difference between survey experiment and observational study

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Difference Between Survey and Experiment

While surveys collected data, provided by the informants, experiments test various premises by trial and error method. This article attempts to shed light on the difference between survey and experiment, have a look.

Content: Survey Vs Experiment

Comparison chart.

Definition of Survey

By the term survey, we mean a method of securing information relating to the variable under study from all or a specified number of respondents of the universe. It may be a sample survey or a census survey. This method relies on the questioning of the informants on a specific subject. Survey follows structured form of data collection, in which a formal questionnaire is prepared, and the questions are asked in a predefined order.

Informants are asked questions concerning their behaviour, attitude, motivation, demographic, lifestyle characteristics, etc. through observation, direct communication with them over telephone/mail or personal interview. Questions are asked verbally to the respondents, i.e. in writing or by way of computer. The answer of the respondents is obtained in the same form.

Definition of Experiment

The term experiment means a systematic and logical scientific procedure in which one or more independent variables under test are manipulated, and any change on one or more dependent variable is measured while controlling for the effect of the extraneous variable. Here extraneous variable is an independent variable which is not associated with the objective of study but may affect the response of test units.

In an experiment, the investigator attempts to observe the outcome of the experiment conducted by him intentionally, to test the hypothesis or to discover something or to demonstrate a known fact. An experiment aims at drawing conclusions concerning the factor on the study group and making inferences from sample to larger population of interest.

Key Differences Between Survey and Experiment

The differences between survey and experiment can be drawn clearly on the following grounds:

• A technique of gathering information regarding a variable under study, from the respondents of the population, is called survey. A scientific procedure wherein the factor under study is isolated to test hypothesis is called an experiment.
• Surveys are performed when the research is of descriptive nature, whereas in the case of experiments are conducted in experimental research.
• The survey samples are large as the response rate is low, especially when the survey is conducted through mailed questionnaire. On the other hand, samples required in the case of experiments is relatively small.
• Surveys are considered suitable for social and behavioural science. As against this, experiments are an important characteristic of physical and natural sciences.
• Field research refers to the research conducted outside the laboratory or workplace. Surveys are the best example of field research. On the contrary, Experiment is an example of laboratory research. A laboratory research is nothing but research carried on inside the room equipped with scientific tools and equipment.
• In surveys, the data collection methods employed can either be observation, interview, questionnaire, or case study. As opposed to experiment, the data is obtained through several readings of the experiment.

While survey studies the possible relationship between data and unknown variable, experiments determine the relationship. Further, Correlation analysis is vital in surveys, as in social and business surveys, the interest of the researcher rests in understanding and controlling relationships between variables. Unlike experiments, where casual analysis is significant.

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November 17, 2016 at 1:08 am

Ishika says

September 9, 2017 at 9:30 pm

The article was quite helpful… Thank you.

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May 21, 2018 at 4:21 pm

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October 31, 2018 at 12:32 am

If I was doing an experiment with Poverty and Education level, which do you think would be more appropriate for me?

Thanks, Chris

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January 7, 2021 at 2:29 am

So interested,

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May 18, 2023 at 5:31 pm

Thank you for explaining the topic

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Experiment vs Observational Study: Similarities & Differences

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

An experiment involves the deliberate manipulation of variables to observe their effect, while an observational study involves collecting data without interfering with the subjects or variables under study.

This article will explore both, but let’s start with some quick explanations:

• Experimental Study : An experiment is a research design wherein an investigator manipulates one or more variables to establish a cause-effect relationship (Tan, 2022). For example, a pharmaceutical company may conduct an experiment to find out if a new medicine for diabetes is effective by administering it to a selected group (experimental group), while not administering it to another group (control group).
• Observational Study : An observational study is a type of research wherein the researcher observes characteristics and measures variables of interest in a subset of a population, but does not manipulate or intervene (Atkinson et al., 2021). An example may be a sociologist who conducts a cross-sectional survey of the population to determine health disparities across different income groups. 

Experiment vs Observational Study

1. experiment.

An experiment is a research method characterized by a high degree of experimental control exerted by the researcher. In the context of academia, it allows for the testing of causal hypotheses (Privitera, 2022).

When conducting an experiment, the researcher first formulates a hypothesis , which is a predictive statement about the potential relationship between at least two variables.

For instance, a psychologist may want to test the hypothesis that participation in physical exercise ( independent variable ) improves the cognitive abilities (dependent variable) of the elderly.

In an experiment, the researcher manipulates the independent variable(s) and then observes the effects on the dependent variable(s). This method of research involves two or more comparison groups—an experimental group that is subjected to the variable being tested and a control group that is not (Sampselle, 2012).

For instance, in the physical exercise study noted above, the psychologist would administer a physical exercise regime to an experimental group of elderly people, while a control group would continue with their usual lifestyle activities .

One of the unique features of an experiment is random assignment . Participants are randomly allocated to either the experimental or control groups to ensure that every participant has an equal chance of being in either group. This reduces the risk of confounding variables and increases the likelihood that the results are attributable to the independent variable rather than another factor (Eich, 2014).

For instance, in the physical exercise example, the psychologist would randomly assign participants to the experimental or control group to reduce the potential impact of external variables such as diet or sleep patterns.

1. Impacts of Films on Happiness: A psychologist might create an experimental study where she shows participants either a happy, sad, or neutral film (independent variable) then measures their mood afterward (dependent variable). Participants would be randomly assigned to one of the three film conditions.

2. Impacts of Exercise on Weight Loss: In a fitness study, a trainer could investigate the impact of a high-intensity interval training (HIIT) program on weight loss. Half of the participants in the study are randomly selected to follow the HIIT program (experimental group), while the others follow a standard exercise routine (control group).

3. Impacts of Veganism on Cholesterol Levels: A nutritional experimenter could study the effects of a particular diet, such as veganism, on cholesterol levels. The chosen population gets assigned either to adopt a vegan diet (experimental group) or stick to their usual diet (control group) for a specific period, after which cholesterol levels are measured.

Read More: Examples of Random Assignment

Strengths and Weaknesses

Read More: Experimental Research Examples

2. Observational Study

Observational research is a non-experimental research method in which the researcher merely observes the subjects and notes behaviors or responses that occur (Ary et al., 2018).

This approach is unintrusive in that there is no manipulation or control exerted by the researcher. For instance, a researcher could study the relationships between traffic congestion and road rage by just observing and recording behaviors at a set of busy traffic lights, without applying any control or altering any variables.

In observational studies, the researcher distinguishes variables and measures their values as they naturally occur. The goal is to capture naturally occurring behaviors , conditions, or events (Ary et al., 2018).

For example, a sociologist might sit in a cafe to observe and record interactions between staff and customers in order to examine social and occupational roles .

There is a significant advantage of observational research in that it provides a high level of ecological validity – the extent to which the data collected reflects real-world situations – as the behaviors and responses are observed in a natural setting without experimenter interference (Holleman et al., 2020)

However, the inability to control various factors that might influence the observations may expose these studies to potential confounding bias , a consideration researchers must take into account (Schober & Vetter, 2020).

1. Behavior of Animals in the Wild: Zoologists often use observational studies to understand the behaviors and interactions of animals in their natural habitats. For instance, a researcher could document the social structure and mating behaviors of a wolf pack over a period of time.

2. Impact of Office Layout on Productivity: A researcher in organizational psychology might observe how different office layouts affect staff productivity and collaboration. This involves the observation and recording of staff interactions and work output without altering the office setting.

3. Foot Traffic and Retail Sales: A market researcher might conduct an observational study on how foot traffic (the number of people passing by a store) impacts retail sales. This could involve observing and documenting the number of walk-ins, time spent in-store, and purchase behaviors.

Read More: Observational Research Examples

Experimental and Observational Study Similarities and Differences

Experimental and observational research both have their place – one is right for one situation, another for the next.

Experimental research is best employed when the aim of the study is to establish cause-and-effect relationships between variables – that is, when there is a need to determine the impact of specific changes on the outcome (Walker & Myrick, 2016).

One of the standout features of experimental research is the control it gives to the researcher, who dictates how variables should be changed and assigns participants to different conditions (Privitera, 2022). This makes it an excellent choice for medical or pharmaceutical studies, behavioral interventions, and any research where hypotheses concerning influence and change need to be tested.

For example, a company might use experimental research to understand the effects of staff training on job satisfaction and productivity.

Observational research , on the other hand, serves best when it’s vital to capture the phenomena in their natural state, without intervention, or when ethical or practical considerations prevent the researcher from manipulating the variables of interest (Creswell & Poth, 2018).

It is the method of choice when the interest of the research lies in describing what is, rather than altering a situation to see what could be (Atkinson et al., 2021).

This approach might be utilized in studies that aim to describe patterns of social interaction, daily routines, user experiences, and so on. A real-world example of observational research could be a study examining the interactions and learning behaviors of students in a classroom setting.

I’ve demonstrated their similarities and differences a little more in the table below:

Experimental and observational research each have their place, depending upon the study. Importantly, when selecting your approach, you need to reflect upon your research goals and objectives, and select from the vast range of research methodologies , which you can read up on in my next article, the 15 types of research designs .

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education . London: Cengage Learning.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . New York: SAGE Publications Ltd.

Creswell, J.W., and Poth, C.N. (2018). Qualitative Inquiry and Research Design: Choosing among Five Approaches . New York: Sage Publications.

Eich, E. (2014). Business Research Methods: A Radically Open Approach . Frontiers Media SA.

Holleman, G. A., Hooge, I. T., Kemner, C., & Hessels, R. S. (2020). The ‘real-world approach’and its problems: A critique of the term ecological validity. Frontiers in Psychology , 11 , 721. doi: https://doi.org/10.3389/fpsyg.2020.00721  

Privitera, G. J. (2022). Research methods for the behavioral sciences . Sage Publications.

Sampselle, C. M. (2012). The Science and Art of Nursing Research . South University Online Press.

Schober, P., & Vetter, T. R. (2020). Confounding in observational research. Anesthesia & Analgesia , 130 (3), 635.

Tan, W. C. K. (2022). Research methods: A practical guide for students and researchers . World Scientific.

Walker, D., and Myrick, F. (2016). Grounded Theory: An Exploration of Process and Procedure . New York: Qualitative Health Research.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

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Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies:  A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies:  Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies:  Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials:  Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups:  Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding:  Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages:  These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations:  However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

• Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
• Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
• Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
• Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
• Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
• Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Frequently asked questions

What is the difference between an observational study and an experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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 analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Statistics By Jim

Making statistics intuitive

What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

• Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
• Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
• Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

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Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
• They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched 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.

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Experimental vs Observational Studies: Differences & Examples

Understanding the differences between experimental vs observational studies is crucial for interpreting findings and drawing valid conclusions. Both methodologies are used extensively in various fields, including medicine, social sciences, and environmental studies. 

Researchers often use observational and experimental studies to gather comprehensive data and draw robust conclusions about their investigating phenomena. 

This blog post will explore what makes these two types of studies unique, their fundamental differences, and examples to illustrate their applications.

What is an Experimental Study?

An experimental study is a research design in which the investigator actively manipulates one or more variables to observe their effect on another variable. This type of study often takes place in a controlled environment, which allows researchers to establish cause-and-effect relationships.

Key Characteristics of Experimental Studies:

• Manipulation: Researchers manipulate the independent variable(s).
• Control: Other variables are kept constant to isolate the effect of the independent variable.
• Randomization: Subjects are randomly assigned to different groups to minimize bias.
• Replication: The study can be replicated to verify results.

Types of Experimental Study

• Laboratory Experiments: Conducted in a controlled environment where variables can be precisely controlled.
• Field Research : These are conducted in a natural setting but still involve manipulation and control of variables.
• Clinical Trials: Used in medical research and the healthcare industry to test the efficacy of new treatments or drugs.

