the validity of a research design

Introducing Research Designs

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We define research design as a combination of decisions within a research process. These decisions enable us to make a specific type of argument by answering the research question. It is the implementation plan for the research study that allows reaching the desired (type of) conclusion. Different research designs make it possible to draw different conclusions. These conclusions produce various kinds of intellectual contributions. As all kinds of intellectual contributions are necessary to increase the body of knowledge, no research design is inherently better than another, only more appropriate to answer a specific question.

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Hunziker, S., Blankenagel, M. (2021). Introducing Research Designs. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_1

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• Reliability vs Validity in Research | Differences, Types & Examples

Reliability vs Validity in Research | Differences, Types & Examples

Published on 3 May 2022 by Fiona Middleton . Revised on 10 October 2022.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research .

Table of contents

Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect your data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalisability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability, or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data .

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are of high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardised questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or the findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid generalisable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession). Ensure that you have enough participants and that they are representative of the population.

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible.

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations, clearly define how specific behaviours or responses will be counted, and make sure questions are phrased the same way each time.

• Standardise the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

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Research designs are intended to provide structure that allows for logical conclusions about the relationship between independent and dependent variables. The investigator must have confidence that extraneous factors have not influenced the outcome. Even with a design that fulfills the requirements of an experiment, such as a randomized controlled trial (RCT), researchers must be vigilant regarding many potential sources of confounding that can obscure the effects of an intervention. Confounders may be extrinsic factors that emerge from the environment and the research situation, or they may be intrinsic factors that represent personal characteristics of the subjects of the study.

In Chapter 10 , we addressed the importance of validity for measurements to have confidence in the meaning of a measured outcome. Here, we are concerned about validity in relation to the design of a research study and interpretation of results. The purpose of this chapter is to examine issues of control that must be addressed in the design and analysis of research. Although these concerns will be presented in the context of explanatory studies, they are also relevant to quasi-experimental and observational designs.

Regardless of the care we take in the design of research, we know that clinical studies seldom have the ability to completely eliminate confounding effects. Although causality can never be demonstrated with complete certainty, the experimental method provides the most convincing evidence of the effect of one variable on another.

The goals of explanatory research correspond to four types of design validity (see Fig. 15-1 ). These form a framework for evaluating experiments: statistical conclusion validity, internal validity, construct validity, and external validity (see Table 15-1 ). 1

Figure 15–1

Four types of design validity. Each form is cumulatively dependent on the components below it.

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Home » Validity – Types, Examples and Guide

Validity – Types, Examples and Guide

Table of Contents

Definition:

Validity refers to the extent to which a concept, measure, or study accurately represents the intended meaning or reality it is intended to capture. It is a fundamental concept in research and assessment that assesses the soundness and appropriateness of the conclusions, inferences, or interpretations made based on the data or evidence collected.

Research Validity

Research validity refers to the degree to which a study accurately measures or reflects what it claims to measure. In other words, research validity concerns whether the conclusions drawn from a study are based on accurate, reliable and relevant data.

Validity is a concept used in logic and research methodology to assess the strength of an argument or the quality of a research study. It refers to the extent to which a conclusion or result is supported by evidence and reasoning.

How to Ensure Validity in Research

Ensuring validity in research involves several steps and considerations throughout the research process. Here are some key strategies to help maintain research validity:

Clearly Define Research Objectives and Questions

Start by clearly defining your research objectives and formulating specific research questions. This helps focus your study and ensures that you are addressing relevant and meaningful research topics.

Use appropriate research design

Select a research design that aligns with your research objectives and questions. Different types of studies, such as experimental, observational, qualitative, or quantitative, have specific strengths and limitations. Choose the design that best suits your research goals.

Use reliable and valid measurement instruments

If you are measuring variables or constructs, ensure that the measurement instruments you use are reliable and valid. This involves using established and well-tested tools or developing your own instruments through rigorous validation processes.

Ensure a representative sample

When selecting participants or subjects for your study, aim for a sample that is representative of the population you want to generalize to. Consider factors such as age, gender, socioeconomic status, and other relevant demographics to ensure your findings can be generalized appropriately.

Address potential confounding factors

Identify potential confounding variables or biases that could impact your results. Implement strategies such as randomization, matching, or statistical control to minimize the influence of confounding factors and increase internal validity.

Minimize measurement and response biases

Be aware of measurement biases and response biases that can occur during data collection. Use standardized protocols, clear instructions, and trained data collectors to minimize these biases. Employ techniques like blinding or double-blinding in experimental studies to reduce bias.

Conduct appropriate statistical analyses

Ensure that the statistical analyses you employ are appropriate for your research design and data type. Select statistical tests that are relevant to your research questions and use robust analytical techniques to draw accurate conclusions from your data.

Consider external validity

While it may not always be possible to achieve high external validity, be mindful of the generalizability of your findings. Clearly describe your sample and study context to help readers understand the scope and limitations of your research.

Peer review and replication

Submit your research for peer review by experts in your field. Peer review helps identify potential flaws, biases, or methodological issues that can impact validity. Additionally, encourage replication studies by other researchers to validate your findings and enhance the overall reliability of the research.

Transparent reporting

Clearly and transparently report your research methods, procedures, data collection, and analysis techniques. Provide sufficient details for others to evaluate the validity of your study and replicate your work if needed.

Types of Validity

There are several types of validity that researchers consider when designing and evaluating studies. Here are some common types of validity:

Internal Validity

Internal validity relates to the degree to which a study accurately identifies causal relationships between variables. It addresses whether the observed effects can be attributed to the manipulated independent variable rather than confounding factors. Threats to internal validity include selection bias, history effects, maturation of participants, and instrumentation issues.

External Validity

External validity concerns the generalizability of research findings to the broader population or real-world settings. It assesses the extent to which the results can be applied to other individuals, contexts, or timeframes. Factors that can limit external validity include sample characteristics, research settings, and the specific conditions under which the study was conducted.

Construct Validity

Construct validity examines whether a study adequately measures the intended theoretical constructs or concepts. It focuses on the alignment between the operational definitions used in the study and the underlying theoretical constructs. Construct validity can be threatened by issues such as poor measurement tools, inadequate operational definitions, or a lack of clarity in the conceptual framework.

Content Validity

Content validity refers to the degree to which a measurement instrument or test adequately covers the entire range of the construct being measured. It assesses whether the items or questions included in the measurement tool represent the full scope of the construct. Content validity is often evaluated through expert judgment, reviewing the relevance and representativeness of the items.

Criterion Validity

Criterion validity determines the extent to which a measure or test is related to an external criterion or standard. It assesses whether the results obtained from a measurement instrument align with other established measures or outcomes. Criterion validity can be divided into two subtypes: concurrent validity, which examines the relationship between the measure and the criterion at the same time, and predictive validity, which investigates the measure’s ability to predict future outcomes.

Face Validity

Face validity refers to the degree to which a measurement or test appears, on the surface, to measure what it intends to measure. It is a subjective assessment based on whether the items seem relevant and appropriate to the construct being measured. Face validity is often used as an initial evaluation before conducting more rigorous validity assessments.

