types of validity in research with examples

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.

table showing the different types of 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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9 Types of Validity in Research

types of validity in research, explained below

Validity refers to whether or not a test or an experiment is actually doing what it is intended to do.

Validity sits upon a spectrum. For example:

Low Validity: Most people now know that the standard IQ test does not actually measure intelligence or predict success in life.
High Validity: By contrast, a standard pregnancy test is about 99% accurate , meaning it has very high validity and is therefore a very reliable test.

There are many ways to determine validity. Most of them are defined below.

Types of Validity

1. face validity.

Face validity refers to whether a scale “appears” to measure what it is supposed to measure. That is, do the questions seem to be logically related to the construct under study.

For example, a personality scale that measures emotional intelligence should have questions about self-awareness and empathy. It should not have questions about math or chemistry.

One common way to assess face validity is to ask a panel of experts to examine the scale and rate it’s appropriateness as a tool for measuring the construct. If the experts agree that the scale measures what it has been designed to measure, then the scale is said to have face validity.

If a scale, or a test, doesn’t have face validity, then people taking it won’t be serious.

Conbach explains it in the following way:

“When a patient loses faith in the medicine his doctor prescribes, it loses much of its power to improve his health. He may skip doses, and in the end may decide doctors cannot help him and let treatment lapse all together. For similar reasons, when selecting a test one must consider how worthwhile it will appear to the participant who takes it and other laymen who will see the results” (Cronbach, 1970, p. 182).

2. Content Validity

Content validity refers to whether a test or scale is measuring all of the components of a given construct. For example, if there are five dimensions of emotional intelligence (EQ), then a scale that measures EQ should contain questions regarding each dimension.

Similar to face validity, content validity can be assessed by asking subject matter experts (SMEs) to examine the test. If experts agree that the test includes items that assess every domain of the construct, then the test has content validity.

For example, the math portion of the SAT contains questions that require skills in many types of math: arithmetic, algebra, geometry, calculus, and many others. Since there are questions that assess each type of math, then the test has content validity.

The developer of the test could ask SMEs to rate the test’s construct validity. If the SMEs all give the test high ratings, then it has construct validity.

3. Construct Validity

Construct validity is the extent to which a measurement tool is truly assessing what it has been designed to assess.

There are two main methods of assessing construct validity: convergent and discriminant validity.

Convergent validity involves taking two tests that are supposed to measure the same construct and administering them to a sample of participants. The higher the correlation between the two tests, the stronger the construct validity.

With divergent validity, two tests that measure completely different constructs are administered to the same sample of participants. Since the tests are measuring different constructs, there should be a very low correlation between the two.

4. Internal Validity

Internal validity refers to whether or not the results of an experiment are due to the manipulation of the independent, or treatment, variables. For example, a researcher wants to examine how temperature affects willingness to help, so they have research participants wait in a room.

There are different rooms, one has the temperature set at normal, one at moderately warm, and the other at very warm.

During the next phase of the study, participants are asked to donate to a local charity before taking part in the rest of the study. The results showed that as the temperature of the room increased, donations decreased.

On the surface, it seems as though the study has internal validity: room temperature affected donations. However, even though the experiment involved three different rooms set at different temperatures, each room was a different size. The smallest room was the warmest and the normal temperature room was the largest.

Now, we don’t know if the donations were affected by room temperature or room size. So, the study has questionable internal validity.

Another way internal validity is assessed is through inter-rater reliability measures, which helps bolster both the validity and reliability of the study.

5. External Validity

External validity refers to whether the results of a study generalize to the real world or other situations. A lot of psychological studies take place in a university lab. Therefore, the setting is not very realistic.

This creates a big problem regarding external validity. Can we say that what happens in a lab would be the same thing that would happen in the real world?

For example, a study on mindfulness involves the researcher randomly assigning different research participants to use one of three mindfulness apps on their phones at home every night for 3 weeks. At the end of three weeks, their level of stress is measured with some high-tech EEG equipment.

This study has external validity because the participants used real apps and they were at home when using those apps. The apps and the home setting are realistic, so the study has external validity.

See More: Examples of External Validity

6. Concurrent Validity

Concurrent validity is a method of assessing validity that involves comparing a new test with an already existing test, or an already established criterion.

For example, a newly developed math test for the SAT will need to be validated before giving it to thousands of students. So, the new version of the test is administered to a sample of college math majors along with the old version of the test.

Scores on the two tests are compared by calculating a correlation between the two. The higher the correlation, the stronger the concurrent validity of the new test.

7. Predictive Validity

Predictive validity refers to whether scores on one test are associated with performance on a given criterion. That is, can a person’s score on the test predict their performance on the criterion?

For example, an IT company needs to hire dozens of programmers for an upcoming project. But conducting interviews with hundreds of applicants is time-consuming and not very accurate at identifying skilled coders.

So, the company develops a test that contains programming problems similar to the demands of the new project. The company assesses predictive validity of the test by having their current programmers take the test and then compare their scores with their yearly performance evaluations.

The results indicate that programmers with high marks in their evaluations also did very well on the test. Therefore, the test has predictive validity.

Now, when new applicants’ take the test, the company can predict how well they will do at the job in the future. People that do well on the predictor variable test will most likely do well at the job.

8. Statistical Conclusion Validity

Statistical conclusion validity refers to whether the conclusions drawn by the authors of a study are supported by the statistical procedures.

For example, did the study apply the correct statistical analyses, were adequate sampling procedures implemented, did the study use measurement tools that are valid and reliable?

If the answers to those questions are all “yes,” then the study has statistical conclusion validity. However, if the some or all of the answers are “no,” then the conclusions of the study are called into question.

Using the wrong statistical analyses or basing the conclusions on very small sample sizes, make the results questionable. If the results are based on faulty procedures, then the conclusions cannot be accepted as valid.

9. Criterion Validity

Criterion validity is sometimes called predictive validity. It refers to how well scores on one measurement device are associated with scores on a given performance domain (the criterion).

For example, how well do SAT scores predict college GPA? Or, to what extent are measures of consumer confidence related to the economy?

An example of low criterion validity is how poorly athletic performance at the NFL’s combine actually predicts performance on the field on gameday. There are dozens of tests that the athletes go through, but about 99% of them have no association with how well they do in games.

However, nutrition and exercise are highly related to longevity (the criterion). Those constructs have criterion validity because hundreds of studies have identified that nutrition and exercise are directly linked to living a longer and healthier life.

There are so many types of validity because the measurement precision of abstract concepts is hard to discern. There can also be confusion and disagreement among experts on the definition of constructs and how they should be measured.

For these reasons, social scientists have spent considerable time developing a variety of methods to assess the validity of their measurement tools. Sometimes this reveals ways to improve techniques, and sometimes it reveals the fallacy of trying to predict the future based on faulty assessment procedures.

Cook, T.D. and Campbell, D.T. (1979) Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin, Boston.

Cohen, R. J., & Swerdlik, M. E. (2005). Psychological testing and assessment: An introduction to tests and measurement (6th ed.). New York: McGraw-Hill.

Cronbach, L. J. (1970). Essentials of Psychological Testing . New York: Harper & Row.

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

Simms, L. (2007). Classical and Modern Methods of Psychological Scale Construction. Social and Personality Psychology Compass, 2 (1), 414 – 433. https://doi.org/10.1111/j.1751-9004.2007.00044.x

Dave Cornell (PhD)

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Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
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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 .

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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Validity & Reliability In Research

A Plain-Language Explanation (With Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Kerryn Warren (PhD) | September 2023

Validity and reliability are two related but distinctly different concepts within research. Understanding what they are and how to achieve them is critically important to any research project. In this post, we’ll unpack these two concepts as simply as possible.