Example of an Experimental Study:

Imagine a study to test the effectiveness of a new drug for reducing blood pressure. Researchers would:

• Randomly assign participants to two groups: receiving the drug and receiving a placebo.
• Ensure that participants do not know their group (double-blind procedure).
• Measure blood pressure before and after the intervention.
• Compare the changes in blood pressure between the two groups to determine the drug’s effectiveness.

What is an Observational Study?

An observational study is a research design in which the investigator observes subjects and measures variables without intervening or manipulating the study environment. This type of study is often used when manipulating impractical or unethical variables.

Key Characteristics of Observational Studies:

• No Manipulation: Researchers do not manipulate the independent variable.
• Natural Setting: Observations are made in a natural environment.
• Causation Limitations: It is difficult to establish cause-and-effect relationships due to the need for more control over variables.
• Descriptive: Often used to describe characteristics or outcomes.

Types of Observational Studies: 

• Cohort Studies : Follow a control group of people over time to observe the development of outcomes.
• Case-Control Studies: Compare individuals with a specific outcome (cases) to those without (controls) to identify factors that might contribute to the outcome.
• Cross-Sectional Studies : Collect data from a population at a single point to analyze the prevalence of an outcome or characteristic.

Example of an Observational Study:

Consider a study examining the relationship between smoking and lung cancer. Researchers would:

• Identify a cohort of smokers and non-smokers.
• Follow both groups over time to record incidences of lung cancer.
• Analyze the data to observe any differences in cancer rates between smokers and non-smokers.

Difference Between Experimental vs Observational Studies

Choosing Between Experimental and Observational Studies

The researchers relied on statistical analysis to interpret the results of randomized controlled trials, building upon the foundations established by prior research.

Use Experimental Studies When:

• Causality is Important: If determining a cause-and-effect relationship is crucial, experimental studies are the way to go.
• Variables Can Be Controlled: When you can manipulate and control the variables in a lab or controlled setting, experimental studies are suitable.
• Randomization is Possible: When random assignment of subjects is feasible and ethical, experimental designs are appropriate.

Use Observational Studies When:

• Ethical Concerns Exist: If manipulating variables is unethical, such as exposing individuals to harmful substances, observational studies are necessary.
• Practical Constraints Apply: When experimental studies are impractical due to cost or logistics, observational studies can be a viable alternative.
• Natural Settings Are Required: If studying phenomena in their natural environment is essential, observational studies are the right choice.

Strengths and Limitations

Experimental studies.

• Establish Causality: Experimental studies can establish causal relationships between variables by controlling and using randomization.
• Control Over Confounding Variables: The controlled environment allows researchers to minimize the influence of external variables that might skew results.
• Repeatability: Experiments can often be repeated to verify results and ensure consistency.

Limitations:

• Ethical Concerns: Manipulating variables may be unethical in certain situations, such as exposing individuals to harmful conditions.
• Artificial Environment: The controlled setting may not reflect real-world conditions, potentially affecting the generalizability of results.
• Cost and Complexity: Experimental studies can be costly and logistically complex, especially with large sample sizes.

Observational Studies

• Real-World Insights: Observational studies provide valuable insights into how variables interact in natural settings.
• Ethical and Practical: These studies avoid ethical concerns associated with manipulation and can be more practical regarding cost and time.
• Diverse Applications: Observational studies can be used in various fields and situations where experiments are not feasible.
• Lack of Causality: It’s easier to establish causation with manipulation, and results are limited to identifying correlations.
• Potential for Confounding: Uncontrolled external variables may influence the results, leading to biased conclusions.
• Observer Bias: Researchers may unintentionally influence outcomes through their expectations or interpretations of data.

Examples in Various Fields

• Experimental Study: Clinical trials testing the effectiveness of a new drug against a placebo to determine its impact on patient recovery.
• Observational Study: Studying the dietary habits of different populations to identify potential links between nutrition and disease prevalence.
• Experimental Study: Conducting a lab experiment to test the effect of sleep deprivation on cognitive performance by controlling sleep hours and measuring test scores.
• Observational Study: Observing social interactions in a public setting to explore natural communication patterns without intervention.

Environmental Science

• Experimental Study: Testing the impact of a specific pollutant on plant growth in a controlled greenhouse setting.
• Observational Study: Monitoring wildlife populations in a natural habitat to assess the effects of climate change on species distribution.

How QuestionPro Research Can Help in Experimental vs Observational Studies

Choosing between experimental and observational studies is a critical decision that can significantly impact the outcomes and interpretations of a study. QuestionPro Research offers powerful tools and features that can enhance both types of studies, giving researchers the flexibility and capability to gather, analyze, and interpret data effectively.

Enhancing Experimental Studies with QuestionPro

Experimental studies require a high degree of control over variables, randomization, and, often, repeated trials to establish causal relationships. QuestionPro excels in facilitating these requirements through several key features:

• Survey Design and Distribution: With QuestionPro, researchers can design intricate surveys tailored to their experimental needs. The platform supports random assignment of participants to different groups, ensuring unbiased distribution and enhancing the study’s validity.
• Data Collection and Management: Real-time data collection and management tools allow researchers to monitor responses as they come in. This is crucial for experimental studies where data collection timing and sequence can impact the results.
• Advanced Analytics: QuestionPro offers robust analytical tools that can handle complex data sets, enabling researchers to conduct in-depth statistical analyses to determine the effects of the experimental interventions.

Supporting Observational Studies with QuestionPro

Observational studies involve gathering data without manipulating variables, focusing on natural settings and real-world scenarios. QuestionPro’s capabilities are well-suited for these studies as well:

• Customizable Surveys: Researchers can create detailed surveys to capture a wide range of observational data. QuestionPro’s customizable templates and question types allow for flexibility in capturing nuanced information.
• Mobile Data Collection: For field research, QuestionPro’s mobile app enables data collection on the go, making it easier to conduct studies in diverse settings without internet connectivity.
• Longitudinal Data Tracking: Observational studies often require data collection over extended periods. QuestionPro’s platform supports longitudinal studies, allowing researchers to track changes and trends.

Experimental and observational studies are essential tools in the researcher’s toolkit. Each serves a unique purpose and offers distinct advantages and limitations. By understanding their differences, researchers can choose the most appropriate study design for their specific objectives, ensuring their findings are valid and applicable to real-world situations.

Whether establishing causality through experimental studies or exploring correlations with observational research designs, the insights gained from these methodologies continue to shape our understanding of the world around us. 

Whether conducting experimental or observational studies, QuestionPro Research provides a comprehensive suite of tools that enhance research efficiency, accuracy, and depth. By leveraging its advanced features, researchers can ensure that their studies are well-designed, their data is robustly analyzed, and their conclusions are reliable and impactful.

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Experimental Studies and Observational Studies

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• First Online: 01 January 2022
• pp 1748–1756
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Experimental studies: Experiments, Randomized controlled trials (RCTs) ; Observational studies: Non-experimental studies, Non-manipulation studies, Naturalistic studies

Definitions

The experimental study is a powerful methodology for testing causal relations between one or more explanatory variables (i.e., independent variables) and one or more outcome variables (i.e., dependent variable). In order to accomplish this goal, experiments have to meet three basic criteria: (a) experimental manipulation (variation) of the independent variable(s), (b) randomization – the participants are randomly assigned to one of the experimental conditions, and (c) experimental control for the effect of third variables by eliminating them or keeping them constant.

In observational studies, investigators observe or assess individuals without manipulation or intervention. Observational studies are used for assessing the mean levels, the natural variation, and the structure of variables, as well as...

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Atalay K, Barrett GF (2015) The impact of age pension eligibility age on retirement and program dependence: evidence from an Australian experiment. Rev Econ Stat 97:71–87. https://doi.org/10.1162/REST_a_00443

Article   Google Scholar  

Bergeman L, Boker SM (eds) (2016) Methodological issues in aging research. Psychology Press, Hove

Google Scholar  

Byrkes CR, Bielak AMA (under review) Evaluation of publication bias and statistical power in gerontological psychology. Manuscript submitted for publication

Campbell DT, Stanley JC (1966) Experimental and quasi-experimental designs for research. Rand-McNally, Chicago

Carpenter D (2010) Reputation and power: organizational image and pharmaceutical regulation at the FDA. Princeton University Press, Princeton

Cavanaugh JC, Blanchard-Fields F (2019) Adult development and aging, 8th edn. Cengage, Boston

Fölster M, Hess U, Hühnel I et al (2015) Age-related response bias in the decoding of sad facial expressions. Behav Sci 5:443–460. https://doi.org/10.3390/bs5040443

Freund AM, Isaacowitz DM (2013) Beyond age comparisons: a plea for the use of a modified Brunswikian approach to experimental designs in the study of adult development and aging. Hum Dev 56:351–371. https://doi.org/10.1159/000357177

Haslam C, Morton TA, Haslam A et al (2012) “When the age is in, the wit is out”: age-related self-categorization and deficit expectations reduce performance on clinical tests used in dementia assessment. Psychol Aging 27:778–784. https://doi.org/10.1037/a0027754

Institute for Social Research (2018) The health and retirement study. Aging in the 21st century: Challenges and opportunities for americans. Survey Research Center, University of Michigan

Jung J (1971) The experimenter’s dilemma. Harper & Row, New York

Leary MR (2001) Introduction to behavioral research methods, 3rd edn. Allyn & Bacon, Boston

Lindenberger U, Scherer H, Baltes PB (2001) The strong connection between sensory and cognitive performance in old age: not due to sensory acuity reductions operating during cognitive assessment. Psychol Aging 16:196–205. https://doi.org/10.1037//0882-7974.16.2.196

Löckenhoff CE, Carstensen LL (2004) Socioemotional selectivity theory, aging, and health: the increasingly delicate balance between regulating emotions and making tough choices. J Pers 72:1395–1424. https://doi.org/10.1111/j.1467-6494.2004.00301.x

Maxwell SE (2015) Is psychology suffering from a replication crisis? What does “failure to replicate” really mean? Am Psychol 70:487–498. https://doi.org/10.1037/a0039400

Menard S (2002) Longitudinal research (2nd ed.). Sage, Thousand Oaks, CA

Mitchell SJ, Scheibye-Knudsen M, Longo DL et al (2015) Animal models of aging research: implications for human aging and age-related diseases. Ann Rev Anim Biosci 3:283–303. https://doi.org/10.1146/annurev-animal-022114-110829

Moher D (1998) CONSORT: an evolving tool to help improve the quality of reports of randomized controlled trials. JAMA 279:1489–1491. https://doi.org/10.1001/jama.279.18.1489

Oxford Centre for Evidence-Based Medicine (2011) OCEBM levels of evidence working group. The Oxford Levels of Evidence 2. Available at: https://www.cebm.net/category/ebm-resources/loe/ . Retrieved 2018-12-12

Patten ML, Newhart M (2018) Understanding research methods: an overview of the essentials, 10th edn. Routledge, New York

Piccinin AM, Muniz G, Sparks C et al (2011) An evaluation of analytical approaches for understanding change in cognition in the context of aging and health. J Geront 66B(S1):i36–i49. https://doi.org/10.1093/geronb/gbr038

Pinquart M, Silbereisen RK (2006) Socioemotional selectivity in cancer patients. Psychol Aging 21:419–423. https://doi.org/10.1037/0882-7974.21.2.419

Redman LM, Ravussin E (2011) Caloric restriction in humans: impact on physiological, psychological, and behavioral outcomes. Antioxid Redox Signal 14:275–287. https://doi.org/10.1089/ars.2010.3253

Rutter M (2007) Proceeding from observed correlation to causal inference: the use of natural experiments. Perspect Psychol Sci 2:377–395. https://doi.org/10.1111/j.1745-6916.2007.00050.x

Schaie W, Caskle CI (2005) Methodological issues in aging research. In: Teti D (ed) Handbook of research methods in developmental science. Blackwell, Malden, pp 21–39

Shadish WR, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin, Boston

Sonnega A, Faul JD, Ofstedal MB et al (2014) Cohort profile: the health and retirement study (HRS). Int J Epidemiol 43:576–585. https://doi.org/10.1093/ije/dyu067

Weil J (2017) Research design in aging and social gerontology: quantitative, qualitative, and mixed methods. Routledge, New York

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Pinquart, M. (2021). Experimental Studies and Observational Studies. In: Gu, D., Dupre, M.E. (eds) Encyclopedia of Gerontology and Population Aging. Springer, Cham. https://doi.org/10.1007/978-3-030-22009-9_573

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Surveys, Experiments, and Observational Studies

Related Topics: Common Core (Statistics & Probability) Common Core for Mathematics

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Section 1.2: Observational Studies versus Designed Experiments

• 1.1 Introduction to the Practice of Statistics
• 1.2 Observational Studies versus Designed Experiments
• 1.3 Random Sampling
• 1.4 Bias in Sampling
• 1.5 The Design of Experiments

By the end of this lesson, you will be able to...

