Qualitative vs Quantitative Research Methods & Data Analysis
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
The main difference between quantitative and qualitative research is the type of data they collect and analyze.
Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
- Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
- Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.
On This Page:
What Is Qualitative Research?
Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.
Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.
Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)
Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).
Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).
Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.
Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.
Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.
Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.
Qualitative Methods
There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .
The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.
The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)
Here are some examples of qualitative data:
Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.
Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.
Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.
Diaries or journals : Written accounts of personal experiences or reflections.
Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.
Qualitative Data Analysis
Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.
Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .
For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .
Key Features
- Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
- Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
- The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
- The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
- The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.
Limitations of Qualitative Research
- Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
- The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
- Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
- The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.
Advantages of Qualitative Research
- Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
- Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
- Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
- Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.
What Is Quantitative Research?
Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.
The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.
Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.
Quantitative Methods
Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.
For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).
Experimental methods limit how research participants react to and express appropriate social behavior.
Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.
There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:
Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .
The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.
Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.
This data can be analyzed to identify brain regions involved in specific mental processes or disorders.
For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.
The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms.
Quantitative Data Analysis
Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.
Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).
- Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
- The research aims for objectivity (i.e., without bias) and is separated from the data.
- The design of the study is determined before it begins.
- For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
- Research is used to test a theory and ultimately support or reject it.
Limitations of Quantitative Research
- Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
- Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
- Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
- Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.
Advantages of Quantitative Research
- Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
- Useful for testing and validating already constructed theories.
- Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
- Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
- Hypotheses can also be tested because of statistical analysis (Antonius, 2003).
Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.
Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.
Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.
Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.
Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.
Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.
Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.
Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage
Further Information
- Mixed methods research
- Designing qualitative research
- Methods of data collection and analysis
- Introduction to quantitative and qualitative research
- Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
- Qualitative research in health care: Analysing qualitative data
- Qualitative data analysis: the framework approach
- Using the framework method for the analysis of
- Qualitative data in multi-disciplinary health research
- Content Analysis
- Grounded Theory
- Thematic Analysis
Educational resources and simple solutions for your research journey
Qualitative vs Quantitative Research: Differences, Examples, and Methods
There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.
Table of Contents
Qualitative v s Quantitative Research
Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.
Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .
What Are the Differences Between Qualitative and Quantitative Research
Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.
| Understanding meanings, exploring ideas, behaviors, and contexts, and formulating theories | Generating and analyzing numerical data, quantifying variables by using logical, statistical, and mathematical techniques to test or prove hypothesis | |
| Limited sample size, typically not representative | Large sample size to draw conclusions about the population | |
| Expressed using words. Non-numeric, textual, and visual narrative | Expressed using numerical data in the form of graphs or values. Statistical, measurable, and numerical | |
| Interviews, focus groups, observations, ethnography, literature review, and surveys | Surveys, experiments, and structured observations | |
| Inductive, thematic, and narrative in nature | Deductive, statistical, and numerical in nature | |
| Subjective | Objective | |
| Open-ended questions | Close-ended (Yes or No) or multiple-choice questions | |
| Descriptive and contextual | Quantifiable and generalizable | |
| Limited, only context-dependent findings | High, results applicable to a larger population | |
| Exploratory research method | Conclusive research method | |
| To delve deeper into the topic to understand the underlying theme, patterns, and concepts | To analyze the cause-and-effect relation between the variables to understand a complex phenomenon | |
| Case studies, ethnography, and content analysis | Surveys, experiments, and correlation studies |
Data Collection Methods
There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:
Qualitative Research Data Collection
- Interviews
- Focus g roups
- Content a nalysis
- Literature review
- Observation
- Ethnography
Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.
Quantitative Research Data Collection
- Surveys/ q uestionnaires
- Experiments
- Secondary data analysis
- Structured o bservations
- Case studies
- Tests and a ssessments
Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.
Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.
Qualitative vs Quantitative Research Outcomes
Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs. Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.
When to Use Qualitative vs Quantitative Research Approach
The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.
Qualitative research approach
Qualitative research approach is used under following scenarios:
- To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.
- Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.
- Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.
Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”
This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?
Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.
Quantitative research approach
Quantitative research approach is used under following scenarios:
- Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.
- Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.
- Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.
Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”
Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.
Mixed methods approach
In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.
Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.
How to Analyze Qualitative and Quantitative Data
When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.
Analyzing qualitative data
Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:
- Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.
- Coding: Data can be arranged in categories based on themes/concepts using coding.
- Theme development: Utilize higher-level organization to group related codes into broader themes.
- Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.
- Reporting: Present findings with quotes or excerpts to illustrate key themes.
Analyzing quantitative data
Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:
- Processing raw data: Check missing values, outliers, or inconsistencies in raw data.
- Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.
- Exploratory data analysis: Usage of visuals to deduce patterns and trends.
- Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).
- Interpretation: Analyze results considering significance and practical implications.
- Validation: Data validation through replication or literature review.
- Reporting: Present findings by means of tables, figures, or graphs.
Benefits and limitations of qualitative vs quantitative research
There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:
Benefits of qualitative research
- Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.
- Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.
- Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.
- Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.
- Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.
Limitations of qualitative research
- Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.
- Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.
- Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.
- Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.
- Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.
Benefits of quantitative research
- Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.
- Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.
- Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.
- Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.
- Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.
Limitations of quantitative research
- Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.
- Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.
- Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.
- Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .
- Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.
Frequently asked questions
- What is the difference between qualitative and quantitative research?
Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.
- What are the types of qualitative research?
Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.
- What are the types of quantitative research?
Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.
- Can you give me examples for qualitative and quantitative research?
Qualitative Research Example:
Research Question: What are the experiences of parents with autistic children in accessing support services?
Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.
Quantitative Research Example:
Research Question: What is the correlation between sleep duration and academic performance in college students?
Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.
Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.
Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place – Get All Access now starting at just $14 a month !
Related Posts
What are the Best Research Funding Sources
What are Experimental Groups in Research
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
- Publications
- Account settings
Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .
- Advanced Search
- Journal List
- J Korean Med Sci
- v.37(16); 2022 Apr 25
A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles
Edward barroga.
1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.
Glafera Janet Matanguihan
2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.
INTRODUCTION
Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6
It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4
There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.
DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES
A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5
On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4
Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8
Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12
CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES
Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13
There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10
TYPES OF RESEARCH QUESTIONS AND HYPOTHESES
Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .
| Quantitative research questions | Quantitative research hypotheses |
|---|---|
| Descriptive research questions | Simple hypothesis |
| Comparative research questions | Complex hypothesis |
| Relationship research questions | Directional hypothesis |
| Non-directional hypothesis | |
| Associative hypothesis | |
| Causal hypothesis | |
| Null hypothesis | |
| Alternative hypothesis | |
| Working hypothesis | |
| Statistical hypothesis | |
| Logical hypothesis | |
| Hypothesis-testing | |
| Qualitative research questions | Qualitative research hypotheses |
| Contextual research questions | Hypothesis-generating |
| Descriptive research questions | |
| Evaluation research questions | |
| Explanatory research questions | |
| Exploratory research questions | |
| Generative research questions | |
| Ideological research questions | |
| Ethnographic research questions | |
| Phenomenological research questions | |
| Grounded theory questions | |
| Qualitative case study questions |
Research questions in quantitative research
In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .
| Quantitative research questions | |
|---|---|
| Descriptive research question | |
| - Measures responses of subjects to variables | |
| - Presents variables to measure, analyze, or assess | |
| What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training? | |
| Comparative research question | |
| - Clarifies difference between one group with outcome variable and another group without outcome variable | |
| Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)? | |
| - Compares the effects of variables | |
| How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells? | |
| Relationship research question | |
| - Defines trends, association, relationships, or interactions between dependent variable and independent variable | |
| Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic? | |
Hypotheses in quantitative research
In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .
| Quantitative research hypotheses | |
|---|---|
| Simple hypothesis | |
| - Predicts relationship between single dependent variable and single independent variable | |
| If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered. | |
| Complex hypothesis | |
| - Foretells relationship between two or more independent and dependent variables | |
| The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable). | |
| Directional hypothesis | |
| - Identifies study direction based on theory towards particular outcome to clarify relationship between variables | |
| Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects. | |
| Non-directional hypothesis | |
| - Nature of relationship between two variables or exact study direction is not identified | |
| - Does not involve a theory | |
| Women and men are different in terms of helpfulness. (Exact study direction is not identified) | |
| Associative hypothesis | |
| - Describes variable interdependency | |
| - Change in one variable causes change in another variable | |
| A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable). | |
| Causal hypothesis | |
| - An effect on dependent variable is predicted from manipulation of independent variable | |
| A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient. | |
| Null hypothesis | |
| - A negative statement indicating no relationship or difference between 2 variables | |
| There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2). | |
| Alternative hypothesis | |
| - Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables | |
| The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2). | |
| Working hypothesis | |
| - A hypothesis that is initially accepted for further research to produce a feasible theory | |
| Dairy cows fed with concentrates of different formulations will produce different amounts of milk. | |
| Statistical hypothesis | |
| - Assumption about the value of population parameter or relationship among several population characteristics | |
| - Validity tested by a statistical experiment or analysis | |
| The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2. | |
| There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan. | |
| Logical hypothesis | |
| - Offers or proposes an explanation with limited or no extensive evidence | |
| If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less. | |
| Hypothesis-testing (Quantitative hypothesis-testing research) | |
| - Quantitative research uses deductive reasoning. | |
| - This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses. | |
Research questions in qualitative research
Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15
There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .
| Qualitative research questions | |
|---|---|
| Contextual research question | |
| - Ask the nature of what already exists | |
| - Individuals or groups function to further clarify and understand the natural context of real-world problems | |
| What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems) | |
| Descriptive research question | |
| - Aims to describe a phenomenon | |
| What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities? | |
| Evaluation research question | |
| - Examines the effectiveness of existing practice or accepted frameworks | |
| How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility? | |
| Explanatory research question | |
| - Clarifies a previously studied phenomenon and explains why it occurs | |
| Why is there an increase in teenage pregnancy (phenomenon) in Tanzania? | |
| Exploratory research question | |
| - Explores areas that have not been fully investigated to have a deeper understanding of the research problem | |
| What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic? | |
| Generative research question | |
| - Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions | |
| How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative? | |
| Ideological research question | |
| - Aims to advance specific ideas or ideologies of a position | |
| Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care? | |
| Ethnographic research question | |
| - Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings | |
| What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis? | |
| Phenomenological research question | |
| - Knows more about the phenomena that have impacted an individual | |
| What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual) | |
| Grounded theory question | |
| - Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups | |
| What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed? | |
| Qualitative case study question | |
| - Assesses a phenomenon using different sources of data to answer “why” and “how” questions | |
| - Considers how the phenomenon is influenced by its contextual situation. | |
| How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan? | |
| Qualitative research hypotheses | |
|---|---|
| Hypothesis-generating (Qualitative hypothesis-generating research) | |
| - Qualitative research uses inductive reasoning. | |
| - This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis. | |
| - Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach. | |
Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15
Hypotheses in qualitative research
Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1
FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES
Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14
The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14
As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.
| Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
|---|---|---|---|
| Research question | Which is more effective between smoke moxibustion and smokeless moxibustion? | “Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” | 1) Vague and unfocused questions |
| 2) Closed questions simply answerable by yes or no | |||
| 3) Questions requiring a simple choice | |||
| Hypothesis | The smoke moxibustion group will have higher cephalic presentation. | “Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group. | 1) Unverifiable hypotheses |
| Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group. | 2) Incompletely stated groups of comparison | ||
| Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” | 3) Insufficiently described variables or outcomes | ||
| Research objective | To determine which is more effective between smoke moxibustion and smokeless moxibustion. | “The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” | 1) Poor understanding of the research question and hypotheses |
| 2) Insufficient description of population, variables, or study outcomes |
a These statements were composed for comparison and illustrative purposes only.
b These statements are direct quotes from Higashihara and Horiuchi. 16
| Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
|---|---|---|---|
| Research question | Does disrespect and abuse (D&A) occur in childbirth in Tanzania? | How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania? | 1) Ambiguous or oversimplistic questions |
| 2) Questions unverifiable by data collection and analysis | |||
| Hypothesis | Disrespect and abuse (D&A) occur in childbirth in Tanzania. | Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania. | 1) Statements simply expressing facts |
| Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania. | 2) Insufficiently described concepts or variables | ||
| Research objective | To describe disrespect and abuse (D&A) in childbirth in Tanzania. | “This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” | 1) Statements unrelated to the research question and hypotheses |
| 2) Unattainable or unexplorable objectives |
a This statement is a direct quote from Shimoda et al. 17
The other statements were composed for comparison and illustrative purposes only.
CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES
To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .
Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.
Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12
In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.
EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES
- EXAMPLE 1. Descriptive research question (quantitative research)
- - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
- “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
- RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
- EXAMPLE 2. Relationship research question (quantitative research)
- - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
- “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
- Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
- EXAMPLE 3. Comparative research question (quantitative research)
- - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
- “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
- RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
- STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
- EXAMPLE 4. Exploratory research question (qualitative research)
- - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
- “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
- EXAMPLE 5. Relationship research question (quantitative research)
- - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
- “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23
EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES
- EXAMPLE 1. Working hypothesis (quantitative research)
- - A hypothesis that is initially accepted for further research to produce a feasible theory
- “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
- “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
- EXAMPLE 2. Exploratory hypothesis (qualitative research)
- - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
- “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
- “Conclusion
- Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
- EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
- “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
- Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
- EXAMPLE 4. Statistical hypothesis (quantitative research)
- - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
- “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
- “Statistical Analysis
- ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27
EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS
- EXAMPLE 1. Background, hypotheses, and aims are provided
- “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
- “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
- “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
- EXAMPLE 2. Background, hypotheses, and aims are provided
- “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
- “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
- EXAMPLE 3. Background, aim, and hypothesis are provided
- “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
- “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
- “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30
Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.
Disclosure: The authors have no potential conflicts of interest to disclose.
Author Contributions:
- Conceptualization: Barroga E, Matanguihan GJ.
- Methodology: Barroga E, Matanguihan GJ.
- Writing - original draft: Barroga E, Matanguihan GJ.
- Writing - review & editing: Barroga E, Matanguihan GJ.
- Bipolar Disorder
- Therapy Center
- When To See a Therapist
- Types of Therapy
- Best Online Therapy
- Best Couples Therapy
- Managing Stress
- Sleep and Dreaming
- Understanding Emotions
- Self-Improvement
- Healthy Relationships
- Student Resources
- Personality Types
- Sweepstakes
- Guided Meditations
- Verywell Mind Insights
- 2024 Verywell Mind 25
- Mental Health in the Classroom
- Editorial Process
- Meet Our Review Board
- Crisis Support
Quantitative vs. Qualitative Research in Psychology
- Key Differences
Quantitative Research Methods
Qualitative research methods.
- How They Relate
In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena—things that happen because of and through human behavior—are especially difficult to grasp with typical scientific models.
At a Glance
Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.
- Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
- Quantitative research involves collecting and evaluating numerical data.
This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.
Qualitative Research vs. Quantitative Research
In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.
Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:
- Self-reports , like surveys or questionnaires
- Observation (often used in experiments or fieldwork)
- Implicit attitude tests that measure timing in responding to prompts
Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.
However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.
Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.
Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.
Used to develop theories
Takes a broad, complex approach
Answers "why" and "how" questions
Explores patterns and themes
Used to test theories
Takes a narrow, specific approach
Answers "what" questions
Explores statistical relationships
Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."
The scientific method follows this general process. A researcher must:
- Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
- Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
- Develop experiments to manipulate the variables
- Collect empirical (measured) data
- Analyze data
Quantitative methods are about measuring phenomena, not explaining them.
Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.
These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.
Basic Assumptions
Quantitative methods assume:
- That the world is measurable
- That humans can observe objectively
- That we can know things for certain about the world from observation
In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.
As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .
Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.
Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.
Correlation and Causation
A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:
- The study was a true experiment.
- The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
- The dependent variable can be measured through a ratio or a scale.
So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.
Pitfalls of Quantitative Research
Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?
As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.
Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.
Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.
While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."
Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.
These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.
Qualitative Approaches
There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:
- Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
- Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
- Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
- Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.
Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.
Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.
There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.
Interpretation
Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).
The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.
Relationship Between Qualitative and Quantitative Research
It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.
These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.
For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).
After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.
By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.
Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.
Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313
Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.
Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.
Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049
Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers . SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927
Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977
Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.
Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research Methods in Psychology . McGraw Hill Education.
By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.
Have a language expert improve your writing
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
- Knowledge Base
Methodology
- What Is Qualitative Research? | Methods & Examples
What Is Qualitative Research? | Methods & Examples
Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.
Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.
Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.
- How does social media shape body image in teenagers?
- How do children and adults interpret healthy eating in the UK?
- What factors influence employee retention in a large organization?
- How is anxiety experienced around the world?
- How can teachers integrate social issues into science curriculums?
Table of contents
Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.
Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.
Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.
| Approach | What does it involve? |
|---|---|
| Grounded theory | Researchers collect rich data on a topic of interest and develop theories . |
| Researchers immerse themselves in groups or organizations to understand their cultures. | |
| Action research | Researchers and participants collaboratively link theory to practice to drive social change. |
| Phenomenological research | Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences. |
| Narrative research | Researchers examine how stories are told to understand how participants perceive and make sense of their experiences. |
Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.
Prevent plagiarism. Run a free check.
Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:
- Observations: recording what you have seen, heard, or encountered in detailed field notes.
- Interviews: personally asking people questions in one-on-one conversations.
- Focus groups: asking questions and generating discussion among a group of people.
- Surveys : distributing questionnaires with open-ended questions.
- Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
- You take field notes with observations and reflect on your own experiences of the company culture.
- You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
- You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.
Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.
For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.
Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.