This post is based on our popular online course, Research Methodology Bootcamp . In the course, we unpack the basics of methodology using straightfoward language and loads of examples. If you’re new to academic research, you definitely want to use this link to get 50% off the course (limited-time offer).

Overview: Validity & Reliability

The big picture
Validity 101
Reliability 101
Key takeaways

First, The Basics…

First, let’s start with a big-picture view and then we can zoom in to the finer details.

Validity and reliability are two incredibly important concepts in research, especially within the social sciences. Both validity and reliability have to do with the measurement of variables and/or constructs – for example, job satisfaction, intelligence, productivity, etc. When undertaking research, you’ll often want to measure these types of constructs and variables and, at the simplest level, validity and reliability are about ensuring the quality and accuracy of those measurements .

As you can probably imagine, if your measurements aren’t accurate or there are quality issues at play when you’re collecting your data, your entire study will be at risk. Therefore, validity and reliability are very important concepts to understand (and to get right). So, let’s unpack each of them.

What Is Validity?

In simple terms, validity (also called “construct validity”) is all about whether a research instrument accurately measures what it’s supposed to measure .

For example, let’s say you have a set of Likert scales that are supposed to quantify someone’s level of overall job satisfaction. If this set of scales focused purely on only one dimension of job satisfaction, say pay satisfaction, this would not be a valid measurement, as it only captures one aspect of the multidimensional construct. In other words, pay satisfaction alone is only one contributing factor toward overall job satisfaction, and therefore it’s not a valid way to measure someone’s job satisfaction.

types of validity in research with examples

Oftentimes in quantitative studies, the way in which the researcher or survey designer interprets a question or statement can differ from how the study participants interpret it . Given that respondents don’t have the opportunity to ask clarifying questions when taking a survey, it’s easy for these sorts of misunderstandings to crop up. Naturally, if the respondents are interpreting the question in the wrong way, the data they provide will be pretty useless . Therefore, ensuring that a study’s measurement instruments are valid – in other words, that they are measuring what they intend to measure – is incredibly important.

There are various types of validity and we’re not going to go down that rabbit hole in this post, but it’s worth quickly highlighting the importance of making sure that your research instrument is tightly aligned with the theoretical construct you’re trying to measure . In other words, you need to pay careful attention to how the key theories within your study define the thing you’re trying to measure – and then make sure that your survey presents it in the same way.

For example, sticking with the “job satisfaction” construct we looked at earlier, you’d need to clearly define what you mean by job satisfaction within your study (and this definition would of course need to be underpinned by the relevant theory). You’d then need to make sure that your chosen definition is reflected in the types of questions or scales you’re using in your survey . Simply put, you need to make sure that your survey respondents are perceiving your key constructs in the same way you are. Or, even if they’re not, that your measurement instrument is capturing the necessary information that reflects your definition of the construct at hand.

If all of this talk about constructs sounds a bit fluffy, be sure to check out Research Methodology Bootcamp , which will provide you with a rock-solid foundational understanding of all things methodology-related. Remember, you can take advantage of our 60% discount offer using this link.

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What Is Reliability?

As with validity, reliability is an attribute of a measurement instrument – for example, a survey, a weight scale or even a blood pressure monitor. But while validity is concerned with whether the instrument is measuring the “thing” it’s supposed to be measuring, reliability is concerned with consistency and stability . In other words, reliability reflects the degree to which a measurement instrument produces consistent results when applied repeatedly to the same phenomenon , under the same conditions .

As you can probably imagine, a measurement instrument that achieves a high level of consistency is naturally more dependable (or reliable) than one that doesn’t – in other words, it can be trusted to provide consistent measurements . And that, of course, is what you want when undertaking empirical research. If you think about it within a more domestic context, just imagine if you found that your bathroom scale gave you a different number every time you hopped on and off of it – you wouldn’t feel too confident in its ability to measure the variable that is your body weight 🙂

It’s worth mentioning that reliability also extends to the person using the measurement instrument . For example, if two researchers use the same instrument (let’s say a measuring tape) and they get different measurements, there’s likely an issue in terms of how one (or both) of them are using the measuring tape. So, when you think about reliability, consider both the instrument and the researcher as part of the equation.

As with validity, there are various types of reliability and various tests that can be used to assess the reliability of an instrument. A popular one that you’ll likely come across for survey instruments is Cronbach’s alpha , which is a statistical measure that quantifies the degree to which items within an instrument (for example, a set of Likert scales) measure the same underlying construct . In other words, Cronbach’s alpha indicates how closely related the items are and whether they consistently capture the same concept .

Reliability reflects whether an instrument produces consistent results when applied to the same phenomenon, under the same conditions.

Recap: Key Takeaways

Alright, let’s quickly recap to cement your understanding of validity and reliability:

Validity is concerned with whether an instrument (e.g., a set of Likert scales) is measuring what it’s supposed to measure
Reliability is concerned with whether that measurement is consistent and stable when measuring the same phenomenon under the same conditions.

In short, validity and reliability are both essential to ensuring that your data collection efforts deliver high-quality, accurate data that help you answer your research questions . So, be sure to always pay careful attention to the validity and reliability of your measurement instruments when collecting and analysing data. As the adage goes, “rubbish in, rubbish out” – make sure that your data inputs are rock-solid.

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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.

Validity in Psychological Tests

Why Measures Like Validity and Reliability are Important

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

James Lacy, MLS, is a fact-checker and researcher.

Content Validity

Criterion-related validity, construct validity, face validity, reliability vs. validity, frequently asked questions.

Validity is the extent to which a test measures what it claims to measure. It is vital for a test to be valid in order for the results to be accurately applied and interpreted.

Psychological assessment is an important part of both experimental research and clinical treatment. One of the greatest concerns when creating a psychological test is whether or not it actually measures what we think it is measuring.

For example, a test might be designed to measure a stable personality trait but instead, it measures transitory emotions generated by situational or environmental conditions. A valid test ensures that the results are an accurate reflection of the dimension undergoing assessment.

Validity isn’t determined by a single statistic, but by a body of research that demonstrates the relationship between the test and the behavior it is intended to measure. There are four types of validity: content validity, criterion-related validity, construct validity, and face validity.

This article discusses what each of these four types of validity is and how they are used in psychological tests. It also explores how validity compares with reliability, which is another important measure of a test's accuracy and usefulness.

When a test has content validity, the items on the test represent the entire range of possible items the test should cover. Individual test questions may be drawn from a large pool of items that cover a broad range of topics.

In some instances where a test measures a trait that is difficult to define, an expert judge may rate each item’s relevance. Because each judge bases their rating on opinion, two independent judges rate the test separately. Items that are rated as strongly relevant by both judges will be included in the final test.

Internal and External Validity

Internal and external validity are used to determine whether or not the results of an experiment are meaningful. Internal validity relates to the way a test is performed, while external validity examines how well the findings may apply in other settings.

A test is said to have criterion-related validity when it has demonstrated its effectiveness in predicting criteria, or indicators, of a construct.

For example, when an employer hires new employees, they will examine different criteria that could predict whether or not a prospective hire will be a good fit for a job. People who do well on a test may be more likely to do well at a job, while people with a low score on a test will do poorly at that job.

There are two different types of criterion validity: concurrent and predictive.

Concurrent Validity

Concurrent validity occurs when criterion measures are obtained at the same time as test scores, indicating the ability of test scores to estimate an individual’s current state. For example, on a test that measures levels of depression, the test would be said to have concurrent validity if it measured the current levels of depression experienced by the test taker.