• distinguish between an observational study and a designed experiment
• identify possible lurking variables
• explain the various types of observational studies

For a quick overview of this section, watch this short video summary:

To begin, we're going to discuss some of the ways to collect data. In general, there are a few standards:

• existing sources
• survey sampling
• designed experiments

Most of us associate the word census with the U.S. Census, but it actually has a broader definition. Here's typical definition:

A census is a list of all individuals in a population along with certain characteristics of each individual.

The nice part about a census is that it gives us all the information we want. Of course, it's usually impossible to get - imagine trying to interview every single ECC student . That'd be over 10,000 interviews!

So if we can't get a census, what do we do? A great source of data is other studies that have already been completed. If you're trying to answer a particular question, look to see if someone else has already collected data about that population. The moral of the story is this: Don't collect data that have already been collected!

Observational Studies versus Designed Experiments

Now to one of the main objectives for this section. Two other very common sources of data are observational studies and designed experiments . We're going to take some time here to describe them and distinguish between them - you'll be expected to be able to do the same in homework and on your first exam.

The easiest examples of observational studies are surveys. No attempt is made to influence anything - just ask questions and record the responses. By definition,

An observational study measures the characteristics of a population by studying individuals in a sample, but does not attempt to manipulate or influence the variables of interest.

For a good example, try visiting the Pew Research Center . Just click on any article and you'll see an example of an observational study. They just sample a particular group and ask them questions.

In contrast, designed experiments explicitly do attempt to influence results. They try to determine what affect a particular treatment has on an outcome.

A designed experiment applies a treatment to individuals (referred to as experimental units or subjects ) and attempts to isolate the effects of the treatment on a response variable .

For a nice example of a designed experiment, check out this article from National Public Radio about the effect of exercise on fitness.

So let's look at a couple examples.

Visit this link from Science Daily , from July 8th, 2008. It talks about the relationship between Post-Traumatic Stress Disorder (PTSD) and heart disease. After reading the article carefully, try to decide whether it was an observational study or a designed experiment

What was it?

This was a tricky one. It was actually an observational study . The key is that the researchers didn't force the veterans to have PTSD, they simply observed the rate of heart disease for those soldiers who have PTSD and the rate for those who do not.

Visit this link from the Gallup Organization , from June 17th, 2008. It looks at what Americans' top concerns were at that point. Read carefully and think of the how the data were collected. Do you think this was an observational study or a designed experiment? Why?

Think carefully about which you think it was, and just as important - why? When you're ready, click the link below.

If you were thinking that this was an observational study , you were right!The key here is that the individuals sampled were just asked what was important to them. The study didn't try to impose certain conditions on people for a set amount of time and see if those conditions affected their responses.

This last example is regarding the "low-carb" Atkins diet, and how it compares with other diets. Read through this summary of a report in the New England Journal of Medicine and see if you can figure out whether it's an observational study or a designed experiment.

As expected, this was a designed experiment , but do you know why? The key here is they forced individuals to maintain a certain diet, and then compared the participants' health at the end.

Probably the biggest difference between observational studies and designed experiments is the issue of association versus causation . Since observational studies don't control any variables, the results can only be associations . Because variables are controlled in a designed experiment, we can have conclusions of causation .

Look back over the three examples linked above and see if all three reported their results correctly. You'll often find articles in newspapers or online claiming one variable caused a certain response in another, when really all they had was an association from doing an observational study.

The discussion of the differences between observational studies and designed experiments may bring up an interesting question - why are we worried so much about the difference?

We already mentioned the key at the end of the previous page, but it bears repeating here:

Observational studies only allow us to claim association ,not causation .

The primary reason behind this is something called a lurking variable (sometimes also termed a confounding factor , among other similar terms).

A lurking variable is a variable that affects both of the variables of interest, but is either not known or is not acknowledged.

Consider the following example, from The Washington Post:

Coffee may have health benefits and may not pose health risks for many people

By Carolyn Butler Tuesday, December 22, 2009

Of all the relationships in my life, by far the most on-again, off-again has been with coffee: From that initial, tentative dalliance in college to a serious commitment during my first real reporting job to breaking up altogether when I got pregnant, only to fail miserably at quitting my daily latte the second time I was expecting. More recently the relationship has turned into full-blown obsession and, ironically, I often fall asleep at night dreaming of the delicious, satisfying cup of joe that awaits, come morning.

[...] Rest assured: Not only has current research shown that moderate coffee consumption isn't likely to hurt you, it may actually have significant health benefits. "Coffee is generally associated with a less health-conscious lifestyle -- people who don't sleep much, drink coffee, smoke, drink alcohol," explains Rob van Dam, an assistant professor in the departments of nutrition and epidemiology at the Harvard School of Public Health. He points out that early studies failed to account for such issues and thus found a link between drinking coffee and such conditions as heart disease and cancer, a link that has contributed to java's lingering bad rep. "But as more studies have been conducted-- larger and better studies that controlled for healthy lifestyle issues --the totality of efforts suggests that coffee is a good beverage choice."

Source: Washington Post

What is this article telling us? If you look at the parts in bold, you can see that Professor van Dam is describing a lurking variable: lifestyle. In past studies, this variable wasn't accounted for. Researchers in the past saw the relationship between coffee and heart disease, and came to the conclusion that the coffee was causing the heart disease.

But since those were only observational studies, the researchers could only claim an association . In that example, the lifestyle choices of individuals was affecting both their coffee use and other risks leading to heart disease. So "lifestyle" would be an example of a lurking variable in that example.

For more on lurking variables, check out this link from The Math Forum and this one from The Psychology Wiki . Both give further examples and illustrations.

With all the problems of lurking variables, there are many good reasons to do an observational study. For one, a designed experiment may be impractical or even unethical (imagine a designed experiment regarding the risks of smoking). Observational studies also tend to cost much less than designed experiments, and it's often possible to obtain a much larger data set than you would with a designed experiment. Still, it's always important to remember the difference in what we can claim as a result of observational studies versus designed experiments.

Types of Observational Studies

There are three major types of observational studies, and they're listed in your text: cross-sectional studies, case-control studies, and cohort studies.

Cross-sectional Studies

This first type of observational study involves collecting data about individuals at a certain point in time. A researcher concerned about the effect of working with asbestos might compare the cancer rate of those who work with asbestos versus those who do not.

Cross-sectional studies are cheap and easy to do, but they don't give very strong results. In our quick example, we can't be sure that those working with asbestos who don't report cancer won't eventually develop it. This type of study only gives a bit of the picture, so it is rarely used by itself. Researchers tend to use a cross-sectional study to first determine if their might be a link, and then later do another study (like one of the following) to further investigate.

Case-control Studies

Case-control studies are frequently used in the medical community to compare individuals with a particular characteristic (this group is the case )with individuals who do not have that characteristic (this group is the control ). Researchers attempt to select homogeneous groups, so that on average, all other characteristics of the individuals will be similar, with only the characteristic in question differing.

One of the most famous examples of this type of study is the early research on the link between smoking and lung cancer in the United Kingdom by Richard Doll and A. Bradford Hill. In the 1950's, almost 80% of adults in the UK were smokers, and the connection between smoking and lung cancer had not yet been established. Doll and Hill interviewed about 700 lung cancer patients to try to determine a possible cause.

This type of study is retrospective ,because it asks the individuals to look back and describe their habits(regarding smoking, in this case). There are clear weaknesses in a study like this, because it expects individuals to not only have an accurate memory, but also to respond honestly. (Think about a study concerning drug use and cognitive impairment.) Not only that, we discussed previously that such a study may prove association , but it cannot prove causation .

Cohort Studies

A cohort describes a group of individuals, and so a cohort study is one in which a group of individuals is selected to participate in a study. The group is then observed over a period of time to determine if particular characteristics affect a response variable.

Based on their earlier research, Doll and Hill began one of the largest cohort studies in 1951. The study was again regarding the link between smoking and lung cancer. The study began with 34,439 male British doctors, and followed them for over 50 years. Doll and Hill first reported findings in 1954 in the British Medical Journal , and then continued to report their findings periodically afterward. Their last report was in 2004,again published in the British Medical Journal . This last report reflected on 50 years of observational data from the cohort.

This last type of study is called prospective , because it begins with the group and then collects data over time. Cohort studies are definitely the most powerful of the observational studies,particularly with the quantity and quality of data in a study like the previous one.

Let's look at some examples.

A recent article in the BBC News Health section described a study concerning dementia and "mid-life ills". According to the article, researches followed more than 11,000 people over a period of 12-14 years. They found that smoking, diabetes, and high blood pressure were all factors in the onset of dementia.

What type of observational study was this? Cross-sectional, case-control,or cohort?

Because the researchers tracked the 11,000 participants, this is a cohort study .

In 1993, the National Institute of Environmental Health Sciences funded a study in Iowa regarding the possible relationship between radon levels and the incidence of cancer. The study gathered information from 413 participants who had developed lung cancer and compared those results with 614 participants who did not have lung cancer.

What type of study was this?

This study was retrospective - gathering information about the group of interest (those with cancer) and comparing them with a control group(those without cancer). This is an example of a case-control study .

Thought his may seem similar to a cross-sectional study, it differs in that the individuals are "matched" (with cancer vs. without cancer)and the individuals are expected to look back in time and describe their time spent in the home to determine their radon exposure.

In 2004, researchers published an article in the New England Journal of Medicine regarding the relationship between the mental health of soldiers exposed to combat stress. The study collected information from soldiers in four combat infantry units either before their deployment to Iraq or three to four months after their return from combat duty.

Since this was simply a survey given over a short period of time to try to examine the effect of combat duty, this was a cross-sectional study. Unlike the previous example, it did not ask the participants to delve into their history, nor did it explicitly "match" soldiers with a particular characteristic.

<< previous section | next section >>

Emma's Research and Television Blog

I <3 science and t.v., observational studies, surveys, quasi-experiments, and experiments.

Across the sciences, researchers use a spectrum of tools or “instruments” to collect information and then make inferences about human preferences and behavior. These tools vary in the degree of control the researcher traditionally has had over the conditions of data collection. Surveys are an instance of such an instrument. Though widely used across social science, business, and even in computer science as user studies, surveys are known to have bugs. Although there are many tools for designing web surveys, few address known problems in survey design.

They have also traditionally varied in their media and the conditions under which they are administered. Some tools we consider are:

Observational studies

Allowing no control over how data are gathered, observational studies are analogous to data mining — if the information is not readily available, the researcher simply cannot get it.