Most types of qualitative data analysis share the same five steps:
- Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
- Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
- Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
- Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
- Identify recurring themes. Link codes together into cohesive, overarching themes.
There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.
| Approach | When to use | Example |
|---|---|---|
| To describe and categorize common words, phrases, and ideas in qualitative data. | A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps. | |
| To identify and interpret patterns and themes in qualitative data. | A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity. | |
| To examine the content, structure, and design of texts. | A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade. | |
| To study communication and how language is used to achieve effects in specific contexts. | A political scientist could use discourse analysis to study how politicians generate trust in election campaigns. |
Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:
- Flexibility
The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.
- Natural settings
Data collection occurs in real-world contexts or in naturalistic ways.
- Meaningful insights
Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.
- Generation of new ideas
Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.
Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:
- Unreliability
The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.
- Subjectivity
Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.
- Limited generalizability
Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .
- Labor-intensive
Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Chi square goodness of fit test
- Degrees of freedom
- Null hypothesis
- Discourse analysis
- Control groups
- Mixed methods research
- Non-probability sampling
- Quantitative research
- Inclusion and exclusion criteria
Research bias
- Rosenthal effect
- Implicit bias
- Cognitive bias
- Selection bias
- Negativity bias
- Status quo bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
There are five common approaches to qualitative research :
- Grounded theory involves collecting data in order to develop new theories.
- Ethnography involves immersing yourself in a group or organization to understand its culture.
- Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
- Phenomenological research involves investigating phenomena through people’s lived experiences.
- Action research links theory and practice in several cycles to drive innovative changes.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
There are various approaches to qualitative data analysis , but they all share five steps in common:
- Prepare and organize your data.
- Review and explore your data.
- Develop a data coding system.
- Assign codes to the data.
- Identify recurring themes.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
Cite this Scribbr article
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved August 24, 2024, from https://www.scribbr.com/methodology/qualitative-research/
Is this article helpful?
Pritha Bhandari
Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, "i thought ai proofreading was useless but..".
I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”
Reference management. Clean and simple.
Qualitative vs. quantitative research - what’s the difference?
What is quantitative research?
What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.
Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.
Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.
The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".
To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.
With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?
With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.
In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.
Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.
Some methods to collect this data include:
- Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
- Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
- Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
- Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
- Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
- Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
- Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.
Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.
The easiest way to describe qualitative research is that it answers the question " why ".
Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.
Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.
Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.
We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.
Some of these data collection methods include:
- Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
- Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
- Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
- Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
- Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
- Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.
Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:
- Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
- Qualitative research allows you to understand concepts or experiences.
Let's look at how you'll use these approaches in a research project a bit closer:
- Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
- Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
- Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
- Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.
Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.
This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.
So, to answer the initial question, the approach you use is up to you. However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.
No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.
Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.
To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.
Considering the above, you can use tools, applications, and programming languages like R to calculate:
- The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
- The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
- Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
- The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.
Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.
Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.
Some of the most common ways to analyze qualitative data include:
- Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️ Learn how to do a content analysis in the guide.
- Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️ Learn how to do a thematic analysis in this guide.
- Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.
We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:
| Focuses on testing hypotheses. Can also be used to determine general facts about a topic. | Focuses on developing an idea or hypotheses. Can also be used to gain a deeper understanding into specific topics. |
| Analysis is mainly done through mathematical or statistical analytics. | Analysis is more interpretive and involves summarizing and categorizing topics or themes and interpreting data. |
| Data is typically expressed in numbers, graphs, tables, or other numerical formats. | Data is generally expressed in words or text. |
| Requires a reasonably large sample size to be reliable. | Requires smaller sample sizes with only a few respondents. |
| Data collection is focused on closed-ended questions. | Data collection is focused on open-ended questions to extract the opinions and views on a particular subject. |
Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.
3 examples of qualitative research would be:
- Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
- Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.
3 examples of quantitative research include:
- Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
- Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.
The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".
The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".
Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research
No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.
Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.
Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.
Qualitative vs. Quantitative Research in Education: Definitions
Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.
Generate Hypotheses with Qualitative Research
Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.
Form Strong Conclusions with Quantitative Research
Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.
Differences in Data Collection Methods
Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.
Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.
An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.
Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.
On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.
Qualitative Research Methods
- Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
- Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
- Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
- Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.
Quantitative Research Methods
- Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
- Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
- Observations : Researchers look at quantifiable patterns and behavior.
- Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.
Choosing a Research Strategy
When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.
For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.
It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.
In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.
Learn How to Put Education Research into Action
Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .
What’s the Difference Between Educational Equity and Equality?
EdD vs. PhD in Education: Requirements, Career Outlook, and Salary
Top Education Technology Jobs for Doctorate in Education Graduates
American University, EdD in Education Policy and Leadership
Edutopia, “2019 Education Research Highlights”
Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”
iMotion, “Qualitative vs. Quantitative Research: What Is What?”
Scribbr, “Qualitative vs. Quantitative Research”
Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”
Typeform, “A Simple Guide to Qualitative and Quantitative Research”
Request Information
AU Program Helper
This AI chatbot provides automated responses, which may not always be accurate. By continuing with this conversation, you agree that the contents of this chat session may be transcribed and retained. You also consent that this chat session and your interactions, including cookie usage, are subject to our privacy policy .
Qualitative vs Quantitative Research 101
A plain-language explanation (with examples).
By: Kerryn Warren (PhD, MSc, BSc) | June 2020
So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.
However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.
In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.
Qualitative vs Quantitative: Overview
- Qualitative analysis 101
- Quantitative analysis 101
- How to choose which one to use
- Data collection and analysis for qualitative and quantitative research
- The pros and cons of both qualitative and quantitative research
- A quick word on mixed methods
Qualitative Research 101: The Basics
The bathwater is hot.
Let us unpack that a bit. What does that sentence mean? And is it useful?
The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .
Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences by systematically coding and analysing the data .
With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.
In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.
But that’s not the only way to analyse bathwater, of course…
Quantitative Research 101: The Basics
The bathwater is 45 degrees Celsius.
Now, what does this mean? How can this be used?
I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .
If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).
With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…
In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:
Hypothesis: Men prefer bathing in lower temperature water than women do.
This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.
So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .
Need a helping hand?
Qualitative vs Quantitative Research: Which one should you use?
And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research.
If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.
On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.
Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.
Let’s look at an example.
Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:
- How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
- What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
- Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).
The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.
Qualitative vs Quantitative Research: Data collection and analysis
The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.
A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.
To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.
On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.
So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.
Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.
There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.
Qualitative vs Quantitative Research: The pros & cons on both sides
Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.
Let’s start with qualitative “pros”
Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.
Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.
There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.
Qualitative research also has its “cons”:
A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?
But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?
Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.
Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.
Now let’s turn to quantitative “pros”:
Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.
Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.
Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.
Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.
But there are also quantitative “cons”:
Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…
It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.
Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.
Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.
Mixed methods: a happy medium?
Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .
In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.
A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!
Recap: Qualitative vs Quantitative Research
So, just to recap what we have learned in this post about the great qual vs quant debate:
- Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
- Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
- Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
- Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
- Mixed methods can be a happy medium, but should be used purposefully.
- Bathwater temperature is a contentious and severely under-studied research topic.
Psst... there’s more!
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
It was helpful
thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena
Waoo! Simplifies language. I have read this several times and had probs. Today it is very clear. Bravo
Trackbacks/Pingbacks
- What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Qualitative, quantitative and mixed-methods are different types of methodologies, distinguished by whether they focus on words, numbers or both. This…
Submit a Comment Cancel reply
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
- Print Friendly
Have a language expert improve your writing
Run a free plagiarism check in 10 minutes, automatically generate references for free.
- Knowledge Base
- Methodology
- What Is Qualitative Research? | Methods & Examples
What Is Qualitative Research? | Methods & Examples
Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.
Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.
Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.
Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.
- How does social media shape body image in teenagers?
- How do children and adults interpret healthy eating in the UK?
- What factors influence employee retention in a large organisation?
- How is anxiety experienced around the world?
- How can teachers integrate social issues into science curriculums?
Table of contents
Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.
Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.
Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.
| Approach | What does it involve? |
|---|---|
| Grounded theory | Researchers collect rich data on a topic of interest and develop theories . |
| Researchers immerse themselves in groups or organisations to understand their cultures. | |
| Researchers and participants collaboratively link theory to practice to drive social change. | |
| Phenomenological research | Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences. |
| Narrative research | Researchers examine how stories are told to understand how participants perceive and make sense of their experiences. |
Prevent plagiarism, run a free check.
Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:
- Observations: recording what you have seen, heard, or encountered in detailed field notes.
- Interviews: personally asking people questions in one-on-one conversations.
- Focus groups: asking questions and generating discussion among a group of people.
- Surveys : distributing questionnaires with open-ended questions.
- Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
- You take field notes with observations and reflect on your own experiences of the company culture.
- You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
- You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.
Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.
For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.
Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.
Most types of qualitative data analysis share the same five steps:
- Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
- Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
- Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
- Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
- Identify recurring themes. Link codes together into cohesive, overarching themes.
There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.
| Approach | When to use | Example |
|---|---|---|
| To describe and categorise common words, phrases, and ideas in qualitative data. | A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps. | |
| To identify and interpret patterns and themes in qualitative data. | A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity. | |
| To examine the content, structure, and design of texts. | A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade. | |
| To study communication and how language is used to achieve effects in specific contexts. | A political scientist could use discourse analysis to study how politicians generate trust in election campaigns. |
Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:
- Flexibility
The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.
- Natural settings
Data collection occurs in real-world contexts or in naturalistic ways.
- Meaningful insights
Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.
- Generation of new ideas
Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.
Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:
- Unreliability
The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.
- Subjectivity
Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.
- Limited generalisability
Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .
- Labour-intensive
Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
There are five common approaches to qualitative research :
- Grounded theory involves collecting data in order to develop new theories.
- Ethnography involves immersing yourself in a group or organisation to understand its culture.
- Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
- Phenomenological research involves investigating phenomena through people’s lived experiences.
- Action research links theory and practice in several cycles to drive innovative changes.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.
There are various approaches to qualitative data analysis , but they all share five steps in common:
- Prepare and organise your data.
- Review and explore your data.
- Develop a data coding system.
- Assign codes to the data.
- Identify recurring themes.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
Cite this Scribbr article
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/
Is this article helpful?
Pritha Bhandari
The differences between qualitative and quantitative research methods
Last updated
15 January 2023
Reviewed by
Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.
All your data in one place
Analyze your qualitative and quantitative data together in Dovetail and uncover deeper insights
- The differences between quantitative and qualitative research
The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.
On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.
- Qualitative vs. quantitative data collection
Focusing on different types of data means that the data collection methods vary.
Quantitative data collection methods
As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.
Qualitative data collection methods
On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records.
- Qualitative vs. quantitative outcomes
The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.
On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.
- How to analyze qualitative and quantitative data
Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.
Analyzing quantitative data
As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.
Analyzing qualitative data
Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation.
Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.
- When to use qualitative vs. quantitative research
Choosing between the two approaches comes down to understanding what your goal is with the research.
Qualitative research approach
Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.
Quantitative research approach
On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions.
Mixed methods approach
Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.
- Benefits and limitations
Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.
Qualitative studies: Pros and cons
This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.
On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.
It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well.
Quantitative studies: Pros and cons
The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic.
It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.
- How to balance qualitative and quantitative research
Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways.
Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?
Open-ended vs. close-ended questions
One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.
An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:
1. How do you compensate for higher prices at the grocery store? (Select all that apply)
A. Purchase fewer items
B. Opt for less expensive choices
C. Take money from other parts of the budget
D. Use a food bank or other charity to fill the gaps
E. Make more food from scratch
2. How do rising prices affect your grocery shopping habits? (Write your answer)
We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs.
Learn more about qualitative research data analysis software
Should you be using a customer insights hub.
Do you want to discover previous research faster?
Do you share your research findings with others?
Do you analyze research data?
Start for free today, add your research, and get to key insights faster
Editor’s picks
Last updated: 18 April 2023
Last updated: 27 February 2023
Last updated: 5 February 2023
Last updated: 16 April 2023
Last updated: 16 August 2024
Last updated: 9 March 2023
Last updated: 30 April 2024
Last updated: 12 December 2023
Last updated: 11 March 2024
Last updated: 4 July 2024
Last updated: 6 March 2024
Last updated: 5 March 2024
Last updated: 13 May 2024
Latest articles
Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.
Get started for free
- Resources Home 🏠
- Try SciSpace Copilot
- Search research papers
- Add Copilot Extension
- Try AI Detector
- Try Paraphraser
- Try Citation Generator
- April Papers
- June Papers
- July Papers
Qualitative and Quantitative Research — Explore the differences
In the research arena, there are two main approaches that researchers can take — qualitative and quantitative research. Understanding the fundamentals of these two methods is crucial for conducting effective research and obtaining accurate results.
This article provides insights into the differences between qualitative and quantitative research and we also discuss how to develop research questions for qualitative and quantitative studies, and how to gather and analyze data using these research approaches. Furthermore, we will examine how to interpret findings from qualitative and quantitative research, as well as identify ethical considerations.
By the end of this comprehensive article, readers will be equipped with the knowledge and tools to apply qualitative and quantitative research to advance knowledge in their respective fields.
What is Qualitative and Quantitative Research?
Qualitative research aims to understand complex phenomena by exploring the subjective experiences and perspectives of individuals. It focuses on gathering in-depth data through techniques such as interviews, observations, and open-ended surveys. This approach allows researchers to delve into the intricacies of the topic, uncovering unique insights that may not be captured through quantitative methods alone.
For example, imagine a study on the impact of social media on mental health. Qualitative research would involve conducting interviews with individuals who have experienced negative effects from excessive social media use. Through these interviews, researchers can gain a deep understanding of the participants' experiences, emotions, and thoughts. They can explore the nuances of how social media affects different aspects of mental health, such as self-esteem, body image, and social comparison.
Conversely, quantitative research involves collecting numerical data and analyzing it using statistical methods to identify patterns, trends, and relationships. This approach allows researchers to generalize their findings to a larger population and calculate statistically significant results. It relies on structured surveys, experiments, and other data collection methods that provide standardized data for analysis.
Continuing with the example of social media and mental health, quantitative research would involve administering surveys to a large sample of individuals. The surveys would include questions that measure various aspects of mental health, such as anxiety, depression, and life satisfaction. By collecting numerical data from a large and diverse sample, researchers can identify trends and relationships between social media use and mental health outcomes.
Both qualitative and quantitative research have their strengths and weaknesses. Qualitative research allows for a deep understanding of the topic, providing rich insights and capturing the context of the participants' experiences. It allows researchers to uncover unique perspectives and shed light on subjective experiences.
On the other hand, quantitative research entails a structured and systematic approach to data collection and analysis, allowing for comparisons and generalizations across different groups and contexts.
However, it is crucial to emphasize that qualitative and quantitative research are not mutually exclusive. They frequently serve as a complement to one another within the realm of research studies. Researchers may use qualitative methods to explore a topic in-depth and generate hypotheses, which can then be tested using quantitative methods. This combination of approaches, known as mixed methods research, allows for a more comprehensive understanding of complex phenomena.
Advantages and Disadvantages of Each Research Method
Qualitative research offers the advantage of generating detailed and nuanced data. It allows researchers to explore complex issues and gain a deeper understanding of participants' thoughts, emotions, and behaviors. However, qualitative research can be time-consuming, and data analysis may be subjective.
In contrast, quantitative research provides objective and quantifiable data, making it easier to draw conclusions and establish causation. It enables researchers to collect data from large samples, increasing the generalizability of findings. Nevertheless, quantitative research may overlook important contextual information and fail to capture the complexities of human experiences. Additionally, it requires a solid understanding of statistical techniques for accurate analysis.
When to Use Qualitative or Quantitative Research?
The choice between qualitative and quantitative research depends on the research questions and objectives. Qualitative research is appropriate when exploring new or complex phenomena, seeking in-depth insights, or generating hypotheses for further investigation. It is particularly useful in social sciences and humanities. On the other hand, quantitative research is suitable when aiming to establish causal relationships, generalize findings to a larger population, or measure phenomena systematically and objectively. It is commonly employed in sciences such as psychology, economics, and medicine.
By considering the nature of the research question, the available resources, and the desired outcomes, researchers can make an informed decision on the appropriate research approach.
How to develop research Questions for Qualitative and Quantitative Studies?
A well-defined research question is essential for conducting meaningful research. In qualitative studies, research questions are exploratory and aim to understand the experiences, perceptions, and meanings of participants. These questions should be open-ended and allow for in-depth exploration of the phenomenon under investigation.
In quantitative research, research questions are often formulated to test hypotheses or examine relationships between variables. These questions should be clear, specific, and measurable to guide data collection and analysis.
Regardless of the research approach, it is crucial to develop research questions that align with the research objectives, is feasible to investigate and contribute to existing knowledge in the field.
Gathering and Analyzing Data
Qualitative research involves collecting data through various techniques, such as interviews, focus groups, and observations. Researchers must establish rapport with participants to encourage open and honest responses. The data collected is then analyzed using methods like thematic analysis and constant comparison to identify patterns, themes, and categories. In quantitative research, data is collected using surveys, experiments, or other structured methods. Researchers aim to obtain a representative sample and ensure the reliability and validity of the data. Statistical analysis techniques, such as descriptive statistics, correlation, and regression, are then applied to conclude.
Regardless of the research approach, it is essential to document the data collection and analysis process thoroughly to ensure transparency and reproducibility.
Interpreting Findings
Interpreting findings from qualitative research involves carefully analyzing the patterns, themes, and categories identified during data analysis. Researchers aim to understand the overarching meaning of the data and draw conclusions based on the participants' experiences and perspectives. The findings are often supported by direct quotes or examples from the data. In quantitative research, findings are interpreted by analyzing statistical results and examining the significance of relationships or differences. Researchers must carefully consider the limitations of the study and the generalizability of the findings. The results are often presented using tables, charts, and graphs for clarity.
