validity formula in research

• Privacy Policy

Home » Validity – Types, Examples and Guide

Validity – Types, Examples and Guide

Table of Contents

Validity is a fundamental concept in research, referring to the extent to which a test, measurement, or study accurately reflects or assesses the specific concept that the researcher is attempting to measure. Ensuring validity is crucial as it determines the trustworthiness and credibility of the research findings.

Research Validity

Research validity pertains to the accuracy and truthfulness of the research. It examines whether the research truly measures what it claims to measure. Without validity, research results can be misleading or erroneous, leading to incorrect conclusions and potentially flawed applications.

How to Ensure Validity in Research

Ensuring validity in research involves several strategies:

• Clear Operational Definitions : Define variables clearly and precisely.
• Use of Reliable Instruments : Employ measurement tools that have been tested for reliability.
• Pilot Testing : Conduct preliminary studies to refine the research design and instruments.
• Triangulation : Use multiple methods or sources to cross-verify results.
• Control Variables : Control extraneous variables that might influence the outcomes.

Types of Validity

Validity is categorized into several types, each addressing different aspects of measurement accuracy.

Internal Validity

Internal validity refers to the degree to which the results of a study can be attributed to the treatments or interventions rather than other factors. It is about ensuring that the study is free from confounding variables that could affect the outcome.

External Validity

External validity concerns the extent to which the research findings can be generalized to other settings, populations, or times. High external validity means the results are applicable beyond the specific context of the study.

Construct Validity

Construct validity evaluates whether a test or instrument measures the theoretical construct it is intended to measure. It involves ensuring that the test is truly assessing the concept it claims to represent.

Content Validity

Content validity examines whether a test covers the entire range of the concept being measured. It ensures that the test items represent all facets of the concept.

Criterion Validity

Criterion validity assesses how well one measure predicts an outcome based on another measure. It is divided into two types:

• Predictive Validity : How well a test predicts future performance.
• Concurrent Validity : How well a test correlates with a currently existing measure.

Face Validity

Face validity refers to the extent to which a test appears to measure what it is supposed to measure, based on superficial inspection. While it is the least scientific measure of validity, it is important for ensuring that stakeholders believe in the test’s relevance.

Importance of Validity

Validity is crucial because it directly affects the credibility of research findings. Valid results ensure that conclusions drawn from research are accurate and can be trusted. This, in turn, influences the decisions and policies based on the research.

Examples of Validity

• Internal Validity : A randomized controlled trial (RCT) where the random assignment of participants helps eliminate biases.
• External Validity : A study on educational interventions that can be applied to different schools across various regions.
• Construct Validity : A psychological test that accurately measures depression levels.
• Content Validity : An exam that covers all topics taught in a course.
• Criterion Validity : A job performance test that predicts future job success.

Where to Write About Validity in A Thesis

In a thesis, the methodology section should include discussions about validity. Here, you explain how you ensured the validity of your research instruments and design. Additionally, you may discuss validity in the results section, interpreting how the validity of your measurements affects your findings.

Applications of Validity

Validity has wide applications across various fields:

• Education : Ensuring assessments accurately measure student learning.
• Psychology : Developing tests that correctly diagnose mental health conditions.
• Market Research : Creating surveys that accurately capture consumer preferences.

Limitations of Validity

While ensuring validity is essential, it has its limitations:

• Complexity : Achieving high validity can be complex and resource-intensive.
• Context-Specific : Some validity types may not be universally applicable across all contexts.
• Subjectivity : Certain types of validity, like face validity, involve subjective judgments.

By understanding and addressing these aspects of validity, researchers can enhance the quality and impact of their studies, leading to more reliable and actionable results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Reliability – Types, Examples and Guide

Construct Validity – Types, Threats and Examples

Content Validity – Measurement and Examples

Inter-Rater Reliability – Methods, Examples and...

Face Validity – Methods, Types, Examples

Internal Validity – Threats, Examples and Guide

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Content Validity: Definition, Examples & Measuring

By Jim Frost Leave a Comment

What is Content Validity?

Content validity is the degree to which a test or assessment instrument evaluates all aspects of the topic, construct, or behavior that it is designed to measure. Do the items fully cover the subject? High content validity indicates that the test fully covers the topic for the target audience. Lower results suggest that the test does not contain relevant facets of the subject matter.

For example, imagine that I designed a test that evaluates how well students understand statistics at a level appropriate for an introductory college course. Content validity assesses my test to see if it covers suitable material for that subject area at that level of expertise. In other words, does my test cover all pertinent facets of the content area? Is it missing concepts?

Learn more about other Types of Validity  and Reliability vs. Validity .

Content Validity Examples

Evaluating content validity is crucial for the following examples to ensure the tests assess the full range of knowledge and aspects of the psychological constructs:

• A test to obtain a license, such as driving or selling real estate.
• Standardized testing for academic purposes, such as the SAT and GRE.
• Tests that evaluate knowledge of subject area domains, such as biology, physics, and literature.
• A scale for assessing anger management.
• A questionnaire that evaluates coping abilities.
• A scale to assess problematic drinking.

How to Measure Content Validity

Measuring content validity involves assessing individual questions on a test and asking experts whether each one targets characteristics that the instrument is designed to cover. This process compares the test against its goals and the theoretical properties of the construct. Researchers systematically determine whether each item contributes, and that no aspect is overlooked.

Factor Analysis

Advanced content validity assessments use multivariate factor analysis to find the number of underlying dimensions that the test items cover. In this context, analysts can use factor analysis to determine whether the items collectively measure a sufficient number and type of fundamental factors. If the measurement instrument does not sufficiently cover the dimensions, the researchers should improve it. Learn more in my Guide to Factor Analysis with an Example .

Content Validity Ratio

For this overview, let’s look at a more intuitive approach.

Most assessment processes in this realm obtain input from subject matter experts. Lawshe* proposed a standard method for measuring content validity in psychology that incorporates expert ratings. This approach involves asking experts to determine whether the knowledge or skill that each item on the test assesses is “essential,” “useful, but not necessary,” or “not necessary.”

His method is essentially a form of inter-rater reliability about the importance of each item. You want all or most experts to agree that each item is “essential.”

Lawshe then proposes that you calculate the content validity ratio (CVR) for each question:

• N e = Number of “essentials” for an item.
• N = Number of experts.

Using this formula, you’ll obtain values ranging from -1 (perfect disagreement) to +1 (perfect agreement) for each question. Values above 0 indicate that more than half the experts agree.

However, it’s essential to consider whether the agreement might be due to chance. Don’t worry! Critical values for the ratio can help you make that determination. These critical values depend on the number of experts. You can find them here: Critical Values for Lawshe’s CVR .

The content validity index (CVI) is the mean CVR for all items and it provides an overall assessment of the measurement instrument. Values closer to 1 are better.

Finally, CVR distinguishes between necessary and unnecessary questions, but it does not identify missing facets.

Lawshe, CH, A Quantitative Approach to Content Validity, Personnel Psychology , 1975, 28, 563-575.

Share this:

Reader Interactions

Comments and questions cancel reply.

Content Validity in Research: Definition & Examples

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

Learn about our Editorial Process

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.

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.

• Content validity is a type of criterion validity that demonstrates how well a measure covers the construct it is meant to represent.
• It is important for researchers to establish content validity in order to ensure that their study is measuring what it intends to measure.
• There are several ways to establish content validity, including expert opinion, focus groups , and surveys.

What Is Content Validity?

Content Validity is the degree to which elements of an assessment instrument are relevant to a representative of the targeted construct for a particular assessment purpose.

This encompasses aspects such as the appropriateness of the items, tasks, or questions to the specific domain being measured and whether the assessment instrument covers a broad enough range of content to enable conclusions to be drawn about the targeted construct (Rossiter, 2008).

One example of an assessment with high content validity is the Iowa Test of Basic Skills (ITBS). The ITBS is a standardized test that has been used since 1935 to assess the academic achievement of students in grades 3-8.

The test covers a wide range of academic skills, including reading, math, language arts, and social studies. The items on the test are carefully developed and reviewed by a panel of experts to ensure that they are fair and representative of the skills being tested.

As a result, the ITBS has high content validity and is widely used by schools and districts to measure student achievement.

Meanwhile, most driving tests have low content validity.  The questions on the test are often not representative of the skills needed to drive safely. For example, many driving permit tests do not include questions about how to parallel park or how to change lanes.