The next best approach is to run a survey. Surveys have similar intent as observational studies, in that they are not meant to have an impact on the subject(s) being studied. However, surveys are known to have flaws that bias results. These flaws are typically related to the language of individual survey questions and the structure and control flow of the survey instrument itself.

True Experiments

If a research is in the position of having a high degree of control over all variables of the experiment, they can randomly assign treatments and perform what is known as a “true experiment”. These experiments require little modeling, since the researcher can simply using hypothesis testing to distinguish between effect and noise.

Quasi-Experiments

Quasi-experiments are similar to true experiments, except they relax some of the requirements of true experiments and are typically concerned with understanding causality.

In the past, there has been little fluidity between these four approaches to data collection, since the media used to implement each was dramatically different. However, with the proliferation of data on the web and the ease of issuing questionnaires on such platforms as facebook, SurveyMonkey, and Mechanical Turk, the implementation of these studies of human preferences and behavior have come to share many core features.

Despite similarities between these tools, quality control techniques for experiments have been largely absent from the design and deployment of surveys. There has been an outpouring of web tools and services for designing and hosting web surveys, aimed at non-programmers. While there are some tools and services available for experiments, they tend to be domain-specific and targeted to niche populations of researchers. The robust statistical approaches used in experimental design should inform survey design, and the general, programmatic approaches to web survey design should be available for experimental design.

3 thoughts on “ Observational Studies, Surveys, Quasi-Experiments, and Experiments ”

This is good, I just got confused on the observational part. I guess by observational you mean what in linguistics we call a corpus study – you just search and analyze information that’s already “out there”, which may involve gathering what’s out there in some systematic way first, and possibly annotating it. But there’s no control over the frequencies of the situations, which leads to sparsity in some areas. The upsides, though, are that things are more naturalistic, you lack task effects (there’s that phrase you wanted to look up), and you can get way more data. With sophisticated statistics (I think data mining is a synonym of that?), you can account for the uncontrolled distribution at least to a point. And when you control your distribution, you run the risk of introducing sampling bias. I don’t know if you want to go into observational studies very much since that’s not what SurveyMan is about, but some of these ideas might help. Also, I don’t know what a quasi-experiment is.

I think you’re right about all the synonyms for observational studies — I think unifying techniques and terminology should be a goal of data science, since many disciplines are doing the same thing, but in slightly different ways.

Regarding what SurveyMan has to offer, it really isn’t focused on observational studies/data mining. There was a picture I drew on the white board for Molly that illustrated how previously experiments and quasi-experiments were grouped together because they were conducted in highly controlled environments (i.e. a lab), whereas surveys and observational studies were conducted in the wild, where you have very little control over the environment. Surveys have traditionally aspired to be like observational studies, but inherently suffer from the “probe effect”. We’re shifting surveys over to the (quasi-)?experiments category, since we’re exercising a higher degree of control than we had before. What’s interesting to me is that this is quite clearly a product of being able to deploy surveys on the web. While this new technology has made surveys more robust, using the same platforms has actually degraded the integrity of experiments — where before they were conducted in a lab, now you have no idea what the conditions are under which the person is taking them. The assumption here is that you’ll drown out the noise caused by these uncontrolled environments by gathering significantly more data from a significantly broader population than before.

re : quasi-experiments. They’re used either when you cannot randomly assign a variable (e.g. it’s hard to reassign sex), or when you hold other variables constant on purpose in order to determine causality. This is something Emery’s quite keen on right now.

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Experiment vs Observational Study: A Deeper Look

When we read about research studies and reports, many are times that we fail to pay attention to the design of the study. For you to know the quality of the research findings, it is paramount to start by understanding some basics of research/study design.

The primary goal of doing a study is to evaluate the relationship between several variables. For example, does eating fast food result in teenagers being overweight? Or does going to college increase the chances of getting a job? Most studies fall into two main categories, observational and experimental studies, but what is the difference? Other widely accepted research types are cohort studies, randomized controls, and case-control studies, but these three are part of either experimental or observational study. Keep reading to understand the difference between observational study and experiment.

What Is An Observational Study?

To understand observational study vs experiment, let us start by looking at each of them.

So, what is an observational study ? This is a form of research where the measurement is done on the selected sample without running a control experiment. Therefore, the researcher observes the impact of a specific risk factor, such as treatment or intervention, without focusing on who is not exposed. It is simply a matter of observing what is happening.

When an observational report is released, it indicates that there might be a relationship between several variables, but this cannot be relied on. It is simply too weak or biased. We will demonstrate this with an example.

A study asking people how they liked a new film that was released a few months ago is a good example of an observational study. The researcher in the study does not have any control over the participants. Therefore, even if the study point to some relationship between the main variables, it is considered too weak. For example, the study did not factor in the possibility of viewers watching other films.

The main difference between an observational study and an experiment is that the latter is randomized . Again, unlike the observational study statistics, which are considered biased and weak, evidence from experimental research is stronger.

Advantages of Observational Studies

If you are thinking of carrying a research and have been wondering whether to go for randomized experiment vs observational study, here are some key advantages of the latter.

• Because the observational study does not require the use of control, it is inexpensive to undertake. Suppose you take the example of a study looking at the impact of introducing a new learning method into a school. In that case, all you need is to ask any interested students to participate in a survey with questions, such as “yes” and “no.”
• Doing observational research can also be pretty simple because you do not have to keep looking into multiple variables, and trying to control some groups.
• Sometimes the observational method is the only way to study some things, such as exposure to specific threats. For example, it might not be ethical to expose people to harmful variables, such as radiation. However, it is possible to study the exposed population living in affected areas using observational studies.

While the advantages of observational research might appear attractive, you need to weigh them against the cons. To run conclusive observational research, you might require a lot of time. Sometimes, this might run for years or decades.

The results from observational studies are also open to a lot of criticism because of confounding biases. For example, a cohort study might conclude that most people who love to meditate regularly suffer less from heart issues. However, this alone might not be the only cause of low cases of heart problems. The people who medicate might also be following healthy diets and doing a lot of exercises to stay healthy.

Types of Observational Studies

Observational studies further branches into several categories, including cohort study, cross-sectional, and case-control. Here is a breakdown of these different types of studies:

• Cohort Study

For study purposes, a “cohort” is a team or group of people who are somehow linked. Example, people born within a specific period might be referred to as a “birth cohort.”

The concept of cohort study edges close to that of experimental research. Here, the researcher records whether every participant in the cohort is affected by the selected variables. In a medical setting, the researcher might want to know whether the cohort population in the study got exposed to a certain variable and if they developed the medical condition of interest. This is the most preferred method of study when urgent response, especially to a public health concern, such as a disease outbreak is reported.

It is important to appreciate that this is different from experimental research because the investigator simply observes but does not determine the exposure status of the participants.

• Case Control Study

In this type of study, the researcher enrolls people with a health issue and another without the problem. Then, the two groups are compared based on exposure. The control group is used to generate an estimate of the expected exposure in the population.

• Cross-Sectional Research

This is the third type of observational type of study, and it involves taking a sample from a population that is exposed to health risk and measuring them to establish the extent of the outcome. This study is very common in health settings when researchers want to know the prevalence of a health condition at any specific moment. For example, in a cross-sectional study, some of the selected persons might have lived with high blood pressure for years, while others might have started seeing the signs recently.

Experimental Studies

Now that you know the observational study definition, we will now compare it with experiment research. So, what is experimental research?

In experimental design, the researcher randomly assigns a selected part of the population some treatment to make a cause and effect conclusion. The random selection of samples is largely what makes the experiment different from the observational study design.

The researcher controls the environment, such as exposure levels, and then checks the response produced by the population. In science, the evidence generated by experimental studies is stronger and less contested compared to that produced by observational studies.

Sometimes, you might find experimental study design being referred to as a scientific study. Always remember that when using experimental studies, you need two groups, the main experiment group (part of the population exposed to a variable) and the control (another group that does not get exposed/ treatment by the researcher).

Benefits of Using Experimental Study Design

Here are the main advantages to expect for using experimental study vs observational experiment.

• Most experimental studies are shorter and smaller compared to observational studies.
• The study, especially the selected sample and control group, is monitored closely to ensure the results are accurate.
• Experimental study is the most preferred method of study when targeting uncontested results.

When using experimental studies, it is important to appreciate that it can be pretty expensive because you are essentially following two groups, the experiment sample and control. The cost also arises from the factor that you might need to control the exposure levels and closely follow the progress before drawing a conclusion.

Observational Study vs Experiment: Examples

Now that we have looked at how each design, experimental and observational, work, we will now turn to examples and identify their application.

To improve the quality of life, many people are trying to quit smoking by following different strategies, but it is true that quitting is not easy. So the methods that are used by smokers include:

• Using drugs to reduce addiction to nicotine.
• Using therapy to train smokers how to stop smoking.
• Combining therapy and drugs.
• Cold turkey (neither of the above).

The variable in the study is (I, II, III, IV), and the outcome or response is success or failure to quit the problem of smoking. If you select to use an observational method, the values of the variables (I, ii, iii, iv) would happen naturally, meaning that you would not control them. In an experimental study, values would be assigned by the researcher, implying that you would tell the participants the methods to use. Here is a demonstration:

• Observational Study: Here, you would imagine a population of people trying to quit smoking. Then, use a survey, such as online or telephone interviews, to reach the smokers trying to stop the habit. After a year later, you reach the same persons again, to enquire whether they were successful. Note that you do not run any control over the population.
• Experimental study: In this case, a representative sample of smokers trying to stop the habit is selected through a survey. Say you reach about 1000. Now, the number is divided into four groups of 250 persons, and each group is allocated one of the four methods above (i, ii, iii, or iv).

The results from the experimental study might be as shown below:

From the results of the experimental study, we can say that combining therapy and drugs method helped most smokers to quit the habit successfully. Therefore, a policy can be developed to adopt the most successful method for helping smokers quit the problem.

It is important to note that both studies commence with a random sample. The difference between an observational study and an experiment is that the sample is divided in the latter while it is not in the former. In the case of the experimental study, the researcher is controlling the main variables and then checking the relationship.

A researcher picked a random sample of learners in a class and asked them about their study habits at home. The data showed that students who used at least 30 minutes to study after school scored better grades than those who never studied at all.

This type of study can be classified as observational because the researcher simply asked the respondents about their study habits after school. Because there was no group given a particular treatment, the study cannot qualify as experimental.

In another study, the researcher randomly picked two groups of students in school to determine the effectiveness of a new study method. Group one was asked to follow the new method for a period of three months, while the other was asked to simply study the way they were used. Then, the researcher checked the scores between the two groups to determine if the new method is better.

So, is this an experimental or observational study? This type of study can be categorized as experimental because the researcher randomly picked two groups of respondents. Then, one group was given some treatment, and the other one was not.

In one of the studies, the researcher took a random sample of people and looked at their eating habits. Then, every member was classified as either healthy or at risk of developing obesity. The researcher also drew recommendations to help people at risk of developing overweight issues to avoid the problem.

This type of study is observational because the researcher took a random sample but did no accord any group a special treatment. The study simply observed the people’s eating habits and classified them.

In one of the studies done in Japan, the researcher wanted to know the levels of radioactive materials in people’s tissues after the bombing of Hiroshima and Nagasaki in 1945. Therefore, he took a random sample of 1000 people in the region and asked them to get checked to determine the levels of radiation in their tissues.

After the study, the researcher concluded that the level of radiation in people’s tissues is still very high and might be associated with different types of diseases being reported in the region. Can you determine what type of study design this is?

The research is an example observational study because it did not have any control. The researcher only observed the levels but did not have any type of control group. Again, there was no special treatment to one of the study populations.

