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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing 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.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 .

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Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

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Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research, what is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

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A variable is a characteristic that can change and have more than one value, such as age, height, and weight. But what are the different types of variables?

Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.

Textual analysis is the method of analysing and understanding the text. We need to look carefully at the text to identify the writer’s context and message.

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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 .

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 .

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 .

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 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.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

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.

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Home » Dissertation Methodology – Structure, Example and Writing Guide

Dissertation Methodology – Structure, Example and Writing Guide

• Table of Contents

Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

• Introduction : This section should explain the importance and goals of your research .
• Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
• Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
• Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
• Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
• Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
• Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

• Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
• Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
• Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
• Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
• Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
• Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
• Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

• Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
• Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
• Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

• If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
• If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

• If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
• If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

• Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
• Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

• Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

• Acknowledgements
• Literature Review (or it may be interspersed throughout the dissertation)
• Methodology
• Results/Findings
• References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

• Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
• Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
• Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
• Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
• Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
• Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
• Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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The Top 3 Types of Dissertation Research Explained

Preparing for your doctoral dissertation takes serious perseverance. You’ve endured years of studies and professional development to get to this point. After sleepless nights and labor-intensive research, you’re ready to present the culmination of all of your hard work. Even with a strong base knowledge, it can be difficult — even daunting — to decide how you will begin writing.

By taking a wide-lens view of the dissertation research process , you can best assess the work you have ahead of you and any gaps in your current research strategy. Subsequently, you’ll begin to develop a timeline so you can work efficiently and cross that finish line with your degree in hand.

What Is a Dissertation?

A dissertation is a published piece of research on a novel topic in your chosen field. Students complete a dissertation as part of a doctoral or PhD program. For most students, a dissertation is the first substantive piece of academic research they will write. 

Because a dissertation becomes a published piece of academic literature that other academics may cite, students must defend it in front of a board of experts consisting of peers in their field, including professors, their advisor, and other industry experts. 

For many students, a dissertation is the first piece of research in a long career full of research. As such, it’s important to choose a topic that’s interesting and engaging.

Types of Dissertation Research

Dissertations can take on many forms, based on research and methods of presentation in front of a committee board of academics and experts in the field. Here, we’ll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree.

1. Qualitative

The first type of dissertation is known as a qualitative dissertation . A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies. This type of research relies on non-numbers-based data collected through things like interviews, focus groups and participant observation. 

The decision to model your dissertation research according to the qualitative method will depend largely on the data itself that you are collecting. For example, dissertation research in the field of education or psychology may lend itself to a qualitative approach, depending on the essence of research. Within a qualitative dissertation research model, a candidate may pursue one or more of the following:

• Case study research
• Autoethnographies
• Narrative research 
• Grounded theory 

Although individual approaches may vary, qualitative dissertations usually include certain foundational characteristics. For example, the type of research conducted to develop a qualitative dissertation often follows an emergent design, meaning that the content and research strategy changes over time. Candidates also rely on research paradigms to further strategize how best to collect and relay their findings. These include critical theory, constructivism and interpretivism, to name a few. 

Because qualitative researchers integrate non-numerical data, their methods of collection often include unstructured interview, focus groups and participant observations. Of course, researchers still need rubrics from which to assess the quality of their findings, even though they won’t be numbers-based. To do so, they subject the data collected to the following criteria: dependability, transferability and validity. 

When it comes time to present their findings, doctoral candidates who produce qualitative dissertation research have several options. Some choose to include case studies, personal findings, narratives, observations and abstracts. Their presentation focuses on theoretical insights based on relevant data points. 

2. Quantitative

Quantitative dissertation research, on the other hand, focuses on the numbers. Candidates employ quantitative research methods to aggregate data that can be easily categorized and analyzed. In addition to traditional statistical analysis, quantitative research also hones specific research strategy based on the type of research questions. Quantitative candidates may also employ theory-driven research, replication-based studies and data-driven dissertations. 

When conducting research, some candidates who rely on quantitative measures focus their work on testing existing theories, while others create an original approach. To refine their approach, quantitative researchers focus on positivist or post-positivist research paradigms. Quantitative research designs focus on descriptive, experimental or relationship-based designs, to name a few. 

To collect the data itself, researchers focus on questionnaires and surveys, structured interviews and observations, data sets and laboratory-based methods. Then, once it’s time to assess the quality of the data, quantitative researchers measure their results against a set of criteria, including: reliability, internal/external validity and construct validity. Quantitative researchers have options when presenting their findings. Candidates convey their results using graphs, data, tables and analytical statements.

3. Mixed-Method

Many PhD candidates also use a hybrid model in which they employ both qualitative and quantitative methods of research. Mixed dissertation research models are fairly new and gaining traction. For a variety of reasons, a mixed-method approach offers candidates both versatility and credibility. It’s a more comprehensive strategy that allows for a wider capture of data with a wide range of presentation optimization. 