Irrespective of the research approach, it is crucial to avoid generalizing beyond the scope of the data and to consider alternative interpretations.
Identifying Ethical Considerations in Qualitative and Quantitative Research
Both qualitative and quantitative research must adhere to ethical guidelines to protect the rights and well-being of participants. Researchers should obtain informed consent, ensure confidentiality, and prevent harm. In qualitative research, building trust and maintaining participant anonymity is crucial. In quantitative research, privacy and data protection are paramount.
Additionally, researchers must consider the potential biases, power dynamics, and conflicts of interest that may influence the research process and findings. Being aware of these ethical considerations helps ensure the integrity and reliability of the research.
How to Write a Research Report Based on Qualitative or Quantitative Data
When writing a research report, it is essential to structure it clearly and concisely. In qualitative research, the report typically includes an introduction, literature review, methodology, findings, discussion, and conclusion. The findings section focuses on the themes and patterns identified during analysis and is supported by quotes or examples from the data.
In quantitative research, the report generally consists of an introduction, literature review, methodology, results, discussion, and conclusion. The results section presents the statistical analysis and findings in a clear and organized manner, often using tables, charts, and graphs.
The report should be written in a scholarly tone, provide sufficient details, and communicate the research findings and implications.
Assessing Reliability and Validity of Qualitative and Quantitative Results
Reliability and validity are crucial considerations in research. In qualitative research, researchers can enhance reliability by using multiple researchers to analyze the data and compare their interpretations. Validity can be strengthened by employing rigorous data collection methods, establishing trustworthiness, and including participant validation.
In quantitative research, reliability can be assessed through test-retest reliability or inter-rater reliability. Validity can be evaluated by examining internal validity, external validity, and construct validity. Additionally, researchers should carefully consider potential confounding variables and ensure proper control measures are in place.
By assessing reliability and validity, researchers can enhance the credibility and trustworthiness of their research findings.
Qualitative and quantitative research are distinct yet complementary approaches to conducting research. Understanding when to use each method, developing appropriate research questions, gathering and analyzing data, interpreting findings, and addressing ethical considerations are all critical aspects of conducting valuable research. By embracing these methodologies and applying them appropriately, researchers can contribute to the advancement of knowledge and make meaningful contributions to their respective fields.
You might also like
ChatPDF Showdown: SciSpace Chat PDF vs. Adobe PDF Reader
Boosting Citations: A Comparative Analysis of Graphical Abstract vs. Video Abstract
The Impact of Visual Abstracts on Boosting Citations
- Skip to main content
- Skip to primary sidebar
- Skip to footer
- QuestionPro
- Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
- Resources Blog eBooks Survey Templates Case Studies Training Help center
Home Market Research
Qualitative vs Quantitative Research: Differences and Examples
Understanding the differences between qualitative vs quantitative research is essential when conducting a research project, as both methods underpin the two key approaches in conducting a study.
In recent blogs, we elaborately discussed quantitative and qualitative research methods b ut what is the difference between the two? Which one is the best? Let’s find out.
Qualitative Research In a nutshell
Qualitative research is a research methodology where “quality” or opinion based research is conducted to derive research conclusions. This type of research is often conversational in nature rather than being quantifiable through empirical research and measurements.
Qualitative research: Methods & Characteristics
1. Conversation : A conversation takes place between the researcher and the respondent. This can be in the form of focus groups , in-depth interviews using telephonic / video / face-to-face conversations.
However, with the rise of online platforms, a bulk of steps in qualitative research involves creating and maintaining online community portals for a more quantifiable and recordable qualitative study.
LEARN ABOUT: Qualitative Interview
2. Conclusions : Research conclusions are subjective in nature when conducting qualitative research. The researcher may derive conclusions based on in-depth analysis of respondent attitude, reason behind responses and understanding of psychological motivations.
Quantitative Research In a nutshell
Quantitative research is a research methodology which uses questions and questionnaires to gather quantifiable data and perform statistical analysis to derive meaningful research conclusions.
Quantitative research: Methods & Characteristics
1. Questions : Quantitative research method uses surveys and polls to gather information on a given subject. There are a variety of question types used based on a nature of the research study.
For Example: If you want to conduct a customer satisfaction quantitative research, the Net Promoter Score is one of the critically acclaimed survey questions for this purpose.
2. Distribution : Quantitative research uses email surveys as the primary mode of gathering responses to questions. Alternatively, technology has given rise to offline distribution methods for relatively remote locations using offline mobile data capture apps. For social sciences and psychological quantitative research, social media surveys are also used to gather data.
3. Statistical Analysis : Quantitative research uses a wide range of data analysis techniques such as Conjoint Analysis , Cross Tabulation and Trend Analysis .
Qualitative vs Quantitative Research
Now let’s compare the qualitative and quantitative research methods in different aspects so that you can choose the right one in your next investigation.:
1. Objective and flow of research
Quantitative research is used in data-oriented research where the objective of research design is to derive “measurable empirical evidence” based on fixed and pre-determined questions. The flow of research, is therefore, decided before the research is conducted.
Where as, qualitative research is used where the objective is research is to keep probing the respondents based on previous answers under the complete discretion of the interviewer. The flow of research is not determined and the researcher / interviewer has the liberty to frame and ask new questions.
2. Respondent sample size
Respondents or sample of a particular panel is much larger for quantitative research such that enough verifiable information is gather to reach a conclusion without opinion bias. In large scale quantitative research, sample size can be in thousands.
Where as, qualitative research inherently uses less sample size because a large sample size makes it difficult of the research to probe respondents. For instance, a typical political focus group study evaluating election candidates involves no more than 5-10 panelists.
3. Information gathering
Quantitative research uses information gathering methods that can be quantified and processed for statistical analysis techniques. Simply put – quantitative research is heavily dependent on “numbers”, data and stats.
LEARN ABOUT: Research Process Steps
Where as, qualitative research uses conversational methods to gather relevant information on a given subject.
4. Post-research response analysis and conclusions
Quantitative research uses a variety of statistical analysis methods to derive quantifiable research conclusions. These are based on mathematical processes applied on the gather data.
Where as, qualitative researc h depends on the interviewer to derive research conclusions based on qualitative conversations held with the respondents. This conclusion is effectively subjective in nature. This is why quantitative research recordings are often reviewed by senior researchers before the final research conclusion is drawn.
Differences between qualitative vs quantitative research
We hope that this information helps you choose your next research method and achieve your goals.
If you want to carry out any qualitative or qualitative research questions , ask about the tools that QuestionPro has available to help you with the qualitative data collection of the data you need. We have functions for all types of research!.
MORE LIKE THIS
Age Gating: Effective Strategies for Online Content Control
Aug 23, 2024
Customer Experience Lessons from 13,000 Feet — Tuesday CX Thoughts
Aug 20, 2024
Insight: Definition & meaning, types and examples
Aug 19, 2024
Employee Loyalty: Strategies for Long-Term Business Success
Other categories.
- Academic Research
- Artificial Intelligence
- Assessments
- Brand Awareness
- Case Studies
- Communities
- Consumer Insights
- Customer effort score
- Customer Engagement
- Customer Experience
- Customer Loyalty
- Customer Research
- Customer Satisfaction
- Employee Benefits
- Employee Engagement
- Employee Retention
- Friday Five
- General Data Protection Regulation
- Insights Hub
- Life@QuestionPro
- Market Research
- Mobile diaries
- Mobile Surveys
- New Features
- Online Communities
- Question Types
- Questionnaire
- QuestionPro Products
- Release Notes
- Research Tools and Apps
- Revenue at Risk
- Survey Templates
- Training Tips
- Tuesday CX Thoughts (TCXT)
- Uncategorized
- What’s Coming Up
- Workforce Intelligence
- Science Notes Posts
- Contact Science Notes
- Todd Helmenstine Biography
- Anne Helmenstine Biography
- Free Printable Periodic Tables (PDF and PNG)
- Periodic Table Wallpapers
- Interactive Periodic Table
- Periodic Table Posters
- Science Experiments for Kids
- How to Grow Crystals
- Chemistry Projects
- Fire and Flames Projects
- Holiday Science
- Chemistry Problems With Answers
- Physics Problems
- Unit Conversion Example Problems
- Chemistry Worksheets
- Biology Worksheets
- Periodic Table Worksheets
- Physical Science Worksheets
- Science Lab Worksheets
- My Amazon Books
Qualitative and Quantitative Data – Definitions and Examples
There are two types of data. Qualitative data is descriptive information about characteristics that are difficult to define or measure or cannot be expressed numerically. Quantitative data is numerical information that can be measured or counted.
Qualitative → Qualities
Quantitative → Quantities
Examples of Qualitative Data
- feelings and emotions
- color (unless it can be written as a specific wavelength of light)
- expressions of more/less, ugly/beautiful, fat/thin, healthy/sickly
Examples of Quantitative Data
Quantitative data can be expressed as numbers. If you can measure it, it can be expressed as a quantity.
- number of objects
- temperature
- percentages
Using Both Types of Data
While quantitative data is easier to analyze, qualitative data is also important.
If you are conducting an experiment using the scientific method, for example, you want to record your observations and data as thoroughly as possible.
Let’s say you want to describe a cat. You would use both types of data. Qualitative data could include its fur color and the color of its eyes, whether its fur is soft or rough, the sounds it makes, how it smells, whether it seems affectionate or aloof, its health, whether it is young or old. Quantitative data could include its length, weight, number of toes on each paw, how high it can jump, how many ounces of food it eats each data, and its body temperature.
Related Posts
Quantitative and Qualitative Research
- Quantitative vs. Qualitative Research
- Find quantitative or qualitative research in CINAHL
- Find quantitative or qualitative research in PsycINFO
- Relevant book titles
Mixed Methods Research
As its name suggests, mixed methods research involves using elements of both quantitative and qualitative research methods. Using mixed methods, a researcher can more fully explore a research question and provide greater insight.
What is Empirical Research?
Empirical research is based on observed and measured phenomena. Knowledge is extracted from real lived experience rather than from theory or belief.
IMRaD: Scholarly journals sometimes use the "IMRaD" format to communicate empirical research findings.
Introduction: explains why this research is important or necessary. Provides context ("literature review").
Methodology: explains how the research was conducted ("research design").
Results: presents what was learned through the study ("findings").
Discussion: explains or comments upon the findings including why the study is important and connecting to other research ("conclusion").
What is Quantitative Research?
Quantitative research gathers data that can be measured numerically and analyzed mathematically. Quantitative research attempts to answer research questions through the quantification of data.
Indicators of quantitative research include:
contains statistical analysis
large sample size
objective - little room to argue with the numbers
types of research: descriptive studies, exploratory studies, experimental studies, explanatory studies, predictive studies, clinical trials
What is Qualitative Research?
Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups.
Indicators of qualitative research include:
interviews or focus groups
small sample size
subjective - researchers are often interpreting meaning
methods used: phenomenology, ethnography, grounded theory, historical method, case study
Video: Empirical Studies: Qualitative vs. Quantitative
This video from usu libraries walks you through the differences between quantitative and qualitative research methods. (5:51 minutes) creative commons attribution license (reuse allowed) https://youtu.be/rzcfma1l6ce.
- << Previous: Home
- Next: Find quantitative or qualitative research in CINAHL >>
- Last Updated: Mar 25, 2024 12:23 PM
- URL: https://libguides.hofstra.edu/quantitative-and-qualitative-research
This site is compliant with the W3C-WAI Web Content Accessibility Guidelines HOFSTRA UNIVERSITY Hempstead, NY 11549-1000 (516) 463-6600 © 2000-2009 Hofstra University
What Is Qualitative vs. Quantitative Study? Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data. In This Article:What is qualitative research, what is quantitative research.
What’s the Difference Between a Qualitative and Quantitative Study?Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university. Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief. Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair. Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies. Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews. These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article. Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure.  How Does Qualitative and Quantitative Research Differ?How are the approaches of quantitative and qualitative research different? In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated. Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis.  Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research. Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further. Sources of Quantitative ResearchSome example methods of quantitative research methods or sources include, but are not limited to, the following:
The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches. Sources of Qualitative ResearchResearchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples:
Examples of Research Questions Best Suited for Qualitative vs. Quantitative MethodsQualitative research questions:.
Quantitative Research Questions:
These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis. Mixed Methods ResearchIn addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.” If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach. When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers? How to Analyze Qualitative DataBelow is a step-by-step overview of how to analyze qualitative data.
How to Analyze Quantitative DataThere are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats. Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview.
 Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology. Pros and Cons of Qualitative ResearchQualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision:
Pros and Cons of Quantitative ResearchQuantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study.
If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills. With flexible course schedules, convenient online classes , scholarships and financial aid , and an inclusive military-friendly culture, higher education has never been more achievable or accessible. At National University, you’ll find opportunities to challenge and hone your research skills in more than 75 accredited graduate and undergraduate programs and fast-paced credential and certificate programs in healthcare, business, engineering, computer science, criminal justice, sociology, accounting, and more. Contact our admissions office to request program information, or apply to National University online today . Learn More About Our University and ScholarshipsJoin our email list!