Importance of Validity

Validity is crucial in research for several reasons:

• Accurate Measurement: Validity ensures that the measurements or observations in a study accurately represent the intended constructs or variables. Without validity, researchers cannot be confident that their results truly reflect the phenomena they are studying. Validity allows researchers to draw accurate conclusions and make meaningful inferences based on their findings.
• Credibility and Trustworthiness: Validity enhances the credibility and trustworthiness of research. When a study demonstrates high validity, it indicates that the researchers have taken appropriate measures to ensure the accuracy and integrity of their work. This strengthens the confidence of other researchers, peers, and the wider scientific community in the study’s results and conclusions.
• Generalizability: Validity helps determine the extent to which research findings can be generalized beyond the specific sample and context of the study. By addressing external validity, researchers can assess whether their results can be applied to other populations, settings, or situations. This information is valuable for making informed decisions, implementing interventions, or developing policies based on research findings.
• Sound Decision-Making: Validity supports informed decision-making in various fields, such as medicine, psychology, education, and social sciences. When validity is established, policymakers, practitioners, and professionals can rely on research findings to guide their actions and interventions. Validity ensures that decisions are based on accurate and trustworthy information, which can lead to better outcomes and more effective practices.
• Avoiding Errors and Bias: Validity helps researchers identify and mitigate potential errors and biases in their studies. By addressing internal validity, researchers can minimize confounding factors and alternative explanations, ensuring that the observed effects are genuinely attributable to the manipulated variables. Validity assessments also highlight measurement errors or shortcomings, enabling researchers to improve their measurement tools and procedures.
• Progress of Scientific Knowledge: Validity is essential for the advancement of scientific knowledge. Valid research contributes to the accumulation of reliable and valid evidence, which forms the foundation for building theories, developing models, and refining existing knowledge. Validity allows researchers to build upon previous findings, replicate studies, and establish a cumulative body of knowledge in various disciplines. Without validity, the scientific community would struggle to make meaningful progress and establish a solid understanding of the phenomena under investigation.
• Ethical Considerations: Validity is closely linked to ethical considerations in research. Conducting valid research ensures that participants’ time, effort, and data are not wasted on flawed or invalid studies. It upholds the principle of respect for participants’ autonomy and promotes responsible research practices. Validity is also important when making claims or drawing conclusions that may have real-world implications, as misleading or invalid findings can have adverse effects on individuals, organizations, or society as a whole.

Examples of Validity

Here are some examples of validity in different contexts:

• Example 1: All men are mortal. John is a man. Therefore, John is mortal. This argument is logically valid because the conclusion follows logically from the premises.
• Example 2: If it is raining, then the ground is wet. The ground is wet. Therefore, it is raining. This argument is not logically valid because there could be other reasons for the ground being wet, such as watering the plants.
• Example 1: In a study examining the relationship between caffeine consumption and alertness, the researchers use established measures of both variables, ensuring that they are accurately capturing the concepts they intend to measure. This demonstrates construct validity.
• Example 2: A researcher develops a new questionnaire to measure anxiety levels. They administer the questionnaire to a group of participants and find that it correlates highly with other established anxiety measures. This indicates good construct validity for the new questionnaire.
• Example 1: A study on the effects of a particular teaching method is conducted in a controlled laboratory setting. The findings of the study may lack external validity because the conditions in the lab may not accurately reflect real-world classroom settings.
• Example 2: A research study on the effects of a new medication includes participants from diverse backgrounds and age groups, increasing the external validity of the findings to a broader population.
• Example 1: In an experiment, a researcher manipulates the independent variable (e.g., a new drug) and controls for other variables to ensure that any observed effects on the dependent variable (e.g., symptom reduction) are indeed due to the manipulation. This establishes internal validity.
• Example 2: A researcher conducts a study examining the relationship between exercise and mood by administering questionnaires to participants. However, the study lacks internal validity because it does not control for other potential factors that could influence mood, such as diet or stress levels.
• Example 1: A teacher develops a new test to assess students’ knowledge of a particular subject. The items on the test appear to be relevant to the topic at hand and align with what one would expect to find on such a test. This suggests face validity, as the test appears to measure what it intends to measure.
• Example 2: A company develops a new customer satisfaction survey. The questions included in the survey seem to address key aspects of the customer experience and capture the relevant information. This indicates face validity, as the survey seems appropriate for assessing customer satisfaction.
• Example 1: A team of experts reviews a comprehensive curriculum for a high school biology course. They evaluate the curriculum to ensure that it covers all the essential topics and concepts necessary for students to gain a thorough understanding of biology. This demonstrates content validity, as the curriculum is representative of the domain it intends to cover.
• Example 2: A researcher develops a questionnaire to assess career satisfaction. The questions in the questionnaire encompass various dimensions of job satisfaction, such as salary, work-life balance, and career growth. This indicates content validity, as the questionnaire adequately represents the different aspects of career satisfaction.
• Example 1: A company wants to evaluate the effectiveness of a new employee selection test. They administer the test to a group of job applicants and later assess the job performance of those who were hired. If there is a strong correlation between the test scores and subsequent job performance, it suggests criterion validity, indicating that the test is predictive of job success.
• Example 2: A researcher wants to determine if a new medical diagnostic tool accurately identifies a specific disease. They compare the results of the diagnostic tool with the gold standard diagnostic method and find a high level of agreement. This demonstrates criterion validity, indicating that the new tool is valid in accurately diagnosing the disease.

Where to Write About Validity in A Thesis

In a thesis, discussions related to validity are typically included in the methodology and results sections. Here are some specific places where you can address validity within your thesis:

Research Design and Methodology

In the methodology section, provide a clear and detailed description of the measures, instruments, or data collection methods used in your study. Discuss the steps taken to establish or assess the validity of these measures. Explain the rationale behind the selection of specific validity types relevant to your study, such as content validity, criterion validity, or construct validity. Discuss any modifications or adaptations made to existing measures and their potential impact on validity.

Measurement Procedures

In the methodology section, elaborate on the procedures implemented to ensure the validity of measurements. Describe how potential biases or confounding factors were addressed, controlled, or accounted for to enhance internal validity. Provide details on how you ensured that the measurement process accurately captures the intended constructs or variables of interest.

Data Collection

In the methodology section, discuss the steps taken to collect data and ensure data validity. Explain any measures implemented to minimize errors or biases during data collection, such as training of data collectors, standardized protocols, or quality control procedures. Address any potential limitations or threats to validity related to the data collection process.

Data Analysis and Results

In the results section, present the analysis and findings related to validity. Report any statistical tests, correlations, or other measures used to assess validity. Provide interpretations and explanations of the results obtained. Discuss the implications of the validity findings for the overall reliability and credibility of your study.