Get Professional Help Whenever You Need It

If you are a researcher, it is very important to be able to define observational study and experiment research before commencing your work. This can help you to determine the different parameters and how to go about the study. As we have demonstrated, observational studies mainly involve gathering the findings from the field without trying to control the variables. Although this study’s results can be contested, it is the most recommended method when using other studies such as experimental design, is unfeasible or unethical.

Experimental studies giving the researcher greater control over the study population by controlling the variables. Although more expensive, it takes a relatively shorter time, and results are less biased.

Now, go ahead and design your study. Always remember that you can seek help from either your lecturer or an expert when designing the study. Once you understand the concept of observational study vs experiment well, research can become so enjoyable and fun.

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1.3 Data Collection and Observational Studies

Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? When we are interested in the effect one variable may have on another, we call the first variable the explanatory variable and the second the response variable . Questions like these are answered using studies and experiments. Proper study design ensures the production of reliable, accurate data.

Data Collection Methods

There are many ways data is commonly collected, each with their own advantages and disadvantages. Some ways data may be collected are:

• Anecdotal evidence
• Observational studies
• Designed (controlled) experiments

The latter two options are more commonly accepted, but we will briefly describe the former first.

Anecdotal Evidence

Consider the following statements seemingly based on data:

• I met two students who took more than seven years to graduate from Duke, so it must take longer to graduate at Duke than at many other colleges.
• A man on the news had an adverse reaction to a vaccine, so it must be dangerous.
• My friend’s dad had a heart attack and died after they gave him a new heart disease drug, so the drug must not work.

Though each conclusion is technically based on data, there are two problems. First, the data in each example only represent one or two cases. Second, and more importantly, it is unclear whether these cases are actually representative of the population. Data collected in this haphazard fashion are called anecdotal evidence. While such evidence may be true and verifiable, be careful of data collected in this way since it may only represent extraordinary or unusual cases. Often, we are more likely to recall anecdotal evidence based on its striking characteristics. For instance, in Case #1 above, we are more likely to remember the two people we met who took seven years to graduate than the six others who graduated in four years. Instead of looking at the most unusual cases, we should examine a sample of many cases that represent the population.

Observational Studies

Researchers perform an observational study when they collect data in a way that does not directly interfere with how the data arise. For instance, researchers may collect information via a questionnaire or survey, review medical or company records, or follow a large group of similar individuals to form hypotheses about why certain diseases develop. In each of these situations, researchers merely observe the data that arise. In general, observational studies can provide evidence of naturally occurring associations between variables, but they cannot by themselves show a causal connection. Why not? Consider the following example.

Suppose an observational study tracking sunscreen use and skin cancer found that the more sunscreen someone used, the more likely the person was to have skin cancer. Does this mean sunscreen causes skin cancer? Some previous research tells us that using sunscreen actually reduces skin cancer risk, so maybe there is another variable that can explain this hypothetical association between sunscreen usage and skin cancer. One important piece of information that is absent may be sun exposure. 

Exposure to the sun is unaccounted for in this simple investigation, even though it stands to reason if someone is out in the sun all day, she is more likely to use sunscreen but also more likely to get skin cancer. Sun exposure here is an example of what we might call a confounding variable . Also known as a lurking or conditional variable, this is a variable that was not accounted for and may actually be important. Confounding variables can cause many misleading, counterintuitive, or even humorous (spurious) correlations.

Observational studies come in two forms: prospective and retrospective . A prospective study identifies individuals and collects information as events unfold. For instance, medical researchers may identify and follow a group of patients over many years to assess the possible influences of behavior on cancer risk. One example of such a study is the Nurses’ Health Study, started in 1976 and expanded in 1989. This prospective study recruits registered nurses and then collects data from them using questionnaires. Retrospective studies collect data after events have taken place (e.g., researchers reviewing past events in medical records). Some datasets may contain both prospectively and retrospectively collected variables.

There are other classifications of observational studies you may encounter, especially in life science and medical contexts. A cohort study follows a group of many similar individuals over time, often producing longitudinal data. A cross-sectional study indicates data collection on a population at one point in time (often prospective). A case-control study compares a group that has a certain characteristic to a group that does not, often taking the form of a retrospective study for rare conditions.

A researcher is studying the relationship between time spent studying in medical school and depression rates among students. The researcher looks at graduated students’ medical records to determine if they have ever seen a psychologist. He also sends out a questionnaire to the same students to ask how much time they spent studying in college. What type of study is this?

Click here for more multimedia resources, including podcasts, videos, lecture notes, and worked examples.

Figure References

Figure 1.3: Jason Leung (2018). “Selective focus photo of red peonies.” Unsplash license. https://unsplash.com/photos/nonlZlChSZQ

Figure 1.4: Kindred Grey (2020). “Sun Exposure Confounding Factors.” CC BY-SA 4.0.

The independent variable in an experiment; the value controlled by researchers

The dependent variable in an experiment; the value that is measured for change at the end of an experiment

Actual values (numbers or words) that are collected from the variables of interest

Evidence that is based on personal testimony and collected informally

Data collection where no variables are manipulated

Data collection where variables are manipulated in a controlled setting

A relationship between variables

A variable that has an effect on a study even though it is neither an explanatory variable nor a response variable

Collecting information as events unfold

Collecting or using data after events have taken place

Longitudinal study where a group of people (typically sharing a common factor) are studied and data is collected for a purpose

Collecting data multiple times on the same individuals over a period of time, usually in fixed increments

Data collection on a population at one point in time (often prospective)

A study that compares a group that has a certain characteristic to a group that does not, often a retrospective study for rare conditions

Significant Statistics Copyright © 2024 by John Morgan Russell, OpenStaxCollege, OpenIntro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Difference Between Observational Study and Experiments

• Categorized under Science | Difference Between Observational Study and Experiments

Observational Study vs Experiments

Observational study and experiments are the two major types of study involved in research. The main difference between these two types of study is in the way the observation is done.

In experiments, the researcher will undertake some experiment and not just make observations. In observational study, the researcher simply makes an observation and arrives at a conclusion.

In an experiment, the researcher manipulates every aspect for deriving a conclusion. In observational study, no experiment is conducted. In this type of study, the researcher relies more on data collected. In observational study, the researcher just observes what has happened in the past and what is happening now and draws conclusions based on these data. But in experiments, the researcher observes things through various studies. In other words, it can be said that there is human intervention in experiments whereas there is no human intervention in observational study. Here are examples for observational study and experiments that could clearly define the differences between the two. Hawthorne studies are a good example for experiments. The studies were conducted at the Hawthorne plant of the Western Electric Company. The study was to see the impact of illumination and productivity. First, the productivity was measured, and then the illumination was modified. After this the productivity was again measured which helped the researchers to arrive at a conclusion. The study to determine the relation between smoking and lung cancer is a typical example for observational study. For this the researchers collected data of both smokers and non-smokers. After this, the researchers would make observations with the help of the data and the statistics collected from each group.

1.The main difference between observational study and experiments is in the way the observation is done. 2.In an experiment, the researcher will undertake some experiment and not just make observations. In observational study, the researcher simply makes an observation and arrives at a conclusion. 3.In observational study, no experiment is conducted. In this type of study the researcher relies more on data collected. 4.In an experiment, the researcher observes things through various studies. 5.There is human intervention in experiments whereas there is no human intervention in observational study. 6.Hawthorne studies are a good example for experiments. 7.The study to determine the relation between smoking and lung cancer is a typical example for observational study.

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Cite APA 7 S, P. (2017, June 22). Difference Between Observational Study and Experiments. Difference Between Similar Terms and Objects. http://www.differencebetween.net/science/difference-between-observational-study-and-experiments/. MLA 8 S, Prabhat. "Difference Between Observational Study and Experiments." Difference Between Similar Terms and Objects, 22 June, 2017, http://www.differencebetween.net/science/difference-between-observational-study-and-experiments/.

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Distinguish between surveys, experiments, and observational studies; relate randomization to each

Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.

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Gait, quality of life, and knee function in advanced knee osteoarthritis: a single-center, prospective, observational study.