In the most common cases, candidates will first use quantitative methods to collect and categorize their data. Then, they’ll rely on qualitative methods to analyze that data and draw meaningful conclusions to relay to their committee panel. 

With a mixed-method approach, although you’re able to collect and analyze a more broad range of data, you run the risk of widening the scope of your dissertation research so much that you’re not able to reach succinct, sustainable conclusions. This is where it becomes critical to outline your research goals and strategy early on in the dissertation process so that the techniques you use to capture data have been thoroughly examined. 

How to Choose a Type of Dissertation Research That’s Right for You

After this overview of application and function, you may still be wondering how to go about choosing a dissertation type that’s right for you and your research proposition. In doing so, you’ll have a couple of things to consider: 

• What are your personal motivations? 
• What are your academic goals? 

It’s important to discern exactly what you hope to get out of your doctoral program . Of course, the presentation of your dissertation is, formally speaking, the pinnacle of your research. However, doctoral candidates must also consider:

• Which contributions they will make to the field
• Who they hope to collaborate with throughout their studies
• What they hope to take away from the experience personally, professionally and academically

Personal Considerations

To discern which type of dissertation research to choose, you have to take a closer look at your learning style, work ethic and even your personality. 

Quantitative research tends to be sequential and patterned-oriented. Steps move in a logical order, so it becomes clear what the next step should be at all times. For most candidates, this makes it easier to devise a timeline and stay on track. It also keeps you from getting overwhelmed by the magnitude of research involved. You’ll be able to assess your progress and make simple adjustments to stay on target. 

On the other hand, maybe you know that your research will involve many interviews and focus groups. You anticipate that you’ll have to coordinate participants’ schedules, and this will require some flexibility. Instead of creating a rigid schedule from the get-go, allowing your research to flow in a non-linear fashion may actually help you accomplish tasks more efficiently, albeit out of order. This also allows you the personal versatility of rerouting research strategy as you collect new data that leads you down other paths. 

After examining the research you need to conduct, consider more broadly: What type of student and researcher are you? In other words, What motivates you to do your best work? 

You’ll need to make sure that your methodology is conducive to the data you’re collecting, and you also need to make sure that it aligns with your work ethic so you set yourself up for success. If jumping from one task to another will cause you extra stress, but planning ahead puts you at ease, a quantitative research method may be best, assuming the type of research allows for this. 

Professional Considerations

The skills you master while working on your dissertation will serve you well beyond the day you earn your degree. Take into account the skills you’d like to develop for your academic and professional future. In addition to the hard skills you will develop in your area of expertise, you’ll also develop soft skills that are transferable to nearly any professional or academic setting. Perhaps you want to hone your ability to strategize a timeline, gather data efficiently or draw clear conclusions about the significance of your data collection. 

If you have considerable experience with quantitative analysis, but lack an extensive qualitative research portfolio, now may be your opportunity to explore — as long as you’re willing to put in the legwork to refine your skills or work closely with your mentor to develop a strategy together. 

Academic Considerations

For many doctoral candidates who hope to pursue a professional career in the world of academia, writing your dissertation is a practice in developing general research strategies that can be applied to any academic project. 

Candidates who are unsure which dissertation type best suits their research should consider whether they will take a philosophical or theoretical approach or come up with a thesis that addresses a specific problem or idea. Narrowing down this approach can sometimes happen even before the research begins. Other times, candidates begin to refine their methods once the data begins to tell a more concrete story.

Next Step: Structuring Your Dissertation Research Schedule

Once you’ve chosen which type of dissertation research you’ll pursue, you’ve already crossed the first hurdle. The next hurdle becomes when and where to fit dedicated research time and visits with your mentor into your schedule. The busyness of day-to-day life shouldn’t prevent you from making your academic dream a reality. In fact, search for programs that assist, not impede, your path to higher levels of academic success. 

Find out more about SNU’s online and on-campus education opportunities so that no matter where you are in life, you can choose the path that’s right for you.

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Everything You Need to Know About Qualitative Vs. Quantitative Research

Students working on their dissertation often get confused about the difference between qualitative and quantitative research approach. Two main differences between these approaches are: (1) the types of data used and (2) how the data are analyzed. Qualitative research deals primarily with text and meaning making, while quantitative research includes the use of with numbers and statistics.

Qualitative Research vs. Quantitative Research

The section highlights the fundamental difference between qualitative and quantitative research designs:

Qualitative Research

Aim: To obtain an enhanced, in-depth understanding of participants’ underlying experiences, opinions, concepts, or problems, and in some cases theory testing and generation.

Sample Size: Small sample sizes are needed.

Expression Unit: Text, dialogue, images, videos, artefacts etc.

Use Case: Used to gain a better understanding of a topic, situation, or problem using participants’ lived experiences.

Data Collection Methods: Interviews with open-ended questions, literature reviews, focus groups, ethnography, and observations expressed in narratives.