Limitations and Future Directions

In the discussion or conclusion section, reflect on the limitations of your study, including limitations related to validity. Acknowledge any potential threats or weaknesses to validity that you encountered during your research. Discuss how these limitations may have influenced the interpretation of your findings and suggest avenues for future research that could address these validity concerns.

Applications of Validity

Validity is applicable in various areas and contexts where research and measurement play a role. Here are some common applications of validity:

Psychological and Behavioral Research

Validity is crucial in psychology and behavioral research to ensure that measurement instruments accurately capture constructs such as personality traits, intelligence, attitudes, emotions, or psychological disorders. Validity assessments help researchers determine if their measures are truly measuring the intended psychological constructs and if the results can be generalized to broader populations or real-world settings.

Educational Assessment

Validity is essential in educational assessment to determine if tests, exams, or assessments accurately measure students’ knowledge, skills, or abilities. It ensures that the assessment aligns with the educational objectives and provides reliable information about student performance. Validity assessments help identify if the assessment is valid for all students, regardless of their demographic characteristics, language proficiency, or cultural background.

Program Evaluation

Validity plays a crucial role in program evaluation, where researchers assess the effectiveness and impact of interventions, policies, or programs. By establishing validity, evaluators can determine if the observed outcomes are genuinely attributable to the program being evaluated rather than extraneous factors. Validity assessments also help ensure that the evaluation findings are applicable to different populations, contexts, or timeframes.

Medical and Health Research

Validity is essential in medical and health research to ensure the accuracy and reliability of diagnostic tools, measurement instruments, and clinical assessments. Validity assessments help determine if a measurement accurately identifies the presence or absence of a medical condition, measures the effectiveness of a treatment, or predicts patient outcomes. Validity is crucial for establishing evidence-based medicine and informing medical decision-making.

Social Science Research

Validity is relevant in various social science disciplines, including sociology, anthropology, economics, and political science. Researchers use validity to ensure that their measures and methods accurately capture social phenomena, such as social attitudes, behaviors, social structures, or economic indicators. Validity assessments support the reliability and credibility of social science research findings.

Market Research and Surveys

Validity is important in market research and survey studies to ensure that the survey questions effectively measure consumer preferences, buying behaviors, or attitudes towards products or services. Validity assessments help researchers determine if the survey instrument is accurately capturing the desired information and if the results can be generalized to the target population.

Limitations of Validity

Here are some limitations of validity:

• Construct Validity: Limitations of construct validity include the potential for measurement error, inadequate operational definitions of constructs, or the failure to capture all aspects of a complex construct.
• Internal Validity: Limitations of internal validity may arise from confounding variables, selection bias, or the presence of extraneous factors that could influence the study outcomes, making it difficult to attribute causality accurately.
• External Validity: Limitations of external validity can occur when the study sample does not represent the broader population, when the research setting differs significantly from real-world conditions, or when the study lacks ecological validity, i.e., the findings do not reflect real-world complexities.
• Measurement Validity: Limitations of measurement validity can arise from measurement error, inadequately designed or flawed measurement scales, or limitations inherent in self-report measures, such as social desirability bias or recall bias.
• Statistical Conclusion Validity: Limitations in statistical conclusion validity can occur due to sampling errors, inadequate sample sizes, or improper statistical analysis techniques, leading to incorrect conclusions or generalizations.
• Temporal Validity: Limitations of temporal validity arise when the study results become outdated due to changes in the studied phenomena, interventions, or contextual factors.
• Researcher Bias: Researcher bias can affect the validity of a study. Biases can emerge through the researcher’s subjective interpretation, influence of personal beliefs, or preconceived notions, leading to unintentional distortion of findings or failure to consider alternative explanations.
• Ethical Validity: Limitations can arise if the study design or methods involve ethical concerns, such as the use of deceptive practices, inadequate informed consent, or potential harm to participants.

Also see  Reliability Vs Validity

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Validity in research: a guide to measuring the right things

Last updated

27 February 2023

Reviewed by

Cathy Heath

Validity is necessary for all types of studies ranging from market validation of a business or product idea to the effectiveness of medical trials and procedures. So, how can you determine whether your research is valid? This guide can help you understand what validity is, the types of validity in research, and the factors that affect research validity.

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• What is validity?

In the most basic sense, validity is the quality of being based on truth or reason. Valid research strives to eliminate the effects of unrelated information and the circumstances under which evidence is collected. 

Validity in research is the ability to conduct an accurate study with the right tools and conditions to yield acceptable and reliable data that can be reproduced. Researchers rely on carefully calibrated tools for precise measurements. However, collecting accurate information can be more of a challenge.

Studies must be conducted in environments that don't sway the results to achieve and maintain validity. They can be compromised by asking the wrong questions or relying on limited data. 

Why is validity important in research?

Research is used to improve life for humans. Every product and discovery, from innovative medical breakthroughs to advanced new products, depends on accurate research to be dependable. Without it, the results couldn't be trusted, and products would likely fail. Businesses would lose money, and patients couldn't rely on medical treatments. 

While wasting money on a lousy product is a concern, lack of validity paints a much grimmer picture in the medical field or producing automobiles and airplanes, for example. Whether you're launching an exciting new product or conducting scientific research, validity can determine success and failure.

• What is reliability?

Reliability is the ability of a method to yield consistency. If the same result can be consistently achieved by using the same method to measure something, the measurement method is said to be reliable. For example, a thermometer that shows the same temperatures each time in a controlled environment is reliable.

While high reliability is a part of measuring validity, it's only part of the puzzle. If the reliable thermometer hasn't been properly calibrated and reliably measures temperatures two degrees too high, it doesn't provide a valid (accurate) measure of temperature. 

Similarly, if a researcher uses a thermometer to measure weight, the results won't be accurate because it's the wrong tool for the job. 

• How are reliability and validity assessed?

While measuring reliability is a part of measuring validity, there are distinct ways to assess both measurements for accuracy. 

How is reliability measured?

These measures of consistency and stability help assess reliability, including:

Consistency and stability of the same measure when repeated multiple times and conditions

Consistency and stability of the measure across different test subjects

Consistency and stability of results from different parts of a test designed to measure the same thing

How is validity measured?

Since validity refers to how accurately a method measures what it is intended to measure, it can be difficult to assess the accuracy. Validity can be estimated by comparing research results to other relevant data or theories.

The adherence of a measure to existing knowledge of how the concept is measured

The ability to cover all aspects of the concept being measured

The relation of the result in comparison with other valid measures of the same concept

• What are the types of validity in a research design?

Research validity is broadly gathered into two groups: internal and external. Yet, this grouping doesn't clearly define the different types of validity. Research validity can be divided into seven distinct groups.

Face validity : A test that appears valid simply because of the appropriateness or relativity of the testing method, included information, or tools used.

Content validity : The determination that the measure used in research covers the full domain of the content.

Construct validity : The assessment of the suitability of the measurement tool to measure the activity being studied.

Internal validity : The assessment of how your research environment affects measurement results. This is where other factors can’t explain the extent of an observed cause-and-effect response.

External validity : The extent to which the study will be accurate beyond the sample and the level to which it can be generalized in other settings, populations, and measures.