Meanwhile, driving license tests often do not test drivers in non-ideal conditions, such as rain or snow. As a result, these tests do not provide an accurate measure of a person’s ability to drive safely.

The higher the content validity of an assessment, the more accurately it can measure what it is intended to measure — the target construct (Rossiter, 2008).

Why is content validity important in research?

Content validity is important in research as it provides confidence that an instrument is measuring what it is supposed to be measuring.

This is particularly relevant when developing new measures or adapting existing ones for use with different populations.

It also has implications for the interpretation of results, as findings can only be accurately applied to groups for which the content validity of the measure has been established.

Step-by-step guide: How to measure content validity?

Haynes et al. (1995) emphasized the importance of content validity and gave an overview of ways to assess it.

One of the first ways of measuring content validity was the Delphi method, which was invented by NASA in 1940 as a way of systematically creating technical predictions. 

The method involves a group of experts who make predictions about the future and then reach a consensus about those predictions. Today, the Delphi method is most commonly used in medicine.

In a content validity study using the Delphi method, a panel of experts is asked to rate the items on an assessment instrument on a scale. The expert panel also has the opportunity to add comments about the items.

After all ratings have been collected, the average item rating is calculated. In the second round, the experts receive summarized results of the first round and are able to make further comments and revise their first-round answers.

This back-and-forth continues until some homogeneity criterion — similarity between the results of researchers — is achieved (Koller et al., 2017).

Lawshie (1975) and Lynn (1986) created numerical methods to assess content validity. Both of these methods require the development of a content validity index (CVI). A content validity index is a statistical measure of the degree to which an assessment instrument covers the content domain of interest.

There are two steps in calculating a content validity index:

• Determining the number of items that should be included in the assessment instrument;
• Determining the percentage of items that actually are included in the assessment instrument.

The first step, determining the number of items that should be included in an assessment instrument, can be done using one of two approaches: item sampling or expert consensus.

Item sampling involves selecting a sample of items from a larger set of items that cover the content domain. The number of items in the sample is then used to estimate the total number of items needed to cover the content domain.

This approach has the advantage of being quick and easy, but it can be biased if the sample of items is not representative of the larger set (Koller et al., 2017).

The second approach, expert consensus, involves asking a group of experts how many items should be included in an assessment instrument to adequately cover the content domain. This approach has the advantage of being more objective, but it can be time-consuming and expensive.

Experts are able to assign these items to dimensions of the construct that they intend to measure and assign relevance values to decide whether an item is a strong measure of the construct.

Although various attempts to numerize the process of measuring content validity exist, there is no systematic procedure that could be used as a general guideline for the evaluation of content validity (Newman et al., 2013).

When is content validity used?

Education assessment.

In the context of educational assessment, validity is the extent to which an assessment instrument accurately measures what it is intended to measure. Validity concerns anyone who is making inferences and decisions about a learner based on data.

This can have deep implications for students’ education and future. For instance, a test that poorly measures students’ abilities can lead to placement in a future course that is unsuitable for the student and, ultimately, to the student’s failure (Obilor, 2022).

There are a number of factors that specifically affect the validity of assessments given to students, such as (Obilor, 2018):

• Unclear Direction: If directions do not clearly indicate to the respondent how to respond to the tool’s items, the validity of the tool is reduced.
• Vocabulary: If the vocabulary of the respondent is poor, and he does not understand the items, the validity of the instrument is affected.
• Poorly Constructed Test Items: If items are constructed in such a way that they have different meanings for different respondents, validity is affected.
• Difficulty Level of Items: In an achievement test, too easy or too difficult test items would not discriminate among students, thereby lowering the validity of the test.
• Influence of Extraneous Factors: Extraneous factors like the style of expression, legibility, mechanics of grammar (spelling, punctuation), handwriting, and length of the tool, amongst others, influence the validity of a tool.
• Inappropriate Time Limit: In a speed test, if enough time limit is given, the result will be invalidated as a measure of speed. In a power test, an inappropriate time limit will lower the validity of the test.

There are a few reasons why interviews may lack content validity . First, interviewers may ask different questions or place different emphases on certain topics across different candidates. This can make it difficult to compare candidates on a level playing field.

Second, interviewers may have their own personal biases that come into play when making judgments about candidates.

Finally, the interview format itself may be flawed. For example, many companies ask potential programmers to complete brain teasers — such as calculating the number of plumbers in Chicago or coding tasks that rely heavily on theoretical knowledge of data structures — even if this knowledge would be used rarely or never on the job.

Questionnaires

Questionnaires rely on the respondents’ ability to accurately recall information and report it honestly. Additionally, the way in which questions are worded can influence responses.

To increase content validity when designing a questionnaire, careful consideration must be given to the types of questions that will be asked.

Open-ended questions are typically less biased than closed-ended questions, but they can be more difficult to analyze.

It is also important to avoid leading or loaded questions that might influence respondents’ answers in a particular direction. The wording of questions should be clear and concise to avoid confusion (Koller et al., 2017).

Is content validity internal or external?

Most experts agree that content validity is primarily an internal issue. This means that the concepts and items included in a test should be based on a thorough analysis of the specific content area being measured.

The items should also be representative of the range of difficulty levels within that content area. External factors, such as the opinions of experts or the general public, can influence content validity, but they are not necessarily the primary determinant.

In some cases, such as when developing a test for licensure or certification, external stakeholders may have a strong say in what is included in the test (Koller et al., 2017).

How can content validity be improved?

There are a few ways to increase content validity. One is to create items that are more representative of the targeted construct. Another is to increase the number of items on the assessment so that it covers a greater range of content.

Finally, experts can review the items on the assessment to ensure that they are fair and representative of the skills being tested (Koller et al., 2017).

How do you test the content validity of a questionnaire?

There are a few ways to test the content validity of a questionnaire. One way is to ask experts in the field to review the questions and provide feedback on whether or not they believe the questions are relevant and cover all important topics.

Another way is to administer the questionnaire to a small group of people and then analyze the results to see if there are any patterns or themes emerging from the responses.

Finally, it is also possible to use statistical methods to test for content validity, although this approach is more complex and usually requires access to specialized software (Koller et al., 2017).

How can you tell if an instrument is content-valid?

There are a few ways to tell if an instrument is content-valid. The first of these involves looking at two subsets of content validity: face and construct validity.

Face validity is a measure of whether or not the items on the test appear to measure what they claim to measure. This is highly subjective but convenient to assess.

Another way is to look at the construct validity, which is whether or not the items on the test measure what they are supposed to measure. Finally, you can also look at the criterion-related validity, which is whether or not the items on the test predict future performance.

What is the difference between content and criterion validity?

Content validity is a measure of how well a test covers the content it is supposed to cover.

Criterion validity, meanwhile, is an index of how well a test correlates with an established standard of comparison or a criterion.

For example, if a measure of criminal behavior is criterion valid, then it should be possible to use it to predict whether an individual will be arrested in the future for a criminal violation, is currently breaking the law, and has a previous criminal record (American Psychological Association).

Are content validity and construct validity the same?

Content validity is not the same as construct validity.

Content validity is a method of assessing the degree to which a measure covers the range of content that it purports to measure.

In contrast, construct validity is a method of assessing the degree to which a measure reflects the underlying construct that it purports to measure.

It is important to note that content validity and construct validity are not mutually exclusive; a measure can be both valid and invalid with respect to content and construct.

However, content validity is a necessary but not sufficient condition for construct validity. That is, a measure cannot be construct valid if it does not first have content validity (Koller et al., 2017).

For example, an academic achievement test in math may have content validity if it contains questions from all areas of math a student is expected to have learned before the test, but it may not have construct validity if it does not somehow relate to tests of similar and different constructs.

How many experts are needed for content validity?

There is no definitive answer to this question as it depends on a number of factors, including the nature of the instrument being validated and the purpose of the validation exercise.

However, in general, a minimum of three experts should be used in order to ensure that the content validity of an instrument is adequately established (Koller et al., 2017).

American Psychological Association. (n.D.). Content Validity. American Psychological Association Dictionary.

Haynes, S. N., Richard, D., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological assessment , 7 (3), 238.

Koller, I., Levenson, M. R., & Glück, J. (2017). What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Frontiers in psychology , 8 , 126.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology , 28 (4), 563-575.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research .

Obilor, E. I. (2018). Fundamentals of research methods and Statistics in Education and Social Sciences. Port Harcourt: SABCOS Printers & Publishers.