Data Analysis Method: Thematic analysis, content analysis, and discourse analysis.

Quantitative Research

Aim: T o generate knowledge, confirm/refute theory, create an understanding of social, psychological and economic processes using numbers. Quantitative studies also aim to increase generalizability of findings.

Sample Size : Large sample of respondents are required.

Expression Unit: Numbers, tables, Graphs, etc.

Use Case : Used to test or confirm a theory/hypothesis

Data Collection Methods: Experiments, quasi-experiment, surveys with closed-ended questions, observations in numerical form, etc.

Data Analysis Method: Simple math or advanced statistical analysis is done using software such as SPSS, Stata, R, SmartPLS, or Excel.

There are many factors to consider when deciding whether to use qualitative or quantitative research for your dissertation. The best way to decide between the two is to select a method that is most appropriate to help you answer your research questions.

Qualitative vs. Quantitative: What to Use & When to Use?

Based on the information presented in the table above you’ll know that qualitative research is primarily used when the researcher wants to gain an in-depth understanding of a selected topic or problem based on the participants’ lived experiences. Quantitative research, on the other hand, is used to test or confirm a theory or hypothesis of interest.

You can use either qualitative or quantitative research - or even both. Some dissertation projects may benefit from using a mixed method research approach. Be sure to keep this information in mind when deciding on the best approach to use for your dissertation.

Understanding Qualitative & Quantitative Research Approach

To select the right research approach, it is critical to have a clear understanding of each method. Many dissertation students find it challenging to understand the practical use of these methods. Let’s break it down further with the help of an example.

Let’s say you surveyed all customers patronizing a restaurant over a seven-day period using questionnaires. In the questionnaire, you ask the customers how they would rate their overall experience at the restaurant on a scale of 1-5. At the end of the seven-day period, you analyzed the collected data and drew conclusions based on the responses. For example, a possible conclusion from the survey could be that the customers gave an average rating of 4 to the restaurant. This data collection and analysis method falls under the quantitative research method.

If the same example were used for the qualitative research method, a series of interviews using a structured, semi-structured, or unstructured guide would replace the questionnaire used in the quantitative method. Instead of simply asking the customers to rate the restaurant, you would ask questions such as:

How happy are you with this restaurant’s food?

What do you love about this restaurant? Why?

What restaurant food items stand out to you? Why?

What suggestions would you give to the restaurant owner for further improvement?

You can add more questions, depending on the objective of your research. It is important to note that quantitative data are captured using questionnaires. On the other hand, because of the volume of data common in qualitative research it is important that the interview/focus group sessions be recorded verbatim, with the permission of the participant, to ensure data accuracy for transcription, coding and analysis.

Qualitative research is often used in exploratory studies, where limited data on a particular phenomenon exists and/or for theory development. If a plethora of research has already been conducted on the topic of interest, then a quantitative approach might be best. However, in some cases a combination of the two, mixed methods, may be more effective, depending on your area of focus and research questions.

Mixed Methods: A Qualitative + Quantitative Research Approach

A mixed methods approach is also used in many dissertation studies where both qualitative and quantitative data are collected to better understand a phenomenon of interest. In essence, this approach is about “mixing” of your quantitative and qualitative data to provide a more complete and synergistic picture than would be possible with a qualitative or quantitative design only.

You might be thinking, how would I “mix” both my qualitative and quantitative data for it to provide coherent and meaningful results, right? Well, there are a number of ways to do this. Let’s dig a bit deeper into the three most commonly used strategies.

1. Use qualitative data to support/explore quantitative findings : This method is called the exploratory sequential design and involves two steps. First, a quantitative instrument (e.g., questionnaire) is designed, administered and analyzed. Then, based on the quantitative findings , a qualitative data collection phase is developed. Specifically, an interview/focus group guide is created with questions that builds on the quantitative findings.

2. Use quantitative data to support qualitative findings . The process is similar to that above with the exception that with this approach, qualitative data are collected and analyzed first. Next, based on the qualitative results, a quantitative data collection and analysis phase is developed. In essence this method allows for the quantitative data to support the qualitative findings.

3. Collect both qualitative and quantitative data at the same time. This is called a convergent design. In this case, data from both designs are collected at roughly the same time point; information is assessed based on the design using parallel constructs for both data type; data are analyzed separately; and the results of both the quantitative and qualitative data are compared. Through comparison, the results can be presented in one of three ways: (1) side-by-side comparison in the results and discussion section; (2) converting the qualitative data into quantitative scores (i.e., assigning values to qualitative responses); or displaying results from both the quantitative and qualitative data using a tabular format (i.e., a joint display).

Your choice of a research method should depend on your data requirements. If you want to test or measure an item or series of items, use the quantitative research approach. To understand a research subject or problem more thoroughly, a qualitative research approach is better. If your research questions require both understanding and testing an idea, choose the mixed research approach.