Statistical conclusion validity: The determination of whether a relationship exists between procedures and outcomes (appropriate sampling and measuring procedures along with appropriate statistical tests).

Criterion-related validity : A measurement of the quality of your testing methods against a criterion measure (like a “gold standard” test) that is measured at the same time.

• Examples of validity

Like different types of research and the various ways to measure validity, examples of validity can vary widely. These include:

A questionnaire may be considered valid because each question addresses specific and relevant aspects of the study subject.

In a brand assessment study, researchers can use comparison testing to verify the results of an initial study. For example, the results from a focus group response about brand perception are considered more valid when the results match that of a questionnaire answered by current and potential customers.

A test to measure a class of students' understanding of the English language contains reading, writing, listening, and speaking components to cover the full scope of how language is used.

• Factors that affect research validity

Certain factors can affect research validity in both positive and negative ways. By understanding the factors that improve validity and those that threaten it, you can enhance the validity of your study. These include:

Random selection of participants vs. the selection of participants that are representative of your study criteria

Blinding with interventions the participants are unaware of (like the use of placebos)

Manipulating the experiment by inserting a variable that will change the results

Randomly assigning participants to treatment and control groups to avoid bias

Following specific procedures during the study to avoid unintended effects

Conducting a study in the field instead of a laboratory for more accurate results

Replicating the study with different factors or settings to compare results

Using statistical methods to adjust for inconclusive data

What are the common validity threats in research, and how can their effects be minimized or nullified?

Research validity can be difficult to achieve because of internal and external threats that produce inaccurate results. These factors can jeopardize validity.

History: Events that occur between an early and later measurement

Maturation: The passage of time in a study can include data on actions that would have naturally occurred outside of the settings of the study

Repeated testing: The outcome of repeated tests can change the outcome of followed tests

Selection of subjects: Unconscious bias which can result in the selection of uniform comparison groups

Statistical regression: Choosing subjects based on extremes doesn't yield an accurate outcome for the majority of individuals

Attrition: When the sample group is diminished significantly during the course of the study

Maturation: When subjects mature during the study, and natural maturation is awarded to the effects of the study

While some validity threats can be minimized or wholly nullified, removing all threats from a study is impossible. For example, random selection can remove unconscious bias and statistical regression. 

Researchers can even hope to avoid attrition by using smaller study groups. Yet, smaller study groups could potentially affect the research in other ways. The best practice for researchers to prevent validity threats is through careful environmental planning and t reliable data-gathering methods. 

• How to ensure validity in your research

Researchers should be mindful of the importance of validity in the early planning stages of any study to avoid inaccurate results. Researchers must take the time to consider tools and methods as well as how the testing environment matches closely with the natural environment in which results will be used.

The following steps can be used to ensure validity in research:

Choose appropriate methods of measurement

Use appropriate sampling to choose test subjects

Create an accurate testing environment

How do you maintain validity in research?

Accurate research is usually conducted over a period of time with different test subjects. To maintain validity across an entire study, you must take specific steps to ensure that gathered data has the same levels of accuracy. 

Consistency is crucial for maintaining validity in research. When researchers apply methods consistently and standardize the circumstances under which data is collected, validity can be maintained across the entire study.

Is there a need for validation of the research instrument before its implementation?

An essential part of validity is choosing the right research instrument or method for accurate results. Consider the thermometer that is reliable but still produces inaccurate results. You're unlikely to achieve research validity without activities like calibration, content, and construct validity.

• Understanding research validity for more accurate results

Without validity, research can't provide the accuracy necessary to deliver a useful study. By getting a clear understanding of validity in research, you can take steps to improve your research skills and achieve more accurate results.

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Validity In Psychology Research: Types & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

In psychology research, validity refers to the extent to which a test or measurement tool accurately measures what it’s intended to measure. It ensures that the research findings are genuine and not due to extraneous factors.

Validity can be categorized into different types based on internal and external validity .

The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure. For example, a test of intelligence should measure intelligence and not something else (such as memory).

Internal and External Validity In Research

Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other confounding factor.

In other words, there is a causal relationship between the independent and dependent variables .

Internal validity can be improved by controlling extraneous variables, using standardized instructions, counterbalancing, and eliminating demand characteristics and investigator effects.

External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity), and over time (historical validity).

External validity can be improved by setting experiments more naturally and using random sampling to select participants.

Types of Validity In Psychology

Two main categories of validity are used to assess the validity of the test (i.e., questionnaire, interview, IQ test, etc.): Content and criterion.

• Content validity refers to the extent to which a test or measurement represents all aspects of the intended content domain. It assesses whether the test items adequately cover the topic or concept.
• Criterion validity assesses the performance of a test based on its correlation with a known external criterion or outcome. It can be further divided into concurrent (measured at the same time) and predictive (measuring future performance) validity.

Face Validity

Face validity is simply whether the test appears (at face value) to measure what it claims to. This is the least sophisticated measure of content-related validity, and is a superficial and subjective assessment based on appearance.

Tests wherein the purpose is clear, even to naïve respondents, are said to have high face validity. Accordingly, tests wherein the purpose is unclear have low face validity (Nevo, 1985).

A direct measurement of face validity is obtained by asking people to rate the validity of a test as it appears to them. This rater could use a Likert scale to assess face validity.

For example:

• The test is extremely suitable for a given purpose
• The test is very suitable for that purpose;
• The test is adequate
• The test is inadequate
• The test is irrelevant and, therefore, unsuitable

It is important to select suitable people to rate a test (e.g., questionnaire, interview, IQ test, etc.). For example, individuals who actually take the test would be well placed to judge its face validity.

Also, people who work with the test could offer their opinion (e.g., employers, university administrators, employers). Finally, the researcher could use members of the general public with an interest in the test (e.g., parents of testees, politicians, teachers, etc.).

The face validity of a test can be considered a robust construct only if a reasonable level of agreement exists among raters.

It should be noted that the term face validity should be avoided when the rating is done by an “expert,” as content validity is more appropriate.

Having face validity does not mean that a test really measures what the researcher intends to measure, but only in the judgment of raters that it appears to do so. Consequently, it is a crude and basic measure of validity.

A test item such as “ I have recently thought of killing myself ” has obvious face validity as an item measuring suicidal cognitions and may be useful when measuring symptoms of depression.

However, the implication of items on tests with clear face validity is that they are more vulnerable to social desirability bias. Individuals may manipulate their responses to deny or hide problems or exaggerate behaviors to present a positive image of themselves.

It is possible for a test item to lack face validity but still have general validity and measure what it claims to measure. This is good because it reduces demand characteristics and makes it harder for respondents to manipulate their answers.

For example, the test item “ I believe in the second coming of Christ ” would lack face validity as a measure of depression (as the purpose of the item is unclear).

This item appeared on the first version of The Minnesota Multiphasic Personality Inventory (MMPI) and loaded on the depression scale.