Recent Resources
Your passion. Our Programs.Choose an area of study, select a degree level. Search the site Modal window with site-search and helpful links Featured Programs
Helpful Links
Terms & Conditions By submitting your information to National University as my electronic signature and submitting this form by clicking the Request Info button above, I provide my express written consent to representatives of National University and National University affiliates (including City University of Seattle) to contact me about educational opportunities. This includes the use of automated technology, such as an automatic dialing system and pre-recorded or artificial voice messages, text messages, and mail, both electronic and physical, to the phone numbers (including cellular) and e-mail address(es) I have provided. I confirm that the information provided on this form is accurate and complete. I also understand that certain degree programs may not be available in all states. Message and data rates may apply. Message frequency may vary. I understand that consent is not a condition to purchase any goods, services or property, and that I may withdraw my consent at any time by sending an email to [email protected] . I understand that if I am submitting my personal data from outside of the United States, I am consenting to the transfer of my personal data to, and its storage in, the United States, and I understand that my personal data will be subject to processing in accordance with U.S. laws, unless stated otherwise in our privacy policy . Please review our privacy policy for more details or contact us at [email protected] . By submitting my information, I acknowledge that I have read and reviewed the Accessibility Statement . By submitting my information, I acknowledge that I have read and reviewed the Student Code of Conduct located in the Catalog . National University Chat Options
qualitative and quantitative header Learning ObjectiveDifferentiate between qualitative and quantitative approaches. Hong is a physical therapist who teaches injury assessment classes at the University of Utah. With the recent change to online for the remainder of the semester, Hong is interested in the impact on students’ skills acquisition for injury assessment. He wants to utilize both quantitative and qualitative approaches—he plans to compare previous student test scores to current student test scores. He also plans to interview current students about their experiences practicing injury assessment skills virtually. What specific study design methods will Hong use? Making sense of the evidencehen conducting a literature search and reviewing research articles, it is important to have a general understanding of the types of research and data you anticipate from different types of studies. In this article, we review two broad categories of study methods, quantitative and qualitative, and discuss some of their subtypes, or designs, and the type of data that they generate. Quantitative vs. qualitative approaches
Quantitative is measurable. It is often associated with a more traditional scientific method of gathering data in an organized, objective manner so that findings can be generalized to other persons or populations. Quantitative designs are based on probabilities or likelihood—it utilizes ‘p’ values, power analysis, and other scientific methods to ensure the rigor and reproducibility of the results to other populations. Quantitative designs can be experimental, quasi-experimental, descriptive, or correlational. Qualitative is usually more subjective , although like quantitative research, it also uses a systematic approach. Qualitative research is generally preferred when the clinical question centers around life experiences or meaning. Qualitative research explores the complexity, depth, and richness of a particular situation from the perspective of the informants—referring to the person or persons providing the information. This may be the patient, the patient’s caregivers, the patient’s family members, etc. The information may also come from the investigator’s or researcher’s observations. At the heart of qualitative research is the belief that reality is based on perceptions and can be different for each person, often changing over time. Study design differences
Quantitative design methodsQuantitative designs typically fall into four categories: experimental, quasi-experimental, descriptive, or correlational. Let’s talk about these different types. But before we begin, we need to briefly review the difference between independent and dependent variables. The independent variable is the variable that is being manipulated, or the one that varies. It is sometimes called the ‘predictor’ or ‘treatment’ variable. The dependent variable is the outcome (or response) variable. Changes in the dependent variables are presumed to be caused or influenced by the independent variable. ExperimentalIn experimental designs, there are often treatment groups and control groups. This study design looks for cause and effect (if A, then B), so it requires having control over at least one of the independent, or treatment variables. Experimental design administers the treatment to some of the subjects (called the ‘experimental group’) and not to others (called the ‘control group’). Subjects are randomly assigned—meaning that they would have an equal chance of being assigned to the control group or the experimental group. This is the strongest design for testing cause and effect relationships because randomization reduces bias. In fact, most researchers believe that a randomized controlled trail is the only kind of research study where we can infer cause (if A, then B). The difficulty with a randomized controlled trial is that the results may not be generalizable in all circumstances with all patient populations, so as with any research study, you need to consider the application of the findings to your patients in your setting. Quasi-experimentalQuasi-Experimental studies also seek to identify a cause and effect (causal) relationship, although they are less powerful than experimental designs. This is because they lack one or more characteristics of a true experiment. For instance, they may not include random assignment or they may not have a control group. As is often the case in the ‘real world’, clinical care variables often cannot be controlled due to ethical, practical, or fiscal concerns. So, the quasi experimental approach is utilized when a randomized controlled trial is not possible. For example, if it was found that the new treatment stopped disease progression, it would no longer be ethical to withhold it from others by establishing a control group. DescriptiveDescriptive studies give us an accurate account of the characteristics of a particular situation or group. They are often used to determine how often something occurs, the likelihood of something occurring, or to provide a way to categorize information. For example, let’s say we wanted to look at the visiting policy in the ICU and describe how implementing an open-visiting policy affected nurse satisfaction. We could use a research tool, such as a Likert scale (5 = very satisfied and 1 = very dissatisfied), to help us gain an understanding of how satisfied nurses are as a group with this policy. CorrelationalCorrelational research involves the study of the relationship between two or more variables. The primary purpose is to explain the nature of the relationship, not to determine the cause and effect. For example, if you wanted to examine whether first-time moms who have an elective induction are more likely to have a cesarean birth than first-time moms who go into labor naturally, the independent variables would be ‘elective induction’ and ‘go into labor naturally’ (because they are the variables that ‘vary’) and the outcome variable is ‘cesarean section.’ Even if you find a strong relationship between elective inductions and an increased likelihood of cesarean birth, you cannot state that elective inductions ‘cause’ cesarean births because we have no control over the variables. We can only report an increased likelihood. Qualitative design methodsQualitative methods delve deeply into experiences, social processes, and subcultures. Qualitative study generally falls under three types of designs: phenomenology, ethnography and grounded theory. PhenomenologyIn this approach, we want to understand and describe the lived experience or meaning of persons with a particular condition or situation. For example, phenomenological questions might ask “What is it like for an adolescent to have a younger sibling with a terminal illness?” or “What is the lived experience of caring for an older house-bound dependent parent?” EthnographyEthnographic studies focus on the culture of a group of people. The assumption behind ethnographies is that groups of individuals evolve into a kind of ‘culture’ that guides the way members of that culture or group view the world. In this kind of study, the research focuses on participant observation, where the researcher becomes an active participant in that culture to understand its experiences. For example, nursing could be considered a professional culture, and the unit of a hospital can be viewed as a subculture. One example specific to nursing culture was a study done in 2006 by Deitrick and colleagues . They used ethnographic methods to examine problems related to answering patient call lights on one medical surgical inpatient unit. The single nursing unit was the ‘culture’ under study. Grounded theoryGrounded theory research begins with a general research problem, selects persons most likely to clarify the initial understanding of the question, and uses a variety of techniques (interviewing, observation, document review to name a few) to discover and develop a theory. For example, one nurse researcher used a grounded theory approach to explain how African American women from different socioeconomic backgrounds make decisions about mammography screening. Because African American women historically have fewer mammograms (and therefore lower survival rates for later stage detection), understanding their decision-making process may help the provider support more effective health promotion efforts. Being able to identify the differences between qualitative and quantitative research and becoming familiar with the subtypes of each can make a literature search a little less daunting. Take the quizThis article originally appeared July 2, 2020. It was updated to reflect current practice on March 21, 2021. Barbara WilsonMary-jean (gigi) austria, tallie casucci. Performing a rapid critical appraisal helps evaluate a study for its worth by ensuring validity, meaningful data, and significance to the patient. Contributors Barb Wilson, Mary Jean Austria, and Tallie Casucci share a checklist of questions to complete a rapid critical appraisal efficiently and effectively. Relationship building isn’t typically the focus of medical training but is a necessary skill for truly excellent clinicians. Deirdre, Joni, Jared and colleagues developed a model to integrate relationship management skills into medical training, helping create a more well-rounded, complete clinician. Medical students Rachel Tsolinas and Sam Wilkinson, along with SOM professor Kathryn Moore, share a practical tool all health care professionals can use to broaden our understanding of how culture influences decisions and events. Subscribe to our newsletterReceive the latest insights in health care impact, improvement, leadership, resilience, and more..  Contact the Accelerate Team 50 North Medical Drive | Salt Lake City, Utah 84132 | 801-587-2157 Qualitative vs. QuantitativeWhile quantitative research is based on numbers and mathematical calculations (aka quantitative data ), qualitative research is based on written or spoken narratives (or qualitative data ). Qualitative and quantitative research techniques are used in marketing , sociology , psychology , public health and various other disciplines. Comparison chart
Type of dataQualitative research gathers data that is free-form and non-numerical, such as diaries, open-ended questionnaires, interviews and observations that are not coded using a numerical system. On the other hand, quantitative research gathers data that can be coded in a numerical form. Examples of quantitative research include experiments or interviews/questionnaires that used closed questions or rating scales to collect information . Applications of Quantitative and Qualitative DataQualitative data and research is used to study individual cases and to find out how people think or feel in detail. It is a major feature of case studies. Quantitative data and research is used to study trends across large groups in a precise way. Examples include clinical trials or censuses. When to use qualitative vs. quantitative research?Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well. It is hard, for example, to conduct in-depth interviews with thousands of people or to analyze their responses to open-ended questions. But it is relatively easier to analyze survey responses from thousands of people if the questions are closed-ended and responses can be mathematically encoded in, say, rating scales or preference ranks. Conversely, qualitative research shines when it is not possible to come up with closed-ended questions. For example, marketers often use focus groups of potential customers to try and gauge what influences brand perception, product purchase decisions, feelings and emotions . In such cases, researchers are usually at very early stages of forming their hypotheses and do not want to limit themselves to their initial understanding. Qualitative research often opens up new options and ideas that quantitative research cannot due to its closed-ended nature. Analysis of dataQualitative data can be difficult to analyze, especially at scale, as it cannot be reduced to numbers or used in calculations. Responses may be sorted into themes, and require an expert to analyze. Different researchers may draw different conclusions from the same qualitative material. Quantitative data can be ranked or put into graphs and tables to make it easier to analyze. Data ExplosionData is being generated at an increasing rate because of the expansion in the number of computing devices and the growth of the Internet . Most of this data is quantitative and special tools and techniques are evolving to analyze this " big data ". Effects of FeedbackThe following diagram illustrates the effects of positive and negative feedback on Qualitative vs Quantitative research: 
Related Comparisons Share this comparison via: If you read this far, you should follow us: "Qualitative vs Quantitative." Diffen.com. Diffen LLC, n.d. Web. 24 Aug 2024. < > Comments: Qualitative vs Quantitative
Edit or create new comparisons in your area of expertise. Stay connected © All rights reserved.  Social Work: Research Overview
Qualitative Vs Quantitative
Quantitative and Qualitative Research Guide
University of Texas Arlington Libraries 702 Planetarium Place · Arlington, TX 76019 · 817-272-3000
 Qualitative Research DefinitionQualitative research methods and examples, advantages and disadvantages of qualitative approaches, qualitative vs. quantitative research, showing qualitative research skills on resumes, what is qualitative research methods and examples. 
 Forage puts students first. Our blog articles are written independently by our editorial team. They have not been paid for or sponsored by our partners. See our full editorial guidelines . Table of ContentsQualitative research seeks to understand people’s experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people’s beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in user experience (UX) design or marketing . Researchers use qualitative approaches to “determine answers to research questions on human behavior and the cultural values that drive our thinking and behavior,” says Margaret J. King, director at The Center for Cultural Studies & Analysis in Philadelphia. Data in qualitative research typically can’t be assessed mathematically — the data is not sets of numbers or quantifiable information. Rather, it’s collections of images, words, notes on behaviors, descriptions of emotions, and historical context. Data is collected through observations, interviews, surveys, focus groups, and secondary research. However, a qualitative study needs a “clear research question at its base,” notes King, and the research needs to be “observed, categorized, compared, and evaluated (along a scale or by a typology chart) by reference to a baseline in order to determine an outcome with value as new and reliable information.”  Quantium Data AnalyticsExplore the power of data and its ability to power breakthrough possibilities for individuals, organisations and societies with this free job simulation from Quantium. Avg. Time: 4 to 5 hours Skills you’ll build: Data validation, data visualisation, data wrangling, programming, data analysis, commercial thinking, statistical testing, presentation skills Who Uses Qualitative Research?Researchers in social sciences and humanities often use qualitative research methods, especially in specific areas of study like anthropology, history, education, and sociology. Qualitative methods are also applicable in business, technology , and marketing spaces. For example, product managers use qualitative research to understand how target audiences respond to their products. They may use focus groups to gain insights from potential customers on product prototypes and improvements or surveys from existing customers to understand what changes users want to see. Other careers that may involve qualitative research include:
 Electronic Arts Product ManagementLearn how product managers leverage qualitative and other types of research to build and improve products in this free job simulation from EA. Avg. Time: 1 to 2 hours Skills you’ll build: Critical thinking, problem solving, performance metrics, written communication, project planning Good research begins with a question, and this question informs the approach used by qualitative researchers. Grounded TheoryGrounded theory is an inductive approach to theory development. In many forms of research, you begin with a hypothesis and then test it to see if you’re correct. In grounded theory, though, you go in without any assumptions and rely on the data you collect to form theories. You start with an open question about a phenomenon you are studying and collect and analyze data until you can form a fully-fledged theory from the information. Example: A company wants to improve its brand and marketing strategies. The company performs a grounded theory approach to solving this problem by conducting interviews and surveys with past, current, and prospective customers. The information gathered from these methods helps the company understand what type of branding and marketing their customer-base likes and dislikes, allowing the team to inductively craft a new brand and marketing strategy from the data. Action ResearchAction research is one part study and one part problem-solving . Through action research, analysts investigate a problem or weakness and develop practical solutions. The process of action research is cyclical —- researchers assess solutions for efficiency and effectiveness, and create further solutions to correct any issues found. Example: A manager notices her employees struggle to cooperate on group projects. She carefully reviews how team members interact with each other and asks them all to respond to a survey about communication. Through the survey and study, she finds that guidelines for group projects are unclear. After changing the guidelines, she reviews her team again to see if there are any changes to their behavior. >>MORE: Explore how action research helps consultants serve clients with Accenture’s Client Research and Problem Identification job simulation . Phenomenological ResearchPhenomenological research investigates a phenomenon in depth, looking at people’s experiences and understanding of the situation. This sort of study is primarily descriptive and seeks to broaden understanding around a specific incident and the people involved. Researchers in phenomenological studies must be careful to set aside any biases or assumptions because the information used should be entirely from the subjects themselves. Example : A researcher wants to better understand the lived experience of college students with jobs. The purpose of this research is to gain insights into the pressures of college students who balance studying and working at the same time. The researcher conducts a series of interviews with several college students, learning about their past and current situations. Through the first few interviews, the researcher builds a relationship with the students. Later discussions are more targeted, with questions prompting the students to discuss their emotions surrounding both work and school and the difficulties and benefits arising from their situation. The researcher then analyzes these interviews, and identifies shared themes to contextualize the experiences of the students.  GE Aerospace Human ResourcesLearn the research and conflict resolution skills necessary for a career in human resources in this free job simulation from GE. Avg. Time: 3 to 4 hours Skills you’ll build: Feedback giving, communication skills, empowering with insights, basics of lean, process mapping, continuous improvement tools, dataset handling in Excel EthnographyEthnography is an immersive study of a particular culture or community. Through ethnographic research, analysts aim to learn about a group’s conventions, social dynamics, and cultural norms. Some researchers use active observation methods, finding ways to integrate themselves into the culture as much as possible. Others use passive observation, watching closely from the outside but not fully immersing themselves. Example: A company hires an external researcher to learn what their company’s culture is actually like. The researcher studies the social dynamics of the employees and may even look at how these employees interact with clients and with each other outside of the office. The goal is to deliver a comprehensive report of the company’s culture and the social dynamics of its employees. Case StudiesA case study is a type of in-depth analysis of a situation. Case studies can focus on an organization, belief system, event, person, or action. The goal of a case study is to understand the phenomenon and put it in a real-world context. Case studies are also commonly used in marketing and sales to highlight the benefits of a company’s products or services. Example: A business performs a case study of its competitors’ strategies. This case study aims to show why the company should adopt a specific business strategy. The study looks at each competitor’s business structure, marketing campaigns, product offerings, and historical growth trends. Then, using this data on other businesses, the researcher can theorize how that strategy would benefit their company. >>MORE: Learn how companies use case study interviews to assess candidates’ research and problem-solving skills. Qualitative research methods are great for generating new ideas. The exploratory nature of qualitative research means uncovering unexpected information, which often leads to new theories and further research topics. Additionally, qualitative findings feel meaningful. These studies focus on people, emotions, and societies and may feel closer to their communities than quantitative research that relies on more mathematical and logical data. However, qualitative research can be unreliable at times. It’s difficult to replicate qualitative studies since people’s opinions and emotions can change quickly. For example, a focus group has a lot of variables that can affect the outcome, and that same group, asked the same questions a year later, may have entirely different responses. The data collection can also be difficult and time-consuming with qualitative research. Ultimately, interviewing people, reviewing surveys, and understanding and explaining human emotions can be incredibly complex. Find your career fitSee what career path is right for you with our free career quiz! While qualitative research deals with data that isn’t easily manipulated by mathematics, quantitative research almost exclusively involves numbers and numerical data. Quantitative studies aim to find concrete details, like units of time, percentages, or statistics. Besides the types of data used, a core difference between quantitative and qualitative research is the idea of control and replication. “Qualitative is less subject to control (as in lab studies) and, therefore, less statistically measurable than quantitative approaches,” says King. One person’s interview about a specific topic can have completely different responses than every other person’s interview since there are so many variables in qualitative research. On the other hand, quantitative studies can often be replicated. For instance, when testing the effects of a new medication, quantifiable data, like blood test results, can be repeated. Qualitative data, though, like how people feel about the medication, may differ from person to person and from moment to moment.  JPMorgan Quantitative ResearchDiscover how bankers use quantitative methods to analyze businesses and industry trends with this free job simulation. Avg. Time: 6 to 7 hours Skills you’ll build: Programming, data analysis, Python, critical thinking, statistics, dynamic programming You can show your experience with qualitative research on your resume in your skills or work experience sections and your cover letter .
Grow your skills and explore your career options with Forage’s free job simulations . Image credit: Canva  Related Posts6 negotiation skills to level up your work life, how to build conflict resolution skills: case studies and examples, what is github uses and getting started, upskill with forage.  Top companies are hiring now! Cookie consentWe use our own and third-party cookies to show you more relevant content based on your browsing and navigation history. Please accept or manage your cookie settings below. Here's our cookie policy
Qualitative vs. quantitative research: A simple guideQuantitative research deals with numbers and statistics, while qualitative research involves pulling information from experiences and stories.  Latest posts on TipsTypeform | 08.2024 Typeform | 07.2024 From Tesla to Tushy, every successful brand is built on a foundation of both quantitative and qualitative research. Marketers and product developers use this zero-party data to frame their advertising strategies, product positioning, and brand voice—basically, everything that goes into designing and selling a product or service. When it comes to qualitative vs. quantitative research, both methods have their benefits and drawbacks in certain applications. We break down what you need to know before running your next round of market research. Qualitative vs. quantitative research: What’s the difference?Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people’s thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so. But don’t be fooled into thinking that one research method is better than the other—both require systematically applied research methods and analysis.