Analyzing Qualitative & Quantitative Data

While data collection is an important part of your dissertation, the analysis will help you in understanding the meaning of the data you collected and your research questions.

If you’re analyzing quantitative data, you will most definitely capitalize on your math and statistics knowledge. But there is no need to worry because you will not be doing any calculations by hand. Quantitative data analysis if often completed through the use of statistical packages such as Stata, R, SPSS, SmartPLS, or other applications. Once the data are analyzed, you will document the findings in the form of graphs and tables, and make inferences based on the findings.

When analyzing qualitative data, you will have to prepare, organize, and review the collected data. Remember, qualitative research typically involves large volumes of data that will require a lot more time to analyze than data collected for quantitative studies. Once all the data are obtained, transcribed verbatim and verified, the researcher reads through all the transcripts to become more familiar with the information shared by the participants and make notes as needed. Next, a data coding system id developed that include codes unique to your study that are assigned to the data. Following these steps will help you identify recurring themes, and where necessary subthemes. Although thematic analysis is most common in qualitative analysis, content analysis or discourse analysis are other data analysis methods used by students and researchers alike.

Because qualitive studies usually have smaller sample sizes, some students use the paper and pen method to analyze their data. However, numerous software packages exists that can aid in the data analysis process including, but not limited to, Dedoose, NVivo, and ATLAS-ti.

1. What’s the Basic Difference Between Quantitative & Qualitative Methods?

The quantitative research method deals with numbers, while the qualitative method analyzes text, videos, images, and artefacts. The quantitative research method is used to test or confirm a theory or hypothesis, and the qualitative research method deals with theory development or a better understanding of the selected topic or problem.

2. What is Mixed Method Research?

A mixed method research approach involves the use of both qualitative and quantitative research in the collect and analysis of data, and subsequent findings used to answer research question(s).

3. How do I Choose Between Quantitative and Qualitative Research?

Your choice should be based on your research questions and dissertation project requirements. If your main aim is to measure something, proceed with the quantitative approach. A qualitative approach is best suited for projects that require an in-depth understanding of a specific topic or phenomenon.

4. What is Data Collection?

Data collection is a process of gathering information during the research phase. Primary and/or secondary data are often used in dissertation studies. Primary data refers to the collection of data by the researcher from the source, and can take the form of being structured, semi-structured or unstructured. Primary data are often collected using surveys (i.e., interviews or questionnaires), observations, experiments, quasi-experiments etc. On the other hand, secondary data includes data that have already been collected by other researchers from the primary source and made available to others for use.

5. How Do I Analyze Qualitative Data?

Qualitative data can be analyzed using various methods. The most common qualitative analysis method is thematic analysis where similarities and differences are examined across participants, afterwhich themes are developed based on participants’ experiences. Other common analysis methods include discourse analysis and content analysis method

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Choosing between types

There are a number of reasons why you may choose one type of dissertation over another. Some are more academic in nature, whilst others tend to be more personal or practical . Academic justifications are important because the person marking your dissertation will expect to see such academic justifications in your final product. Personal and practical justifications are similarly important, not because these are something that a marker is looking for, but because the dissertation process can be tough. As a result, many of the decisions you make throughout the dissertation process (e.g., the choice of sampling strategy or data analysis techniques) will be influenced by factors such as cost, ease, convenience, and what skills you have or can learn in time. We briefly discuss these considerations below, and explain how they may influence the particular choice of dissertation type; after all, the academic, personal and practical justifications for a quantitative dissertation are different for qualitative or mixed methods dissertations.

Academic justifications

You'll almost always been able to find an academic justification for your choice of dissertation, whether qualitative, quantitative or mixed methods. These academic justifications include factors that are generally philosophical or theoretical , or which refer to a particular research problem or idea .

The reasons that act as a justification for your dissertation will often become clear when you decide on the route you will follow within one of these three types of dissertation (i.e., a qualitative, quantitative or mixed methods dissertation). We have chosen not to go into any more detail about such academic justifications now because they are so specific to the route that you choose. However, you'll learn about these justifications in detail in the Quantitative Dissertations part of Lærd Dissertation, where you can choose between one of three routes (i.e., Route #1: Replication-based dissertations , Route #2: Data-driven dissertations , and Route #3: Theory-driven dissertations ).

Personal or practical justifications

One of the major challenges of doing a dissertation, especially if you are an undergraduate, is uncertainty : Can I plan out the dissertation process from the start? Will I be able to finish on time? Can I get my head around the research paradigms and research designs that guide my choice of dissertation (i.e., qualitative, quantitative or mixed methods)? Do I have the right skills to analyse qualitative or quantitative data? What software packages will I have to learn to do this, if any?

Dissertations are often worth a good proportion of your final year mark, if not the grade of your entire degree, so how tolerant you are to uncertainty matters. On this basis, think about the following:

Am I a bit of a planning freak?