Because most of the original normative sample of the MMPI were good Christians, only a depressed Christian would think Christ is not coming back. Thus, for this particular religious sample, the item does have general validity but not face validity.

Construct Validity

Construct validity assesses how well a test or measure represents and captures an abstract theoretical concept, known as a construct. It indicates the degree to which the test accurately reflects the construct it intends to measure, often evaluated through relationships with other variables and measures theoretically connected to the construct.

Construct validity was invented by Cronbach and Meehl (1955). This type of content-related validity refers to the extent to which a test captures a specific theoretical construct or trait, and it overlaps with some of the other aspects of validity

Construct validity does not concern the simple, factual question of whether a test measures an attribute.

Instead, it is about the complex question of whether test score interpretations are consistent with a nomological network involving theoretical and observational terms (Cronbach & Meehl, 1955).

To test for construct validity, it must be demonstrated that the phenomenon being measured actually exists. So, the construct validity of a test for intelligence, for example, depends on a model or theory of intelligence .

Construct validity entails demonstrating the power of such a construct to explain a network of research findings and to predict further relationships.

The more evidence a researcher can demonstrate for a test’s construct validity, the better. However, there is no single method of determining the construct validity of a test.

Instead, different methods and approaches are combined to present the overall construct validity of a test. For example, factor analysis and correlational methods can be used.

Convergent validity

Convergent validity is a subtype of construct validity. It assesses the degree to which two measures that theoretically should be related are related.

It demonstrates that measures of similar constructs are highly correlated. It helps confirm that a test accurately measures the intended construct by showing its alignment with other tests designed to measure the same or similar constructs.

For example, suppose there are two different scales used to measure self-esteem:

Scale A and Scale B. If both scales effectively measure self-esteem, then individuals who score high on Scale A should also score high on Scale B, and those who score low on Scale A should score similarly low on Scale B.

If the scores from these two scales show a strong positive correlation, then this provides evidence for convergent validity because it indicates that both scales seem to measure the same underlying construct of self-esteem.

Concurrent Validity (i.e., occurring at the same time)

Concurrent validity evaluates how well a test’s results correlate with the results of a previously established and accepted measure, when both are administered at the same time.

It helps in determining whether a new measure is a good reflection of an established one without waiting to observe outcomes in the future.

If the new test is validated by comparison with a currently existing criterion, we have concurrent validity.

Very often, a new IQ or personality test might be compared with an older but similar test known to have good validity already.

Predictive Validity

Predictive validity assesses how well a test predicts a criterion that will occur in the future. It measures the test’s ability to foresee the performance of an individual on a related criterion measured at a later point in time. It gauges the test’s effectiveness in predicting subsequent real-world outcomes or results.

For example, a prediction may be made on the basis of a new intelligence test that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out, then the test has predictive validity.

Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin , 52, 281-302.

Hathaway, S. R., & McKinley, J. C. (1943). Manual for the Minnesota Multiphasic Personality Inventory . New York: Psychological Corporation.

Kelley, T. L. (1927). Interpretation of educational measurements. New York : Macmillan.

Nevo, B. (1985). Face validity revisited . Journal of Educational Measurement , 22(4), 287-293.

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1.2: Internal and External Validity

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Internal and External Validity

Developing a research design should be more than just a matter of convenience (although practicality is an important element, which we touched on in the last section). Not all designs are created equally and there are trade-offs we make when opting for one type of design over another. The two major components of an assessment of a research design are its internal validity and its external validity. Internal validity basically means we can make a causal statement within the context of our study. We have internal validity if, for our study, we can say our independent variable caused our dependent variable. Often times, the major challenge is the issue of spuriousness. We have to ask if our design allows us to say our independent variable makes our dependent variable vary systematically as it changes and that those changes in the dependent variable are not due to some third or extraneous variable/factor.

The second basis for evaluating your research design is to assess its external validity . External validity means that we can generalize the results of our study. It asks whether our findings are applicable in other settings. Here we consider what population we are interested in generalizing to. We might be interested in adult Americans, but if we have studied a sample of first-year college students then we might not be able to generalize to our target population. As you can see here, the sampling method is the key. The quality of the sampling method you choose is directly tied to your ability to generalize the findings of one particular study to the entire population. Typically a representative sampling method gives us the best chance to generalize the findings to our target population, thus gives the study high external validity. By contrast, when a non-representative sampling methods is used, it reduces generalizability, i.e., the external validity of the study.

Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.

Reliability alone is not enough, measures need to be reliable, as well as, valid. For example, if a weight measuring scale is wrong by 4kg (it deducts 4 kg of the actual weight), it can be specified as reliable, because the scale displays the same weight every time we measure a specific item. However, the scale is not valid because it does not display the actual weight of the item.

Research validity can be divided into two groups: internal and external. It can be specified that “internal validity refers to how the research findings match reality, while external validity refers to the extend to which the research findings can be replicated to other environments” (Pelissier, 2008, p.12).

Moreover, validity can also be divided into five types:

1. Face Validity is the most basic type of validity and it is associated with a highest level of subjectivity because it is not based on any scientific approach. In other words, in this case a test may be specified as valid by a researcher because it may seem as valid, without an in-depth scientific justification.

Example: questionnaire design for a study that analyses the issues of employee performance can be assessed as valid because each individual question may seem to be addressing specific and relevant aspects of employee performance.

2. Construct Validity relates to assessment of suitability of measurement tool to measure the phenomenon being studied. Application of construct validity can be effectively facilitated with the involvement of panel of ‘experts’ closely familiar with the measure and the phenomenon.

Example: with the application of construct validity the levels of leadership competency in any given organisation can be effectively assessed by devising questionnaire to be answered by operational level employees and asking questions about the levels of their motivation to do their duties in a daily basis.

3. Criterion-Related Validity involves comparison of tests results with the outcome. This specific type of validity correlates results of assessment with another criterion of assessment.

Example: nature of customer perception of brand image of a specific company can be assessed via organising a focus group. The same issue can also be assessed through devising questionnaire to be answered by current and potential customers of the brand. The higher the level of correlation between focus group and questionnaire findings, the high the level of criterion-related validity.

4. Formative Validity refers to assessment of effectiveness of the measure in terms of providing information that can be used to improve specific aspects of the phenomenon.

Example: when developing initiatives to increase the levels of effectiveness of organisational culture if the measure is able to identify specific weaknesses of organisational culture such as employee-manager communication barriers, then the level of formative validity of the measure can be assessed as adequate.

5. Sampling Validity (similar to content validity) ensures that the area of coverage of the measure within the research area is vast. No measure is able to cover all items and elements within the phenomenon, therefore, important items and elements are selected using a specific pattern of sampling method depending on aims and objectives of the study.

Example: when assessing a leadership style exercised in a specific organisation, assessment of decision-making style would not suffice, and other issues related to leadership style such as organisational culture, personality of leaders, the nature of the industry etc. need to be taken into account as well.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Reliability and Validity – Definitions, Types & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023

A researcher must test the collected data before making any conclusion. Every  research design  needs to be concerned with reliability and validity to measure the quality of the research.