OBILOR, E. I. P., & MIWARI, G. U. P. (2022). Content Validity in Educational Assessment.

Newman, Isadore, Janine Lim, and Fernanda Pineda. “Content validity using a mixed methods approach: Its application and development through the use of a table of specifications methodology.” Journal of Mixed Methods Research 7.3 (2013): 243-260.

Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management , 19 (4), 380-388.

What is Content Validity? (Definition & Example)

The term  content validity refers to how well a survey or test measures the construct that it sets out to measure.

For example, suppose a professor wants to test the overall knowledge of his students in the subject of elementary statistics. His test would have content validity if:

• The test covers every topic of elementary statistics that he taught in the class.
• The test does not cover unrelated topics such as history, economics, biology, etc.

A test lacks content validity if it doesn’t cover all aspects of a construct it sets out to measure or if it covers topics that are unrelated to the construct in any way.

When is Content Validity Used?

In practice, content validity is often used to assess the validity of tests that assess content knowledge. Examples include:

Example 1: Statistics Final Exam

A final exam at the end of a semester for a statistics course would have content validity if it covers every topic discussed in the course and excludes all other irrelevant  topics.

Example 2: Pilot’s License

An exam that tests whether or not individuals have enough knowledge to acquire their pilot’s license would have content validity if it includes questions that cover every possible topic discussed in a pilot’s course and exclude all other questions that aren’t relevant for the license.

Example 3: Real Estate License

An exam that tests whether or not individuals possess enough knowledge to get a real estate license would have content validity if it covers every topic that needs to be understood by a real estate agent and excludes all other questions that aren’t relevant.

In each situation, content validity can help determine if a test covers all aspects of the construct that it sets out to measure.

How to Measure Content Validity

In a 1975 paper , C.H. Lawshe developed the following technique to assess content validity:

Step 1: Collect data from subject matter experts.

Lawshe proposed that each subject matter expert (SME) on a judging panel should respond to the question:

“Is the skill or knowledge measured by this item ‘essential,’ ‘useful, but not essential,’ or ‘not necessary’ to the performance of the job?”

Each SME should provide this response to each question on a test.

Step 2: Calculate the content validity ratio.

Next, Lawshe proposed the following formula to quantify the content validity ratio of each question on the test:

Content Validity Ratio = (n e – N/2) / (N/2)

• n e : The number of subject matter experts indicating “essential”
• N: The total number of SME panelists

If the content validity ratio for a given question falls below a certain critical value, it’s likely that the question is not measuring the construct of interest as well as it should.

The following table shows the critical values based on the number of SME panelists:

The content validity index, denoted as CVI, is the mean content validity ratio of all questions on a test. The closer the CVI is to 1, the higher the overall content validity of a test.

The following example shows how to calculate content validity for a certain test.

Example: Measuring Content Validity

Suppose we ask a panel of 10 judges to rate 6 items on a test. The green boxes in the following table shows which judges rated each item as an “essential” item:

The content validity ratio for the first item would be calculated as:

Content Validity Ratio = (n e – N/2) / (N/2) = (9 – 10/2) / (10/2) =  0.8

We could calculate the content validity ratio for each item in a similar manner:

From the critical values table, we can see that an item is considered to have content validity for a panel of 10 judges only if it has a CVR value above 0.62.

For this particular test, only three of the items pass this threshold.

Lastly, we can also calculate the content validity index (CVI) of the entire test as the average of all the CVR values:

CVI = (0.8 -0.2 + 1 + 0.8 + 0.6 + 0) / 6 =  0.5

This CVI value is quite low, which indicates that the test likely doesn’t measure the construct of interest as well as it could.

It would be recommended to remove or modify the items that have low CVR values to improve the overall content validity of the test.

Content Validity vs. Face Validity

Content validity is different from face validity , which is when a survey or test appears valid at face value to both the individuals who take it and the individuals who administer it.

Face validity is a less technical way of assessing the validity of a test and it’s often used just used as a quick way to detect whether or not a test should be modified in some way before being used.

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

One Reply to “What is Content Validity? (Definition & Example)”

please were did you get the critical value for each number of panelist from?

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Join the Statology Community

Sign up to receive Statology's exclusive study resource: 100 practice problems with step-by-step solutions. Plus, get our latest insights, tutorials, and data analysis tips straight to your inbox!

By subscribing you accept Statology's Privacy Policy.

What is the Significance of Validity in Research?

Introduction

• What is validity in simple terms?

Internal validity vs. external validity in research

Uncovering different types of research validity, factors that improve research validity.

In qualitative research , validity refers to an evaluation metric for the trustworthiness of study findings. Within the expansive landscape of research methodologies , the qualitative approach, with its rich, narrative-driven investigations, demands unique criteria for ensuring validity.

Unlike its quantitative counterpart, which often leans on numerical robustness and statistical veracity, the essence of validity in qualitative research delves deep into the realms of credibility, dependability, and the richness of the data .

The importance of validity in qualitative research cannot be overstated. Establishing validity refers to ensuring that the research findings genuinely reflect the phenomena they are intended to represent. It reinforces the researcher's responsibility to present an authentic representation of study participants' experiences and insights.

This article will examine validity in qualitative research, exploring its characteristics, techniques to bolster it, and the challenges that researchers might face in establishing validity.

At its core, validity in research speaks to the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure or understand. It's about ensuring that the study investigates what it purports to investigate. While this seems like a straightforward idea, the way validity is approached can vary greatly between qualitative and quantitative research .

Quantitative research often hinges on numerical, measurable data. In this paradigm, validity might refer to whether a specific tool or method measures the correct variable, without interference from other variables. It's about numbers, scales, and objective measurements. For instance, if one is studying personalities by administering surveys, a valid instrument could be a survey that has been rigorously developed and tested to verify that the survey questions are referring to personality characteristics and not other similar concepts, such as moods, opinions, or social norms.

Conversely, qualitative research is more concerned with understanding human behavior and the reasons that govern such behavior. It's less about measuring in the strictest sense and more about interpreting the phenomenon that is being studied. The questions become: "Are these interpretations true representations of the human experience being studied?" and "Do they authentically convey participants' perspectives and contexts?"

Differentiating between qualitative and quantitative validity is crucial because the research methods to ensure validity differ between these research paradigms. In quantitative realms, validity might involve test-retest reliability or examining the internal consistency of a test.

In the qualitative sphere, however, the focus shifts to ensuring that the researcher's interpretations align with the actual experiences and perspectives of their subjects.

This distinction is fundamental because it impacts how researchers engage in research design , gather data , and draw conclusions . Ensuring validity in qualitative research is like weaving a tapestry: every strand of data must be carefully interwoven with the interpretive threads of the researcher, creating a cohesive and faithful representation of the studied experience.

While often terms associated more closely with quantitative research, internal and external validity can still be relevant concepts to understand within the context of qualitative inquiries. Grasping these notions can help qualitative researchers better navigate the challenges of ensuring their findings are both credible and applicable in wider contexts.

Internal validity

Internal validity refers to the authenticity and truthfulness of the findings within the study itself. In qualitative research , this might involve asking: Do the conclusions drawn genuinely reflect the perspectives and experiences of the study's participants?

Internal validity revolves around the depth of understanding, ensuring that the researcher's interpretations are grounded in participants' realities. Techniques like member checking , where participants review and verify the researcher's interpretations , can bolster internal validity.

External validity

External validity refers to the extent to which the findings of a study can be generalized or applied to other settings or groups. For qualitative researchers, the emphasis isn't on statistical generalizability, as often seen in quantitative studies. Instead, it's about transferability.

It becomes a matter of determining how and where the insights gathered might be relevant in other contexts. This doesn't mean that every qualitative study's findings will apply universally, but qualitative researchers should provide enough detail (through rich, thick descriptions) to allow readers or other researchers to determine the potential for transfer to other contexts.

Try out a free trial of ATLAS.ti today

See how you can turn your data into critical research findings with our intuitive interface.

Looking deeper into the realm of validity, it's crucial to recognize and understand its various types. Each type offers distinct criteria and methods of evaluation, ensuring that research remains robust and genuine. Here's an exploration of some of these types.

Construct validity

Construct validity is a cornerstone in research methodology . It pertains to ensuring that the tools or methods used in a research study genuinely capture the intended theoretical constructs.

In qualitative research , the challenge lies in the abstract nature of many constructs. For example, if one were to investigate "emotional intelligence" or "social cohesion," the definitions might vary, making them hard to pin down.