If you are, you may prefer to take on a quantitative dissertation rather than qualitative dissertation . One of the broad advantages of quantitative dissertations is that they tend to be more sequential in nature, such that you can often set out, right from the start of the dissertation process, the various stages you will need to go through in order to answer your research questions or hypotheses. This is because in quantitative dissertations, it is far less common to change major components of the research process (e.g., your research questions or hypotheses, or research design), after you've decided what these are going to be, which you typically do at the very start of the dissertation process. Not only does this make it possible to plan what you will be doing from month-to-month, but it also reduces the uncertainty through the dissertation process. You'll see in the Quantitative Dissertations section how we have been able to provide comprehensive, step-by-step guides to walk you through the dissertation process, as well as chapter-by-chapter guides to show you how to write up.

By contrast, qualitative dissertations are not sequential, but reflexive and emergent in nature, which means that what you planned to do at the start of the dissertation process is more likely to have to be modified. Such modification takes place because one of the tenets of qualitative research is flexibility to allow for things that are learnt during the research process to be integrated (e.g., initial interviews may suggest that you need to add or omit a particular research question). Whilst such changes may only happen a few times, and may be minor in many cases, they do add an element of uncertainty. At a basic level, imagine the difference between knowing how many participants you need to have to fill in your questionnaire, and therefore, roughly how long this will take (i.e., a quantitative dissertation ), as opposed to being quite uncertain how many interviews you need to arrange to collect sufficient data to answer your research questions (i.e., a qualitative dissertation ). Whilst these might sound like small points, it can mean having to put aside another month to collect sufficient interview data in a qualitative dissertation compared with a quantitative one.

What are my strong points?

Whilst qualitative and quantitative dissertations are more than just the use of qualitative or qualitative research methods and data, there is no escaping the fact that qualitative dissertations use qualitative research methods and collect qualitative data (i.e., from unstructured interviews, focus groups, participant observation, etc.), and quantitative dissertations use quantitative research methods, collecting quantitative data (i.e., from data sets, surveys, structured interviews, structured observation, etc.). If you've spent your degree working with quantitative research designs (e.g., randomized control trials, pre- and post-test designs, relationship-based designs, etc.), as well as quantitative research methods and data, the logical choice might be to take on a quantitative dissertation . The same can be said for qualitative dissertations , since in both cases, the learning curve will be a lot higher if you're completely unaccustomed to the components that make up these different types of dissertation.

What am I interested in?

At the end of the day, the dissertation process is a long one, lasting around 6 months (in most cases). If you're not interested in experimental research, you prefer working with more unstructured research methods (e.g., depth interviews, unstructured observation, etc.), or you hate quantitative data analysis (i.e., any form of statistics), taking on a quantitative dissertation may not be a good idea. The same can be said for qualitative dissertations , which require a lot of perseverance and dedication, especially during the data collection process, which can be time consuming and requires a lot of toeing-and-froing. Choose a type of dissertation that is going to keep you interested, and which you will not find boring or demoralizing.

If you're taking on a qualitative dissertation , we wish you good luck (although you will still be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation). However, if you're taking on a quantitative dissertation (or a mixed methods dissertation that is mainly quantitative in its focus), go to the Quantitative Dissertations part of Lærd Dissertation now. We have extensive guides to help you through the process.

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Literature Reviews

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Qualitative researchers TEND to:

Researchers using qualitative methods tend to:

• t hink that social sciences cannot be well-studied with the same methods as natural or physical sciences
• feel that human behavior is context-specific; therefore, behavior must be studied holistically, in situ, rather than being manipulated
• employ an 'insider's' perspective; research tends to be personal and thereby more subjective.
• do interviews, focus groups, field research, case studies, and conversational or content analysis.

Image from https://www.editage.com/insights/qualitative-quantitative-or-mixed-methods-a-quick-guide-to-choose-the-right-design-for-your-research?refer-type=infographics

Qualitative Research (an operational definition)

Qualitative Research: an operational description

Purpose : explain; gain insight and understanding of phenomena through intensive collection and study of narrative data

Approach: inductive; value-laden/subjective; holistic, process-oriented

Hypotheses: tentative, evolving; based on the particular study

Lit. Review: limited; may not be exhaustive

Setting: naturalistic, when and as much as possible

Sampling : for the purpose; not necessarily representative; for in-depth understanding

Measurement: narrative; ongoing

Design and Method: flexible, specified only generally; based on non-intervention, minimal disturbance, such as historical, ethnographic, or case studies

Data Collection: document collection, participant observation, informal interviews, field notes

Data Analysis: raw data is words/ ongoing; involves synthesis

Data Interpretation: tentative, reviewed on ongoing basis, speculative

• Qualitative research with more structure and less subjectivity
• Increased application of both strategies to the same study ("mixed methods")
• Evidence-based practice emphasized in more fields (nursing, social work, education, and others).