Predictive Validity

Predictive validity is when the criterion measures are obtained at a time after the test. Examples of tests with predictive validity are career or aptitude tests , which are helpful in determining who is likely to succeed or fail in certain subjects or occupations.

A test has construct validity if it demonstrates an association between the test scores and the prediction of a theoretical trait. Intelligence tests are one example of measurement instruments that should have construct validity. A valid intelligence test should be able to accurately measure the construct of intelligence rather than other characteristics, such as memory or education level.

Essentially, construct validity looks at whether a test covers the full range of behaviors that make up the construct being measured. The procedure here is to identify necessary tasks to perform a job like typing, design, or physical ability.

In order to demonstrate the construct validity of a selection procedure, the behaviors demonstrated in the selection should be a representative sample of the behaviors of the job.

Face validity is one of the most basic measures of validity. Essentially, researchers are simply taking the validity of the test at face value by looking at whether it appears to measure the target variable. On a measure of happiness , for example, the test would be said to have face validity if it appeared to actually measure levels of happiness.

Obviously, face validity only means that the test looks like it works. It does not mean that the test has been proven to work. However, if the measure seems to be valid at this point, researchers may investigate further in order to determine whether the test is valid and should be used in the future.

A survey asking people which political candidate they plan to vote for would be said to have high face validity, while a complex test used as part of a psychological experiment that looks at a variety of values, characteristics, and behaviors might be said to have low face validity because the exact purpose of the test is not immediately clear, particularly to the participants.

While validity examines how well a test measures what it is intended to measure, reliability refers to how consistent the results are. There are four ways to assess reliability:

Internal consistency : Internal consistency examines the consistency of different items within the same test.
Inter-rater : In this method, multiple independent judges score the test on its reliability.
Parallel or alternate forms : This approach uses different forms of the same test and compares the results.
Test-retest : This measures the reliability of results by administering the same test at different points in time.

It's important to remember that a test can be reliable without being valid. Consistent results do not always indicate that a test is measuring what researchers designed it to.

External validity is how well the results of a test apply in other settings. The findings of a test with strong external validity will apply to practical situations and take real-world variables into account.

Internal validity examines the procedures and structure of a test to determine how well it was conducted and whether or not its results are valid. A test with strong internal validity will establish cause and effect and should eliminate alternative explanations for the findings.

Reliability is an examination of how consistent and stable the results of an assessment are. Validity refers to how well a test actually measures what it was created to measure. Reliability measures the precision of a test, while validity looks at accuracy.

An example of reliability in psychology research would be administering a personality test multiple times in a row to see if the person has the same result. If the score is the same or similar on each test, it is an indicator that the test is reliable.

Content validity is measured by checking to see whether the content of a test accurately depicts the construct being tested. Generally, experts on the subject matter would determine whether or not a test has acceptable content validity.

Validity can be demonstrated by showing a clear relationship between the test and what it is meant to measure. This can be done by showing that a study has one (or more) of the four types of validity: content validity, criterion-related validity, construct validity, and/or face validity.

Newton PE, Shaw SD. Standards for talking and thinking about validity . Psychol Methods . 2013;18(3):301-19. doi:10.1037/a0032969

Cizek GJ. Defining and distinguishing validity: Interpretations of score meaning and justifications of test use . Psychol Methods . 2012;17(1):31-43. doi:10.1037/a0026975

Committee on Psychological Testing, Including Validity Testing, for Social Security Administration Disability Determinations; Board on the Health of Select Populations; Institute of Medicine. Psychological Testing in the Service of Disability Determination . Washington, DC; 2015.

Lin WL., Yao G. Criterion validity . In: Michalos AC, ed. Encyclopedia of Quality of Life and Well-Being Research . Springer, Dordrecht; 2014. doi:10.1007/978-94-007-0753-5_618

Lin WL., Yao G. Concurrent validity . In: Michalos AC, ed. Encyclopedia of Quality of Life and Well-Being Research . Springer, Dordrecht; 2014. doi:10.1007/978-94-007-0753-5_516

Lin WL., Yao G. Predictive validity . In: Michalos AC, eds. Encyclopedia of Quality of Life and Well-Being Research . Springer, Dordrecht; 2014. doi:10.1007/978-94-007-0753-5_2241

Ginty AT. Construct validity . In: Gellman MD, Turner JR, eds. Encyclopedia of Behavioral Medicine . Springer, New York, NY; 2013. doi:10.1007/978-1-4419-1005-9_861

Johnson E. Face validity . In: Volkmar FR, ed. Encyclopedia of Autism Spectrum Disorders . Springer, New York, NY; 2013. doi:10.1007/978-1-4419-1698-3_308

Almanasreh E, Moles R, Chen TF. Evaluation of methods used for estimating content validity . Res Social Adm Pharm . 2019;15(2):214-221. doi:10.1016/j.sapharm.2018.03.066

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Reliability vs. Validity in Research: Types & Examples

Explore how reliability vs validity in research determines quality. Learn the differences and types + examples. Get insights!

When it comes to research, getting things right is crucial. That’s where the concepts of “Reliability vs Validity in Research” come in.

Imagine it like a balancing act – making sure your measurements are consistent and accurate at the same time. This is where test-retest reliability, having different researchers check things, and keeping things consistent within your research plays a big role.

As we dive into this topic, we’ll uncover the differences between reliability and validity, see how they work together, and learn how to use them effectively.

Understanding Reliability vs. Validity in Research

When it comes to collecting data and conducting research, two crucial concepts stand out: reliability and validity.

These pillars uphold the integrity of research findings, ensuring that the data collected and the conclusions drawn are both meaningful and trustworthy. Let’s dive into the heart of the concepts, reliability, and validity, to comprehend their significance in the realm of research truly.

What is reliability?

Reliability refers to the consistency and dependability of the data collection process. It’s like having a steady hand that produces the same result each time it reaches for a task.

In the research context, reliability is all about ensuring that if you were to repeat the same study using the same reliable measurement technique, you’d end up with the same results. It’s like having multiple researchers independently conduct the same experiment and getting outcomes that align perfectly.

Imagine you’re using a thermometer to measure the temperature of the water. You have a reliable measurement if you dip the thermometer into the water multiple times and get the same reading each time. This tells you that your method and measurement technique consistently produce the same results, whether it’s you or another researcher performing the measurement.

What is validity?

On the other hand, validity refers to the accuracy and meaningfulness of your data. It’s like ensuring that the puzzle pieces you’re putting together actually form the intended picture. When you have validity, you know that your method and measurement technique are consistent and capable of producing results aligned with reality.

Think of it this way; Imagine you’re conducting a test that claims to measure a specific trait, like problem-solving ability. If the test consistently produces results that accurately reflect participants’ problem-solving skills, then the test has high validity. In this case, the test produces accurate results that truly correspond to the trait it aims to measure.

In essence, while reliability assures you that your data collection process is like a well-oiled machine producing the same results, validity steps in to ensure that these results are not only consistent but also relevantly accurate.

Together, these concepts provide researchers with the tools to conduct research that stands on a solid foundation of dependable methods and meaningful insights.

Types of Reliability

Let’s explore the various types of reliability that researchers consider to ensure their work stands on solid ground.

High test-retest reliability

Test-retest reliability involves assessing the consistency of measurements over time. It’s like taking the same measurement or test twice – once and then again after a certain period. If the results align closely, it indicates that the measurement is reliable over time. Think of it as capturing the essence of stability.

Inter-rater reliability

When multiple researchers or observers are part of the equation, interrater reliability comes into play. This type of reliability assesses the level of agreement between different observers when evaluating the same phenomenon. It’s like ensuring that different pairs of eyes perceive things in a similar way.