1. Introduction

2. materials and methods, statistical analysis, 4. discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Scheuing, W.J.; Reginato, A.M.; Deeb, M.; Acer Kasman, S. The Burden of Osteoarthritis: Is It a Rising Problem? Best Pract. Res. Clin. Rheumatol. 2023 , 37 , 101836. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Akimoto, T.; Kawamura, K.; Wada, T.; Ishihara, N.; Yokota, A.; Suginoshita, T.; Yokoyama, S. Evaluation of Gait Cycle Time Variability in Patients with Knee Osteoarthritis Using a Triaxial Accelerometer. medRxiv 2022 , 34 , 140–145. [ Google Scholar ] [ CrossRef ]
• Neuprez, A.; Neuprez, A.H.; Kurth, W.; Gillet, P.; Bruyère, O.; Reginster, J.-Y. Profile of Osteoarthritic Patients Undergoing Hip or Knee Arthroplasty, a Step toward a Definition of the “Need for Surgery”. Aging Clin. Exp. Res. 2018 , 30 , 315–321. [ Google Scholar ] [ CrossRef ]
• Pua, Y.-H.; Poon, C.L.-L.; Seah, F.J.-T.; Thumboo, J.; Clark, R.A.; Tan, M.-H.; Chong, H.-C.; Tan, J.W.-M.; Chew, E.S.-X.; Yeo, S.-J. Predicting Individual Knee Range of Motion, Knee Pain, and Walking Limitation Outcomes Following Total Knee Arthroplasty. Acta Orthop. 2019 , 90 , 179–186. [ Google Scholar ] [ CrossRef ]
• Li, H.; Hu, S.; Zhao, R.; Zhang, Y.; Huang, L.; Shi, J.; Li, P.; Wei, X. Gait Analysis of Bilateral Knee Osteoarthritis and Its Correlation with Western Ontario and McMaster University Osteoarthritis Index Assessment. Medicina 2022 , 58 , 1419. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Abellan Van Kan, G.; Rolland, Y.; Andrieu, S.; Bauer, J.; Beauchet, O.; Bonnefoy, M.; Cesari, M.; Donini, L.M.; Gillette-Guyonnet, S.; Inzitari, M.; et al. Gait Speed at Usual Pace as a Predictor of Adverse Outcomes in Community-Dwelling Older People an International Academy on Nutrition and Aging (IANA) Task Force. J. Nutr. Health Aging 2009 , 13 , 881–889. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Freijo, V.; Navarro, C.; Molina, B.; Villalba, J. Low Correlation between Gait and Quality of Life in Advanced Knee Osteoarthritis. J. Funct. Morphol. Kinesiol. 2023 , 8 , 77. [ Google Scholar ] [ CrossRef ]
• Fransen, B.L.; Pijnappels, M.; Butter, I.K.; Burger, B.J.; van Dieën, J.H.; Hoozemans, M.J.M. Patients’ Perceived Walking Abilities, Daily-Life Gait Behavior and Gait Quality before and 3 Months after Total Knee Arthroplasty. Arch. Orthop. Trauma Surg. 2022 , 142 , 1189–1196. [ Google Scholar ] [ CrossRef ]
• Berliner, J.L.; Brodke, D.J.; Chan, V.; SooHoo, N.F.; Bozic, K.J. Can Preoperative Patient-Reported Outcome Measures Be Used to Predict Meaningful Improvement in Function After TKA? Clin. Orthop. Relat. Res. 2017 , 475 , 149–157. [ Google Scholar ] [ CrossRef ]
• Thuraisingam, S.; Dowsey, M.; Manski-Nankervis, J.-A.; Spelman, T.; Choong, P.; Gunn, J.; Chondros, P. Developing Prediction Models for Total Knee Replacement Surgery in Patients with Osteoarthritis: Statistical Analysis Plan. Osteoarthr. Cartil. Open 2020 , 2 , 100126. [ Google Scholar ] [ CrossRef ]
• Kluge, F.; Hannink, J.; Pasluosta, C.; Klucken, J.; Gaßner, H.; Gelse, K.; Eskofier, B.M.; Krinner, S. Pre-Operative Sensor-Based Gait Parameters Predict Functional Outcome after Total Knee Arthroplasty. Gait Posture 2018 , 66 , 194–200. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mawarikado, Y.; Inagaki, Y.; Fujii, T.; Imagita, H.; Fukumoto, T.; Kubo, T.; Shirahase, M.; Kido, A.; Tanaka, Y. Preoperative Factors Affecting Patient-Reported Outcome Measures for Total Knee Arthroplasty. Prog. Rehabil. Med. 2022 , 7 , 20220049. [ Google Scholar ] [ CrossRef ]
• Yocum, D.; Elashoff, B.; Verta, P.; Armock, G.; Yergler, J. Patient Reported Outcomes Do Not Correlate to Functional Knee Recovery and Range of Motion in Total Knee Arthroplasty. J. Orthop. 2023 , 43 , 36–40. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Naili, J.E.; Iversen, M.D.; Esbjörnsson, A.-C.; Hedström, M.; Schwartz, M.H.; Häger, C.K.; Broström, E.W. Deficits in Functional Performance and Gait One Year after Total Knee Arthroplasty despite Improved Self-Reported Function. Knee Surg. Sports Traumatol. Arthrosc. 2017 , 25 , 3378–3386. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bonnefoy-Mazure, A.; Armand, S.; Sagawa, Y.; Suvà, D.; Miozzari, H.; Turcot, K. Knee Kinematic and Clinical Outcomes Evolution Before, 3 Months, and 1 Year After Total Knee Arthroplasty. J. Arthroplast. 2017 , 32 , 793–800. [ Google Scholar ] [ CrossRef ]
• Kellgren, J.H.; Lawrence, J.S. Radiological Assessment of Osteo-Arthrosis. Ann. Rheum. Dis. 1957 , 16 , 494–502. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Charnley, J.; Halley, D.K. Rate of Wear in Total Hip Replacement. Clin. Orthop. Relat. Res. 1975 , 112 , 170–179. [ Google Scholar ] [ CrossRef ]
• Gieysztor, E.; Pecuch, A.; Kowal, M.; Borowicz, W.; Paprocka-Borowicz, M. Pelvic Symmetry Is Influenced by Asymmetrical Tonic Neck Reflex during Young Children’s Gait. Int. J. Environ. Res. Public Health 2020 , 17 , 4759. [ Google Scholar ] [ CrossRef ]
• Insall, J.N.; Dorr, L.D.; Scott, R.D.; Norman, W. Rationale, of The Knee Society Clinical Rating System. Clin. Orthop. Relat. Res. 1989 , 248 , 13–14. [ Google Scholar ] [ CrossRef ]
• Vaquero, J.; Longo, U.G.; Forriol, F.; Martinelli, N.; Vethencourt, R.; Denaro, V. Reliability, Validity and Responsiveness of the Spanish Version of the Knee Injury and Osteoarthritis Outcome Score (KOOS) in Patients with Chondral Lesion of the Knee. Knee Surg. Sports Traumatol. Arthrosc. 2014 , 22 , 104–108. [ Google Scholar ] [ CrossRef ]
• Hernandez, G.; Garin, O.; Pardo, Y.; Vilagut, G.; Pont, À.; Suárez, M.; Neira, M.; Rajmil, L.; Gorostiza, I.; Ramallo-Fariña, Y.; et al. Validity of the EQ–5D–5L and Reference Norms for the Spanish Population. Qual. Life Res. 2018 , 27 , 2337–2348. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hinkle, D.E.; Wiersma, W.; Jurs, S.G. Applied Statistics for the Behavioral Sciences , 5th ed.; Houghton Mifflin: London, UK, 2003. [ Google Scholar ]
• Cooper, N.A.; Rakel, B.A.; Zimmerman, B.; Tonelli, S.M.; Herr, K.A.; Clark, C.R.; Noiseux, N.O.; Callaghan, J.J.; Sluka, K.A. Predictors of Multidimensional Functional Outcomes after Total Knee Arthroplasty. J. Orthop. Res. 2017 , 35 , 2790–2798. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, H.; Yan, S.; Fang, C.; Guo, X.; Zhang, K. Clinical Evaluation and Gait Characteristics before and after Total Knee Arthroplasty Based on a Portable Gait Analyzer. Orthop. Surg. 2016 , 8 , 360–366. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rajgopal, A.; Aggarwal, K.; Khurana, A.; Rao, A.; Vasdev, A.; Pandit, H. Gait Parameters and Functional Outcomes After Total Knee Arthroplasty Using Persona Knee System With Cruciate Retaining and Ultracongruent Knee Inserts. J. Arthroplast. 2017 , 32 , 87–91. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Boekesteijn, R.J.; van Gerven, J.; Geurts, A.C.H.; Smulders, K. Objective Gait Assessment in Individuals with Knee Osteoarthritis Using Inertial Sensors: A Systematic Review and Meta-Analysis. Gait Posture 2022 , 98 , 109–120. [ Google Scholar ] [ CrossRef ]
• Astephen Wilson, J.L.; Dunbar, M.J.; Hubley-Kozey, C.L. Knee Joint Biomechanics and Neuromuscular Control During Gait Before and After Total Knee Arthroplasty Are Sex-Specific. J. Arthroplast. 2015 , 30 , 118–125. [ Google Scholar ] [ CrossRef ]
• Kwon, S.; Perera, S.; Pahor, M.; Katula, J.A.; King, A.C.; Groessl, E.J.; Studenski, S.A. What Is a Meaningful Change in Physical Performance? Findings from a Clinical Trial in Older Adults (the LIFE-P Study). J. Nutr. Health Aging 2009 , 13 , 538–544. [ Google Scholar ] [ CrossRef ]
• Gilbert, A.L.; Song, J.; Cella, D.; Chang, R.W.; Dunlop, D.D. What Is an Important Difference in Gait Speed in Adults With Knee Osteoarthritis? Arthritis Care Res. 2021 , 73 , 559–565. [ Google Scholar ] [ CrossRef ]
• Berghmans, D.D.P.; Lenssen, A.F.; Emans, P.J.; de Bie, R.A. Functions, Disabilities and Perceived Health in the First Year after Total Knee Arthroplasty; a Prospective Cohort Study. BMC Musculoskelet. Disord. 2018 , 19 , 250. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Belmonte, R.; Tejero, M.; Aguirrezabal, A.; Boza, R.; Guillén, A.; Escalada, F. Análisis de La Marcha a Los 6 Meses de Una Prótesis Total de Rodilla: Evolución y Correlación Con Variables Clínicas. Rehabilitacion 2010 , 44 , 130–136. [ Google Scholar ] [ CrossRef ]
• Papakostidou, I.; Dailiana, Z.H.; Papapolychroniou, T.; Liaropoulos, L.; Zintzaras, E.; Karachalios, T.S.; Malizos, K.N. Factors Affecting the Quality of Life after Total Knee Arthroplasties: A Prospective Study. BMC Musculoskelet. Disord. 2012 , 13 , 116. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Connelly, J.W.; Galea, V.P.; Rojanasopondist, P.; Matuszak, S.J.; Ingelsrud, L.H.; Nielsen, C.S.; Bragdon, C.R.; Huddleston, J.I.; Malchau, H.; Troelsen, A. Patient Acceptable Symptom State at 1 and 3 Years After Total Knee Arthroplasty. J. Bone Jt. Surg. 2019 , 101 , 995–1003. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nanjo, K.; Ikeda, T.; Nagashio, N.; Sakai, T.; Jinno, T. Preoperative Predictors of Instrumental Activities of Daily Living Disability in Older Adults 6 Months after Knee Arthroplasty. Arch. Phys. Med. Rehabil. 2023 , 104 , 1260–1267. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pinzone, O.; Schwartz, M.H.; Baker, R. Comprehensive Non-Dimensional Normalization of Gait Data. Gait Posture 2016 , 44 , 68–73. [ Google Scholar ] [ CrossRef ]

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Freijo, V.; Navarro, C.; Villalba, J. Gait, Quality of Life, and Knee Function in Advanced Knee Osteoarthritis: A Single-Center, Prospective, Observational Study. J. Clin. Med. 2024 , 13 , 5392. https://doi.org/10.3390/jcm13185392

Freijo V, Navarro C, Villalba J. Gait, Quality of Life, and Knee Function in Advanced Knee Osteoarthritis: A Single-Center, Prospective, Observational Study. Journal of Clinical Medicine . 2024; 13(18):5392. https://doi.org/10.3390/jcm13185392

Freijo, Valentín, Claudia Navarro, and Jordi Villalba. 2024. "Gait, Quality of Life, and Knee Function in Advanced Knee Osteoarthritis: A Single-Center, Prospective, Observational Study" Journal of Clinical Medicine 13, no. 18: 5392. https://doi.org/10.3390/jcm13185392

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NPR fact-checked the Harris-Trump presidential debate. Here's what we found

Vice President and Democratic presidential candidate Kamala Harris and former President and Republican presidential candidate Donald Trump speak during a presidential debate. Saul Loeb/AFP via Getty Images hide caption

Vice President Harris and former President Donald Trump faced off Tuesday in their first — and possibly only — debate of the 2024 campaign, taking questions on key issues like the border, the economy and abortion.

With the candidates virtually tied in the polls, and just 55 days until Election Day, Trump and Harris sought to define their visions for America in front of a national audience and deflect attacks from the other side.

NPR reporters fact-checked the candidates' claims in real time . Here's what they found:

NPR's White House Correspondent Discusses The Debate | Morning Edition | NPR

TRUMP: "I had no inflation, virtually no inflation. They had the highest inflation, perhaps in the history of our country, because I've never seen a worse period of time. People can't go out and buy cereal or bacon or eggs or anything else."

Inflation soared to a four-decade high of 9.1% in 2022, according to the consumer price index. While inflation has since fallen to 2.9% (as of July), prices — particularly food prices — are still higher than many Americans would like.

Other countries have also faced high inflation in the wake of the pandemic, as tangled supply chains struggled to keep pace with surging demand. Russia’s invasion of Ukraine also fueled inflation by driving up energy and food prices worldwide.

Government spending in the U.S. under both the Biden-Harris administration and Trump also may have contributed, putting more money in people’s pockets and enabling them to keep spending in the face of high prices.

While high prices are a source of frustration for many Americans, the average worker has more buying power today than she did before the pandemic. Since February 2020 (just before the pandemic took hold in the U.S.), consumer prices have risen 21.6% while average wages have risen 23%.

Many prices were depressed early in the pandemic, however, so the comparison is less flattering if you start the clock when President Biden and Vice President Harris took office. Since early 2021, consumer prices have risen 19.6%, while average wages have risen 16.9%. Wage gains have been outpacing price increases for over a year, so that gap should eventually close.

— NPR economics correspondent Scott Horsley

2024 Election

Taylor swift endorses kamala harris in instagram post after the debate.

HARRIS: "Donald Trump left us the worst unemployment since the Great Depression."

At the height of the Great Depression in 1933, the national unemployment rate was near 25%, according to the Franklin D. Roosevelt Presidential Library.

At the start of the COVID pandemic, the unemployment rate peaked at 14.8% in April 2020, a level not seen since 1948, according to the Congressional Research Service.

But by the time Trump left office, unemployment had fallen to a lower, but still elevated, level. The January 2021 unemployment rate was 6.3%.

— NPR producer Lexie Schapitl

Immigration

TRUMP: "You see what's happening with towns throughout the United States. You look at Springfield, Ohio, you look at Aurora in Colorado. They are taking over the towns. They're taking over buildings. They're going in violently. These are the people that she and Biden let into our country, and they're destroying our country. They're dangerous. They're at the highest level of criminality, and we have to get them out."