To bolster construct validity, it is important to clearly and transparently define the concepts being studied. In addition, researchers may triangulate data from multiple sources , ensuring that different viewpoints converge towards a shared understanding of the construct. Furthermore, they might delve into iterative rounds of data collection, refining their methods with each cycle to better align with the conceptual essence of their focus.

Content validity

Content validity's emphasis is on the breadth and depth of the content being assessed. In other words, content validity refers to capturing all relevant facets of the phenomenon being studied. Within qualitative paradigms, ensuring comprehensive representation is paramount. If, for instance, a researcher is using interview protocols to understand community perceptions of a local policy, it's crucial that the questions encompass all relevant aspects of that policy. This could range from its implementation and impact to public awareness and opinion variations across demographic groups.

Enhancing content validity can involve expert reviews where subject matter experts evaluate tools or methods for comprehensiveness. Another strategy might involve pilot studies , where preliminary data collection reveals gaps or overlooked aspects that can be addressed in the main study.

Ecological validity

Ecological validity refers to the genuine reflection of real-world situations in research findings. For qualitative researchers, this means their observations , interpretations , and conclusions should resonate with the participants and context being studied.

If a study explores classroom dynamics, for example, studying students and teachers in a controlled research setting would have lower ecological validity than studying real classroom settings. Ecological validity is important to consider because it helps ensure the research is relevant to the people being studied. Individuals might behave entirely different in a controlled environment as opposed to their everyday natural settings.

Ecological validity tends to be stronger in qualitative research compared to quantitative research , because qualitative researchers are typically immersed in their study context and explore participants' subjective perceptions and experiences. Quantitative research, in contrast, can sometimes be more artificial if behavior is being observed in a lab or participants have to choose from predetermined options to answer survey questions.

Qualitative researchers can further bolster ecological validity through immersive fieldwork, where researchers spend extended periods in the studied environment. This immersion helps them capture the nuances and intricacies that might be missed in brief or superficial engagements.

Face validity

Face validity, while seemingly straightforward, holds significant weight in the preliminary stages of research. It serves as a litmus test, gauging the apparent appropriateness and relevance of a tool or method. If a researcher is developing a new interview guide to gauge employee satisfaction, for instance, a quick assessment from colleagues or a focus group can reveal if the questions intuitively seem fit for the purpose.

While face validity is more subjective and lacks the depth of other validity types, it's a crucial initial step, ensuring that the research starts on the right foot.

Criterion validity

Criterion validity evaluates how well the results obtained from one method correlate with those from another, more established method. In many research scenarios, establishing high criterion validity involves using statistical methods to measure validity. For instance, a researcher might utilize the appropriate statistical tests to determine the strength and direction of the linear relationship between two sets of data.

If a new measurement tool or method is being introduced, its validity might be established by statistically correlating its outcomes with those of a gold standard or previously validated tool. Correlational statistics can estimate the strength of the relationship between the new instrument and the previously established instrument, and regression analyses can also be useful to predict outcomes based on established criteria.

While these methods are traditionally aligned with quantitative research, qualitative researchers, particularly those using mixed methods , may also find value in these statistical approaches, especially when wanting to quantify certain aspects of their data for comparative purposes. More broadly, qualitative researchers could compare their operationalizations and findings to other similar qualitative studies to assess that they are indeed examining what they intend to study.

In the realm of qualitative research , the role of the researcher is not just that of an observer but often as an active participant in the meaning-making process. This unique positioning means the researcher's perspectives and interactions can significantly influence the data collected and its interpretation . Here's a deep dive into the researcher's pivotal role in upholding validity.

Reflexivity

A key concept in qualitative research, reflexivity requires researchers to continually reflect on their worldviews, beliefs, and potential influence on the data. By maintaining a reflexive journal or engaging in regular introspection, researchers can identify and address their own biases , ensuring a more genuine interpretation of participant narratives.

Building rapport

The depth and authenticity of information shared by participants often hinge on the rapport and trust established with the researcher. By cultivating genuine, non-judgmental, and empathetic relationships with participants, researchers can enhance the validity of the data collected.

Positionality

Every researcher brings to the study their own background, including their culture, education, socioeconomic status, and more. Recognizing how this positionality might influence interpretations and interactions is crucial. By acknowledging and transparently sharing their positionality, researchers can offer context to their findings and interpretations.

Active listening

The ability to listen without imposing one's own judgments or interpretations is vital. Active listening ensures that researchers capture the participants' experiences and emotions without distortion, enhancing the validity of the findings.

Transparency in methods

To ensure validity, researchers should be transparent about every step of their process. From how participants were selected to how data was analyzed , a clear documentation offers others a chance to understand and evaluate the research's authenticity and rigor .

Member checking

Once data is collected and interpreted, revisiting participants to confirm the researcher's interpretations can be invaluable. This process, known as member checking , ensures that the researcher's understanding aligns with the participants' intended meanings, bolstering validity.

Embracing ambiguity

Qualitative data can be complex and sometimes contradictory. Instead of trying to fit data into preconceived notions or frameworks, researchers must embrace ambiguity, acknowledging areas of uncertainty or multiple interpretations.

Make the most of your research study with ATLAS.ti

From study design to data analysis, let ATLAS.ti guide you through the research process. Download a free trial today.

Validity in research: a guide to measuring the right things

Last updated

27 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

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

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• What is validity?

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

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

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

Why is validity important in research?

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

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

• What is reliability?

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

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

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

• How are reliability and validity assessed?

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

How is reliability measured?

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

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

Consistency and stability of the measure across different test subjects

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

How is validity measured?

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

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

The ability to cover all aspects of the concept being measured

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

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

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

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

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

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

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

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

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

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

• Examples of validity

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

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

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

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

• Factors that affect research validity

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

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

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

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

Randomly assigning participants to treatment and control groups to avoid bias

Following specific procedures during the study to avoid unintended effects

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

Replicating the study with different factors or settings to compare results

Using statistical methods to adjust for inconclusive data

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

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

History: Events that occur between an early and later measurement

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

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

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

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

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

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

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

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

• How to ensure validity in your research

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

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

Choose appropriate methods of measurement

Use appropriate sampling to choose test subjects

Create an accurate testing environment

How do you maintain validity in research?

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

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

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

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

• Understanding research validity for more accurate results

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

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 6 October 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 9 March 2023

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 5: Psychological Measurement

Reliability and Validity of Measurement

Learning Objectives

• Define reliability, including the different types and how they are assessed.
• Define validity, including the different types and how they are assessed.
• Describe the kinds of evidence that would be relevant to assessing the reliability and validity of a particular measure.

Again, measurement involves assigning scores to individuals so that they represent some characteristic of the individuals. But how do researchers know that the scores actually represent the characteristic, especially when it is a construct like intelligence, self-esteem, depression, or working memory capacity? The answer is that they conduct research using the measure to confirm that the scores make sense based on their understanding of the construct being measured. This is an extremely important point. Psychologists do not simply  assume  that their measures work. Instead, they collect data to demonstrate  that they work. If their research does not demonstrate that a measure works, they stop using it.

As an informal example, imagine that you have been dieting for a month. Your clothes seem to be fitting more loosely, and several friends have asked if you have lost weight. If at this point your bathroom scale indicated that you had lost 10 pounds, this would make sense and you would continue to use the scale. But if it indicated that you had gained 10 pounds, you would rightly conclude that it was broken and either fix it or get rid of it. In evaluating a measurement method, psychologists consider two general dimensions: reliability and validity.

Reliability

Reliability  refers to the consistency of a measure. Psychologists consider three types of consistency: over time (test-retest reliability), across items (internal consistency), and across different researchers (inter-rater reliability).

Test-Retest Reliability

When researchers measure a construct that they assume to be consistent across time, then the scores they obtain should also be consistent across time.  Test-retest reliability  is the extent to which this is actually the case. For example, intelligence is generally thought to be consistent across time. A person who is highly intelligent today will be highly intelligent next week. This means that any good measure of intelligence should produce roughly the same scores for this individual next week as it does today. Clearly, a measure that produces highly inconsistent scores over time cannot be a very good measure of a construct that is supposed to be consistent.

Assessing test-retest reliability requires using the measure on a group of people at one time, using it again on the  same  group of people at a later time, and then looking at  test-retest correlation  between the two sets of scores. This is typically done by graphing the data in a scatterplot and computing Pearson’s  r . Figure 5.2 shows the correlation between two sets of scores of several university students on the Rosenberg Self-Esteem Scale, administered two times, a week apart. Pearson’s r for these data is +.95. In general, a test-retest correlation of +.80 or greater is considered to indicate good reliability.