Internal reliability

Internal consistency dives into the harmony among different items within a measurement tool aiming to assess the same concept. This often comes into play in surveys or questionnaires, where participants respond to various items related to a single construct. If the responses to these items consistently reflect the same underlying concept, the measurement is said to have high internal consistency.

Types of validity

Let’s explore the various types of validity that researchers consider to ensure their work stands on solid ground.

Content validity

It delves into whether a measurement truly captures all dimensions of the concept it intends to measure. It’s about making sure your measurement tool covers all relevant aspects comprehensively.

Imagine designing a test to assess students’ understanding of a history chapter. It exhibits high content validity if the test includes questions about key events, dates, and causes. However, if it focuses solely on dates and omits causation, its content validity might be questionable.

Construct validity

It assesses how well a measurement aligns with established theories and concepts. It’s like ensuring that your measurement is a true representation of the abstract construct you’re trying to capture.

Criterion validity

Criterion validity examines how well your measurement corresponds to other established measurements of the same concept. It’s about making sure your measurement accurately predicts or correlates with external criteria.

Differences between reliability and validity in research

Let’s delve into the differences between reliability and validity in research.

While both reliability and validity contribute to trustworthy research, they address distinct aspects. Reliability ensures consistent results, while validity ensures accurate and relevant results that reflect the true nature of the measured concept.

Example of Reliability and Validity in Research

In this section, we’ll explore instances that highlight the differences between reliability and validity and how they play a crucial role in ensuring the credibility of research findings.

Example of reliability

Imagine you are studying the reliability of a smartphone’s battery life measurement. To collect data, you fully charge the phone and measure the battery life three times in the same controlled environment—same apps running, same brightness level, and same usage patterns.

If the measurements consistently show a similar battery life duration each time you repeat the test, it indicates that your measurement method is reliable. The consistent results under the same conditions assure you that the battery life measurement can be trusted to provide dependable information about the phone’s performance.

Example of validity

Researchers collect data from a group of participants in a study aiming to assess the validity of a newly developed stress questionnaire. To ensure validity, they compare the scores obtained from the stress questionnaire with the participants’ actual stress levels measured using physiological indicators such as heart rate variability and cortisol levels.

If participants’ scores correlate strongly with their physiological stress levels, the questionnaire is valid. This means the questionnaire accurately measures participants’ stress levels, and its results correspond to real variations in their physiological responses to stress.

Validity assessed through the correlation between questionnaire scores and physiological measures ensures that the questionnaire is effectively measuring what it claims to measure participants’ stress levels.

In the world of research, differentiating between reliability and validity is crucial. Reliability ensures consistent results, while validity confirms accurate measurements. Using tools like QuestionPro enhances data collection for both reliability and validity. For instance, measuring self-esteem over time showcases reliability, and aligning questions with theories demonstrates validity.

QuestionPro empowers researchers to achieve reliable and valid results through its robust features, facilitating credible research outcomes. Contact QuestionPro to create a free account or learn more!

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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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Indian J Psychol Med
v.40(5); Sep-Oct 2018

Internal, External, and Ecological Validity in Research Design, Conduct, and Evaluation

Chittaranjan andrade.

Department of Psychopharmacology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

Reliability and validity describe desirable psychometric characteristics of research instruments. The concept of validity is also applied to research studies and their findings. Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts. Ecological validity examines, specifically, whether the study findings can be generalized to real-life settings; thus ecological validity is a subtype of external validity. These concepts are explained using examples so that readers may understand why the consideration of internal, external, and ecological validity is important for designing and conducting studies, and for understanding the merits of published research.

DID CATIE HAVE EXTERNAL VALIDITY?

The answer is both yes and no. CATIE[ 1 ] was designed as an effectiveness study; that is, a study with relevance to real-world settings. The CATIE findings are relevant to clinical practice in the USA but are of questionable relevance in India. One reason is that, in the USA, where CATIE was conducted, the primary outcome, time to all-cause treatment discontinuation, is substantially patient-influenced, whereas in India, where families supervise treatment, it is largely caregiver-determined. Another and more important reason is that the healthcare delivery system in clinical practice is strikingly different in the two countries. Thus CATIE has good external validity for clinical practice in the USA but not in India.

RELIABILITY AND VALIDITY

Reliability and validity are concepts that are applied to instruments such as rating scales and screening tools. Validity describes how well an instrument does what it is supposed to do. For example, does an instrument that screens for depression do so with high sensitivity and specificity? Reliability describes the consistency with which results are obtained. For example, if an instrument that rates the severity of depression is administered to the same patient twice within the span of an hour, are the scores obtained closely similar? Different types of reliability and validity describe desirable psychometric properties of research and clinical instruments.[ 2 , 3 ] Validity can also be applied to laboratory and clinical studies, and to their findings, as well, as the sections below show.

INTERNAL VALIDITY

Internal validity examines whether the manner in which a study was designed, conducted, and analyzed allows trustworthy answers to the research questions in the study. For example, improper randomization, inadvertent unblinding of patients or raters, excessive use of rescue medication, and missing data can all undermine the fidelity of the results and conclusions of a randomized controlled trial (RCT). That is, the internal validity of the RCT is compromised. Internal validity is based on judgment and is not a computed statistic.

Internal validity examines the extent to which systematic error (bias) is present. Such systematic error can arise through selection bias, performance bias, detection bias, and attrition bias.[ 4 ] If internal validity is compromised, it can occasionally be improved, for example, by a modified plan of analysis. However, biases can be often fatal as, for example, if double-blind ratings were not obtained in an RCT.

EXTERNAL VALIDITY

External validity examines whether the findings of a study can be generalized to other contexts.[ 4 ] Studies are conducted on samples, and if sampling was random, the sample is representative of the population, and so the results of a study can validly be generalized to the population from which the sample was drawn. But results may not be generalizable to other populations. Thus external validity is poor for studies with sociodemographic restrictions; studies that exclude severely ill and suicidal patients, or patients with personality disorders, substance use disorders, and medical comorbidities; studies that disallow concurrent treatments; and so on. External validity is also limited in short-term studies of patients who need to be treated for months to years. External validity, like internal validity, is based on judgment and is not a computed statistic.

ECOLOGICAL VALIDITY

Ecological validity examines whether the results of a study can be generalized to real-life settings.[ 5 ] How is this different from external validity? External validity asks whether the findings of a study can be generalized to patients with characteristics that are different from those in the study, or patients who are treated in a different way, or patients who are followed up for longer durations. In contrast, ecological validity specifically examines whether the findings of a study can be generalized to naturalistic situations, such as clinical practice in everyday life. Ecological validity is, therefore, a subtype of external validity. The ecological validity of an instrument can be computed as a correlation between ratings obtained with that instrument and an appropriate measure in naturalistic practice or in everyday life. The ecological validity of a study is a judgment and is not a computed statistic.

Ecological validity was originally invoked in the context of laboratory studies that required to be generalized to real-life situations.[ 5 ] Thus, laboratory studies of the neuropsychological and psychomotor impairments produced by psychotropic drugs have poor ecological validity because what is studied in relaxed, rested, and healthy subjects tested in a controlled environment is very different from demands that stressed patients face in everyday life. In fact, these cognitive and psychomotor tests, especially when based on computerized tasks, have no parallel in everyday life. How much less ecological validity, then, would research in animal models of different neuropsychiatric states have for patients in clinical practice? This explains why drugs that work in animal models often fail in humans.[ 6 ]

On a parting note, a good understanding of the concepts of internal, external, and ecological validity is necessary to properly design and conduct studies and to evaluate the merits and applications of published research.