What is Reliability?

Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.

Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.

Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.

What is the Validity?

Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid. 

If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid. 

Example:  Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.

Example:  Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.

Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.

Example:  If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.

Internal Vs. External Validity

One of the key features of randomised designs is that they have significantly high internal and external validity.

Internal validity  is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the  variables .

Example: age, level, height, and grade.

External validity  is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.

Also, read about Inductive vs Deductive reasoning in this article.

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Threats to Interval Validity

Threats of external validity, how to assess reliability and validity.

Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through  various statistical methods  depending on the types of validity, as explained below:

Types of Reliability

Types of validity.

As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity. 

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How to Increase Reliability?

• Use an appropriate questionnaire to measure the competency level.
• Ensure a consistent environment for participants
• Make the participants familiar with the criteria of assessment.
• Train the participants appropriately.
• Analyse the research items regularly to avoid poor performance.

How to Increase Validity?

Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:

• The reactivity should be minimised at the first concern.
• The Hawthorne effect should be reduced.
• The respondents should be motivated.
• The intervals between the pre-test and post-test should not be lengthy.
• Dropout rates should be avoided.
• The inter-rater reliability should be ensured.
• Control and experimental groups should be matched with each other.

How to Implement Reliability and Validity in your Thesis?

According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:

Frequently Asked Questions

What is reliability and validity in research.

Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes.

What is validity?

Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility.

What is reliability?

Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability.

What is reliability in psychology?

In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions.

What is test retest reliability?

Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time.

How to improve reliability of an experiment?

• Standardise procedures and instructions.
• Use consistent and precise measurement tools.
• Train observers or raters to reduce subjective judgments.
• Increase sample size to reduce random errors.
• Conduct pilot studies to refine methods.
• Repeat measurements or use multiple methods.
• Address potential sources of variability.

What is the difference between reliability and validity?

Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research.

Are interviews reliable and valid?

Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised.

Are IQ tests valid and reliable?

IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns.

Are questionnaires reliable and valid?

Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity.

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Validity is the best available approximation to the truth of a given proposition, inference, or conclusion.

The first thing we have to ask is: “validity of what ?” When we think about validity in research, most of us think about research components. We might say that a measure is a valid one, or that a valid sample was drawn, or that the design had strong validity. But all of those statements are technically incorrect. Measures, samples and designs don’t ‘have’ validity – only propositions can be said to be valid. Technically, we should say that a measure leads to valid conclusions or that a sample enables valid inferences, and so on. It is a proposition, inference or conclusion that can ‘have’ validity.

We make lots of different inferences or conclusions while conducting research. Many of these are related to the process of doing research and are not the major hypotheses of the study. Nevertheless, like the bricks that go into building a wall, these intermediate process and methodological propositions provide the foundation for the substantive conclusions that we wish to address. For instance, virtually all social research involves measurement or observation. And, whenever we measure or observe we are concerned with whether we are measuring what we intend to measure or with how our observations are influenced by the circumstances in which they are made. We reach conclusions about the quality of our measures – conclusions that will play an important role in addressing the broader substantive issues of our study. When we talk about the validity of research, we are often referring to these to the many conclusions we reach about the quality of different parts of our research methodology.

We subdivide validity into four types. Each type addresses a specific methodological question. In order to understand the types of validity, you have to know something about how we investigate a research question. Because all four validity types are really only operative when studying causal questions, we will use a causal study to set the context.

The figure shows that there are really two realms that are involved in research. The first, on the top, is the land of theory. It is what goes on inside our heads as researchers. It is where we keep our theories about how the world operates. The second, on the bottom, is the land of observations. It is the real world into which we translate our ideas – our programs, treatments, measures and observations. When we conduct research, we are continually flitting back and forth between these two realms, between what we think about the world and what is going on in it. When we are investigating a cause-effect relationship, we have a theory (implicit or otherwise) of what the cause is ( the cause construct ). For instance, if we are testing a new educational program, we have an idea of what it would look like ideally. Similarly, on the effect side, we have an idea of what we are ideally trying to affect and measure ( the effect construct ). But each of these, the cause and the effect, has to be translated into real things, into a program or treatment and a measure or observational method. We use the term operationalization to describe the act of translating a construct into its manifestation. In effect, we take our idea and describe it as a series of operations or procedures. Now, instead of it only being an idea in our minds, it becomes a public entity that anyone can look at and examine for themselves. It is one thing, for instance, for you to say that you would like to measure self-esteem (a construct). But when you show a ten-item paper-and-pencil self-esteem measure that you developed for that purpose, others can look at it and understand more clearly what you intend by the term self-esteem.

Now, back to explaining the four validity types. They build on one another, with two of them ( conclusion and internal ) referring to the land of observation on the bottom of the figure, one of them ( construct ) emphasizing the linkages between the bottom and the top, and the last ( external ) being primarily concerned about the range of our theory on the top. Imagine that we wish to examine whether use of a World Wide Web (WWW) Virtual Classroom improves student understanding of course material. Assume that we took these two constructs, the cause construct (the WWW site) and the effect (understanding), and operationalized them – turned them into realities by constructing the WWW site and a measure of knowledge of the course material. Here are the four validity types and the question each addresses:

Conclusion Validity: In this study, is there a relationship between the two variables?

In the context of the example we’re considering, the question might be worded: in this study, is there a relationship between the WWW site and knowledge of course material? There are several conclusions or inferences we might draw to answer such a question. We could, for example, conclude that there is a relationship. We might conclude that there is a positive relationship. We might infer that there is no relationship. We can assess the conclusion validity of each of these conclusions or inferences.

Internal Validity: Assuming that there is a relationship in this study, is the relationship a causal one?

Just because we find that use of the WWW site and knowledge are correlated, we can’t necessarily assume that WWW site use causes the knowledge. Both could, for example, be caused by the same factor. For instance, it may be that wealthier students who have greater resources would be more likely to use have access to a WWW site and would excel on objective tests. When we want to make a claim that our program or treatment caused the outcomes in our study, we can consider the internal validity of our causal claim.

Construct Validity: Assuming that there is a causal relationship in this study , can we claim that the program reflected well our construct of the program and that our measure reflected well our idea of the construct of the measure?

In simpler terms, did we implement the program we intended to implement and did we measure the outcome we wanted to measure? In yet other terms, did we operationalize well the ideas of the cause and the effect? When our research is over, we would like to be able to conclude that we did a credible job of operationalizing our constructs – we can assess the construct validity of this conclusion.

External Validity: Assuming that there is a causal relationship in this study between the constructs of the cause and the effect , can we generalize this effect to other persons, places or times?

We are likely to make some claims that our research findings have implications for other groups and individuals in other settings and at other times. When we do, we can examine the external validity of these claims.

Notice how the question that each validity type addresses presupposes an affirmative answer to the previous one. This is what we mean when we say that the validity types build on one another. The figure shows the idea of cumulativeness as a staircase, along with the key question for each validity type.