Again, high test-retest correlations make sense when the construct being measured is assumed to be consistent over time, which is the case for intelligence, self-esteem, and the Big Five personality dimensions. But other constructs are not assumed to be stable over time. The very nature of mood, for example, is that it changes. So a measure of mood that produced a low test-retest correlation over a period of a month would not be a cause for concern.

Internal Consistency

A second kind of reliability is  internal consistency , which is the consistency of people’s responses across the items on a multiple-item measure. In general, all the items on such measures are supposed to reflect the same underlying construct, so people’s scores on those items should be correlated with each other. On the Rosenberg Self-Esteem Scale, people who agree that they are a person of worth should tend to agree that that they have a number of good qualities. If people’s responses to the different items are not correlated with each other, then it would no longer make sense to claim that they are all measuring the same underlying construct. This is as true for behavioural and physiological measures as for self-report measures. For example, people might make a series of bets in a simulated game of roulette as a measure of their level of risk seeking. This measure would be internally consistent to the extent that individual participants’ bets were consistently high or low across trials.

Like test-retest reliability, internal consistency can only be assessed by collecting and analyzing data. One approach is to look at a  split-half correlation . This involves splitting the items into two sets, such as the first and second halves of the items or the even- and odd-numbered items. Then a score is computed for each set of items, and the relationship between the two sets of scores is examined. For example, Figure 5.3 shows the split-half correlation between several university students’ scores on the even-numbered items and their scores on the odd-numbered items of the Rosenberg Self-Esteem Scale. Pearson’s  r  for these data is +.88. A split-half correlation of +.80 or greater is generally considered good internal consistency.

Perhaps the most common measure of internal consistency used by researchers in psychology is a statistic called  Cronbach’s α  (the Greek letter alpha). Conceptually, α is the mean of all possible split-half correlations for a set of items. For example, there are 252 ways to split a set of 10 items into two sets of five. Cronbach’s α would be the mean of the 252 split-half correlations. Note that this is not how α is actually computed, but it is a correct way of interpreting the meaning of this statistic. Again, a value of +.80 or greater is generally taken to indicate good internal consistency.

Interrater Reliability

Many behavioural measures involve significant judgment on the part of an observer or a rater.  Inter-rater reliability  is the extent to which different observers are consistent in their judgments. For example, if you were interested in measuring university students’ social skills, you could make video recordings of them as they interacted with another student whom they are meeting for the first time. Then you could have two or more observers watch the videos and rate each student’s level of social skills. To the extent that each participant does in fact have some level of social skills that can be detected by an attentive observer, different observers’ ratings should be highly correlated with each other. Inter-rater reliability would also have been measured in Bandura’s Bobo doll study. In this case, the observers’ ratings of how many acts of aggression a particular child committed while playing with the Bobo doll should have been highly positively correlated. Interrater reliability is often assessed using Cronbach’s α when the judgments are quantitative or an analogous statistic called Cohen’s κ (the Greek letter kappa) when they are categorical.

Validity  is the extent to which the scores from a measure represent the variable they are intended to. But how do researchers make this judgment? We have already considered one factor that they take into account—reliability. When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever. As an absurd example, imagine someone who believes that people’s index finger length reflects their self-esteem and therefore tries to measure self-esteem by holding a ruler up to people’s index fingers. Although this measure would have extremely good test-retest reliability, it would have absolutely no validity. The fact that one person’s index finger is a centimetre longer than another’s would indicate nothing about which one had higher self-esteem.

Discussions of validity usually divide it into several distinct “types.” But a good way to interpret these types is that they are other kinds of evidence—in addition to reliability—that should be taken into account when judging the validity of a measure. Here we consider three basic kinds: face validity, content validity, and criterion validity.

Face Validity

Face validity  is the extent to which a measurement method appears “on its face” to measure the construct of interest. Most people would expect a self-esteem questionnaire to include items about whether they see themselves as a person of worth and whether they think they have good qualities. So a questionnaire that included these kinds of items would have good face validity. The finger-length method of measuring self-esteem, on the other hand, seems to have nothing to do with self-esteem and therefore has poor face validity. Although face validity can be assessed quantitatively—for example, by having a large sample of people rate a measure in terms of whether it appears to measure what it is intended to—it is usually assessed informally.

Face validity is at best a very weak kind of evidence that a measurement method is measuring what it is supposed to. One reason is that it is based on people’s intuitions about human behaviour, which are frequently wrong. It is also the case that many established measures in psychology work quite well despite lacking face validity. The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) measures many personality characteristics and disorders by having people decide whether each of over 567 different statements applies to them—where many of the statements do not have any obvious relationship to the construct that they measure. For example, the items “I enjoy detective or mystery stories” and “The sight of blood doesn’t frighten me or make me sick” both measure the suppression of aggression. In this case, it is not the participants’ literal answers to these questions that are of interest, but rather whether the pattern of the participants’ responses to a series of questions matches those of individuals who tend to suppress their aggression.

Content Validity

Content validity  is the extent to which a measure “covers” the construct of interest. For example, if a researcher conceptually defines test anxiety as involving both sympathetic nervous system activation (leading to nervous feelings) and negative thoughts, then his measure of test anxiety should include items about both nervous feelings and negative thoughts. Or consider that attitudes are usually defined as involving thoughts, feelings, and actions toward something. By this conceptual definition, a person has a positive attitude toward exercise to the extent that he or she thinks positive thoughts about exercising, feels good about exercising, and actually exercises. So to have good content validity, a measure of people’s attitudes toward exercise would have to reflect all three of these aspects. Like face validity, content validity is not usually assessed quantitatively. Instead, it is assessed by carefully checking the measurement method against the conceptual definition of the construct.

Criterion Validity

Criterion validity  is the extent to which people’s scores on a measure are correlated with other variables (known as  criteria ) that one would expect them to be correlated with. For example, people’s scores on a new measure of test anxiety should be negatively correlated with their performance on an important school exam. If it were found that people’s scores were in fact negatively correlated with their exam performance, then this would be a piece of evidence that these scores really represent people’s test anxiety. But if it were found that people scored equally well on the exam regardless of their test anxiety scores, then this would cast doubt on the validity of the measure.

A criterion can be any variable that one has reason to think should be correlated with the construct being measured, and there will usually be many of them. For example, one would expect test anxiety scores to be negatively correlated with exam performance and course grades and positively correlated with general anxiety and with blood pressure during an exam. Or imagine that a researcher develops a new measure of physical risk taking. People’s scores on this measure should be correlated with their participation in “extreme” activities such as snowboarding and rock climbing, the number of speeding tickets they have received, and even the number of broken bones they have had over the years. When the criterion is measured at the same time as the construct, criterion validity is referred to as concurrent validity ; however, when the criterion is measured at some point in the future (after the construct has been measured), it is referred to as predictive validity (because scores on the measure have “predicted” a future outcome).

Criteria can also include other measures of the same construct. For example, one would expect new measures of test anxiety or physical risk taking to be positively correlated with existing measures of the same constructs. This is known as convergent validity .

Assessing convergent validity requires collecting data using the measure. Researchers John Cacioppo and Richard Petty did this when they created their self-report Need for Cognition Scale to measure how much people value and engage in thinking (Cacioppo & Petty, 1982) [1] . In a series of studies, they showed that people’s scores were positively correlated with their scores on a standardized academic achievement test, and that their scores were negatively correlated with their scores on a measure of dogmatism (which represents a tendency toward obedience). In the years since it was created, the Need for Cognition Scale has been used in literally hundreds of studies and has been shown to be correlated with a wide variety of other variables, including the effectiveness of an advertisement, interest in politics, and juror decisions (Petty, Briñol, Loersch, & McCaslin, 2009) [2] .

Discriminant Validity

Discriminant validity , on the other hand, is the extent to which scores on a measure are not correlated with measures of variables that are conceptually distinct. For example, self-esteem is a general attitude toward the self that is fairly stable over time. It is not the same as mood, which is how good or bad one happens to be feeling right now. So people’s scores on a new measure of self-esteem should not be very highly correlated with their moods. If the new measure of self-esteem were highly correlated with a measure of mood, it could be argued that the new measure is not really measuring self-esteem; it is measuring mood instead.