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Conflicts of interest.

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Home » Internal Validity – Threats, Examples and Guide

Internal Validity – Threats, Examples and Guide

Table of Contents

Internal Validity

Definition:

Internal validity refers to the extent to which a research study accurately establishes a cause-and-effect relationship between the independent variable (s) and the dependent variable (s) being investigated. It assesses whether the observed changes in the dependent variable(s) are actually caused by the manipulation of the independent variable(s) rather than other extraneous factors.

How to Increase Internal Validity

To enhance internal validity, researchers need to carefully design and conduct their studies. Here are some considerations for improving internal validity:

Random Assignment: Use random assignment to allocate participants to different groups in experimental studies. Random assignment helps ensure that the groups are comparable, minimizing the influence of individual differences on the results.
Control Group: Include a control group in experimental studies. This group should be similar to the experimental group but not exposed to the treatment or intervention being tested. The control group helps establish a baseline against which the effects of the treatment can be compared.
Control Extraneous Variables: Identify and control for extraneous variables that could potentially influence the relationship being studied. This can be achieved through techniques like matching participants, using homogeneous samples, or statistically controlling for the variables.
Standardized Procedures: Use standardized procedures and protocols across all participants and conditions. This helps ensure consistency in the administration of the study, reducing the potential for systematic biases.
Counterbalancing: In studies with multiple conditions or treatment sequences, employ counterbalancing techniques. This involves systematically varying the order of conditions or treatments across participants to eliminate any potential order effects.
Minimize Experimenter Bias: Take steps to minimize experimenter bias or expectancy effects. These biases can inadvertently influence the behavior of participants or the interpretation of results. Using blind or double-blind procedures, where the experimenter is unaware of the conditions or group assignments, can help mitigate these biases.
Use Reliable and Valid Measures: Ensure that the measures used in the study are reliable and valid. Reliable measures yield consistent results, while valid measures accurately assess the construct being measured.
Pilot Testing: Conduct pilot testing before the main study to refine the study design and procedures. Pilot testing helps identify potential issues, such as unclear instructions or unforeseen confounds, and allows for necessary adjustments to enhance internal validity.
Sample Size: Increase the sample size to improve statistical power and reduce the likelihood of random variation influencing the results. Adequate sample sizes increase the generalizability and reliability of the findings.
Researcher Bias: Researchers need to be aware of their own biases and take steps to minimize their impact on the study. This can be done through careful experimental design, blind data collection and analysis, and the use of standardized protocols.

Threats To Internal Validity

Several threats can undermine internal validity and compromise the validity of research findings. Here are some common threats to internal validity:

Events or circumstances that occur during the course of a study and affect the outcome, making it difficult to attribute the results solely to the treatment or intervention being studied.

Changes that naturally occur in participants over time, such as physical or psychological development, which can influence the results independently of the treatment or intervention.

Testing Effects

The act of being tested or measured on a particular variable in an initial assessment may influence participants’ subsequent responses. This effect can arise due to familiarity with the test or increased sensitization to the topic being studied.

Instrumentation

Changes or inconsistencies in the measurement tools or procedures used across different stages or conditions of the study. If the measurement methods are not standardized or if there are variations in the administration of tests, it can lead to measurement errors and threaten internal validity.

Selection Bias

When there are systematic differences between the characteristics of individuals selected for different groups or conditions in a study. If participants are not randomly assigned to groups or conditions, the results may be influenced by pre-existing differences rather than the treatment itself.

Attrition or Dropout

The loss of participants from a study over time can introduce bias if those who drop out differ systematically from those who remain. The characteristics of participants who drop out may affect the outcomes and compromise internal validity.

Regression to The Mean

The tendency for extreme scores on a variable to move closer to the average on subsequent measurements. If participants are selected based on extreme scores, their scores are likely to regress toward the mean in subsequent measurements, leading to erroneous conclusions about the effectiveness of a treatment.

Diffusion of Treatment

When participants in one group of a study receive knowledge or benefits from participants in another group, it can dilute the treatment effect and compromise internal validity. This can occur through communication or sharing of information among participants.

Demand Characteristics

Cues or expectations within a study that may influence participants to respond in a certain way or guess the purpose of the research. Participants may modify their behavior to align with perceived expectations, leading to biased results.

Experimenter Bias

Biases or expectations on the part of the researchers that may unintentionally influence the study’s outcomes. Researchers’ behavior, interactions, or inadvertent cues can impact participants’ responses, introducing bias and threatening internal validity.

Types of Internal Validity

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

Construct validity

Refers to the extent to which the operational definitions of the variables used in the study accurately represent the theoretical concepts they are intended to measure. It ensures that the measurements or manipulations used in the study accurately reflect the intended constructs.

Statistical Conclusion Validity

Relates to the degree to which the statistical analysis accurately reflects the relationships between variables. It involves ensuring that the appropriate statistical tests are used, the data is analyzed correctly, and the reported findings are reliable.

Internal Validity of Causal Inferences

Focuses on establishing a cause-and-effect relationship between the independent variable (treatment or intervention) and the dependent variable (outcome or response variable). It involves eliminating alternative explanations or confounding factors that could account for the observed relationship.

Temporal Precedence

Ensures that the cause (independent variable) precedes the effect (dependent variable) in time. It establishes the temporal sequence necessary for making causal claims.

Covariation

Refers to the presence of a relationship or association between the independent variable and the dependent variable. It ensures that changes in the independent variable are accompanied by corresponding changes in the dependent variable.

Elimination of Confounding Variables

Involves controlling for and minimizing the influence of extraneous variables that could affect the relationship between the independent and dependent variables. It helps isolate the true effect of the independent variable on the dependent variable.

Selection bias Control

Ensures that the process of assigning participants to different groups or conditions (randomization) is unbiased. Random assignment helps create equivalent groups, reducing the influence of participant characteristics on the dependent variable.

Controlling for Testing Effects

Involves minimizing the impact of repeated testing or measurement on participants’ responses. Counterbalancing, using control groups, or employing appropriate time intervals between assessments can help control for testing effects.

Controlling for Experimenter Effects

Aims to minimize the influence of the experimenter on participants’ responses. Blinding, using standardized protocols, or automating data collection processes can reduce the potential for experimenter bias.

Replication

Conducting the study multiple times with different samples or settings to verify the consistency and generalizability of the findings. Replication enhances internal validity by ensuring that the observed effects are not due to chance or specific characteristics of the study sample.

Internal Validity Examples

Here are some real-time examples that illustrate internal validity:

Drug Trial: A pharmaceutical company conducts a clinical trial to test the effectiveness of a new medication for treating a specific disease. The study uses a randomized controlled design, where participants are randomly assigned to receive either the medication or a placebo. The internal validity is high because the random assignment helps ensure that any observed differences between the groups can be attributed to the medication rather than other factors.

Education Intervention: A researcher investigates the impact of a new teaching method on student performance in mathematics. The researcher selects two comparable groups of students from the same school and randomly assigns one group to receive the new teaching method while the other group continues with the traditional method. By controlling for factors such as the school environment and student characteristics, the study enhances internal validity by isolating the effects of the teaching method.

Psychological Experiment: A psychologist conducts an experiment to examine the relationship between sleep deprivation and cognitive performance. Participants are randomly assigned to either a sleep-deprived group or a control group. The internal validity is strengthened by manipulating the independent variable (amount of sleep) and controlling for other variables that could influence cognitive performance, such as age, gender, and prior sleep habits.