Some Other Guidelines

• Guide for formatting Graphs and Tables
• Critical Appraisal Checklist for an Article On Qualitative Research

Quantitative researchers TEND to:

Researchers using quantitative methods tend to:

• think that both natural and social sciences strive to explain phenomena with confirmable theories derived from testable assumptions
• attempt to reduce social reality to variables, in the same way as with physical reality
• try to tightly control the variable(s) in question to see how the others are influenced.
• Do experiments, have control groups, use blind or double-blind studies; use measures or instruments.

Quantitative Research (an operational definition)

Quantitative research: an operational description

Purpose: explain, predict or control phenomena through focused collection and analysis of numberical data

Approach: deductive; tries to be value-free/has objectives/ is outcome-oriented

Hypotheses : Specific, testable, and stated prior to study

Lit. Review: extensive; may significantly influence a particular study

Setting: controlled to the degree possible

Sampling: uses largest manageable random/randomized sample, to allow generalization of results to larger populations

Measurement: standardized, numberical; "at the end"

Design and Method: Strongly structured, specified in detail in advance; involves intervention, manipulation and control groups; descriptive, correlational, experimental

Data Collection: via instruments, surveys, experiments, semi-structured formal interviews, tests or questionnaires

Data Analysis: raw data is numbers; at end of study, usually statistical

Data Interpretation: formulated at end of study; stated as a degree of certainty

This page on qualitative and quantitative research has been adapted and expanded from a handout by Suzy Westenkirchner. Used with permission.

Images from https://www.editage.com/insights/qualitative-quantitative-or-mixed-methods-a-quick-guide-to-choose-the-right-design-for-your-research?refer-type=infographics.

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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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.

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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.

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What is the difference between quantitative and qualitative?

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 in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

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.

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

• 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

Related Articles

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Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Tips for a qualitative dissertation

Veronika Williams

17 October 2017

Tips for students

This blog is part of a series for Evidence-Based Health Care MSc students undertaking their dissertations.

Undertaking an MSc dissertation in Evidence-Based Health Care (EBHC) may be your first hands-on experience of doing qualitative research. I chatted to Dr. Veronika Williams, an experienced qualitative researcher, and tutor on the EBHC programme, to find out her top tips for producing a high-quality qualitative EBHC thesis.

1) Make the switch from a quantitative to a qualitative mindset

It’s not just about replacing numbers with words. Doing qualitative research requires you to adopt a different way of seeing and interpreting the world around you. Veronika asks her students to reflect on positivist and interpretivist approaches: If you come from a scientific or medical background, positivism is often the unacknowledged status quo. Be open to considering there are alternative ways to generate and understand knowledge.

2) Reflect on your role

Quantitative research strives to produce “clean” data unbiased by the context in which it was generated.  With qualitative methods, this is neither possible nor desirable.  Students should reflect on how their background and personal views shape the way they collect and analyse their data. This will not only add to the transparency of your work but will also help you interpret your findings.

3)  Don’t forget the theory

Qualitative researchers use theories as a lens through which they understand the world around them. Veronika suggests that students consider the theoretical underpinning to their own research at the earliest stages. You can read an article about why theories are useful in qualitative research  here.

4) Think about depth rather than breadth

Qualitative research is all about developing a deep and insightful understanding of the phenomenon/ concept you are studying. Be realistic about what you can achieve given the time constraints of an MSc.  Veronika suggests that collecting and analysing a smaller dataset well is preferable to producing a superficial, rushed analysis of a larger dataset.

5) Blur the boundaries between data collection, analysis and writing up

Veronika strongly recommends keeping a research diary or using memos to jot down your ideas as your research progresses. Not only do these add to your audit trail, these entries will help contribute to your first draft and the process of moving towards theoretical thinking. Qualitative researchers move back and forward between their dataset and manuscript as their ideas develop. This enriches their understanding and allows emerging theories to be explored.

6) Move beyond the descriptive

When analysing interviews, for example, it can be tempting to think that having coded your transcripts you are nearly there. This is not the case!  You need to move beyond the descriptive codes to conceptual themes and theoretical thinking in order to produce a high-quality thesis.  Veronika warns against falling into the pitfall of thinking writing up is, “Two interviews said this whilst three interviewees said that”.

7) It’s not just about the average experience

When analysing your data, consider the outliers or negative cases, for example, those that found the intervention unacceptable.  Although in the minority, these respondents will often provide more meaningful insight into the phenomenon or concept you are trying to study.

8) Bounce ideas

Veronika recommends sharing your emerging ideas and findings with someone else, maybe with a different background or perspective. This isn’t about getting to the “right answer” rather it offers you the chance to refine your thinking.  Be sure, though, to fully acknowledge their contribution in your thesis.

9) Be selective

In can be a challenge to meet the dissertation word limit.  It won’t be possible to present all the themes generated by your dataset so focus! Use quotes from across your dataset that best encapsulate the themes you are presenting.  Display additional data in the appendix.  For example, Veronika suggests illustrating how you moved from your coding framework to your themes.