What is qualitative research and data?Qualitative research is like the Sherlock Holmes of the research world—it seeks to uncover the hidden stories, motivations, and intricacies that numbers can't reveal. Instead of crunching data, it dives deep into people's experiences, thoughts, and feelings to help explain certain behaviors and patterns. In qualitative research, it's not about numbers but rather words, pictures, and observations. You'll collect rich, unstructured data via interviews, focus group discussions, or open-ended surveys. Say you're a marketing rep keen on understanding how people perceive your smartphone brand. First, you organize a series of in-depth interviews with smartphone users, asking open-ended questions about their experiences with the brand. Participants share stories about their interactions, likes, dislikes, and emotional connections with the product. You also delve into social media posts, online reviews, and forum discussions to gauge the brand's online reputation. As you analyze this data, patterns begin to emerge. You find that users consistently describe the brand as "innovative" and "user-friendly." However, you also discover a recurring frustration with battery life and customer support. Qualitative research not only provides you with insights into how people perceive the brand but also dives into the emotional nuances behind their perceptions. Armed with this knowledge, you can fine-tune your advertising campaigns and product improvements to align with your target audience's genuine feelings and experiences. Pros and cons of qualitative researchQualitative research is your go-to when you want to explore the human side of data. It's like having a heart-to-heart conversation with your research subjects. Just keep in mind that, like any detective work, it comes with its own quirks and challenges. Deep insights: It's great at uncovering the "whys" and "hows" behind human behavior, providing rich insights that quantitative data can miss. Flexible and exploratory: Qualitative research allows for flexibility, so you can adapt your questions and approach when you face the unexpected. Humanizing data: Unlike numbers, qualitative research humanizes data by bringing stories and personal experiences to the forefront. It's perfect for capturing human nuances and emotions. Subjectivity: Different researchers might draw different conclusions from the same data based on their own personal feelings, experiences, or opinions, so it's crucial to stay aware of potential bias. Resource-intensive: Qualitative research demands time and effort. Conducting interviews, transcribing, and analyzing data is a labor-intensive process, which might not suit all budgets or timelines. Smaller samples: Your pool of participants tends to be smaller compared to quantitative research, making it challenging to generalize findings to a larger population. It's like diving deep into a few personal stories rather than looking at the bigger picture. Can’t always be automated: Unlike quantitative research, where you can automate data collection and analysis with software, qualitative research relies heavily on human interaction and interpretation. You can, however, create a survey with open-ended questions to collect qualitative data. Better yet, try our VideoAsk feature, which allows you to ask questions via pre-recorded video and lets respondents answer in video, voice, or text format, preserving that ever-important human element that defines qualitative data.  What is quantitative research and data?Quantitative research is all about numbers, statistics, and cold, hard data. It’s more structured and objective and helps reduce researcher biases . It gets at the “what” of a person’s behavior by answering questions like how many, how often, and to what extent? Let’s look at quantitative research in action. Imagine you're trying to pinpoint the target market for your new fitness app. You survey the app's users, collecting data on their age, gender, location, and fitness habits. The data reveals that 75% of your target users are ages 18-34, with a nearly even split between men and women. You also notice that users in urban areas are 20% more likely to use your app regularly than those in rural areas. Quantitative research doesn't stop at just counting, though. It's also about analyzing data to spot trends and differences. In this case, it's clear that your core audience consists of younger adults in urban settings, and you can tailor your marketing strategies and app features to better cater to this demographic. So, if you're a number-crunching, stats-loving kind of researcher, quantitative research is your jam.  Pros and cons of quantitative researchIn a nutshell, quantitative research is your go-to when you want solid, numerical answers. But remember, it won't tell you the whole story, and sometimes, life's questions are a bit too complex for a numbers-only approach. Keep these pros and cons in mind when running your next quantitative study: Precision with numbers: Quantitative research is like a laser-guided missile for numbers. It offers precise measurements and statistical analysis, which is great when you need concrete answers. Reproducibility: It's a cookie-cutter approach—your methods and results can be replicated by others, making it a cornerstone of scientific rigor. Generalizability: You can often apply findings to a larger population—if it works for one group, it might work for a similar one. Limited bias: Quantitative research can be a bias-buster. With structured surveys, standardized data collection methods, and statistical analysis, it's easier to minimize researcher bias and keep the study objective. Fewer resources: If you're watching your budget, quantitative research may give you more bang for your buck. It often requires fewer resources in terms of time, personnel, and money, making it a practical choice, especially for smaller-scale research projects. Limited depth: While it's king of numbers, quantitative research can be a bit shallow in understanding. It's like knowing the “what” but not the “why.” Context ignored: Sometimes context gets lost in a sea of numbers, and you might miss the bigger picture. Inflexibility: If your research question isn't easily quantifiable, you might end up with results that are difficult to decipher. Not everything can be counted or measured. Which is better: Qualitative or quantitative research?It’s a trick question. We’re not pitting qualitative and quantitative research against each other. However, one may prove more useful than the other, depending on your research goals. For example, it’s best to stick with qualitative research when: You want to explore in-depth: Choose qualitative research when you need a deep understanding of a complex phenomenon, like customer perceptions or human behavior. It's like peeling back the layers of an onion to uncover the core. You need to generate hypotheses: Qualitative research is fantastic for generating ideas or hypotheses that you can later test with quantitative research. You value the human perspective: If you want to capture emotions, stories, and personal experiences, opt for qualitative research. It's your go-to when you're interested in "the why" rather than just "the what." On the other hand, quantitative research may prove more valuable if: You need to measure and quantify: If you're after hard numbers, like percentages, averages, or correlations, quantitative research is your go-to. You want to generalize to a larger population: Quantitative research allows you to make statistically valid generalizations to a broader audience. If you plan to reach a wide market, this is your best bet. You prefer structured and standardized data collection: When consistency and minimizing bias are critical, quantitative research methods like surveys and online tests provide a structured and uniform approach. However, you aren’t limited to just one type of research method. You can use both qualitative and quantitative data to give you the most insightful information when: You need a comprehensive understanding: Sometimes, using both qualitative and quantitative research sequentially is the ideal approach. Start with qualitative research to explore a topic, identify key variables, and generate hypotheses. Then, use quantitative research to test those hypotheses on a larger scale, ensuring a more comprehensive understanding. You want to validate findings: When you've conducted qualitative research and want to make sure your findings are not just anecdotal, quantitative research can validate and generalize your insights to a broader population. You're tackling a complex problem: For multifaceted issues, using both approaches can provide a well-rounded view. Qualitative research can uncover the depth and nuances, while quantitative research can quantify the extent of the issue and help prioritize actions. Quantitative research provides evidence and predictions. Qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need answered. Research methodsQuantitative and qualitative research methods are systematic ways of collecting data and testing hypotheses. And guess what? It’s something you already do all the time. We constantly take in information from our surroundings to figure out how to interact with the people around us. The same goes for market research . A company tries to learn more about their customers and the market. Why? To develop an effective marketing plan or tweak one they already have. The method you use to do this depends on the data that will answer your key questions. Qualitative research methodsHere are some of the most common qualitative research methods: In-depth interviews: Known as IDI in market research circles, in-depth interviews are ideal for digging into people’s attitudes and experiences. Case studies: In-depth analysis of a single case or a few cases are best suited for investigating unique or complex cases in depth Focus groups: These are effective for getting several opinions in a conversational format. Participants lead the discussion, while a facilitator guides the conversation through a list of topics, questions, or projective exercises. Participant observation: Simply engaging and observing your audience day-to-day provides a firsthand view of how people interact in real-life situations. Historical research: Exploring historical documents and records helps you examine the past through primary and secondary sources, contributing to our understanding of historical events and trends and how they may relate to the current scenario. Qualitative surveys: Surveys comprised of open-ended questions provide an automated way to receive qualitative data through a quantitative approach.. Ethnography: Ethnography is a broad market research approach that involves all of the methods above in order to gain a comprehensive understanding of the culture or community being studied. Quantitative research methodsHere are some of the most common quantitative research methods: Surveys: Surveys conducted online, over the phone, and even in person with structured interview questionnaires are an efficient way of collecting data from a large pool of participants. Polls: Polls are one- or two-question surveys that are often used to gauge public opinion on an important matter (or a frivolous matter—it’s your poll). Because polls are only one or two questions, analysis is pretty much immediate. Structured observation: This is a structured form of ethnography used to measure certain actions or behaviors, such as tracking how many boxes of cereal people pick up before choosing one to purchase. Experiment: Market researchers conduct controlled, manipulated, or randomized experiments to understand how specific variables influence outcomes through methods like A/B testing or pilot testing. Quizzes: Answering a few general questions to find out which Harry Potter character you are may seem like fun and games, but interactive quizzes are a great tool for gathering information while keeping your audience engaged. Secondary data analysis: This cost-effective research method taps into big existing datasets like government databases or company records to pull relevant data. Mixed research methodsMixed research methods combine both qualitative and quantitative approaches to provide a comprehensive understanding of the question at hand. Some of the most common mixed research methods include: User testing: You’ve heard the phrase “Show, don’t tell.” So rather than asking people to explain their experiences, why not have them show you? User testing can tell you where you thrive and fall short, so you can adjust your marketing strategy accordingly. Help transcripts: Live chat or call transcripts can yield both qualitative and quantitative data. Reading and coding them can help you understand people’s pain points and challenges throughout your conversion funnel. Customer reviews: Look beyond your own surveys and check sites like Yelp or Google reviews. What are people saying about you? What do they like and dislike? The things people say and how often they say it can yield robust qualitative and quantitative data. Data analysisData analysis is the search for patterns in data, followed by the interpretation of that information to help explain why those patterns are there. It’s important to keep in mind that quantitative and qualitative data aren't mutually exclusive. Qualitative data can be translated into quantitative data. For example, you could count the number of times interviewees used a particular word to describe your product to yield quantitative data. Similarly, quantitative methods of analysis require you to explain what the patterns mean and connect them to other parts of your business—a qualitative exercise! Qualitative data analysis exampleQualitative data can be difficult to analyze since it’s largely made up of text, images, videos, and open-ended responses instead of numbers. Examples of qualitative data analysis include: Thematic analysis: Identifying and categorizing recurring themes, patterns, or concepts within the data to uncover the most prevalent and significant themes in your dataset Content analysis: Examining large amounts of text, visuals, or audio content to identify themes or patterns Discourse analysis: Dissecting the language used in the data to understand how individuals or groups construct meaning and social reality through their discourse Cross-case analysis: Comparing and contrasting multiple cases to identify commonalities and differences, helping to develop broader insights Quantitative data analysis exampleQuantitative data analysis is all about crunching numbers. It can involve presenting data models such as graphs, charts, tables, probabilities, and more. Tools like Excel, R, and Stata make it easy to track quantitative data like: Average scores and means The number of times a specific response is recorded Connections or potential cause-and-effect relationships between two or more variables The reliability and validity of results Get the right data with TypeformCongrats—you’ve learned all about the differences between qualitative vs. quantitative research. Now, the key to successful data collection is iteration. That doesn’t mean doing the same thing again and again. It means continually returning to your questions, methods, and data to spark new ideas and insights that'll level up your research —and your business. Typeform makes it easy to design and automate forms that collect both quantitative and qualitative data—no extensive interviews or focus groups required. With conditional formatting and various question types, you can gather the information you need to get more customers.  About the author Lydia is a content marketer with experience across both the B2B and B2C landscapes. Besides marketing and content, she's really into her dog Louie. Liked that? Check these out: Demographic survey question guide: Examples and best practices Demographic survey questions include age, gender, and income. Learn more about demographic question best practices, examples, and more. Typeform | 05.2024  How to survey your target audience Target audience surveys are an effective way to gather information about your ideal customer to guide marketing efforts, assess product-market fit, and more. Typeform | 11.2023  41 user experience (UX) survey questions [examples + template] User experience survey questions gauge your strengths and weaknesses from the folks using your services. Find out what to ask on your next UX survey. Typeform | 03.2024  Understanding Qualitative and Quantitative Data
 Written by:  Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights. IntroductionIn the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts. This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis. Read More: Exploring 5 Statistical Data Analysis Techniques with Real-World Examples Defining Qualitative Data Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject. This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups. Characteristics of Qualitative Data
Examples of Qualitative Data
Defining Quantitative Data Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data. Characteristics of Quantitative Data
Examples of Quantitative Data
Key Differences Between Qualitative and Quantitative DataUnderstanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:  When to Use Qualitative DataQualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice: Exploratory ResearchWhen investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses. Understanding ContextQualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis. Gaining Insights into Attitudes and BehavioursWhen the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights. Developing TheoriesQualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook. When to Use Quantitative DataQuantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice: Testing HypothesesWhen researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses. Measuring VariablesQuantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys. Generalising FindingsWhen the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions. Identifying Patterns and TrendsQuantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development. Integrating Qualitative and Quantitative Data While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights. Benefits of IntegrationIntegrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.
Examples of Integration
Challenges and Considerations While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type: Challenges of Qualitative DataThe challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data: Subjectivity and BiasOne of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data. Data OverloadQualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked. Lack of StructureQualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data. Time-Consuming NatureQualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers. Challenges of Quantitative DataQuantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations. Limits in Capturing ComplexityQuantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours. Chances for MisinterpretationNumbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data. Influence of Measurement ErrorsDue to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis. Lack of ContextQuantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied. Sample Size LimitationsSmall sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations. Confirmation BiasResearchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights. In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives. By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making. As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike. Frequently Asked QuestionsWhat is the primary difference between qualitative and quantitative data. The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses. When Should I Use Qualitative Data in My Research?Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important. Can Qualitative and Quantitative Data Be Used Together?Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights. Reviewed by:  Post written by: Smith Alex Artificial Intelligence Web Scraping vs. Web Crawling: Understanding the Differences Python Metaprogramming: Unlocking the Power of Code ManipulationYou may also like. 
Top Data Analytics Projects in 2024 for Beginners to Experienced
 Explaining Four Types of Analytics With Examples
Integrations What's new? In-Product Prompts Participant Management Interview Studies Prototype Testing Card Sorting Tree Testing Live Website Testing Automated Reports Templates Gallery Choose from our library of pre-built mazes to copy, customize, and share with your own users Browse all templates Financial Services Tech & Software Product Designers Product Managers User Researchers By use case Concept & Idea Validation Wireframe & Usability Test Content & Copy Testing Feedback & Satisfaction Content Hub Educational resources for product, research and design teams Explore all resources Question Bank Maze Research Success Hub Guides & Reports Help Center Future of User Research Report The Optimal Path Podcast  User Research Aug 19, 2024 • 17 minutes read Qualitative research examples: How to unlock, rich, descriptive insightsQualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.  Armin Tanovic Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points. There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact. In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them. Qualitative UX research made easyConduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.  7 Qualitative research methods: An overviewThere are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented. Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.
1. User interviewsA user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives. Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights. When to use user interviewsInterviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way. Interviews can be used at any stage in the product design and development process, being particularly helpful during:
How to conduct user interviews: The basics
💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform. User interviews: A qualitative research exampleLet’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'. You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide. One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?” Most respondents answer with a resounding and passionate ‘no’ with one of them expanding: “For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.” You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles. You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs. 2. Focus groupsA focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group. When to use focus groupsYou should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews. Focus groups can be used before, during, and after a product launch. They’re ideal:
How to conduct focus group studies: The basics
The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights. Focus groups: A qualitative research exampleYou’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles. However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle. Time to talk to users. You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why. A dialog develops: Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.” Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.” Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.” Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.” Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next. A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees. 3. Ethnographic researchEthnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives. Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations. When to use ethnographic researchEthnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:
How to conduct ethnographic research:
While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants. ⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants. Ethnographic research: A qualitative research exampleYou're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO. The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface. You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?” The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.” You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details. 4. Qualitative observationQualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research. When to qualitative observationQualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:
How to conduct qualitative observation:
Qualitative observation: An qualitative research exampleYou’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study. Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website. Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform. 5. Case study researchCase studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users. When to use case studiesCase studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it. As for specific phases in the product design and development process:
How to conduct case studies:
Case study research: A qualitative research exampleYour team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns. To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow. With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points. Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns. 6. Secondary researchSecondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective. Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application. When to use secondary researchRecord keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:
Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process. How to conduct secondary research:
Secondary research: A qualitative research exampleSafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research. Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached. After checking your other social media channels and review sites, the issue pops up again. With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security. By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users. 7. Open-ended surveysOpen-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses. When to use open-ended surveysUser surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:
How to conduct open-ended surveys:
Open-ended surveys: A qualitative research exampleYou're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions. To better understand if users find the new feature helpful, you create an open-ended, in-app survey. The questions you ask your users:
Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time. To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly. 💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports. Qualitative research vs. quantitative research: What’s the difference?Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different. Here are some of the key differences between qualitative research and quantitative research .
Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation. Conduct qualitative research with MazeYou’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why. So, what about how —how do you identify your 'what' and your 'why'? The answer is with a user research tool like Maze. Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster. Frequently asked questions about qualitative research examplesWhat is qualitative research? Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions. Can a study be both qualitative and quantitative? Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights. For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers. Is qualitative or quantitative research better? The choice between qualitative and quantitative research depends upon your research goals and objectives. Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions. What are some approaches to qualitative research? There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:
 Qualitative vs. quantitative data analysis: How do they differ? Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction. What is qualitative data?Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1 What is quantitative data?Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2 Key difference between qualitative and quantitative dataIt's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing. Data Types and NatureExamples of qualitative data types in learning analytics:
Examples of quantitative data types:
Methods of CollectionQualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data. Qualitative research methods Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:
Quantitative research methods Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:
Analysis techniquesQualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis. Qualitative data analysis methods Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3 Quantitative analysis techniques The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4 Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4 Qualitative and quantitative research toolsFrom the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types. Qualitative research software: NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5 ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6 SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7 R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8 Applications in Educational ResearchBoth quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions. Master Data Analysis with an M.S. in Learning Sciences From SMUWhether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals. For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.
Return to SMU Online Learning Sciences Blog Southern Methodist University has engaged Everspring , a leading provider of education and technology services, to support select aspects of program delivery. This will only take a moment
 Qualitative Research Questionnaire – Types & ExamplesPublished by Alvin Nicolas at August 19th, 2024 , Revised On August 20, 2024 Before you start your research, the first thing you need to identify is the research method . Depending on different factors, you will either choose a quantitative or qualitative study. Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data , qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also be a valuable instrument in qualitative studies. Let’s explore what qualitative research questionnaires are and how you can create one. What Is A Qualitative Research QuestionnaireQualitative research questionnaires are a structured or semi-structured set of questions designed to gather detailed, open-ended participant responses. It allows you to uncover underlying reasons and opinions and provides insights into a particular phenomenon. While quantitative questionnaires often have closed-ended questions and numerical responses, a qualitative questionnaire encourages participants to express themselves freely. Before you design your questionnaire, you should know exactly what you need so you can keep your questions specific enough for the participants to understand. For example:
Types of Qualitative Research Questions With ExamplesNow that you are familiar with what qualitative research questions are, let’s look at the different types of questions you can use in your survey . Descriptive QuestionsThese are used to explore and describe a phenomenon in detail. It helps answer the “what” part of the research, and the questions are mostly foundational. Example: How do students experience online learning? Comparative QuestionsThis type allows you to compare and contrast different groups or situations. You can explore the differences and similarities to highlight the impact of specific variables. Example: How do the study habits of first-year and fourth-year university students differ? Interpretive QuestionsThese questions help you understand the meanings people attach to experiences or phenomena by answering the “how” and “why”. Example: What does “success” mean to entrepreneurs? Evaluative QuestionsYou can use these to assess the quality or value of something. These allow you to understand the outcomes of various situations. Example: How effective is the new customer service training program? Process-Oriented QuestionsTo understand how something happens or develops over time, researchers often use process-oriented questions. Example: How do individuals develop their career goals? Exploratory QuestionsThese allow you to discover new perspectives on a topic. However, you have to be careful that there must be no preconceived notions or research biases to it. Example: What are the emerging trends in the mobile gaming industry? How To Write Qualitative Research Questions?For your study to be successful, it is important to consider designing a questionnaire for qualitative research critically, as it will shape your research and data collection. Here is an easy guide to writing your qualitative research questions perfectly. Tip 1: Understand Your Research GoalsMany students start their research without clear goals, and they have to make substantial changes to their study in the middle of the research. This wastes time and resources. Before you start crafting your questions, it is important to know your research objectives. You should know what you aim to discover through your research, or what specific knowledge gaps you are going to fill. With the help of a well-defined research focus, you can develop relevant and meaningful information. Tip 2: Choose The Structure For Research QuestionsThere are mostly open-ended questionnaires in qualitative research. They begin with words like “how,” “what,” and “why.” However, the structure of your research questions depends on your research design . You have to consider using broad, overarching questions to explore the main research focus, and then add some specific probes to further research the particular aspects of the topic. Tip 3: Use Clear LanguageThe more clear and concise your research questions are, the more effective and free from ambiguity they will be. Do not use complex terminology that might confuse participants. Try using simple and direct language that accurately conveys your intended meaning. Here is a table to explain the wrong and right ways of writing your qualitative research questions.