Quasi-Experimental Study: A researcher investigates the impact of a new traffic law on accident rates in a specific city. Since random assignment is not feasible, the researcher selects two similar neighborhoods: one where the law is implemented and another where it is not. By comparing accident rates before and after the law’s implementation in both areas, the study attempts to establish a causal relationship while acknowledging potential confounding variables, such as driver behavior or road conditions.

Workplace Training Program: An organization introduces a new training program aimed at improving employee productivity. To assess the effectiveness of the program, the company implements a pre-post design where performance metrics are measured before and after the training. By tracking changes in productivity within the same group of employees, the study attempts to attribute any improvements to the training program while controlling for individual differences.

Applications of Internal Validity

Internal validity is a crucial concept in research design and is applicable across various fields of study. Here are some applications of internal validity:

Experimental Research

Internal validity is particularly important in experimental research, where researchers manipulate independent variables to determine their effects on dependent variables. By ensuring strong internal validity, researchers can confidently attribute any observed changes in the dependent variable to the manipulation of the independent variable, establishing a cause-and-effect relationship.

Quasi-experimental Research

Quasi-experimental studies aim to establish causal relationships but lack random assignment to groups. Internal validity becomes crucial in such designs to minimize alternative explanations for the observed effects. Careful selection and control of potential confounding variables help strengthen internal validity in quasi-experimental research.

Observational Studies

While observational studies may not involve experimental manipulation, internal validity is still relevant. Researchers need to identify and control for confounding variables to establish a relationship between variables of interest and rule out alternative explanations for observed associations.

Program Evaluation

Internal validity is essential in evaluating the effectiveness of interventions, programs, or policies. By designing rigorous evaluation studies with strong internal validity, researchers can determine whether the observed outcomes can be attributed to the specific intervention or program being evaluated.

Clinical Trials

Internal validity is critical in clinical trials to determine the effectiveness of new treatments or therapies. Well-designed randomized controlled trials (RCTs) with strong internal validity can provide reliable evidence on the efficacy of interventions and guide clinical decision-making.

Longitudinal Studies

Longitudinal studies track participants over an extended period to examine changes and establish causal relationships. Maintaining internal validity throughout the study helps ensure that observed changes in the dependent variable(s) are indeed caused by the independent variable(s) under investigation and not other factors.

Psychology and Social Sciences

Internal validity is pertinent in psychological and social science research. Researchers aim to understand human behavior and social phenomena, and establishing strong internal validity allows them to draw accurate conclusions about the causal relationships between variables.

Advantages of Internal Validity

Internal validity is essential in research for several reasons. Here are some of the advantages of having high internal validity in a study:

Causal Inference: Internal validity allows researchers to make valid causal inferences. When a study has high internal validity, it establishes a cause-and-effect relationship between the independent variable (treatment or intervention) and the dependent variable (outcome). This provides confidence that changes in the dependent variable are genuinely due to the manipulation of the independent variable.
Elimination of Confounding Factors: High internal validity helps eliminate or control confounding factors that could influence the relationship being studied. By systematically accounting for potential confounds, researchers can attribute the observed effects to the intended independent variable rather than extraneous variables.
Accuracy of Measurements: Internal validity ensures accurate and reliable measurements. Researchers employ rigorous methods to measure variables, reducing measurement errors and increasing the validity and precision of the data collected.
Replicability and Generalizability: Studies with high internal validity are more likely to yield consistent results when replicated by other researchers. This is important for the advancement of scientific knowledge, as replication strengthens the validity of findings and allows for the generalizability of results across different populations and settings.
Intervention Effectiveness: High internal validity helps determine the effectiveness of interventions or treatments. By controlling for confounding factors and utilizing robust research designs, researchers can accurately assess whether an intervention produces the desired outcomes or effects.
Enhanced Decision-making: Studies with high internal validity provide a solid basis for decision-making. Policymakers, practitioners, and professionals can rely on research with high internal validity to make informed decisions about the implementation of interventions or treatments in real-world settings.
Validity of Theory Development: Internal validity contributes to the development and refinement of theories. By establishing strong cause-and-effect relationships, researchers can build and test theories, enhancing our understanding of underlying mechanisms and contributing to theoretical advancements.
Scientific Credibility: Research with high internal validity enhances the overall credibility of the scientific field. Studies that prioritize internal validity uphold the rigorous standards of scientific inquiry and contribute to the accumulation of reliable knowledge.

Limitations of Internal Validity

While internal validity is crucial for research, it is important to recognize its limitations. Here are some limitations or considerations associated with internal validity:

Artificial Experimental Settings: Research studies with high internal validity often take place in controlled laboratory settings. While this allows for rigorous control over variables, it may limit the generalizability of the findings to real-world settings. The controlled environment may not fully capture the complexity and variability of natural settings, potentially affecting the external validity of the study.
Demand Characteristics and Experimenter Effects: Participants in a study may behave differently due to demand characteristics or their awareness of being in a research setting. They might alter their behavior to align with their perceptions of the expected or desired responses, which can introduce bias and compromise internal validity. Similarly, experimenter effects, such as unintentional cues or biases conveyed by the researcher, can influence participant responses and affect internal validity.
Selection Bias: The process of selecting participants for a study may introduce biases and limit the generalizability of the findings. For example, if participants are not randomly selected or if they self-select into the study, the sample may not represent the larger population, impacting both internal and external validity.
Reactive or Interactive Effects: Participants’ awareness of being observed or their exposure to the experimental manipulation may elicit reactive or interactive effects. These effects can influence their behavior, leading to artificial responses that may not be representative of their natural behavior in real-world situations.
Limited Sample Characteristics: The characteristics of the sample used in a study can affect internal validity. If the sample is not diverse or representative of the population of interest, it can limit the generalizability of the findings. Additionally, small sample sizes may reduce statistical power and increase the likelihood of chance findings.
Time-related Factors: Internal validity can be influenced by factors related to the timing of the study. For example, the immediate effects observed in a short-term study may not reflect the long-term effects of an intervention. Additionally, history or maturation effects occurring during the course of the study may confound the relationship being studied.
Exclusion of Complex Variables: To establish internal validity, researchers often simplify the research design by focusing on a limited number of variables. While this allows for controlled experimentation, it may neglect the complex interactions and multiple factors that exist in real-world situations. This limitation can impact the ecological validity and external validity of the findings.
Publication Bias: Publication bias occurs when studies with significant or positive results are more likely to be published, while studies with null or negative results remain unpublished or overlooked. This bias can distort the body of evidence and compromise the overall internal validity of the research field.

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Validity: Types & Examples

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Research validity concerns the degree to which a study accurately represents what it aims to investigate. It addresses the credibility and relevance of the research and its outcomes.

There are primarily two types of validity:

Internal validity: It ensures the research design and its execution accurately reflect the cause-and-effect relationship being studied.
External validity : It relates to how well the study's results can be generalized to other settings, populations, or circumstances.

Are you running a research project and want to ensure its validity? We are here to help you! In this blog post, we will shed more light on every aspect of this important criteria. Get ready to learn everything about test accuracy and its types. We will cover different cases and tell you how to determine whether your research is valid. This article is jam-packed with examples so you can fully understand how things work. Shall we get started?

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What Is Validity: Definition

Validity in research is an estimate that shows how precisely your measurement method works. In other words, it tells whether the study outcomes are accurate and can be applied to the real-world setting. Research accuracy is usually considered in quantitative studies. For instance, research aimed at examining aggression in teens but which, in fact, measures low self-esteem will be invalid. Your research will only be accurate if the tool or method you are studying measures exactly what it is expected to measure. Unlike reliability, here results shouldn’t necessarily be consistent in similar situations. However, you should pay attention to other important aspects. We will cover them in detail down below. Also read and find out our blog about validity vs reliability . You will get more facts for a better understanding.