10) Don’t panic!

There will be a stage during analysis and write up when it seems undoable.  Unlike quantitative researchers who begin analysis with a clear plan, qualitative research is more of a journey. Everything will fall into place by the end.  Be sure, though, to allow yourself enough time to make sense of the rich data qualitative research generates.

Related course:

Qualitative research methods.

Short Course

• Quantitative and Qualitative Research - Main Rules | DissertationOwl
• Quantitative vs Qualitative Research

The Main Difference between Qualitative Research and Quantitative Research

When the researcher must use qualitative or quantitative research, how to get qualitative research data, why collect quantitative research data, get help on qualitative and quantitative research.

Qualitative and quantitative researches are two complementary methods the researcher can combine in a study to get data that is both wide-reaching and deep. If to explain simply, quantitative data gets you the numbers to prove the broad general points of your research. And qualitative data brings you the data and the depth to understand their full implications.

When you want to receive good results from these methods in your study, it’s important for a researcher to understand the differences between them. In our article, you will find the main information about these methods for research.

Needless to say, not everyone can make good research, so if you feel stuck with your paper, we can help to use qualitative and quantitative methods to complete your research properly. Trust our writing team and receive a successful research paper without stress!

Qualitative data collects data that seeks to describe a subject. Needless to say, a qualitative survey is less structured: it goes deep into the topic at hand to gain data about someone’s motivations, attitudes, and thinking. It gives a depth of understanding to your research questions, as well as makes the results harder to analyze.

Quantitative data is designed to collect cold, hard fact like numbers. Quantitative data is always well-structured and statistical . It provides great support when a researcher has to draw general conclusions from the main research.

Quantitative data can help you when you need to see the entire picture. Qualitative data gives the details and also provides your survey results with its own "voice". Let’s see how the researcher can use these methods in their dissertation research analysis .

Formulating hypotheses: Qualitative research helps you gather detailed information on a topic. You can use it to initiate your research by discovering the problems or opportunities people are thinking about. Those ideas can become hypotheses to be proven through your quantitative research.

Validating your hypotheses: Quantitative research will get you numbers that you can apply to the statistical analysis in order to validate your hypotheses. Was that problem real or it's just someone’s perception? The obtained data will help you to make decisions based on objective observations.

Finding general answers: Quantitative research usually has more respondents compared to qualitative research because it is easier to conduct a multiple-choice survey than a series of focus groups or interviews. But it can help you answer questions like: Do people prefer a particular company to its competitors? Which of your company’s services are most important? What writing team is the most appealing? etc.

Incorporating the human element. Qualitative research can help in the final stage of your research project . The quotes you obtained from open-ended questions can put a "voice" to the numbers of your results. For example, it helps your customers to describe your company in their own words to find blind spots. Qualitative data will show you that easily.

There are many methods the researcher can use to conduct qualitative research that will get detailed information on their subjects of interest.

• Interviews. One-on-one conversations that go deep into the topic at hand.
• Case studies. Collections of client stories from in-depth interviews.
• Expert opinions. High-quality information from well-informed sources.
• Focus groups. In-person or online conversation with small groups of people to listen to their views on a product or topic.
• Open-ended survey questions. A text box in a survey that lets the respondent express their thoughts on the matter at hand freely.
• Observational research. Observing people during the course of their habitual routines to understand how they interact with a product, for example.

However, this open-ended method of research does not always lend itself to bringing you the most accurate results to big questions. Analyzing the results is hard because different people use different words and phrases to describe their points of view. They may not even talk about the same thing if they find space to roam with their responses. Read our example of quantitative research questions: 

How long have you been a client of our company?

• This is my first purchase
• Less than six months
• Six months to a year
• 1 or 2 years
• 3 and more years
• I haven’t made a purchase yet

In some cases, it may be more effective to make quantitative research with your questions.

Qualitative survey questions can run the risk of being too vague. To avoid confusing your respondents, you may ask questions like, “What do you think about our internet service?” Instead, you could ask a closed-ended, quantitative question like in the example below:

The internet service is reliable:

• Most of the time
• About half the time
• Once in a while

Qualitative questions take longer to answer. Survey respondents don’t always have big patience to reflect on what they are being asked and write long responses that accurately express their views. It’s much faster to choose one of several pre-loaded options in a questionnaire. Using quantitative questions helps you get more questions in your survey and more responses out of it. Once you have all of your survey responses collected, be sure to present them in an attractive way. With online tools like this free chart maker , you can easily generate pie charts to display your results

Feel stuck with your qualitative or quantitative research? No problem, we have a great decision for your writing problems ! Ask our professional and talented researchers for qualified help and get successful papers on time easily. Choose the best researcher on qualitative and quantitative research methods and get your competed and well-done analysis on time! Surprise your teacher with amazing results and perfect document. Contact us right now to get more information about our service.