Trump attacked Harris and Biden's records on immigration, arguing that they're failing to stem people from other countries from entering the U.S. and causing violence.

In the last two years, more than 40,000 Venezuelan immigrants have arrived in the Denver metro area. And it is true that many now live in Aurora.

A few weeks ago, a video of gang members in an Aurora, Colo., apartment building had right-wing media declaring the city's takeover by Venezuelan gangs. NPR looked into these claims .

Indian migrants drive surge in northern U.S. border crossings

Shortly after the video appeared, Colorado's Republican Party sent a fundraising letter claiming the state is under violent attack, and Venezuelan gangs have taken over Aurora.

It's also true Aurora police have recently arrested 10 members of a Venezuelan gang called Tren de Aragua. But Aurora's interim police chief, Heather Morris, says there's no evidence of a gang takeover of apartment buildings in her city.

What's more, violent crime — including murder, robbery and rape — is way down nationwide, according to the most recent data from the FBI . Notably, analysts predict violent crime rates this year will fall back down to where they were before they surged during the pandemic and may even approach a 50-year low.

Trump also claims that migrants are driving up crime rates in the U.S. That is not true. Researchers from Stanford University found that since the 1960s, immigrants have been 60% less likely to be incarcerated than people born in the U.S. The Cato Institute, a libertarian think tank, found undocumented immigrants in Texas were 37% less likely to be convicted of a crime.

— NPR immigration correspondent Jasmine Garsd and criminal justice reporter Meg Anderson

TRUMP: "In Springfield, they're eating the dogs. The people that came in, they're eating the cats. They're eating the pets of the people that live there."

This remark refers to a debunked, dehumanizing claim that Haitian migrants living in Springfield, Ohio, are abducting pets and eating them .

Untangling Disinformation

Jd vance spreads debunked claims about haitian immigrants eating pets.

The claim, which local police say is baseless, first circulated among far-right activists, local Republicans and neo-Nazis before being picked up by congressional leaders, vice presidential candidate JD Vance and others. A well-known advocate for the Haitian community says she received a wave of racist harassment after Vance shared the theory on social media.

The Springfield News-Sun reported that local police said that incidents of pets being stolen or eaten were "not something that's on our radar right now." The paper said the unsubstantiated claim seems to have started with a post in a Springfield Facebook group that was widely shared across social media.

The claim is the latest example of Trump leaning into anti-immigrant rhetoric. Since entering the political arena in 2015, Trump accused immigrants of being criminals, rapists, or "poisoning the blood of our nation."

— NPR immigration correspondent Jasmine Garsd

TRUMP: "A lot of these illegal immigrants coming in, [Democrats] are trying to get them to vote."

It is illegal for noncitizens to vote in federal elections, and there is no credible evidence that it has happened in significant numbers, or that there is an effort underway to illegally register undocumented immigrants to vote this election.

Voter registration forms require voters to sign an oath — under penalty of perjury — that they are U.S. citizens. If a noncitizen lies about their citizenship on a registration form and votes, they have created a paper trail of a crime that is punishable with jail time and deportation.

“The deterrent is incredibly strong,” David Becker, executive director of the Center for Election Innovation and Research, told NPR.

Illegal crossings hit Biden-era low as migrants wait longer for entry

Election officials routinely verify information on voter registration forms, which ask registrants for either a driver’s license number or the last four digits of Social Security numbers.

In 2016, the Brennan Center for Justice surveyed local election officials in 42 jurisdictions with high immigrant populations and found 30 cases of suspected noncitizens voting out of 23.5 million votes cast, or 0.0001%.

Georgia Secretary of State Brad Raffensperger launched an audit in 2022 that found fewer than 1,700 suspected noncitizens had attempted to register to vote over the past 25 years. None were able to vote.

— NPR disinformation reporter Jude Joffe-Block

TRUMP: "[Harris] was the border czar. Remember that she was the border czar."

Republicans have taken to calling Harris the "border czar" as a way to blame her for increased migration to the U.S. and what they see as border security policy failures of the Biden administration.

There is no actual "border czar" position. In 2021, President Biden tasked Harris with addressing the root causes of migration from Central America.

As Republicans attack Harris on immigration, here’s what her California record reveals

The "root causes strategy ... identifies, prioritizes, and coordinates actions to improve security, governance, human rights, and economic conditions in the region," the White House said in a statement. "It integrates various U.S. government tools, including diplomacy, foreign assistance, public diplomacy, and sanctions."

While Harris has been scrutinized on the right, immigration advocates have also criticized Harris, including for comments in 2021 where she warned prospective migrants, "Do not come."

TRUMP: "You could do abortions in the seventh month, the eighth month, the ninth month, and probably after birth."

As ABC News anchor Linsey Davis mentioned during her real-time fact check, there is no state where it is legal to kill a baby after birth (Trump called it "execution"). A report from KFF earlier this year also noted that abortions “after birth” are illegal in every state.

According to the Pew Research Center, the overwhelming majority of abortions — 93% — take place during the first trimester. Pew says 1% take place after 21 weeks. Most of those take place before 24 weeks, the approximate timeline for fetal viability, according to a report by KFF Health News.

Trump repeats the false claim that Democrats support abortion 'after birth' in debate

A separate analysis from KFF earlier this year noted that later abortions are expensive to obtain and offered by relatively few providers, and often occur because of medical complications or because patients face barriers earlier in their pregnancies.

“Nowhere in America is a woman carrying a pregnancy to term and asking for an abortion. That isn’t happening; it’s insulting to the women of America,” Harris said.

Harris also invoked religion in her response, arguing that “one does not have to abandon their faith” to agree that the government should not control reproductive health decisions.

As Davis also noted, Trump has offered mixed messages about abortion over the course of the campaign. He has bragged about his instrumental role in overturning Roe v. Wade , while appearing to backpedal on an issue that polling makes clear is a liability for Republicans.

— NPR political correspondent Sarah McCammon

Afghanistan

TRUMP: The U.S. withdrawal from Afghanistan "was one of the most incompetently handled situations anybody has ever seen."

Trump and Republicans in Congress say President Biden is to blame for the fall of Kabul to the Taliban three years ago, and the chaotic rush at the airport where 13 U.S. troops died in a suicide bomb attack that killed nearly 200 Afghan civilians trying to flee. Of late, Republicans have been emphasizing Harris’ role . But the Afghanistan war spanned four U.S. presidencies , and it's important to note that it was the Trump administration that signed a peace deal that was basically a quick exit plan.

Trump regularly claims there were no casualties in Afghanistan for 18 months under his administration, and it’s not true, according to Pentagon records.

— NPR veterans correspondent Quil Lawrence

Military policy

HARRIS: “There is not one member of the military who is in active duty in a combat zone in any war zone around the world for the first time this century.”

This is a common administration talking point, and it's technically true. But thousands of troops in Iraq and on the Syrian border are still in very dangerous terrain. U.S. troops died in Jordan in January on a base that keeps watch over the war with ISIS in Syria.

HARRIS: "I will not ban fracking. I have not banned fracking as vice president United States, and in fact, I was the tie-breaking vote on the inflation Reduction Act which opened new leases for fracking."

When she first ran for president in 2019, Harris had said she was firmly in favor of banning fracking — a stance she later abandoned when she joined President Biden’s campaign as his running mate.

In an interview with CNN last month, Harris attempted to explain why her position has changed from being against fracking to being in favor of it.

“What I have seen is that we can grow, and we can increase a clean energy economy without banning fracking,” Harris told CNN’s Dana Bash.

Harris says she won't ban fracking. What to know about the controversial topic

Under the Biden-Harris administration, the U.S. produced a record amount of oil last year — averaging 12.9 million barrels per day. That eclipsed the previous record of 12.3 million barrels per day, set under Trump in 2019. 2023 was also a record year for domestic production of natural gas . Much of the domestic boom in oil and gas production is the result of hydraulic fracturing or “fracking” techniques .

In addition to record oil and gas production, the Biden-Harris administration has also coincided with rapid growth of solar and wind power . Meanwhile, coal has declined as a source of electricity.

Health care

TRUMP: "I had a choice to make: Do I save [the Affordable Care Act] and make it as good as it can be, or do I let it rot? And I saved it."

During his presidency, Trump undermined the Affordable Care Act in many ways — for instance, by slashing funding for advertising and free "navigators" who help people sign up for a health insurance plan on HealthCare.gov. And rather than deciding to "save" the ACA, he tried hard to get Congress to repeal it, and failed. When pushed Tuesday on what health policy he would put in its place, he said he has "concepts of a plan."

Shots - Health News

Amid medicaid's 'unwinding,' many states work to expand health care access.

The Biden administration has reversed course from Trump's management of the Affordable Care Act. Increased subsidies have made premiums more affordable in the marketplaces, and enrollment has surged. The uninsurance rate has dropped to its lowest point ever during the Biden administration.

The Affordable Care Act was passed in 2010 and is entrenched in the health care system. Republicans successfully ran against Obamacare for about a decade, but it has faded as a campaign issue this year.

— NPR health policy correspondent Selena Simmons-Duffin

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Who is Hispanic?

Debates over who is Hispanic have often fueled conversations about identity among Americans who trace their heritage to Latin America or Spain .

So, who is considered Hispanic in the United States today? How exactly do the federal government and others count the Hispanic population? And what role does race play in deciding who counts as Hispanic?

We’ll answer these and other common questions here.

To answer the question of who is Hispanic, this analysis draws on about five decades of U.S. Census Bureau data and about two decades of Pew Research Center surveys of Hispanic adults in the United States.

National counts of the Latino population come from the Census Bureau’s decennial census (this includes P.L. 94-171 census data ) and official population estimates . The bureau’s American Community Survey (ACS) provides demographic details such as race, country of origin and intermarriage rates. Some ACS data was accessed through IPUMS USA from the University of Minnesota.

Views of Hispanic identity draw on the Center’s National Survey of Latinos (NSL), which is fielded in English and Spanish. The survey has been conducted online since 2019, primarily through the Center’s American Trends Panel (ATP), which is recruited through national, random sampling of residential addresses. This way nearly all adults have a chance of selection. The survey is weighted to be representative of the U.S. Hispanic adult population by gender, Hispanic origin, partisan affiliation, education and other categories. Read more about the ATP’s methodology . The NSL was conducted by phone from 2002 to 2018.

Read further details on how the Census Bureau asked about race and ethnicity and coded responses in the 2020 census. Here is a full list of origin groups that were coded as Hispanic in the 2020 census.

How many Hispanics are in the U.S. today?

The Census Bureau estimates there were 65.2 million Hispanics in the U.S. as of July 1, 2023, a new high. They made up more than 19% of the nation’s population .

How are Hispanics identified and counted in government surveys, public opinion polls and other studies?

Before diving into the details, keep in mind that some surveys ask about Hispanic origin and race separately, following current Census Bureau practices – though these are soon to change.

One way to count Hispanics is to include those who say they are Hispanic, with no exceptions – that is, you are Hispanic if you say you are. Pew Research Center uses this approach in our surveys, as do other polling firms such as Gallup and voter exit polls .

The Census Bureau largely counts Hispanics this way, too, but with some exceptions. If respondents select only the “Other Hispanic” category and write in only non-Hispanic responses such as “Irish,” the Census Bureau recodes the response as non-Hispanic.

However, beginning in 2020 , the bureau widened the lens to include a relatively small number of people who did not check a Hispanic box on the census form but answered the race question in a way that implied a Hispanic background. As a result, someone who answered the race question by saying that they are “Mexican” or “Argentinean” was counted as Hispanic, even if they did not check the Hispanic box.