Types of Validity

There are many various types of validity . They fall into two main categories:

Experimental.

Each of these categories are different depending on what they are designed to identify. Let’s begin with explaining the classical definitions of these groups. Expect to find great examples to get a complete picture about the different types of research accuracy.

Test Validity

Above we have mentioned that your research should have accurate methods of measurement and broad generalisability to be valid. And while the latter is related to the experimental studies (more on this later), the former is the main focus of a test validity. In a nutshell, a test validity is the degree to which any test applied in research correctly measures the target object or phenomena. It is usually used in psychological or educational tests. It tells how much your supporting evidence and theory prove the interpretation of your test outcomes. Below we will discuss the primary types you may encounter while measuring the accuracy of your test. Each of these types focuses on different aspects of research precision.

Construct Validity: Definition

Construct validity allows us to find out if an instrument used for measurement is actually what we're trying to measure. It's the most important factor in determining the general accuracy of a method. A construct is any feature or trait that researchers can’t examine. But it can be easily assessed through observation of other indicators connected with it. Constructs may refer to the characteristics of people, such as intelligence, weight, or anxiety. They could also imply larger concepts that apply to social or business groups. For example, these can be race inequality or corporate sustainability. Construct validity example

Content Validity: Definition

Now you may wonder what content validity is. Content validity determines the degree to which a test can represent all characteristics of a construct. In order to get an accurate outcome, the material used in assessment should consider all related aspects of the subject matter under the test. If certain aspects are not included in the measurements or when inapplicable elements are integrated, then the accuracy of such method is vulnerable. Content validity example

Face Validity: Definition

Face validity, also known as logical, is the extent to which a subjective measurement of content relevancy is accurate. Here, experts need to provide their opinion on whether a method assesses any phenomenon intended. This estimate is more personal and, thus, can be prone to prejudice. However, it’s a good measurement instrument if you are doing a preliminary assessment. Face validity example

Criterion Validity: Definition

A final measure of accuracy is criterion related validity. It shows how well your test represents or predicts a criterion. Here, you should understand what a criterion variable is. So let’s sort these things out. A criterion variable is something that is being predicted in your study. It’s otherwise called a response variable or a dependent variable. Criterion variables are usually considered valid. To determine criterion accuracy, you need to compare your test outcomes with the criterion variable (the one that is believed to be true). If your results differ from this criterion, then your test is invalid. Criterion validity example

There are three types of criterion accuracy:

Postdictive.

We will cover the two fist types down below as they are rather widely used in research.

Predictive Validity: Definition

Predictive validity is an estimate that shows whether the test accurately predicts what it intends to predict. For example, you may want to know whether your prediction of any phenomena or human behavior is precise. Accordingly, if your assumptions are justified over time, this indicates that your measurement method has a high predictive accuracy. Example of predictive validity

Concurrent Validity: Definition

Concurrent validity, as its name suggests, shows how accurate the results are if the information about a predictor and criterion are obtained simultaneously. It can also mean the situation when one test is substituted with another test. This way, researchers can stay on budget. Concurrent validity example

Experimental Validity

Experimental validity determines whether an experiment design is built correctly. Without a properly constructed study design, you won’t be able to get valid research results. With this in mind, your research design should justify such factors to be valid:

Have accurate results.
Identify some relationship between variables.
Be generalized to other situations.

Based on this, there are three main types of experimental validity:

Internal validity When a cause-and-effect relationship is determined properly and not affected by other variables. If you can identify any causal connection between your treatment and subject’s reaction, then your experiment is internally accurate.
External validity When research results can be applied to other similar populations. If you can employ your findings in other contexts, then your research has a high external accuracy.
Statistical conclusion validity When your conclusion about causal relationship is correct. Any conclusion that you make should be solely based on data. Otherwise, it will be considered invalid.

If you need more information about this kind of validity, read the internal validity vs external validity article on our platform.

Validity: Key Takeaways

Identifying how thoroughly a student addressed different types of validity in their study is an important factor in any research critique. How well a scientist considers all factors determines whether research ‘makes sense’ and can be developed further. A high-quality study should offer evidence that proves the accuracy of chosen measurement methods. Make sure you consider each factor so you can conduct worthwhile research.

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Frequently Asked Questions About Validity

1. what is a concurrent validity design.

A concurrent validity design is a study where two measurement tests are carried out simultaneously. One of these tests is already well established, while the other one is new. Once two tests are done, researchers compare the outcomes to see if a fresh approach works.

3. What is a good discriminant validity?

To make sure that your study has a good discriminant validity, you need to prove that concepts which shouldn’t be related don’t have any connection. There is no standard score for this estimate. However, an outcome around 0.75-0.85 implies there is a discriminant accuracy.

2. How do you determine predictive validity?

To determine predictive validity you should compare the performance or behavior during the test with the subsequent behavior for which this test was developed. If you find a strong correlation and results are as expected, then your test is accurate.

4. Why is validity important in research?

It’s important to have a high research validity because it allows us to identify what questions should be included in the questionnaire. Besides, it guides researchers in the right direction. In accurate research, a chosen method will measure what is intended to be measured.

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There aren’t any exact metrics that can help you measure aggression. However, you can rely on the related symptoms such as agitation and frequent irritability. To ensure construct accuracy, you should build a questionnaire that will help you assess the construct of aggression, but not the other constructs.

You are designing a test in psychology to identify whether students understood how social cognition works. The test should cover every aspect of this construct. If any details are missing, then such results might not fully represent an overall understanding. Likewise, if you fail to include relevant details emphasized during your course, the test outcomes will also be invalid.

You are studying how post-traumatic stress disorder develops. You review a questionnaire where most questions are focused on the stages of shock after experiencing some traumatic event. On the face of it, this questionnaire seems to be valid.

You want to identify whether the hours students study affects a criterion variable – academic performance. If your test’s outcomes are similar to an already established criterion, then your test has a decent criterion validity.

A good example of this estimate, will be any test showing academic performance at school. You predict how precisely this method will measure future performance.

A great example of this estimate is a written English test that replaces an in-person examination with a teacher. Imagine that you want to assess academic success of thousands of students. One-to-one examinations might be too expensive. For this reason, you can conduct an affordable test which will measure performance in a similar manner.

Reliability and Validity of the Elkins Hypnotizability Scale within a Clinical Sample

Affiliation.

1 Mind-Body Medicine Research Laboratory, Department of Psychology and Neuroscience, Baylor University, Waco, Texas, USA.
PMID: 37399308
PMCID: PMC10403209 (available on 2024-07-03 )
DOI: 10.1080/00207144.2023.2226179

Abstract in English, German, French, Spanish

Hypnotherapy is used in clinical settings to treat mental and physical health-related conditions. Hypnotic response can be measured through hypnotizability scales to help interventionists personalize treatment plans to suit the patients' individualized hypnotic abilities. Examples of these scales are the Elkins Hypnotizability Scale (EHS) and the Stanford Hypnotic Susceptibility Scale, Form C (SHSS:C). According to the previous literature, these scales have good discriminating ability and internal consistency (α = 0.85) in collegiate samples, but the psychometric properties of the EHS for a targeted clinical population have not been determined yet. This study assessed said properties, and results showed adequate reliability of the EHS in a targeted clinical sample and strong convergent validity of the EHS to the SHSS:C. The authors conclude that the EHS is a strong and useful measure of hypnotizability that is pleasant, safe, brief, and sensible to individualities in hypnotic ability found in diverse clinical samples.