For any inference or conclusion, there are always possible threats to validity – reasons the conclusion or inference might be wrong. Ideally, one tries to reduce the plausibility of the most likely threats to validity, thereby leaving as most plausible the conclusion reached in the study. For instance, imagine a study examining whether there is a relationship between the amount of training in a specific technology and subsequent rates of use of that technology. Because the interest is in a relationship, it is considered an issue of conclusion validity. Assume that the study is completed and no significant correlation between amount of training and adoption rates is found. On this basis it is concluded that there is no relationship between the two. How could this conclusion be wrong – that is, what are the “threats to validity”? For one, it’s possible that there isn’t sufficient statistical power to detect a relationship even if it exists. Perhaps the sample size is too small or the measure of amount of training is unreliable. Or maybe assumptions of the correlational test are violated given the variables used. Perhaps there were random irrelevancies in the study setting or random heterogeneity in the respondents that increased the variability in the data and made it harder to see the relationship of interest. The inference that there is no relationship will be stronger – have greater conclusion validity – if one can show that these alternative explanations are not credible. The distributions might be examined to see if they conform with assumptions of the statistical test, or analyses conducted to determine whether there is sufficient statistical power.

The theory of validity, and the many lists of specific threats, provide a useful scheme for assessing the quality of research conclusions. The theory is general in scope and applicability, well-articulated in its philosophical suppositions, and virtually impossible to explain adequately in a few minutes. As a framework for judging the quality of evaluations it is indispensable and well worth understanding.

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• J Bras Pneumol
• v.44(3); May-Jun 2018

Internal and external validity: can you apply research study results to your patients?

Cecilia maria patino.

1 . Methods in Epidemiologic, Clinical, and Operations Research-MECOR-program, American Thoracic Society/Asociación Latinoamericana del Tórax, Montevideo, Uruguay.

2 . Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Juliana Carvalho Ferreira

3 . Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo (SP) Brasil.

CLINICAL SCENARIO

In a multicenter study in France, investigators conducted a randomized controlled trial to test the effect of prone vs. supine positioning ventilation on mortality among patients with early, severe ARDS. They showed that prolonged prone-positioning ventilation decreased 28-day mortality [hazard ratio (HR) = 0.39; 95% CI: 0.25-0.63]. 1

STUDY VALIDITY

The validity of a research study refers to how well the results among the study participants represent true findings among similar individuals outside the study. This concept of validity applies to all types of clinical studies, including those about prevalence, associations, interventions, and diagnosis. The validity of a research study includes two domains: internal and external validity.

Internal validity is defined as the extent to which the observed results represent the truth in the population we are studying and, thus, are not due to methodological errors. In our example, if the authors can support that the study has internal validity, they can conclude that prone positioning reduces mortality among patients with severe ARDS. The internal validity of a study can be threatened by many factors, including errors in measurement or in the selection of participants in the study, and researchers should think about and avoid these errors.

Once the internal validity of the study is established, the researcher can proceed to make a judgment regarding its external validity by asking whether the study results apply to similar patients in a different setting or not ( Figure 1 ). In the example, we would want to evaluate if the results of the clinical trial apply to ARDS patients in other ICUs. If the patients have early, severe ARDS, probably yes, but the study results may not apply to patients with mild ARDS . External validity refers to the extent to which the results of a study are generalizable to patients in our daily practice, especially for the population that the sample is thought to represent.

Lack of internal validity implies that the results of the study deviate from the truth, and, therefore, we cannot draw any conclusions; hence, if the results of a trial are not internally valid, external validity is irrelevant. 2 Lack of external validity implies that the results of the trial may not apply to patients who differ from the study population and, consequently, could lead to low adoption of the treatment tested in the trial by other clinicians.

INCREASING VALIDITY OF RESEARCH STUDIES

To increase internal validity, investigators should ensure careful study planning and adequate quality control and implementation strategies-including adequate recruitment strategies, data collection, data analysis, and sample size. External validity can be increased by using broad inclusion criteria that result in a study population that more closely resembles real-life patients, and, in the case of clinical trials, by choosing interventions that are feasible to apply. 2

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Internal Validity vs. External Validity in Research

Both help determine how meaningful the results of the study are

Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

Verywell / Bailey Mariner

• Internal Validity
• External Validity

Internal validity is a measure of how well a study is conducted (its structure) and how accurately its results reflect the studied group.

External validity relates to how applicable the findings are in the real world. These two concepts help researchers gauge if the results of a research study are trustworthy and meaningful.

Conclusions are warranted

Controls extraneous variables

Eliminates alternative explanations

Focus on accuracy and strong research methods

Findings can be generalized

Outcomes apply to practical situations

Results apply to the world at large

Results can be translated into another context

What Is Internal Validity in Research?

Internal validity is the extent to which a research study establishes a trustworthy cause-and-effect relationship. This type of validity depends largely on the study's procedures and how rigorously it is performed.

Internal validity is important because once established, it makes it possible to eliminate alternative explanations for a finding. If you implement a smoking cessation program, for instance, internal validity ensures that any improvement in the subjects is due to the treatment administered and not something else.

Internal validity is not a "yes or no" concept. Instead, we consider how confident we can be with study findings based on whether the research avoids traps that may make those findings questionable. The less chance there is for "confounding," the higher the internal validity and the more confident we can be.

Confounding refers to uncontrollable variables that come into play and can confuse the outcome of a study, making us unsure of whether we can trust that we have identified the cause-and-effect relationship.

In short, you can only be confident that a study is internally valid if you can rule out alternative explanations for the findings. Three criteria are required to assume cause and effect in a research study:

• The cause preceded the effect in terms of time.
• The cause and effect vary together.
• There are no other likely explanations for the relationship observed.

Factors That Improve Internal Validity

To ensure the internal validity of a study, you want to consider aspects of the research design that will increase the likelihood that you can reject alternative hypotheses. Many factors can improve internal validity in research, including:

• Blinding : Participants—and sometimes researchers—are unaware of what intervention they are receiving (such as using a placebo on some subjects in a medication study) to avoid having this knowledge bias their perceptions and behaviors, thus impacting the study's outcome
• Experimental manipulation : Manipulating an independent variable in a study (for instance, giving smokers a cessation program) instead of just observing an association without conducting any intervention (examining the relationship between exercise and smoking behavior)
• Random selection : Choosing participants at random or in a manner in which they are representative of the population that you wish to study
• Randomization or random assignment : Randomly assigning participants to treatment and control groups, ensuring that there is no systematic bias between the research groups
• Strict study protocol : Following specific procedures during the study so as not to introduce any unintended effects; for example, doing things differently with one group of study participants than you do with another group

Internal Validity Threats

Just as there are many ways to ensure internal validity, there is also a list of potential threats that should be considered when planning a study.