When they created the Need for Cognition Scale, Cacioppo and Petty also provided evidence of discriminant validity by showing that people’s scores were not correlated with certain other variables. For example, they found only a weak correlation between people’s need for cognition and a measure of their cognitive style—the extent to which they tend to think analytically by breaking ideas into smaller parts or holistically in terms of “the big picture.” They also found no correlation between people’s need for cognition and measures of their test anxiety and their tendency to respond in socially desirable ways. All these low correlations provide evidence that the measure is reflecting a conceptually distinct construct.

Key Takeaways

• Psychological researchers do not simply assume that their measures work. Instead, they conduct research to show that they work. If they cannot show that they work, they stop using them.
• There are two distinct criteria by which researchers evaluate their measures: reliability and validity. Reliability is consistency across time (test-retest reliability), across items (internal consistency), and across researchers (interrater reliability). Validity is the extent to which the scores actually represent the variable they are intended to.
• Validity is a judgment based on various types of evidence. The relevant evidence includes the measure’s reliability, whether it covers the construct of interest, and whether the scores it produces are correlated with other variables they are expected to be correlated with and not correlated with variables that are conceptually distinct.
• The reliability and validity of a measure is not established by any single study but by the pattern of results across multiple studies. The assessment of reliability and validity is an ongoing process.
• Practice: Ask several friends to complete the Rosenberg Self-Esteem Scale. Then assess its internal consistency by making a scatterplot to show the split-half correlation (even- vs. odd-numbered items). Compute Pearson’s  r too if you know how.
• Discussion: Think back to the last college exam you took and think of the exam as a psychological measure. What construct do you think it was intended to measure? Comment on its face and content validity. What data could you collect to assess its reliability and criterion validity?
• Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42 , 116–131. ↵
• Petty, R. E, Briñol, P., Loersch, C., & McCaslin, M. J. (2009). The need for cognition. In M. R. Leary & R. H. Hoyle (Eds.), Handbook of individual differences in social behaviour (pp. 318–329). New York, NY: Guilford Press. ↵

The consistency of a measure.

The consistency of a measure over time.

The consistency of a measure on the same group of people at different times.

Consistency of people’s responses across the items on a multiple-item measure.

Method of assessing internal consistency through splitting the items into two sets and examining the relationship between them.

A statistic in which α is the mean of all possible split-half correlations for a set of items.

The extent to which different observers are consistent in their judgments.

The extent to which the scores from a measure represent the variable they are intended to.

The extent to which a measurement method appears to measure the construct of interest.

The extent to which a measure “covers” the construct of interest.

The extent to which people’s scores on a measure are correlated with other variables that one would expect them to be correlated with.

In reference to criterion validity, variables that one would expect to be correlated with the measure.

When the criterion is measured at the same time as the construct.

when the criterion is measured at some point in the future (after the construct has been measured).

When new measures positively correlate with existing measures of the same constructs.

The extent to which scores on a measure are not correlated with measures of variables that are conceptually distinct.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Family Med Prim Care
• v.4(3); Jul-Sep 2015

Validity, reliability, and generalizability in qualitative research

Lawrence leung.

1 Department of Family Medicine, Queen's University, Kingston, Ontario, Canada

2 Centre of Studies in Primary Care, Queen's University, Kingston, Ontario, Canada

In general practice, qualitative research contributes as significantly as quantitative research, in particular regarding psycho-social aspects of patient-care, health services provision, policy setting, and health administrations. In contrast to quantitative research, qualitative research as a whole has been constantly critiqued, if not disparaged, by the lack of consensus for assessing its quality and robustness. This article illustrates with five published studies how qualitative research can impact and reshape the discipline of primary care, spiraling out from clinic-based health screening to community-based disease monitoring, evaluation of out-of-hours triage services to provincial psychiatric care pathways model and finally, national legislation of core measures for children's healthcare insurance. Fundamental concepts of validity, reliability, and generalizability as applicable to qualitative research are then addressed with an update on the current views and controversies.

Nature of Qualitative Research versus Quantitative Research

The essence of qualitative research is to make sense of and recognize patterns among words in order to build up a meaningful picture without compromising its richness and dimensionality. Like quantitative research, the qualitative research aims to seek answers for questions of “how, where, when who and why” with a perspective to build a theory or refute an existing theory. Unlike quantitative research which deals primarily with numerical data and their statistical interpretations under a reductionist, logical and strictly objective paradigm, qualitative research handles nonnumerical information and their phenomenological interpretation, which inextricably tie in with human senses and subjectivity. While human emotions and perspectives from both subjects and researchers are considered undesirable biases confounding results in quantitative research, the same elements are considered essential and inevitable, if not treasurable, in qualitative research as they invariable add extra dimensions and colors to enrich the corpus of findings. However, the issue of subjectivity and contextual ramifications has fueled incessant controversies regarding yardsticks for quality and trustworthiness of qualitative research results for healthcare.

Impact of Qualitative Research upon Primary Care

In many ways, qualitative research contributes significantly, if not more so than quantitative research, to the field of primary care at various levels. Five qualitative studies are chosen to illustrate how various methodologies of qualitative research helped in advancing primary healthcare, from novel monitoring of chronic obstructive pulmonary disease (COPD) via mobile-health technology,[ 1 ] informed decision for colorectal cancer screening,[ 2 ] triaging out-of-hours GP services,[ 3 ] evaluating care pathways for community psychiatry[ 4 ] and finally prioritization of healthcare initiatives for legislation purposes at national levels.[ 5 ] With the recent advances of information technology and mobile connecting device, self-monitoring and management of chronic diseases via tele-health technology may seem beneficial to both the patient and healthcare provider. Recruiting COPD patients who were given tele-health devices that monitored lung functions, Williams et al. [ 1 ] conducted phone interviews and analyzed their transcripts via a grounded theory approach, identified themes which enabled them to conclude that such mobile-health setup and application helped to engage patients with better adherence to treatment and overall improvement in mood. Such positive findings were in contrast to previous studies, which opined that elderly patients were often challenged by operating computer tablets,[ 6 ] or, conversing with the tele-health software.[ 7 ] To explore the content of recommendations for colorectal cancer screening given out by family physicians, Wackerbarth, et al. [ 2 ] conducted semi-structure interviews with subsequent content analysis and found that most physicians delivered information to enrich patient knowledge with little regard to patients’ true understanding, ideas, and preferences in the matter. These findings suggested room for improvement for family physicians to better engage their patients in recommending preventative care. Faced with various models of out-of-hours triage services for GP consultations, Egbunike et al. [ 3 ] conducted thematic analysis on semi-structured telephone interviews with patients and doctors in various urban, rural and mixed settings. They found that the efficiency of triage services remained a prime concern from both users and providers, among issues of access to doctors and unfulfilled/mismatched expectations from users, which could arouse dissatisfaction and legal implications. In UK, a care pathways model for community psychiatry had been introduced but its benefits were unclear. Khandaker et al. [ 4 ] hence conducted a qualitative study using semi-structure interviews with medical staff and other stakeholders; adopting a grounded-theory approach, major themes emerged which included improved equality of access, more focused logistics, increased work throughput and better accountability for community psychiatry provided under the care pathway model. Finally, at the US national level, Mangione-Smith et al. [ 5 ] employed a modified Delphi method to gather consensus from a panel of nominators which were recognized experts and stakeholders in their disciplines, and identified a core set of quality measures for children's healthcare under the Medicaid and Children's Health Insurance Program. These core measures were made transparent for public opinion and later passed on for full legislation, hence illustrating the impact of qualitative research upon social welfare and policy improvement.