Hypnotherapie wird im klinischen Bereich zur Behandlung seelisch und körperlich bedingter Leiden eingesetzt. Die hypnotische Reaktion kann mittels Hypnotisierbarkeitsskalen gemessen werden um den Therapeuten personalisierte Behandlungspläne an die Hand zu geben, angepasst an die je individuellen hypnotischen Fähigkeiten der Patienten. Beispiele für diese Messinstrumente sind die Elkins Hypnotizability Scale (EHS) und die Stanford Hypnotic Susceptibility Scale, Form C (SKSS:C). Entsprechend der Literatur verfügen diese Skalen über gutes Diskriminierungsvermögen und interne Konsistenz (α = 0.85) in Hochschulstichproben, indessen sind die psychometrischen Eigenschaften des EHS für die klinische Zielpopulation noch nicht ermittelt worden. Diese Studie ermittelt die genannten Eigenschaften und die Ergebnisse zeigten eine angemessene Reliabilität der EHS in einer anvisierten klinischen Stichprobe und eine hohe Übereinstimmung der Validität von EHS du SHSS:C. Die Autoren folgern, dass die EHS ein starkes und nützliches Messinstrument für Hypnotisierbarkeit ist und angenehm, sicher, nicht zeitaufwändig und sensibel für die jeweils individuellen Fähigkeiten in unterschiedlichen klinischen Stichproben.

A lida I ost -P eter

Dipl.-Psych.

L’hypnothérapie est utilisée en milieu clinique pour traiter les troubles mentaux et physiques liés à la santé. La réponse hypnotique peut être mesurée à l’aide d’échelles d’hypnotisabilité pour aider les intervenants à personnaliser les plans de traitement en fonction des capacités hypnotiques individuelles des patients. Des exemples de ces échelles sont l’échelle d’hypnotisabilité d’Elkins (EHS) et l’échelle de sensibilité hypnotique de Stanford, forme C (SHSS :C). Selon la littérature antérieure, ces échelles ont une bonne capacité discriminante et une cohérence interne (α = 0,85) dans les échantillons de population universitaire, mais les propriétés psychométriques de l’EHS pour une population clinique ciblée n’ont pas encore été déterminées. Cette étude a évalué ces propriétés et les résultats ont montré une fiabilité adéquate de l’EHS dans un échantillon clinique ciblé et une forte validité convergente de l’EHS vers le SHSS : C. Les auteurs concluent que l’EHS est une mesure solide et utile de l’hypnotisabilité qui est agréable, sûre, brève et sensible aux particularités des capacités hypnotiques trouvées dans divers échantillons cliniques.

G érard F itoussi , M.D.

President-elect of the European Society of Hypnosis

C ameron A lldredge y G ary R. E lkins

Las teorías de estado y no estado de la hipnosis han dominado el campo durante décadas y han ayudado al avance de la hipnosis clínica y científicamente. Sin embargo, se quedan cortos de varias maneras, incluida la consideración suficiente de los procesos inconscientes/experienciales. Nuestra nueva teoría se basa en la teoría del yo cognitivo-experiencial de Epstein, un modelo de proceso dual que brinda una comprensión integral del sistema racional y el sistema experiencial y destaca que, si bien interactúan de manera sinérgica, sus características y modos de operación difieren enormemente. El sistema racional, influenciado por la lógica y la razón, exige recursos cognitivos y opera esforzadamente con un afecto mínimo. Por el contrario, el sistema experiencial está impulsado emocionalmente, es asociativo y codifica la realidad en imágenes y sentimientos sin un esfuerzo consciente. Nuestra teoría, la teoría experiencial adaptativa, postula que la respuesta hipnótica compleja es atribuible a la capacidad de un individuo para adaptarse y cambiar deliberadamente del procesamiento principalmente dentro del sistema racional al sistema experiencial. Una mayor asociación con el sistema experiencial produce alteraciones en el procesamiento de la realidad, lo que permite que las sugestiones hipnóticas se internalicen y actúen sin una interferencia excesiva del sistema racional.

V anessa M uñiz

Baylor University, Waco, Texas, USA

Keywords: Clinical sample; Elkins Hypnotizability Scale; hypnotizability; psychometrics; test reliability; test validity.

Publication types

Research Support, N.I.H., Extramural
Hypnosis* / methods
Hypnotics and Sedatives
Psychometrics
Reproducibility of Results

Grants and funding

R01 AT009384/AT/NCCIH NIH HHS/United States
U01 AT004634/AT/NCCIH NIH HHS/United States

COMMENTS

The 4 Types of Validity in Research
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The concept of validity is also applied to research studies and their findings. Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts. Ecological validity examines, specifically, whether the ...
Internal Validity
Internal Validity. Definition: Internal validity refers to the extent to which a research study accurately establishes a cause-and-effect relationship between the independent variable(s) and the dependent variable(s) being investigated. It assesses whether the observed changes in the dependent variable(s) are actually caused by the manipulation of the independent variable(s) rather than other ...
Validity: Definition, Types & Examples in Research
It addresses the credibility and relevance of the research and its outcomes. There are primarily two types of validity: Internal validity: It ensures the research design and its execution accurately reflect the cause-and-effect relationship being studied. External validity: It relates to how well the study's results can be generalized to other ...
PDF Validity and reliability in quantitative studies
to consider validity and reliability of the data collection tools (instruments) when either conducting or critiquing research. There are three major types of validity. These are described in table 1. The ﬁrst category is content validity. This category looks at whether the instrument adequately covers all
Reliability and Validity of the Elkins Hypnotizability Scale within a
Examples of these scales are the Elkins Hypnotizability Scale (EHS) and the Stanford Hypnotic Susceptibility Scale, Form C (SHSS:C). According to the previous literature, these scales have good discriminating ability and internal consistency (α = 0.85) in collegiate samples, but the psychometric properties of the EHS for a targeted clinical ...

Validity In Psychology Research: Types & Examples

Internal and External Validity In Research

Types of Validity In Psychology

Face Validity

For example:

Construct Validity

Convergent validity

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

Predictive Validity

9 Types of Validity in Research

Types of Validity

2. Content Validity

3. Construct Validity

4. Internal Validity

5. External Validity

6. Concurrent Validity

7. Predictive Validity

8. Statistical Conclusion Validity

9. Criterion Validity

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Reliability vs Validity in Research | Differences, Types & Examples

Table of contents

What is reliability?

What is validity?

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Types of reliability

Types of validity

Ensuring validity

Ensuring reliability

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Fiona Middleton

Validity & Reliability In Research

Overview: Validity & Reliability

First, The Basics…

What Is Validity?

Need a helping hand?

What Is Reliability?

Recap: Key Takeaways

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Reliability and Validity – Definitions, Types & Examples

What is Reliability?

What is the Validity?

Internal Vs. External Validity

Looking for reliable dissertation support?

Threats to Interval Validity

Types of Reliability

Does your Research Methodology Have the Following?

How to Increase Reliability?

How to Increase Validity?

How to Implement Reliability and Validity in your Thesis?

Frequently Asked Questions

What is validity?

What is reliability?

What is reliability in psychology?

What is test retest reliability?

How to improve reliability of an experiment?

What is the difference between reliability and validity?

Are interviews reliable and valid?

Are IQ tests valid and reliable?

Are questionnaires reliable and valid?

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Validity in Psychological Tests

Content Validity

Internal and External Validity

Concurrent Validity

Predictive Validity

Reliability vs. Validity in Research: Types & Examples

Understanding Reliability vs. Validity in Research

What is reliability?

What is validity?

Types of Reliability

High test-retest reliability

Inter-rater reliability

Internal reliability

Types of validity

Content validity