From the available data, the exact number of respondents affected by this change is difficult to determine. But it appears to be about 1% of Hispanics or fewer, according to a Pew Research Center analysis of U.S. Census Bureau data.

How do Hispanics identify their race in Census Bureau surveys?

In the eyes of the Census Bureau, Hispanics can be of any race, because “Hispanic” is an ethnicity and not a race. However, this distinction is subject to debate . A 2015 Center survey found that 17% of Hispanic adults said being Hispanic is mainly a matter of race, while 29% said it is mainly a matter of ancestry. Another 42% said it is mainly a matter of culture.

Nonetheless, the Census Bureau’s 2022 American Community Survey (ACS) provides the self-reported racial identity of Hispanics: 22.5 million single-race Hispanics identified only as “some other race.” This group mostly includes those who wrote in a Hispanic origin or nationality as their race. Another 10.7 million identified as White. Fewer Hispanics identified as American Indian (1.5 million), Black (1.0 million) or Asian (300,000).

Multiracial Hispanics

Another roughly 27.5 million Hispanics identified as more than one race in 2022, up from just 3 million in 2010.

Growth in the number of multiracial Hispanics comes primarily from those who identify as White and “some other race.” That population grew from 1.6 million to 24.9 million between 2010 and 2022. The number of Hispanics who identify as White and no other race declined from 26.7 million to 10.7 million.

The sharp increase in multiracial Hispanics could be due to several factors, including changes to the census form introduced in 2020 that added more space for written responses to the race question and growing racial diversity among Hispanics. This explanation is supported by the fact that almost 25 million of the Hispanics who identified as two or more races in 2022 were coded as “some other race” (and wrote in a response) and one of the specific races (such as Black or White). About 2.6 million Hispanics identified with two or more of the five major races offered in the census.

Changes for the 2030 census

The 2030 census will combine the race and ethnicity questions , a change that other federal surveys will implement in coming years. The new question will add checkboxes for “Hispanic or Latino” and “Middle Eastern or North African” among other race groups long captured in Census Bureau surveys.

Officials hope the changes will reduce the number of Americans who choose the “Some other race” category, especially among Hispanics . However, it’s worth noting that public feedback has raised a variety of concerns, including that combining the race and ethnicity questions could lead to an undercount of the nation’s Afro-Latino population .

Is there an official definition of Hispanic or Latino?

In 1976, Congress passed a law that required the government to collect and analyze data for a specific ethnic group: “Americans of Spanish origin or descent.” That legislation defined this group as “Americans [who] identify themselves as being of Spanish-speaking background and trace their origin or descent from Mexico, Puerto Rico, Cuba, Central and South America, and other Spanish-speaking countries.” This includes around 20 Spanish-speaking nations from Latin America and Spain itself, but not Portugal or Portuguese-speaking Brazil.

To implement this law, the U.S. Office of Management and Budget (OMB) developed Statistical Policy Directive No. 15 (SPD 15) in 1977, then revised it in 1997 and again in March 2024. In the most recent revision, OMB updated racial and ethnic definitions when it announced the combined race and ethnicity question. The current definition of “ Hispanic or Latino ” is “individuals of Mexican, Puerto Rican, Salvadoran, Cuban, Dominican, Guatemalan, and other Central or South American or Spanish culture or origin.”

The Census Bureau first asked everybody in the U.S. about Hispanic ethnicity in 1980. But it made some efforts before then to count people who today would be considered Hispanic. The Census Bureau also has a long history of changing labels and shifting categories . In the 1930 census, for example, the race question had a category for “Mexican.”

The first major attempt to estimate the size of the nation’s Hispanic population came in 1970 and prompted widespread concerns among Hispanic organizations about an undercount. A portion of the U.S. population (5%) was asked if their origin or descent was from the following categories: “Mexican, Puerto Rican, Cuban, Central or South American, Other Spanish” or “No, none of these.”

This approach indeed undercounted about 1 million Hispanics. Many second-generation Hispanics did not select one of the Hispanic groups because the question did not include terms like “Mexican American.” The question wording also resulted in hundreds of thousands of people living in the Central or Southern regions of the U.S. being mistakenly included in the “Central or South American” category.

By 1980, the current approach – in which someone is asked if they are Hispanic – had taken hold, with some changes to the question and response categories since then. In 2000, for example, the term “Latino” was added to make the question read, “Is this person Spanish/Hispanic/Latino?”

What’s the difference between Hispanic and Latino?

“Hispanic” and “Latino” are pan-ethnic terms meant to describe – and summarize – the population of people of that ethnic background living in the U.S. In practice, the Census Bureau often uses the term “Hispanic” or “Hispanic or Latino.”

Some people have drawn sharp distinctions between these two terms . For example, some say that Hispanics are from Spain or from Spanish-speaking countries in Latin America, which matches the federal definition, and Latinos are people from Latin America, regardless of language. In this definition, Latinos would include people from Brazil (where Portuguese is the official language) but not Spain or Portugal.

Pan-ethnic labels like Hispanic and Latino, though widely used, are not universally embraced by the population being labeled. Our 2023 National Survey of Latinos shows a preference for other terms to describe identity: 52% of respondents most often described themselves by their family’s country of origin, while 30% used the terms Hispanic, Latino, Latinx or Latine, and 17% most often described themselves as American.

The 2023 survey also finds varying preferences for pan-ethnic labels: 52% of Hispanics prefer to describe themselves as Hispanic, 29% prefer Latino, 2% prefer Latinx, 1% prefer Latine and 15% have no preference.

What is ‘Latinx’ and who uses it?

Latinx is a pan-ethnic identity term that has emerged in recent years as an alternative to Hispanic and Latino. Some news and entertainment outlets, corporations , local governments and universities use it to describe the nation’s Hispanic population.

However, its popularity has brought increased scrutiny in the U.S. and abroad . Some critics say it ignores the gendered forms of Spanish language, while others see Latinx as a gender- and LGBTQ+-inclusive term . Adding to the debate, some state lawmakers favor banning the use of the term entirely in government documents; Arkansas has done so already .

A 2023 survey found that awareness of Latinx has doubled among U.S. Hispanics since 2019, with growth across all major demographic subgroups. Still, the share of Hispanic adults who use Latinx to describe themselves is statistically unchanged: In 2023, 4% said they use it, compared with 3% in 2019.

Latinx is also broadly unpopular among Latinos who know the term. Three-in-four Latino adults who are aware of Latinx say the term should not be used to describe Hispanics or Latinos.

The emergence of Latinx coincides with a global movement to introduce gender-neutral nouns and pronouns into many languages that have traditionally used male or female constructions. In the U.S., Latinx first appeared more than a decade ago, and it was added to a widely used English dictionary in 2018.

What is ‘Latine’ and who uses it?

Latine is another pan-ethnic term that has emerged in recent years. Our 2023 survey found that 18% of U.S. Hispanics have heard of the term.

Similar to familiarity with Latinx, awareness of Latine varies by age, education and sexual orientation. Among Latinos, awareness of Latine is highest among those ages 18 to 29 (22%), college graduates (24%) and lesbian, gay and bisexual adults (32%).

How do factors like language, parental background and last name affect whether someone is considered Hispanic?

Many U.S. Hispanics have an inclusive view of what it means to be Hispanic:

• 78% of Hispanic adults said in a 2022 Center survey that speaking Spanish is not required to be considered Hispanic. English-dominant Hispanics were more likely than Spanish-dominant Hispanics to say so (93% vs. 64%).
• 33% of Hispanic adults said in a 2019 survey that having two Hispanic parents is not an essential part of what being Hispanic means to them. Another 34% said it was important but not essential and 32% said it was essential.
• 84% of Hispanic adults said in a 2015 survey that having a Spanish last name is not required.

Views of Hispanic identity may change in the coming decades as broad societal changes, such as rising intermarriage rates, produce an increasingly diverse and multiracial U.S. population .

Today, many Hispanic families include people who are not Hispanic:

Spouses: Among all married Hispanics in 2022, 22% had a spouse who is not Hispanic. And in a 2023 Center survey , 27% of Hispanics with a spouse or partner said their spouse or partner is not Hispanic.

Newlyweds: In 2022, 30% of Hispanic newlyweds married someone who is not Hispanic. Among them, 41% of those born in the U.S. married someone who is not Hispanic, compared with 11% of immigrant newlyweds, according to an analysis of ACS data.

Parents: Our 2015 survey found that 15% of U.S. Hispanic adults had at least one parent who is not Hispanic. This share rose to 29% among the U.S. born and 48% among the third or higher generation – those born in the U.S. to parents who were also U.S. born.

What role does skin color play in whether someone is Hispanic?

In surveys like those from the Census Bureau, skin color does not play a role in determining who is Hispanic or not. However, as with race, Latinos can have many different skin tones. A 2021 Center survey of Latino adults showed respondents a palette of 10 skin colors and asked them to choose which one most closely resembled their own.

Latinos reported having a variety of skin tones, reflecting the diversity within the group. Eight-in-ten Latinos selected one of the four lightest skin colors. By contrast, only 3% selected one of the four darkest skin colors.

A majority of Latino adults (57%) say skin color shapes their daily life experiences at least somewhat. Similar shares say having a lighter skin color helps Latinos get ahead in the U.S. (59%) and that having a darker skin color hurts Latinos’ ability to get ahead (62%).

Are Afro-Latinos Hispanic?

Afro-Latino identity is distinct from and can exist alongside a person’s Hispanic identity. Afro-Latinos’ life experiences are shaped by race, skin tone and other factors in ways that differ from other Hispanics. While most Afro-Latinos identify as Hispanic or Latino, not all do, according to our estimates based on a survey of U.S. adults conducted in 2019 and 2020.

In 2020, about 6 million Afro-Latino adults lived in the U.S., making up about 2% of the U.S. adult population and 12% of the adult Latino population. About one-in-seven Afro-Latinos – an estimated 800,000 adults – do not identify as Hispanic.

Are Brazilians, Portuguese, Belizeans and Filipinos considered Hispanic?

Officially, Brazilians are not considered Hispanic or Latino because the federal government’s definition applies only to those of “Spanish culture or origin.” In most cases, people who report their Hispanic or Latino ethnicity as Brazilian in Census Bureau surveys are later recategorized – or “back coded” – as not Hispanic or Latino . The same is true for people with origins in Belize, the Philippines and Portugal.

An error in how the Census Bureau processed data from a 2020 national survey omitted some of this coding and provided a rare window into how Brazilians (and other groups) living in the U.S. view their identity.

In 2020, at least 416,000 Brazilians — more than two-thirds of Brazilians in the U.S. — described themselves as Hispanic or Latino on the ACS and were mistakenly counted that way. Only 14,000 Brazilians were counted as Hispanic in 2019, and 16,000 were in 2021.

The large number of Brazilians who self-identified as Hispanic or Latino highlights how their view of their own identity does not necessarily align with official government definitions. It also underscores that being Hispanic or Latino means different things to different people .

How many people with Hispanic ancestry do not identify as Hispanic?

Of the 42.7 million adults with Hispanic ancestry living in the U.S. in 2015, an estimated 5 million people, or 11%, said they do not identify as Hispanic or Latino , according to a 2015-16 Center survey. These people aren’t counted as Hispanic in our surveys.

Notably, Hispanic self-identification varies across immigrant generations. Among immigrants from Latin America, nearly all identify as Hispanic. But by the fourth generation, only half of people with Hispanic heritage in the U.S. identify as Hispanic.

Note: This is an update of a post originally published on May 28, 2009.

• Hispanic/Latino Identity
• Racial & Ethnic Identity

Mark Hugo Lopez is director of race and ethnicity research at Pew Research Center .

Jens Manuel Krogstad is a senior writer and editor at Pew Research Center .

Jeffrey S. Passel is a senior demographer at Pew Research Center .

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