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Qualitative vs. Quantitative Methodology

31st July 2021

What is the Difference Between Qualitative and Quantitative Methodology?

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Imperial County Office of Education. “Qualitative and Quantitative Research.” Accessed April 12, 2014. http://www.icoe.org/webfm_send/1936 .

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A Common Student Question: Which Is Easier, Quantitative or Qualitative Research?

Leading a peer mentoring scheme means that I get a constant stream of messages and emails from first- and second-year psychology students. It is that time of the year where second-year students are choosing their final-year units and planning what they want to do for their final year research project (their dissertation).

The most common question I receive is: ‘Which is easier, quantitative or qualitative research?’ Of course, some researchers will have some biased views on this – probably based on what they are involved with themselves. But any good researcher will know that there is no straightforward answer to this question.

I remind students that they need to consider their research question. I conceptualise it between two questions: ‘w hat’ and ‘w hy’

The ‘what’ questions typically relate to a research question that requires a quantitative analysis to get a view of what variables influence other variables, or even how and to what extent one variable influences other variables (note that I use influence here, but such a question may also seek to establish causality).

The ‘why’ question, in my mind, would typically require a qualitative analysis . Why are students not receptive to feedback? Why is there a spike in teenage STD contraction? These questions will require asking samples from the population you’re interested in.

Of course, as with most things, there are some exceptions to this rule. For example, a ‘what’ question may require a qualitative analysis. Such as: ‘How does stress at work relate to quality of life in people working night shifts?’ This inevitably means seeking out a sample of people working nights shift.

Alternatively, a ‘why’ question may require a quantitative analysis. But researchers tend to form these ‘why’ questions in the way of a hypothesis. They may have an initial ‘why’ question, but then reflect this in an experimental hypothesis. For example: Why a consumer behaves in a certain why or how they’d feel if a certain situation were to take place.

A lot of students are also concerned about the time consumption of research for a final-year dissertation project. It is important to recognise that one approach (quantitative versus qualitative) is not necessarily faster than the other.

I conceptualise the time consumption of the methods as following, and find this helps students (for quantitative, then qualitative respectively):

data collection > data analysis data collection < data analysis

I have also noticed something peculiar, and I believe I may have experienced this myself before getting more involved in research: statistics anxiety .

Many students are coming to me asking how hard statistics is and whether they will get lots of support from their supervisors on their ‘independent’ projects.

I know many current final-year students, and second-year students, who are opting for a qualitative research project just to avoid running statistical analyses. It is apparent that this reasoning for choosing a qualitative project is a wrong one, especially the aforementioned discussion on choosing a method based on your research question.

A ‘why’ question may require a quantitative analysis.

This raises an important question: Are universities failing to engage students in Research Methods and Statistics? Unfortunately, in my own opinion (as a student), the answer to this question is yes, yes they are.

However, there is a way to fix this. Universities need to realise that the current way of teaching Research Methods and Statistics is failing. I have had countless lectures on different statistical tests, which are important, but I have had to retain knowledge on different pieces of logic and philosophy, which is impractical. At the end of the day, the real world of research does not require this knowledge. It requires you to:

• Formulate a research question;
• Read the literature;
• Design an experiment (or qualitative alternative);
• Collect data;
• Analyse that data;
• Interpret your results.

In my second-year I had a multiple choice question section of my examination, which I strongly believe was pointless on many levels. I failed this section of the examination. The second half of the examination required me to read some SPSS outputs, interpret them, and write up design, results, and discussion (first paragraph) sections of a laboratory report.

I excelled this section of the examination. This, of course, is far more representative of real-life research. I also wonder why students are not being assessed on their quantitative knowledge via using software such as SPSS – this is one of the most common statistics software that researchers use in the real world .

Universities have a duty to teach students to decide for themselves which is most important.

The concern that I have here is that Research Methods and Statistics is not being taught, nor examined, in a practical or realistic way. Another concern I have is that universities are giving the limelight to quantitative methodology, and not giving enough to qualitative methodology. In my first- and second-year, I had six lab classes that were quantitative-based and only two that were qualitative-based – both of which based on thematic analysis and nothing else.

This will lead students to believing that qualitative methodology is secondary to quantitative methodology. I cannot help but find the irony in this. Psychologists, with a wealth of knowledge on behaviour and attitudes, are still yet to develop curricula that will make the researchers of tomorrow. Universities have a duty to teach students to decide for themselves which is most important. In the case of those lab classes I mentioned above, surely this should be a 50/50 split. 

I think academia needs to reflect about the current way in which Research Methods and Statistics is taught. The discipline really must pay attention to the apparent trade-off between quantitative and qualitative methodology and the impression that it makes on students.

Callum Mogridge is an undergraduate psychology student at the University of Manchester. He leads the peer support on the degree programme. 

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