Tip 4: Check Relevance With Research GoalsOnce you have developed some questions, check if they align with your research objectives. You must ensure that each question contributes to your overall research questions. After this, you can eliminate any questions that do not serve a clear purpose in your study. Tip 5: Concentrate On A Single ThemeWhile it is tempting to cover multiple aspects of a topic in one question, it is best to focus on a single theme per question. This helps to elicit focused responses from participants. Moreover, you have to avoid combining unrelated concepts into a single question. If your main research question is complicated, you can create sub-questions with a “ladder structure”. These allow you to understand the attributes, consequences, and core values of your research. For example, let’s say your main broad research question is:
The intermediate questions may be:
Types Of Survey Questionnaires In Qualitative ResearchIt is important to consider your research objectives, target population, resources and needed depth of research when selecting a survey method. The main types of qualitative surveys are discussed below. Face To Face SurveysFace-to-face surveys involve direct interaction between the researcher and the participant. This method allows observers to capture non-verbal cues, body language, and facial expressions, and helps adapt questions based on participant responses. They also let you clarify any misunderstandings. Moreover, there is a higher response rate because of personal interaction. Example: A researcher conducting a study on consumer experiences with a new product might visit participants’ homes to conduct a detailed interview. Telephone SurveysThese type of qualitative research survey questionnaires provide a less intrusive method for collecting qualitative data. The benefits of telephone surveys include, that it allows you to collect data from a wider population. Moreover, it is generally less expensive than face-to-face interviews and interviews can be conducted efficiently. Example: A market research firm might conduct telephone surveys to understand customer satisfaction with a telecommunication service. Online SurveysOnline survey questionnaires are a convenient and cost-effective way to gather qualitative data. You can reach a wide audience quickly, and participants may feel more comfortable sharing sensitive information because of anonymity. Additionally, there are no travel or printing expenses. Example: A university might use online surveys to explore students’ perceptions of online learning experiences. Strengths & Limitations Of Questionnaires In Qualitative ResearchQuestionnaires are undoubtedly a great data collection tool. However, it comes with its fair share of advantages and disadvantages. Let’s discuss the benefits of questionnaires in qualitative research and their cons as well.
Qualitative Research Questionnaire ExampleHere is a concise qualitative research questionnaire sample for research papers to give you a better idea of its format and how it is presented. Thank you for participating in our survey. We value your feedback on our new mobile app. Your responses will help us improve the applications and better meet your needs. Demographic Information
Are questionnaires quantitative or qualitative research?A survey research questionnaire can have both qualitative and quantitative questions. The qualitative questions are mostly open-ended, and quantitative questions take the form of yes/no, or Likert scale rating. Can we use questionnaires in qualitative research?Yes, survey questionnaires can be used in qualitative research for data collection. However, instead of a Likert scale or rating, you can post open-ended questions to your respondents. The participants can provide detailed responses to the questions asked. Why are questionnaires good for qualitative research?In qualitative research, questionnaires allow you to collect qualitative data. The open-ended and unstructured questions help respondents present their ideas freely and provide insights. You May Also LikeThis article presents the key advantages and disadvantages of secondary research so you can select the most appropriate research approach for your study. In correlational research, a researcher measures the relationship between two or more variables or sets of scores without having control over the variables. Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers. USEFUL LINKS LEARNING RESOURCES  COMPANY DETAILS 
Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Loading metrics Open Access Peer-reviewed Research Article “I do think that accessibility is a really major thing that has come [out] of [the] pandemic”: The lived experiences of resilience and health-related quality of life among a diverse sample of graduate students during the COVID-19 pandemicRoles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Visualization, Writing – original draft * E-mail: [email protected] Affiliations Health and Rehabilitation Sciences, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada, Department of Neurobiology, University of California San Diego, San Diego, California, United States of America, Center for Empathy and Social Justice in Human Health, T. Denny Sanford Institute for Empathy and Compassion, University of California San Diego, San Diego, California, United States of America, Department of Psychology, Faculty of Science, Wilfrid Laurier University, Waterloo, Ontario, Canada  Roles Conceptualization, Visualization, Writing – review & editing Affiliations Health and Rehabilitation Sciences, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada, School of Health Studies, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada, Children’s Health Research Institute, London, Ontario, Canada Affiliations Health and Rehabilitation Sciences, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada, School of Health Studies, Faculty of Health Sciences, The University of Western Ontario, London, Ontario, Canada Roles Conceptualization, Supervision, Visualization, Writing – review & editing
The COVID-19 pandemic has negatively impacted the mental health and wellbeing of post-secondary students. Resilience has been found to serve as a protective factor against mental distress among students during the pandemic. Despite the plethora of research that exists on post-secondary students during this crisis, most studies exploring students’ health and resilience are quantitative and lack diversity. To date, the lived experiences of health-related quality of life (HRQOL) and resilience among graduate students representing diversity in age, gender, ethnicity, parental status, university, degree, and faculty during the COVID-19 pandemic remain unknown. As a part of a larger study, the purpose of this qualitative paper was to understand the lived experiences of resilience and HRQOL among a diverse sample of graduate students approximately 18 months into the COVID-19 pandemic in Ontario, Canada. A total of 14 students participated in semi-structured interviews exploring HRQOL domains, factors that supported/undermined participants’ resilience, challenges/barriers to being resilient, and participants’ inner strength. Thematic analysis revealed 5 themes: (1) cultural influences on resilience; (2) the role of privilege/power in shaping resilience; (3) how life stage and past experiences support resilience; (4) how the COVID-19 pandemic has undermined the resilience of equity-deserving groups; and (5) the role of disability/chronic pain. This work presents a unique dichotomy between how the COVID-19 pandemic has disrupted the lives of some graduate students, while simultaneously creating opportunities for others to thrive. Findings from this work underscore the importance of creating inclusive and accessible educational spaces to support graduate students’ resilience and HRQOL currently, and in times of crisis. Citation: Shillington KJ, Burke SM, Mantler T, Irwin JD (2024) “I do think that accessibility is a really major thing that has come [out] of [the] pandemic”: The lived experiences of resilience and health-related quality of life among a diverse sample of graduate students during the COVID-19 pandemic. PLoS ONE 19(8): e0309171. https://doi.org/10.1371/journal.pone.0309171 Editor: Mary Diane Clark, Lamar University, UNITED STATES OF AMERICA Received: May 8, 2024; Accepted: August 6, 2024; Published: August 20, 2024 Copyright: © 2024 Shillington et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Data requests can be sent to Western University's Research Ethics Board ( [email protected] ). In order to maintain participant anonymity, data cannot be uploaded to the repository, in line with the study approval received from the Non-Medical Research Ethics Board at the host institution. It was stated to study participants, and agreed upon when they consented to participate, that all data would be kept strictly confidential. The qualitative data may contain sensitive participant information that cannot be shared, even in de-identified formats. Funding: The author(s) received no specific funding for this work. Competing interests: The authors declare that they have no conflicts of interest. IntroductionThe COVID-19 pandemic has altered the lives of individuals, including post-secondary students [ 1 – 8 ]. In March 2020, following the World Health Organization’s (WHO) declaration of a global pandemic, the Ontario provincial government implemented public health protections that significantly impacted the lives of post-secondary students, such as stay-at-home orders that resulted in the closure of academic institutions. As such, many students were required to study from home and learn remotely during this timeframe. The ongoing COVID-19 pandemic [ 9 ] continues to be associated with challenges for students [ 10 ] and in January 2024, the WHO acknowledged that COVID-19 remains a major threat and urged member states not to dismantle established safety protocols [ 11 ]. It is not surprising then, that the COVID-19 pandemic has negatively impacted some students’ health-related quality of life (HRQOL) [ 1 – 8 ]—a measure of perceived mental, physical, and social wellbeing [ 12 ]. With respect to students’ mental wellbeing during the pandemic, Lorenzo and colleagues [ 6 ] conducted a longitudinal study (December 2020-January 2022) to assess changes in depression, anxiety, and stress scores over time among a cohort of post-secondary students ( N = 1465; 33.7% graduate; 66.1% undergraduate) from Ontario, Canada. The authors concluded that participants reported mental health problems at each time point, with 52.4% exceeding the clinical cut-off scores for depression and anxiety [ 6 ]. Additionally, Ewing and colleagues [ 4 ] conducted semi-structured interviews with Ontario post-secondary ( N = 20; first-year undergraduate) students in the Winter of 2021 to explore the psychological impacts of the COVID-19 pandemic on students with and without pre-existing mental health concerns. The authors found that post-secondary students with pre-existing mental health problems reported being able to cope more effectively with the pandemic than students without pre-existing mental health problems (Ewing et al., 2022). However, all students reported new and unique stressors associated with the pandemic, including worry for the health of loved ones and themselves, the unpredictability of future waves of the virus, and concerns regarding delays in degree completion [ 4 ]. Further, King and colleagues [ 5 ] investigated the impact of the COVID-19 pandemic on the mental health of three first-year undergraduate student cohorts: (1) 2018 (pre-pandemic); (2) 2019 (transitional); and (3) 2020 (during pandemic) at a university in Ontario, Canada. The authors found that all cohorts had high levels of clinically significant symptoms entering university; however, cohorts studying during the pandemic reported clinically higher levels of depression and anxiety compared to the pre-pandemic cohort [ 5 ]. Moreover, in a global meta-analysis that examined the prevalence of clinically significant depression and anxiety among post-secondary students ( N = 1,732,456) during the early stages of the COVID-19 pandemic, the authors found that 30.6% and 28.2% of post-secondary students reported clinical levels of depression and anxiety, respectively [ 8 ]. The COVID-19 pandemic has not only impacted post-secondary students’ mental wellbeing, it has also impacted their physical and social wellbeing. Notably, in February 2021, Ahmad and Aires [ 1 ] conducted focus groups with undergraduate students ( N = 37) at a Canadian university to ascertain the impact of the pandemic on students’ daily life, stress, and coping. Participants reported changes to their daily routine that negatively impacted their physical health and wellbeing, including a decline in physical activity, disrupted sleep, and poor dietary habits [ 1 ]. Further, participants described how the COVID-19 pandemic impacted their social connectivity, noting increased feelings of social isolation and longing for in-person classes and activities [ 1 ]. The lack of social connectedness reported by participants was not a unique experience, as it has been commonly reported by authors in the literature [ 2 , 7 ]. Specifically, in July of 2020, Appleby and colleagues [ 2 ] investigated the impact of the COVID-19 pandemic on the experiences and mental health of undergraduate students ( N = 3,013) at a university in Ontario, Canada and found that 56% of students reported feeling lonely, restless, and anxious as a result of social isolation. Participants described limited in-person interactions with peers, friends, and family which, in turn, negatively impacted their wellbeing [ 2 ]. Similarly, in March-April 2021, Morava and colleagues [ 7 ] conducted a series of focus groups with undergraduate students ( N = 37) at an Ontario university, with the purpose of exploring how the COVID-19 pandemic was affecting students’ communities of learning. Participants described a lack of social interaction and connection, resulting in feelings of isolation for many [ 7 ]. While it is evident that the COVID-19 pandemic has been negatively impacting the HRQOL of post-secondary students, specifically with respect to their mental, physical, and social wellbeing, less is known regarding how the pandemic has been impacting graduate students more specifically. To date, the majority of studies that have reported on the mental health and wellbeing of post-secondary students during the pandemic either focused on undergraduate students or combined cohorts of undergraduate and graduate students [ 1 – 8 ]. While there is value in combining cohorts (e.g., increased sample size), group differences are not typically considered, which is particularly concerning given the fact that undergraduate and graduate students respond to and experience stress differently [ 13 , 14 ]. For example, in a pre-pandemic study exploring the relationship between stress and mental health, Wyatt and Oswalt [ 13 ] found that graduate students reported higher levels of stress compared to undergraduate students and were more likely to seek mental health services. Given that the COVID-19 pandemic introduced new stressors (e.g., fear of infection) [ 15 ] while exacerbating pre-existing concerns (e.g., mental health problems, social isolation, financial insecurity) [ 1 , 16 , 17 ], understanding the experiences of graduate students during the pandemic is particularly important given their problematic stress levels pre-pandemic [ 18 ]. The Student Experience in the Research University (SERU) Consortium survey was administered to graduate and professional students ( n = 15,346) from May to July 2020 with the purpose of assessing graduate students’ mental health during the COVID-19 pandemic in the United States [ 16 ]. The authors concluded that 32% of graduate and professional students screened positive for depression, while 39% screened positive for anxiety [ 16 ]. Notably, the prevalence of depression was 2 times higher, and the prevalence of anxiety was 1.5 times higher among graduate and professional students in 2020 compared to 2019 [ 16 ]. Further, to explore what graduate students found to be particularly worrisome early in the pandemic, Kee [ 15 ] conducted a qualitative study with 7 participants during Spring 2020 at a college in the United States. Students reported experiencing fear and anxiety, especially regarding the uncertainty surrounding the virus, which were compounded by personal responsibilities [ 15 ]. Additionally, graduate students described coping mechanisms including setting work-life boundaries, receiving social support from online communities, as well as maintaining hope [ 15 ]. It is worth noting that historically, when navigating stressful experiences, graduate students have been found to be more likely than undergraduate students to use active coping strategies [ 19 ]; bolstering resilience is one method that can help students cope [ 20 ]. Though resilience is difficult to define due to varied conceptualizations, it can be broadly understood as a dynamic process wherein psychosocial and environmental factors interact to enable an individual to survive, grow and even thrive despite exposure to adversity [ 21 – 23 ]. Importantly, resilience has been found to serve as a protective factor against psychological distress during the COVID-19 pandemic [ 24 ]. In a longitudinal study conducted by Riehm and colleagues [ 24 ], the association between resilience and mental distress early in the COVID-19 pandemic (March-August 2020) was assessed among a sample of adults ( N = 6,008) living in the United States. The authors found that those who reported low to moderate levels of resilience were more likely to experience mental distress, in comparison to those who reported high levels of resilience [ 24 ]. The relationship between mental health and resilience has also been explored among graduate students globally during the COVID-19 pandemic [ 25 , 26 ]. Clark and colleagues [ 25 ] explored the association between resilience and psychological distress among health profession graduate students living in the United States during January-March 2021 of the COVID-19 pandemic. The authors concluded that higher resilience scores were associated with fewer burnout symptoms and improved overall wellbeing among graduate students [ 25 ]. Further, Oducado and colleagues [ 26 ] examined the impact of resilience on perceived stress, anxiety, and fear among graduate students ( N = 203) in the Philippines during the COVID-19 pandemic (Summer 2020). While participants experienced moderate to high levels of stress, anxiety, and fear, resilience had a protective influence, such that it was negatively associated with stress and fear [ 26 ]. It is clear that resilience plays an important role in helping to protect graduate students against mental health problems during the COVID-19 pandemic. It is evident, based on the above review of literature, that the COVID-19 pandemic has negatively impacted the HRQOL (mental, physical, and social wellbeing) of post-secondary students, including graduate students, in Ontario, Canada. Additionally, resilience has been found to serve as a protective factor against psychological distress among graduate students and adults globally [ 24 ]. To date, however, the lived experiences of HRQOL and resilience among diverse samples of graduate students during the COVID-19 pandemic remains unknown. This is particularly important as the COVID-19 pandemic has disproportionately impacted the health of marginalized groups [ 27 – 30 ]. Specifically, Boserup and colleagues [ 27 ] used data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC) and concluded that COVID-19 infection, death, and hospitalization rates were higher among racial/ethnic minority groups than White people in the United States. The authors also found that older age was associated with an increase in COVID-19-related deaths [ 27 ]. Similarly, in a rapid review conducted by Lebrasseur and colleagues [ 28 ], it was reported that the COVID-19 pandemic has perpetuated ageism, negative psychological symptoms, and poor physical health among older adults. In addition to racial/ethnic minorities and older adults, the COVID-19 pandemic has negatively impacted sexual and gender minority groups. Notably, Moore and colleagues [ 30 ] found that during the early stages of the COVID-19 pandemic (March-June 2020), adults in sexual and gender minority groups who were living in the United States reported increased symptoms of depression and anxiety and lower levels of social support compared to their cisgender counterparts. Parents have also been described as an equity-deserving population and in a study conducted by Kerr and colleagues [ 29 ], they reported high levels of depression and anxiety, as well as parental burnout and negative emotions such as worry and anger during the early months of the pandemic (April-May 2020). It is thus evident that the COVID-19 pandemic has disproportionately impacted equity-deserving groups including racial/ethnic and sexual and gender minorities, older adults, and parents; however, less is known about how the pandemic has impacted the HRQOL and resilience of graduate students belonging to these groups. Of the aforementioned studies that reported on the HRQOL and/or resilience of post-secondary students in Ontario, Canada, the study populations were primarily comprised of White, cisgender students [ 2 , 5 , 6 , 25 , 26 ]. Additionally, the majority of the described studies were quantitative in nature [ 2 , 5 , 6 , 8 , 25 , 26 ]. Only three studies were qualitative [ 1 , 4 , 7 ], and of these studies, only one reported demographic characteristics of participants [ 4 ]. This is worth noting as qualitative work lends itself to a more nuanced understanding of participants’ lived experiences [ 31 ], which is particularly important when exploring groups that are historically silenced [ 32 ]. Given that the COVID-19 pandemic has had a disproportionate impact on the health of marginalized groups such as people of colour, those of low socioeconomic status [ 33 ], sexual and gender minorities [ 30 ], and parents [ 29 ], understanding the lived experiences of graduate students who belong to these groups is critical. To our knowledge, researchers have not used purposive sampling to explore the resilience and HRQOL of a diverse sample of graduate students, making this a notable gap in the literature. To this end, and as a part of a larger mixed-methods study titled, A C ross-Sectional Examination of Ontario Graduate Students’ Levels of Resilience A nd Health- R elated Quality of Lif E During the COVID-19 Pandemic (CARE) , the purpose of this qualitative study was to understand the lived experiences of resilience and HRQOL among a diverse sample of graduate students approximately 18 months into the COVID-19 pandemic in Ontario, Canada. Study design and proceduresEthics approval for the CARE study was received from the host institution’s Non-Medical Research Ethics Board (NMREB #119239). Participants were recruited for the study through the host institution’s mass emailer system and social media platforms (i.e., Twitter, Instagram, Facebook, LinkedIn). To be eligible for the study, individuals needed to be enrolled as a full-time graduate student at an Ontario university and be able to read, write, and speak English. Interested participants were asked to click the link on the study advertisements, which brought them to a Qualtrics XM survey (Qualtrics, Provo, UT) that included the letter of information, eligibility and consent process, as well as the questionnaires a more fulsome account of the quantitative methods can be found in Shillington et al. [ 34 ]. At the end of the survey, participants were redirected to a new link where they were invited to submit their name and email address if they were interested in participating in a follow-up interview. The semi-structured interviews were intended to build from the previous findings reported in Shillington et al. [ 34 ], by capturing participants’ lived experiences of resilience and HRQOL during the COVID-19 pandemic. Given the variability in graduate student demographics, purposive sampling based on participant demographic criteria was used to obtain a diverse sample of interview participants (per the guidance of Lavrakas [ 35 ]). To achieve a diverse sample, research assistants compiled the demographics of students who expressed interest in participating in an interview. Next, a sample of graduate students were purposefully selected to represent diversity in age, gender, ethnicity, parental status, university, degree, and faculty. Additionally, students from different universities, degrees (e.g., masters, doctoral, professional), and faculties were considered. Those who were invited to participate in an interview were given the letter of information and asked to provide their availability. The number of interviews conducted was based on the work of Guest and colleagues [ 36 ], who suggested that theoretical saturation was likely to be achieved by the 12th interview. As such, 14 interviews were conducted with an additional two serving to ensure saturation had, in fact, been reached. At the beginning of each interview, participants were asked to provide verbal consent and, in an effort to diminish social desirability bias, were reminded that there were no right or wrong answers and they could refuse to answer any questions [ 37 ]. To support data credibility [ 38 ], the interviewer member checked between questions by summarizing participant responses and reflecting back. The interviews took place via Zoom ( n = 13) or the telephone ( n = 1) with a trained interviewer (KS) and ranged from 60–90 minutes in length. All interviews were transcribed verbatim by a member of the research team. Positionality and epistemological approachAll authors share the epistemological and ontological position of post-positivism. In sharing this stance, the authors believe that researchers should serve as independent observers of the social world, such that social realities should be understood from the perspective of the participant, rather than the researcher [ 39 ]. The authors acknowledge that participants are “active subjects who are productive of their social reality, not simply the objects of social forces” [39, p. 3]. Accordingly, the authors recognize their own perspectives and biases as they interact with the social worlds they are researching and acknowledge that they impose a second level of interpretation during data collection and analysis [ 39 ]. Further, given the nature of the current study and in the spirit of self-reflexivity, it is worth noting that the authors identify as White women, who have completed post-graduate degrees. At the time the research was conducted, the lead author was a graduate student, similar to participants in the study. The authors acknowledge that they might not share the same lived experiences of graduate students of diverse backgrounds; however, all authors interact with and have a vested interest in the wellbeing of graduate students. To this end, it is possible that the authors’ positionality influenced the research process. The COVID-19 pandemic context during data collectionPrior to data collection for the present study, which occurred March 14–29, 2022, Ontario had returned to a modified Step Two of the Roadmap to Reopen framework due to rapid spread of the COVID-19 Omicron variant [ 40 ]. This resulted in the closure of indoor dining and gyms, limits on indoor and outdoor gatherings, lower capacities at retail stores and malls, and the closure of schools and in-person learning [ 40 ]. By March 2022, schools had reopened, in-person learning resumed, and masks were no longer mandated in education settings [ 41 ]. Data collectionThe interviewer (KS) followed a semi-structured interview guide ( S1 Appendix ) that asked participants to share their views, insights, and reflections about the previously collected quantitative findings from the CARE study [ 34 ]. Specifically, the interviewer shared findings from the previously validated Connor-Davidson Resilience Scale-10 (measuring resilience) [ 42 ] and the RAND 36-Item Health Survey 1.0 (measuring HRQOL) [ 43 ], by verbally providing the average scores and their interpretations. The interviewer then asked participants to discuss the extent to which the findings resonated or did not resonate with them by providing additional details and examples relative to their lived experiences. While the interview data were collected in March 2022, participants were asked to reflect back on the timeframe that quantitative data was collected (August-September 2021). Additionally, to expand our understanding of students’ experiences of resilience conceptualized by Rossouw and Rossouw [ 44 ], participants were asked to describe what supported and undermined their resilience with respect to: (1) vision; (2) composure; (3) tenacity; (4) reasoning; (5) collaboration; and (6) health. When prompted, the interviewer provided examples of each domain to aid in participants’ understanding. Further, participants were asked about challenges/barriers they faced to being resilient, as well as what contributed to their inner strength. Data collection and analysis were guided by principles of credibility, dependability, confirmability, and transferability [ 38 ] ( Table 1 ). Additionally, to support the credibility of the data and minimize bias, the principal investigator served as a peer debriefer during data collection and analysis [ 38 ]. This involved the lead author and principal investigator meeting throughout the data collection stage and before analysis to explore the lead author’s personal values and perspectives that had the potential of biasing the interpretation of the data [ 45 ]. The principal investigator posed questions to aid the lead author in understanding her positionality in the inquiry [ 38 ]. Following analysis, the lead author and principal investigator reconvened to ensure the themes and associated quotes had minimal bias.
https://doi.org/10.1371/journal.pone.0309171.t001 Data analysisInterview data were analyzed by question (i.e., deductively) using an inductive content analysis approach [ 46 ], following the method for thematic analysis outlined by Braun and Clarke [ 47 ]. First, the lead author who conducted the interviews (KS) and the principal investigator (JI) met to discuss and create a preliminary codebook, based on commonalities that emerged during the interview process, as well as reflexive notes from the interviews [ 47 ]. The notes generated contributed to data triangulation, a method used to ensure credibility [ 38 , 47 ]. The initial codebook consisted of preliminary themes and associated descriptions. Next, random coding dyads were generated and consisted of the lead author (KS) and three research assistants (JC; JB; ZR), who were each initially assigned 5 transcripts (representing 10 out of 14 transcripts) to analyze using the preliminary coding structure. The use of multiple coders was to support confirmability [ 38 ]. The researchers then independently and simultaneously familiarized themselves with the data by reading and re-reading the transcripts while making notes regarding the codebook and associated themes and descriptions [ 47 ]. During this process, the researchers generated initial codes and then collated the codes into existing, modified, and/or new themes [ 47 ]. Following this process, the dyads met individually and then with the larger group to discuss inconsistencies, resolve disagreements, and bring forward any new perspectives that emerged during their read throughs [ 47 ]. This was an iterative process that involved going through the preliminary codebook (i.e., each theme and associated description) and providing space for members of the coding team to challenge the initial themes; this process continued until consensus was reached among the group [ 47 ]. As a result, refinements were made to the codebook, inclusive of the themes and working definitions [ 47 ]. Lastly, the researchers reviewed the agreed upon themes by checking to ensure that they related to the coded extracts (step 1) and then to the entire dataset (step 2) [ 47 ]. Finally, the researchers defined and named the themes to be included in the analysis [ 47 ]. Once the researchers agreed upon the coding structure, each coder was assigned 7 transcripts (i.e., two coders per transcript). The coding team then independently analyzed their assigned transcripts using the finalized codebook [ 47 ]. Upon completion, the lead author merged the analysis outputs into one report. In an effort to enable researchers to replicate the study and to support dependability [ 38 ], the methods and study procedures were documented in detail. Of the 376 participants who completed the CARE survey, 151 expressed interest in a follow-up interview, and 14 completed a semi-structured interview. The average age of interview participants was 30.3 years ( SD = 9.4), though participants ranged in age from 23 to 58 years old. One fifth of interview participants identified as non-binary (21.4%), 28.6% identified as male, and 50.0% identified as female. The majority of participants identified as non-European origins including Asian (14.3%), other North American (14.3%), Caribbean (7.1%), Latin, Central, and South American (7.1%), African (7.1%), Middle Eastern (7.1%), and mixed origins (7.1%). All participants reported residing off-campus (100%), and the majority lived in London, Ontario (57.1%) and attended Western University (85.7%). Most participants reported being single (64.3%), while some participants reported living with parents/guardians (28.6%), a spouse/partner (37.5%), friends/roommates (21.4%), children (21.4%), and/or siblings (14.3%). Additionally, 14.3% of participants reported being the primary caregiver for an individual with a health condition/disability. The majority of participants were employed (85.7%) and pursuing a Master’s (42.9%) or Doctoral degree (50%). Full demographic details of interview participants can be found in Table 2 , while full demographic details for all participants can be found in Shillington and colleagues [ 34 ]. https://doi.org/10.1371/journal.pone.0309171.t002 Five themes emerged from the data: (1) cultural influences on resilience; (2) the role of privilege/power in shaping resilience; (3) how life stage and past experiences support resilience; (4) how the COVID-19 pandemic has undermined the resilience of equity-deserving groups; and (5) the role of disability/chronic pain. In the subsequent paragraphs, themes are presented within each broader research question category (i.e., resilience and health-related quality of life) along with illustrative quotes. Although quotes may be relevant to more than one theme, they are presented in the theme in which the quote was deemed to best fit. Cultural influences on resilience.Some participants in the current study described how culture can influence a person’s resilience. For one participant (N05), being an international student was “proof” of their resilience, saying: I [do] consider myself a resilient person… [I’m] going back to the fact of being an international student, but I believe that if you’re going through the entire process of moving to another country, understanding a total[ly] different culture… communicating in a different language, or things like that… That’s a really strong proof of resilience. Another participant (N02) identified as being an Occupational Therapy student wherein their program was comprised of students with cultural similarities, saying: Maybe another thing to add about our program is that I know… they really don’t accept international students. So pretty much everyone is from Canada and a lot of people are from Ontario… A lot of people have their own friend groups locally…as well as the friend groups within the program. And then there’s a lot of cultural similarities… We’re all around the same age, so it’s very easy to get along with each other… I know in other graduate programs there is probably a lot more international students who might feel a lot more isolated or not be able to fit in quite as easily. Interestingly, one participant described feeling as though they were from a different culture due to their age. This participant (N09) said, “It’s almost like a cultural difference. It’s almost like age is like a… I’m being… perceived as if I’m from a different culture….” Another participant (N12) underscored how the practice of meditation and mindfulness has a cultural importance to their resilience saying, “…a dedicated meditation and mindfulness practice and yoga practice… is very important for myself in a cultural sense….” Further, one participant (N08) highlighted the intersection of culture and inclusivity to support their resilience saying, “Being around people who are like-minded is really important, and being in spaces that are community-based is really important. So be that, a queer community space or… I volunteered for a queer library for a while and that was fantastic….” Thus, even though participants’ interpretations and understanding of culture differed, they described how it influenced their resilience in various ways. The role of privilege/power in shaping resilience.Many participants underscored privilege as a factor that supported their resilience, especially with regard to having financial freedom. This was emphasized by one individual (N02) who said: I am in a very lucky… more privileged position, in that my parents are supporting the cost of my degree. So, I don’t have to work during the school year, which is a big benefit for me, and which I [can] see is very draining for some of my classmates. And I’m assuming to be very draining for a lot of other grad students who aren’t as lucky or in as comfortable of a position. Similarly, another participant (N08) described how being open to opportunities can support resilience while acknowledging that not everyone is given equal opportunity, saying: I think as much as one door closes, another will open, and you need to just look for that opportunity. And again, I am aware that this comes with a little bit of privilege to say that. If you’re in a situation where you are living pay cheque to pay cheque, this is probably a lot less true. But for somebody who has the privilege that I do, opportunities will present themselves to you if you take them…. Additionally, one individual (N09) reflected on the increased ease to be resilient if you have financial stability saying, “I had a good childhood, good family, good income. And those things have made it so that it’s easier, I think, to carry on. If you’re not… incredibly worried about finances or… the basics, then… maybe you can be more resilient.” Another individual (N07) highlighted how the COVID-19 pandemic helped to shed light on inequities and the role that privilege plays in shaping resilience saying: I think that I’m quite fortunate… I realize that I have a lot of privilege that comes… with my financial stability, having a supportive partner, with my ethnic background… I realize and identify that as we’ve gone through the pandemic, we’ve certainly brought a lot more awareness to racism and discrimination and how that affects our health and our position, our susceptibility to infectious disease. So, I think I’ve become more aware of the privileges that I do have because it would not be the same for me if I had a different skin colour or different economic status, right? If I didn’t have the job that I have with the support that I have… Some of which comes from hard work, investment in my education early on when others in my same life stage weren’t, but some of it is just pure luck as well, right? In the up-bringing I had, the family I was born into, the country I was born in, for example… So, I think that’s important to identify…. Thus, while many participants identified as being resilient, they also acknowledged how privilege played a role in their resilience. Conversely, some students described an unbalanced power dynamic in academia, which negatively impacted their resilience. This was emphasized by one participant (N12) who said: I think it just stems from norms in academia in any case, right? Which is like, you’re not to speak truth to power [chuckles], you’re not to really question how things are set up… I would argue that a lot of people our age do see that academic norms are quite harmful and they’re like, ‘[It] seems very unnecessary, why don’t we do it differently?’ But then for profs… who have made their careers in those spaces, they kind of become agents of reproducing those kind of norms, even though they’ve… been negatively affected by them [the norms] themselves. Similarly, another participant (N03) highlighted challenges within the student-supervisor dynamic, specific to the COVID-19 pandemic, saying: It felt like there was kind of a lack of… empathy, or just [a lack of] understanding of where we were coming from… It’s a little bit challenging to be resilient when it feels like the person on the other side doesn’t necessarily understand… like what you’re going through. Because it is a unique experience to be trying to do grad school, especially while there’s a pandemic going on. And so, I think when there’s that miscommunication or lack of understanding, it becomes a little bit more difficult to be resilient because it doesn’t feel like you’re necessarily supported the way that you want to be. How life stage and past experiences support resilience.Participants described factors that supported their resilience, including the stage of life they were in and learning from past experiences. Even though many participants described going through difficult times, they spoke to how those experiences helped to strengthen their resilience, making participants more equipped to deal with future hardships. This was emphasized by one participant (N04) who reflected on how age can impact resilience, by describing the difference between going through challenging life experiences when they were young compared to now, saying: I guess having been resilient from a young age has helped me to understand now that I can deal with things a lot better than I thought I could… I have a self-awareness that we’ve established comes from resilience. I’ve been able to, again, ask for those supports… [and] identify or learn skills to put into my toolbox… So, learning new skills to self-regulate or to cope… maybe healthier or more productive ways of coping than the ways I used when I was having to be resilient from the beginning, without the help. Another participant (N10) echoed this sentiment as they “considered [themself to be] a highly resilient person given several [life] experiences.” Many participants perceived their resilience to be high because of having gone through challenging experiences, as highlighted by one participant (N11) who said: I feel like my perception of my own resilience is higher now because… I have the hindsight of thinking, ‘Oh, I’ve succeeded in these difficult situations and come out unscathed mostly so… Clearly, I can do it,’ which I feel like makes it easier to be optimistic moving forward about resilience. Reflecting on previous life experiences to overcome current challenges was commonly reported by individuals, as another participant (N12) emphasized the importance of “remembering that you can go through some incredibly, incredibly difficult times… but at the end of the day… your failures are not going to be as high stakes as they feel at the time.” Additionally, one participant (N07) highlighted how their life stage played a role in their resilience saying: I don’t know the average age of graduate students, but I think I’m probably a bit older. I’m knocking on the door of the fourth decade of my life… I had a big space between my master’s and my doctorate and so I think… learning through that probably helps me, for sure… and knowing that sometimes it takes experience, practice, and just hard work…. For this participant, being of older age and having more life experience contributed to their resilience. How the COVID-19 pandemic has undermined the resilience of equity-deserving groups.Participants described how the COVID-19 pandemic, inclusive of the protections implemented and the associated uncertainty, has undermined the resilience of equity-deserving groups including parents and immunocompromised and disabled people. Notably, one individual (N07) discussed the challenge of balancing evolving information, anxieties, and parenting responsibilities, saying: Of course, at the beginning of the pandemic there was a lot of unknowns and anxiety and concerns, especially clinically with the evolving information… So… it’s just a bit challenging to balance, especially [with]…school age children who I don’t have childcare [for] because of COVID. Other individuals underscored how the length of the pandemic has undermined their resilience, including the lack of support for immunocompromised and disabled people. For example, one participant (N03) said: The longer things [the pandemic has gone] on… the less resilient I felt… Initially… you don’t really know how long things will be the way they are and so… the hope aspect plays into it… It’s easier to… be resilient and… buckle up and go along for the ride. But I think the longer things continue to go on, it just felt a little bit more challenging. Another participant (N08) described the pandemic as “eye-opening” regarding a lack of support for at-risk groups saying, “It’s been really eye-opening to see how many people are not in support of immunocompromised or disabled people and are not willing to wear masks for [those] groups… It’s a little shocking….” Whether it was managing multiple roles, the length of the pandemic, or the lack of compassion for at-risk groups, it is evident that the COVID-19 pandemic has undermined the resilience of equity-deserving people in the current study. Health-related quality of lifeThe role of disability/chronic pain.. Participants reflected upon how having chronic pain and/or a disability might impact an individual’s role as a graduate student. For example, one individual (N07) emphasized that ill health does not always manifest as visible symptoms saying, “It’s hard to know… other people’s personal health, especially when you just… see them in class and… that might not represent their health… you know? Maybe they have a big chronic disease, and they appear fine in class, but they struggle outside of that….” Another individual (N01), while reflecting on the challenges they experienced during a time when they had broken their foot and that “[i]f I had a longer-term disability, I could see how that would be impactful.” Other participants who identified as having a disability described how it impacted their day-to-day as a graduate student. One individual (N11) with a physical disorder described changing limitation experiences based on their medical condition, saying, “I find most of the time I don’t have any limitations, and then some of the time… I have different flare-up issues, and on those particular days… I do have issues.” Another participant described a history of knee pain, having undergone four knee surgeries. For this individual, the lockdown period of the COVID-19 pandemic has had a negative impact on their disability as they were not able to rely on certain exercises/activities at the gym, due to gym closures, to manage their pain (N06). In contrast, one participant (N08) emphasized how the COVID-19 pandemic has increased accessibility, which in turn improved their academic performance. Pre-pandemic, this individual (N08) described regularly missing class due to health issues, which meant they were “always running to catch up with everybody else” instead of being on track with the rest of the class. Thus, “having [the] opportunity to actually attend every single class [remotely]… radically changed [their] performance”, such that they were able to “perform to [their] highest capabilities as a student.” Additionally, this participant (N08) emphasized: There’s been such a… disparate experience of the pandemic based on disability, be that mental health or a physical disability… I feel like so many of the benefits that were brought to me by the pandemic by having classes online, have extremely negatively impacted other people. Like the average take on online classes is [a] strong hatred, strong dislike [by some other students], right? And I’m over here like ‘No, this is the best class I’ve ever had!’ …I do think that accessibility is a really major thing that has come [out] of [the] pandemic and I think that the binary distinction has really increased between the two because of things like online classes. So yeah, I think… [the] disabled experience is very different sometimes. Interestingly, another participant (N04) suggested that “…the people who, because of the pandemic, are thriving in the online learning environment or in the hybrid environment, have seen an increase in their health.” Therefore, while few participants in the current study identified as having a disability, it is important to highlight their experiences to provide adequate support, especially because the COVID-19 pandemic has improved accessibility in educational settings. The purpose of this qualitative study was to understand the lived experiences of resilience and HRQOL among a diverse sample of graduate students approximately 18 months into the COVID-19 pandemic in Ontario, Canada. Participants described how culture, life stage, and past experiences can influence resilience, the role of privilege/power in shaping resilience, and ways in which the COVID-19 pandemic has undermined the resilience of equity-deserving groups. Additionally, participants underscored the role of disability and chronic pain as they related to daily living during the pandemic. Though the understanding and conceptualization of culture differed among participants, many underscored the ways in which culture intersected with resilience. For one participant, being an international student was “proof” of their resilience, as they experienced unique stressors including moving to another country and learning a new culture and language. This finding aligns with work conducted by Larcombe and colleagues [ 48 ], who suggested that international students are relatively resilient and are able to cope with the unique stressors they encounter. Specifically, the authors concluded that international students in their study reported higher levels of self-compassion than domestic students, suggesting that self-compassion might buffer against the stressors that international students encounter and foster resilience [ 48 ]. This said, Larcombe and colleagues [ 48 , 49 ] also recommended enhancing environmental psychosocial resources such as social connections and belonging, as these resources have been found to predict student wellbeing. This recommendation aligns with findings from the current study, as one participant emphasized how being a part of “like-minded” community-based groups, such as queer community spaces, can support resilience. In line with this finding, Abreu and colleagues [ 50 ] explored how community and cultural values have informed the experiences of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people ( N = 130) during the COVID-19 pandemic (May-June 2020). The authors concluded that participants relied on intergenerational resilience passed down from their ancestors in the LGBTQ community to help them navigate the COVID-19 pandemic [ 50 ]. In addition to culture, life stage and past experiences were identified by participants as factors that can influence resilience. Specifically, some participants identified that being of older age supported their resilience. This finding is not surprising, as Hayman and colleagues [ 51 ] concluded that late life “is characterised by a unique balance between losses, associated with vulnerability and [social, psychological, and attitudinal] resource restrictions, and potential gains based upon wisdom, experience, autonomy and accumulated systems of support, providing a specific context for the expression of resilience” (p. 577). The authors also suggested that resilience may be higher in older adults compared to other ages, as a result of past life experiences [ 51 ]. This aligns with findings from the current study, as many participants perceived their resilience to be high as a result of having gone through challenging life experiences. This is not surprising, as some evidence suggests that while experiencing adverse life events can have short-term negative impacts, it can also lead to increased resilience in the long-term [ 52 ]. Specifically, in a report by Seery [ 52 ], the author concluded that individuals with some prior lifetime adversity were less likely to be negatively affected by recent adversity compared to those with no history of adversity or high lifetime adversity. Thus, it is not surprising that participants in the current study attributed their resilience to having experienced difficult life experiences. Graduate students in the current study also identified privilege as a factor that contributed to resilience. The relationship between privilege and resilience is largely unknown in the literature; however, lay literature suggests that people tend to assume members of marginalized groups are resilient because of their identities [ 53 ]. Interestingly, some students in our study suggested that it might be easier to be resilient because of their privileges (e.g., economic status, race), providing a differing perspective than the aforementioned discourse. It is worth noting that while 64% of study participants identified as a person of colour, more research is needed to explore the relationship between resilience and privilege. Importantly, researchers argue that resilience should be revisited and redefined in light of associated inherent biases and racism, as the field is dominated by White middle-class voices [ 54 ]. In contrast to privilege, some participants in the current study discussed how the norms of academia can be harmful, including power imbalances and a lack of empathy from supervisors. These findings align with what is reported by Cho and Hayter [ 18 ] in their systematic review (2000–2018; N = 34 studies) on the impact of stress among graduate students. Specifically, these authors concluded that the academic community and environment can either positively or negatively impact student stress [ 18 ]. Common sources of stress among graduate students included academic demands, funding sources, individual circumstances, and level of support from peers and advisors [ 18 ]. Notably, Cho and Hayter [ 18 ] concluded that graduate students who experienced positive advising, mentoring, and support tended to experience less stress. While participants described several factors that supported their resilience, they also highlighted factors that undermined their resilience; most notably, the COVID-19 pandemic. Participants underscored how the COVID-19 pandemic has disproportionately impacted equity-deserving groups including parents and individuals who are immunocompromised and/or disabled. Notably, one participant underscored difficulty balancing the unknowns of the pandemic, evolving information, and parenting school-aged children without childcare. This finding aligns with previous qualitative work, as participants in a study conducted by Obeng and colleagues [ 55 ] emphasized that the early months of the pandemic were particularly stressful for parents who had to work and homeschool their children simultaneously during the week. Similarly, Petts and colleagues [ 56 ] examined whether loss of childcare and new homeschooling demands were associated with employment outcomes for parents during the early months of the COVID-19 pandemic (April 2020). For parents of school-aged children, the authors concluded that homeschooling was associated with adverse employment outcomes among mothers, but not fathers [ 56 ]. This is particularly concerning given that 50% of participants in the current study identified as female and 21% reported that children were members of their households. Parents are not the only group that have been disproportionately affected by the COVID-19 pandemic, as a participant in the current study also highlighted a lack of support towards immunocompromised and disabled people. This finding aligns with work conducted by Rechner and colleagues [ 57 ], who conducted a qualitative study with individuals with disabilities and their caregivers, service providers, and elected governmental representatives in Delaware, United States. Participants in this study ( N = 25) emphasized how the disability community has been overlooked in response to the COVID-19 pandemic. Additionally, schools provide one-on-one support for children with disabilities similar to the support provided by day programs for adults with disabilities; however, much of this support was lost as a result of school and business closures during the early months of the pandemic [ 57 ]. This resulted in additional responsibilities for caregivers during the day and changes in daily routines, a finding that is particularly noteworthy given that 14% of participants in the current study identified as a primary caregiver for individuals with a health condition/disability. Some participants in the current study described the role of disability/chronic pain in relation to their identity as a graduate student. Notably, participants who did not overtly identify as having a disability/chronic pain empathized with those who did, while others described varied lived experiences. Specifically, one student underscored how the pandemic created barriers for them, while another highlighted how the pandemic improved accessibility. This is one of the first studies to shed light on the lived experiences of disability/chronic pain among students during the COVID-19 pandemic; however, in a pre-pandemic study where Hamilton and colleagues [ 58 ] explored the experiences of students ( N = 77) with chronic illnesses in the United Kingdom, participants described feeling undervalued because the university did not prioritize disability-related needs and accessibility. In contrast, a participant in the current study emphasized how the COVID-19 pandemic “levelled the playing field” and improved accessibility by shifting to an online/hybrid learning format. Benson [ 59 ] argues that hybrid learning is an example of “universal design”, which has been recommended by researchers in the education sector as an inclusive approach to engage students with disabilities in the learning process [ 60 ]. Thus, while the dominate discourse in the literature highlights the disadvantages of online learning (e.g., lack of interaction, technical difficulties, increased screen time), as well as students’ desire to return in-person [ 1 ], consideration needs to be given to students with disabilities and chronic illnesses; this study is one of the first to shed light on this perspective. LimitationsThis paper is not without limitations. First, while the authors used purposive sampling to obtain a diverse sample of graduate students, they were limited to participants who completed the CARE survey and expressed interest in interview participation. Thus, while a notable strength of the study is the diverse population, it is worth noting that half of the participants identified as female and over a quarter were of European origins. In the future, researchers interested in conducting research with minority populations may wish to specifically target ethnic and gender diverse groups. Second, the interview guide was informed by the quantitative data from the larger study [ 34 ], which might have influenced participants’ responses, as students were asked the extent to which they resonated with study findings. This said, to mitigate social desirability bias, participants were reminded at the start of each interview that there were no right or wrong answers and that the research team was only interested in what was true for them. Further, given that the interview guide was informed by the quantitative results, the time between quantitative and qualitative data collection was a limitation. Specifically, quantitative data collection occurred between August and September 2021; however, to allow time for analysis and interview scheduling, qualitative data were not collected until March 2022. Thus, when asked the extent to which they resonated with study findings, participants had to reflect back to the time of quantitative data collection which, given the evolving nature of the COVID-19 pandemic, was a challenge. In the future, researchers may wish to shorten the timeframe between quantitative and qualitative data collection or develop an interview guide that is not informed by quantitative data. The purpose of this qualitative paper was to understand the lived experiences of resilience and HRQOL among a diverse sample of graduate students during the COVID-19 pandemic in Ontario, Canada. Findings from this study underscore factors that support and undermine graduate students’ resilience, as well as how the pandemic impacted the health of participants. Notably, this paper shed light on the role of disability and chronic pain as they related to daily living during the pandemic. This work presents a unique dichotomy between how the COVID-19 pandemic disrupted the lives of some graduate students, while simultaneously creating opportunities for others to thrive. Findings from this study underscore the importance of creating inclusive and accessible educational spaces to support graduate students’ resilience and HRQOL. Researchers may wish to build off these study findings by exploring the relationship between privilege and resilience, as well as investigating students with disabilities’ perceptions of the online learning environment, and how it might have changed throughout the ongoing COVID-19 pandemic. Supporting informationS1 appendix.. https://doi.org/10.1371/journal.pone.0309171.s001 AcknowledgmentsWe would like to thank the participants of this study, as well as Jayme Burke, Jaclyn Cook, and Zoha Raza for their assistance on the project.
 Investigating experiences of stigma and its impact on substance use recovery among residents of recovery residences in the United States: A scoping review protocol. BSGH020
The objective of this scoping review is to systematically review the literature on stigma experienced by residents in recovery residences and its impact on substance use recovery outcomes. The review will use the PRISMA-ScR framework to identify studies focused on stigma and recovery in recovery residences published in English within the United States since 2000, including qualitative, quantitative, and mixed-methods studies. Data will be extracted and analyzed thematically to identify gaps in the literature and inform future research and policy development. Preliminary findings suggest that stigma, including labeling and discrimination, significantly hinders recovery by promoting secrecy and withdrawal among residents. Proximity to recovery residences has been shown to reduce community stigma, indicating the potential for better integration and acceptance. This study aims to provide a comprehensive understanding of stigma in recovery residences, its effects on substance use recovery, and recommendations for creating supportive recovery environments. The significance of this study lies in its potential to inform policy, practice, and research, highlighting the need for stigma reduction to improve recovery outcomes in recovery residences. By addressing gaps in the literature, the findings will contribute to developing more effective interventions and supportive environments for individuals recovering from substance use. Competing Interest StatementThe authors have declared no competing interest. Funding StatementThe author(s) received no specific funding for this work. Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. The details of the IRB/oversight body that provided approval or exemption for the research described are given below: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Data AvailabilityNo datasets were generated or analyzed during the current study. View the discussion thread. Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article.  Citation Manager Formats
Subject Area
|
COMMENTS
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test ...
Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys ...
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).
Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc. Qualitative research question examples
Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality. Qualitative research allows you to understand concepts or experiences. Let's look at how you'll use these approaches in a research project a bit closer: Formulating a hypothesis.
For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for ...
This is an important cornerstone of the scientific method. Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average.
Qualitative research is the opposite of quantitative research, which involves collecting and analysing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history. Qualitative research question examples
Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions. On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships.
However, qualitative research can be time-consuming, and data analysis may be subjective. In contrast, quantitative research provides objective and quantifiable data, making it easier to draw conclusions and establish causation. It enables researchers to collect data from large samples, increasing the generalizability of findings.
Quantitative research is used in data-oriented research where the objective of research design is to derive "measurable empirical evidence" based on fixed and pre-determined questions. The flow of research, is therefore, decided before the research is conducted. Where as, qualitative research is used where the objective is research is to ...
Qualitative data describes, while quantitative data is expressed using numbers. (dirkcuys) There are two types of data. Qualitative data is descriptive information about characteristics that are difficult to define or measure or cannot be expressed numerically. Quantitative data is numerical information that can be measured or counted.
Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. Indicators of qualitative research include:
It explores the "how" and "why" of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the "when" and "where," utilizing tools like surveys, experiments, and ...
Quantitative designs can be experimental, quasi-experimental, descriptive, or correlational. Qualitative is usually more subjective, although like quantitative research, it also uses a systematic approach. Qualitative research is generally preferred when the clinical question centers around life experiences or meaning.
Because qualitative and quantitative studies collect different types of data, their data collection methods differ considerably. Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society.
Qualitative research is defined as an exploratory method that aims to understand complex phenomena, often within their natural settings, by examining subjective experiences, beliefs, attitudes, and behaviors. Unlike quantitative research, which focuses on numerical measurements and statistical analysis, qualitative research employs a range of ...
Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well.
Tags: apa, qualitative_research, quantitative_research_methods, social work, social work research Except where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .
Qualitative data focuses on descriptive variables that cannot be measured or assigned a numeric value. An example of this is the color of fruit, or the feelings of a person to a specific stimulus.
Qualitative research seeks to understand people's experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people's beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in ...
Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people's thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so.
Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.
Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user's attitudes and opinions.
Key difference between qualitative and quantitative data. It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing. Data Types and Nature. Examples of qualitative data types in learning analytics:
Before you start your research, the first thing you need to identify is the research method.Depending on different factors, you will either choose a quantitative or qualitative study.. Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences.
Further, given that the interview guide was informed by the quantitative results, the time between quantitative and qualitative data collection was a limitation. Specifically, quantitative data collection occurred between August and September 2021; however, to allow time for analysis and interview scheduling, qualitative data were not collected ...
The objective of this scoping review is to systematically review the literature on stigma experienced by residents in recovery residences and its impact on substance use recovery outcomes. The review will use the PRISMA-ScR framework to identify studies focused on stigma and recovery in recovery residences published in English within the United States since 2000, including qualitative ...