• Attrition : Participants dropping out or leaving a study, which means that the results are based on a biased sample of only the people who did not choose to leave (and possibly who all have something in common, such as higher motivation)
• Confounding : A situation in which changes in an outcome variable can be thought to have resulted from some type of outside variable not measured or manipulated in the study
• Diffusion : This refers to the results of one group transferring to another through the groups interacting and talking with or observing one another; this can also lead to another issue called resentful demoralization, in which a control group tries less hard because they feel resentful over the group that they are in
• Experimenter bias : An experimenter behaving in a different way with different groups in a study, which can impact the results (and is eliminated through blinding)
• Historical events : May influence the outcome of studies that occur over a period of time, such as a change in the political leader or a natural disaster that occurs, influencing how study participants feel and act
• Instrumentation : This involves "priming" participants in a study in certain ways with the measures used, causing them to react in a way that is different than they would have otherwise reacted
• Maturation : The impact of time as a variable in a study; for example, if a study takes place over a period of time in which it is possible that participants naturally change in some way (i.e., they grew older or became tired), it may be impossible to rule out whether effects seen in the study were simply due to the impact of time
• Statistical regression : The natural effect of participants at extreme ends of a measure falling in a certain direction due to the passage of time rather than being a direct effect of an intervention
• Testing : Repeatedly testing participants using the same measures influences outcomes; for example, if you give someone the same test three times, it is likely that they will do better as they learn the test or become used to the testing process, causing them to answer differently

What Is External Validity in Research?

External validity refers to how well the outcome of a research study can be expected to apply to other settings. This is important because, if external validity is established, it means that the findings can be generalizable to similar individuals or populations.

External validity affirmatively answers the question: Do the findings apply to similar people, settings, situations, and time periods?

Population validity and ecological validity are two types of external validity. Population validity refers to whether you can generalize the research outcomes to other populations or groups. Ecological validity refers to whether a study's findings can be generalized to additional situations or settings.

Another term called transferability refers to whether results transfer to situations with similar characteristics. Transferability relates to external validity and refers to a qualitative research design.

Factors That Improve External Validity

If you want to improve the external validity of your study, there are many ways to achieve this goal. Factors that can enhance external validity include:

• Field experiments : Conducting a study outside the laboratory, in a natural setting
• Inclusion and exclusion criteria : Setting criteria as to who can be involved in the research, ensuring that the population being studied is clearly defined
• Psychological realism : Making sure participants experience the events of the study as being real by telling them a "cover story," or a different story about the aim of the study so they don't behave differently than they would in real life based on knowing what to expect or knowing the study's goal
• Replication : Conducting the study again with different samples or in different settings to see if you get the same results; when many studies have been conducted on the same topic, a meta-analysis can also be used to determine if the effect of an independent variable can be replicated, therefore making it more reliable
• Reprocessing or calibration : Using statistical methods to adjust for external validity issues, such as reweighting groups if a study had uneven groups for a particular characteristic (such as age)

External Validity Threats

External validity is threatened when a study does not take into account the interaction of variables in the real world. Threats to external validity include:

• Pre- and post-test effects : When the pre- or post-test is in some way related to the effect seen in the study, such that the cause-and-effect relationship disappears without these added tests
• Sample features : When some feature of the sample used was responsible for the effect (or partially responsible), leading to limited generalizability of the findings
• Selection bias : Also considered a threat to internal validity, selection bias describes differences between groups in a study that may relate to the independent variable—like motivation or willingness to take part in the study, or specific demographics of individuals being more likely to take part in an online survey
• Situational factors : Factors such as the time of day of the study, its location, noise, researcher characteristics, and the number of measures used may affect the generalizability of findings

While rigorous research methods can ensure internal validity, external validity may be limited by these methods.

Internal Validity vs. External Validity

Internal validity and external validity are two research concepts that share a few similarities while also having several differences.

Similarities

One of the similarities between internal validity and external validity is that both factors should be considered when designing a study. This is because both have implications in terms of whether the results of a study have meaning.

Both internal validity and external validity are not "either/or" concepts. Therefore, you always need to decide to what degree a study performs in terms of each type of validity.

Each of these concepts is also typically reported in research articles published in scholarly journals . This is so that other researchers can evaluate the study and make decisions about whether the results are useful and valid.

Differences

The essential difference between internal validity and external validity is that internal validity refers to the structure of a study (and its variables) while external validity refers to the universality of the results. But there are further differences between the two as well.

For instance, internal validity focuses on showing a difference that is due to the independent variable alone. Conversely, external validity results can be translated to the world at large.

Internal validity and external validity aren't mutually exclusive. You can have a study with good internal validity but be overall irrelevant to the real world. You could also conduct a field study that is highly relevant to the real world but doesn't have trustworthy results in terms of knowing what variables caused the outcomes.

Examples of Validity

Perhaps the best way to understand internal validity and external validity is with examples.

Internal Validity Example

An example of a study with good internal validity would be if a researcher hypothesizes that using a particular mindfulness app will reduce negative mood. To test this hypothesis, the researcher randomly assigns a sample of participants to one of two groups: those who will use the app over a defined period and those who engage in a control task.

The researcher ensures that there is no systematic bias in how participants are assigned to the groups. They do this by blinding the research assistants so they don't know which groups the subjects are in during the experiment.

A strict study protocol is also used to outline the procedures of the study. Potential confounding variables are measured along with mood , such as the participants' socioeconomic status, gender, age, and other factors. If participants drop out of the study, their characteristics are examined to make sure there is no systematic bias in terms of who stays in.

External Validity Example

An example of a study with good external validity would be if, in the above example, the participants used the mindfulness app at home rather than in the laboratory. This shows that results appear in a real-world setting.

To further ensure external validity, the researcher clearly defines the population of interest and chooses a representative sample . They might also replicate the study's results using different technological devices.

A Word From Verywell

Setting up an experiment so that it has both sound internal validity and external validity involves being mindful from the start about factors that can influence each aspect of your research.

It's best to spend extra time designing a structurally sound study that has far-reaching implications rather than to quickly rush through the design phase only to discover problems later on. Only when both internal validity and external validity are high can strong conclusions be made about your results.

San Jose State University. Internal and external validity .

Michael RS. Threats to internal & external validity: Y520 strategies for educational inquiry .

Pahus L, Burgel PR, Roche N, Paillasseur JL, Chanez P. Randomized controlled trials of pharmacological treatments to prevent COPD exacerbations: applicability to real-life patients . BMC Pulm Med . 2019;19(1):127. doi:10.1186/s12890-019-0882-y

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

Research design: measurement, reliability, and validity

• PMID: 7395882

The concept of measuring constructs is discussed. An explanation of reliability and validity of measures is presented. Reliability is consistency in measurement over repeated measures. Reliable measures are those with low random (chance) errors. Reliability is assessed by one of four methods: retest, alternative-form test, split-halves test, or internal consistency test. Validity is measuring what is intended to be measured. Valid measures are those with low nonrandom (systematic) errors. Validity is assessed by one of three methods: content validation, criterion-related validation, and construct validation. It is particularly important for the researcher in the behavioral or social sciences to assure himself that measures used are both reliable and valid.

• Models, Theoretical
• Research Design*
• Statistics as Topic