Overall Criteria for Quality in Qualitative Research

Given the diverse genera and forms of qualitative research, there is no consensus for assessing any piece of qualitative research work. Various approaches have been suggested, the two leading schools of thoughts being the school of Dixon-Woods et al. [ 8 ] which emphasizes on methodology, and that of Lincoln et al. [ 9 ] which stresses the rigor of interpretation of results. By identifying commonalities of qualitative research, Dixon-Woods produced a checklist of questions for assessing clarity and appropriateness of the research question; the description and appropriateness for sampling, data collection and data analysis; levels of support and evidence for claims; coherence between data, interpretation and conclusions, and finally level of contribution of the paper. These criteria foster the 10 questions for the Critical Appraisal Skills Program checklist for qualitative studies.[ 10 ] However, these methodology-weighted criteria may not do justice to qualitative studies that differ in epistemological and philosophical paradigms,[ 11 , 12 ] one classic example will be positivistic versus interpretivistic.[ 13 ] Equally, without a robust methodological layout, rigorous interpretation of results advocated by Lincoln et al. [ 9 ] will not be good either. Meyrick[ 14 ] argued from a different angle and proposed fulfillment of the dual core criteria of “transparency” and “systematicity” for good quality qualitative research. In brief, every step of the research logistics (from theory formation, design of study, sampling, data acquisition and analysis to results and conclusions) has to be validated if it is transparent or systematic enough. In this manner, both the research process and results can be assured of high rigor and robustness.[ 14 ] Finally, Kitto et al. [ 15 ] epitomized six criteria for assessing overall quality of qualitative research: (i) Clarification and justification, (ii) procedural rigor, (iii) sample representativeness, (iv) interpretative rigor, (v) reflexive and evaluative rigor and (vi) transferability/generalizability, which also double as evaluative landmarks for manuscript review to the Medical Journal of Australia. Same for quantitative research, quality for qualitative research can be assessed in terms of validity, reliability, and generalizability.

Validity in qualitative research means “appropriateness” of the tools, processes, and data. Whether the research question is valid for the desired outcome, the choice of methodology is appropriate for answering the research question, the design is valid for the methodology, the sampling and data analysis is appropriate, and finally the results and conclusions are valid for the sample and context. In assessing validity of qualitative research, the challenge can start from the ontology and epistemology of the issue being studied, e.g. the concept of “individual” is seen differently between humanistic and positive psychologists due to differing philosophical perspectives:[ 16 ] Where humanistic psychologists believe “individual” is a product of existential awareness and social interaction, positive psychologists think the “individual” exists side-by-side with formation of any human being. Set off in different pathways, qualitative research regarding the individual's wellbeing will be concluded with varying validity. Choice of methodology must enable detection of findings/phenomena in the appropriate context for it to be valid, with due regard to culturally and contextually variable. For sampling, procedures and methods must be appropriate for the research paradigm and be distinctive between systematic,[ 17 ] purposeful[ 18 ] or theoretical (adaptive) sampling[ 19 , 20 ] where the systematic sampling has no a priori theory, purposeful sampling often has a certain aim or framework and theoretical sampling is molded by the ongoing process of data collection and theory in evolution. For data extraction and analysis, several methods were adopted to enhance validity, including 1 st tier triangulation (of researchers) and 2 nd tier triangulation (of resources and theories),[ 17 , 21 ] well-documented audit trail of materials and processes,[ 22 , 23 , 24 ] multidimensional analysis as concept- or case-orientated[ 25 , 26 ] and respondent verification.[ 21 , 27 ]

Reliability

In quantitative research, reliability refers to exact replicability of the processes and the results. In qualitative research with diverse paradigms, such definition of reliability is challenging and epistemologically counter-intuitive. Hence, the essence of reliability for qualitative research lies with consistency.[ 24 , 28 ] A margin of variability for results is tolerated in qualitative research provided the methodology and epistemological logistics consistently yield data that are ontologically similar but may differ in richness and ambience within similar dimensions. Silverman[ 29 ] proposed five approaches in enhancing the reliability of process and results: Refutational analysis, constant data comparison, comprehensive data use, inclusive of the deviant case and use of tables. As data were extracted from the original sources, researchers must verify their accuracy in terms of form and context with constant comparison,[ 27 ] either alone or with peers (a form of triangulation).[ 30 ] The scope and analysis of data included should be as comprehensive and inclusive with reference to quantitative aspects if possible.[ 30 ] Adopting the Popperian dictum of falsifiability as essence of truth and science, attempted to refute the qualitative data and analytes should be performed to assess reliability.[ 31 ]

Generalizability

Most qualitative research studies, if not all, are meant to study a specific issue or phenomenon in a certain population or ethnic group, of a focused locality in a particular context, hence generalizability of qualitative research findings is usually not an expected attribute. However, with rising trend of knowledge synthesis from qualitative research via meta-synthesis, meta-narrative or meta-ethnography, evaluation of generalizability becomes pertinent. A pragmatic approach to assessing generalizability for qualitative studies is to adopt same criteria for validity: That is, use of systematic sampling, triangulation and constant comparison, proper audit and documentation, and multi-dimensional theory.[ 17 ] However, some researchers espouse the approach of analytical generalization[ 32 ] where one judges the extent to which the findings in one study can be generalized to another under similar theoretical, and the proximal similarity model, where generalizability of one study to another is judged by similarities between the time, place, people and other social contexts.[ 33 ] Thus said, Zimmer[ 34 ] questioned the suitability of meta-synthesis in view of the basic tenets of grounded theory,[ 35 ] phenomenology[ 36 ] and ethnography.[ 37 ] He concluded that any valid meta-synthesis must retain the other two goals of theory development and higher-level abstraction while in search of generalizability, and must be executed as a third level interpretation using Gadamer's concepts of the hermeneutic circle,[ 38 , 39 ] dialogic process[ 38 ] and fusion of horizons.[ 39 ] Finally, Toye et al. [ 40 ] reported the practicality of using “conceptual clarity” and “interpretative rigor” as intuitive criteria for assessing quality in meta-ethnography, which somehow echoed Rolfe's controversial aesthetic theory of research reports.[ 41 ]

Food for Thought

Despite various measures to enhance or ensure quality of qualitative studies, some researchers opined from a purist ontological and epistemological angle that qualitative research is not a unified, but ipso facto diverse field,[ 8 ] hence any attempt to synthesize or appraise different studies under one system is impossible and conceptually wrong. Barbour argued from a philosophical angle that these special measures or “technical fixes” (like purposive sampling, multiple-coding, triangulation, and respondent validation) can never confer the rigor as conceived.[ 11 ] In extremis, Rolfe et al. opined from the field of nursing research, that any set of formal criteria used to judge the quality of qualitative research are futile and without validity, and suggested that any qualitative report should be judged by the form it is written (aesthetic) and not by the contents (epistemic).[ 41 ] Rolfe's novel view is rebutted by Porter,[ 42 ] who argued via logical premises that two of Rolfe's fundamental statements were flawed: (i) “The content of research report is determined by their forms” may not be a fact, and (ii) that research appraisal being “subject to individual judgment based on insight and experience” will mean those without sufficient experience of performing research will be unable to judge adequately – hence an elitist's principle. From a realism standpoint, Porter then proposes multiple and open approaches for validity in qualitative research that incorporate parallel perspectives[ 43 , 44 ] and diversification of meanings.[ 44 ] Any work of qualitative research, when read by the readers, is always a two-way interactive process, such that validity and quality has to be judged by the receiving end too and not by the researcher end alone.

In summary, the three gold criteria of validity, reliability and generalizability apply in principle to assess quality for both quantitative and qualitative research, what differs will be the nature and type of processes that ontologically and epistemologically distinguish between the two.

Source of Support: Nil.

Conflict of Interest: None declared.

• How it works

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.

Looking for reliable dissertation support?

We hear you.

• Whether you want a full dissertation written or need help forming a dissertation proposal, we can help you with both.
• Get different dissertation services at ResearchProspect and score amazing grades!

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. 

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

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:

Segments	Explanation
	All the planning about reliability and validity will be discussed here, including the chosen samples and size and the techniques used to measure reliability and validity.
	Please talk about the level of reliability and validity of your results and their influence on values.
	Discuss the contribution of other researchers to improve reliability and validity. 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. You May Also Like A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data. Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers. Ethnography is a type of research where a researcher observes the people in their natural environment. Here is all you need to know about ethnography. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works History, Facts & Figures YSM Dean & Deputy Deans YSM Administration Department Chairs YSM Executive Group YSM Board of Permanent Officers FAC Documents Current FAC Members Appointments & Promotions Committees Ad Hoc Committees and Working Groups Chair Searches Leadership Searches Organization Charts Faculty Demographic Data Professionalism Reporting Data 2022 Diversity Engagement Survey State of the School Archive Faculty Climate Survey: YSM Results Strategic Planning Mission Statement & Process Beyond Sterling Hall COVID-19 Series Workshops Previous Workshops Departments & Centers Find People Biomedical Data Science Health Equity Inflammation Neuroscience Global Health Diabetes and Metabolism Policies & Procedures Media Relations A to Z YSM Lab Websites A-Z Faculty List A-Z Staff List A to Z Abbreviations Dept. Diversity Vice Chairs & Champions Dean’s Advisory Council on Lesbian, Gay, Bisexual, Transgender, Queer and Intersex Affairs Website Minority Organization for Retention and Expansion Website Office for Women in Medicine and Science Committee on the Status of Women in Medicine Website Director of Scientist Diversity and Inclusion Diversity Supplements Frequently Asked Questions Recruitment By Department & Program News & Events Executive Committee Aperture: Women in Medicine Self-Reflection Portraits of Strength Mindful: Mental Health Through Art Event Photo Galleries Additional Support MD-PhD Program PA Online Program Joint MD Programs How to Apply Advanced Health Sciences Research Clinical Informatics & Data Science Clinical Investigation Medical Education Visiting Student Programs Special Programs & Student Opportunities Residency & Fellowship Programs Center for Med Ed Organizational Chart Leadership & Staff Committee Procedural Info (Login Required) Faculty Affairs Department Teams Recent Appointments & Promotions Academic Clinician Track Clinician Educator-Scholar Track Clinican-Scientist Track Investigator Track Traditional Track Research Ranks Instructor/Lecturer Social Work Ranks Voluntary Ranks Adjunct Ranks Other Appt Types Appointments Reappointments Transfer of Track Term Extensions Timeline for A&P Processes Interfolio Faculty Search Interfolio A&P Processes Yale CV Part 1 (CV1) Yale CV Part 2 (CV2) Samples of Scholarship Teaching Evaluations Letters of Evaluation Dept A&P Narrative A&P Voting Faculty Affairs Staff Pages OAPD Faculty Workshops Leadership & Development Seminars List of Faculty Mentors Incoming Faculty Orientation Faculty Onboarding Past YSM Award Recipients Past PA Award Recipients Past YM Award Recipients International Award Recipients Nominations Calendar OAPD Newsletter Fostering a Shared Vision of Professionalism Academic Integrity Addressing Professionalism Concerns Consultation Support for Chairs & Section Chiefs Policies & Codes of Conduct First Fridays Faculty Facing Caregiving Need Fund for Physician-Scientist Mentorship Grant Library Grant Writing Course Mock Study Section Research Paper Writing Establishing a Thriving Research Program Funding Opportunities Join Our Voluntary Faculty Child Mental Health: Fostering Wellness in Children Faculty Resources Research by Keyword Research by Department Research by Global Location Translational Research Research Cores & Services Program for the Promotion of Interdisciplinary Team Science (POINTS) CEnR Steering Committee Experiential Learning Subcommittee Goals & Objectives Faculty & Staff Issues List Print Magazine PDFs Print Newsletter PDFs YSM Events Newsletter Social Media Patient Care INFORMATION FOR Residents & Fellows Researchers Yale Research Team Investigates Difference Between Baby Formula and Human Milk’s Effect on Intestinal Growth Woman picking up baby bottle. A new article published in Gastro Hep Advances by a team of Yale researchers and led by Lauren Smith, MD, hospital resident, finds parental milk and donor human milk support intestinal health and epithelial growth and differentiation, while formula specifically inhibits certain growth factors and prevents differentiation. Researchers also discovered that growth and improvement for certain cell types, like enteroendocrine cells that support proper digestion and peristalsis, were improved specifically with parental milk. Corresponding author Liza Konnikova, MD, PhD , associate professor of immunobiology and of pediatrics (neonatal-perinatal medicine), explained, “We’re still unclear about whether there are simply good factors in breast milk, detrimental factors in formula, or if it’s some combination of both. But some takeaways from this paper are that formula can be detrimental to epithelial growth directly, and that both types of human milk were shown to induce epithelial growth.” The team says that if the factors in human milk that drive these intestinal improvements can be identified through more research, the possibility exists to then supplement formula-fed infants, or improve formula overall, to promote intestinal health and development. That can have profound clinical implications. Smith spoke about how future research may look based on what they have discovered. She said, “What we found is that nutritional exposures have a profound impact on the development of the fetal intestinal epithelium. We found that fetal intestinal tissue fed with formula had a more inflammatory immune profile, while those that were fed with human milk had improved growth, earlier differentiation into mature cell types, and a more homeostatic immunophenotype. We think these effects may explain why we see differences in rates of gastrointestinal complications of prematurity, such as necrotizing enterocolitis, between formula-fed and milk-fed preterm infants. Further research to clarify the mechanism behind this could lead to the ability to supplement the critical factors driving this effect in preterm infants at risk for gastrointestinal complications, reducing morbidity and mortality.” Researchers from the University of Pittsburgh were also involved in this study. Click here to learn more about the authors and read the full paper in Gastro Hep Advances. Featured in this article Liza Konnikova, MD/PhD, FAAP Associate Professor; CyTOF Core Director, Medicine Sarah N. Taylor, MD, MSCR Professor of Pediatrics (Neonatal-Perinatal Medicine); Chief, Section of Neonatal-Perinatal Medicine, Pediatrics; Director of Clinical Research, Pediatrics; Professor, Chronic Disease Epidemiology Weihong Gu Postdoctoral Associate Tessa Kehoe Clinical Research Nurse 2 Kerri St Denis Madison S. Strine, PhD Postdoctoral Associate The state of AI in early 2024: Gen AI adoption spikes and starts to generate value If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead. About the authors This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital. Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value. AI adoption surges Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training. Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2). Future frontiers: Navigating the next wave of tech innovations Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond. Gen AI adoption is most common in the functions where it can create the most value Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents. Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use. Investments in gen AI and analytical AI are beginning to create value The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years. Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales. Inaccuracy: The most recognized and experienced risk of gen AI use As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use. Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7). Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them. In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate. Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability. Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent. Bringing gen AI capabilities to bear The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs. Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement. Gen AI high performers are excelling despite facing challenges Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations. To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models. What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do. Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling. In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management. About the research The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office. They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work. This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office. Explore a career with us Related articles. Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale A generative AI reset: Rewiring to turn potential into value in 2024 Implementing generative AI with speed and safety Our Hiring Process Faculty Careers Site ↗ View All Staff Jobs Join Talent Community Administrative Center for Talented Youth Summer Programs Development & Alumni Relations Facilities/Skilled Trades Grants & Contracts Health Care Human Resources Information Technology Student Services Johns Hopkins strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. The seasonal flu vaccine requirement remains unchanged. Exceptions may be provided under certain circumstances.  Click here for more information. Join Our Talent Community Center for Talented Youth On-Campus Summer Programs Faculty Careers ↗ Student Employment ↗ Current JHU Employees: Internal Career Site ↗ Johns Hopkins strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. The seasonal flu vaccine requirement remains unchanged. Exceptions may be provided under certain circumstances.  Click here for more information. . Research assistant. Our team is seeking a Research Assistant (RA) to support Dr. Tahilin Sanchez Karver’s federally-funded projects on Latina/o/x HIV prevention and care navigation and support strategies. The RA will be responsible for recruiting and enrolling study participants and actively engage in qualitative and quantitative data collection and analysis. We are looking for individuals who are self-motivated, possess strong organizational abilities, can manage multiple tasks, and adhere to deadlines. Proficiency in written and spoken Spanish at a native or advanced level is highly preferred.  This RA role contributes significantly to the development of strategies for HIV navigation and support strategies for Latino/a/x communities both locally and abroad. The ideal candidate will be culturally aware, possess a high level of emotional intelligence, and be highly organized. Demonstrating excellent judgment in handling sensitive situations, the RA will build and sustain trust with the communities served. Working independently with a high degree of organization and dependability, this position offers opportunities for mixed methods analysis, literature review, and writing scientific manuscripts according to project requirements. Specific Duties & Responsibilities Assist with recruitment and enrollment of study participants. Assist with data collection, including conducting participant observations and interviews. Assist with coding and analyzing qualitative and quantitative data (Stata and Atlas.ti experience preferred but not required). Input and manage data in Qualtrics, REDCap, or similar systems. Contribute to reports, publications, and project summaries. Willingness to travel to study sites when needed. Special Knowledge, Skills, & Abilities Excellent oral and written communication skills. Spanish proficiency required. Excellent work attitude and professionalism in working closely with physicians and scientists. Ability to think and work independently and as part of a team. Strong desire to contribute to community based participatory research. Bachelor's Degree in related discipline. Additional related experience may substitute for required education, to the extent permitted by the JHU equivalency formula. Spanish-English bilingual. Classified Title: Research Assistant  Role/Level/Range: ACRO40/E/03/CD   Starting Salary Range: $17.20 - $30.30 HRLY (Commensurate with experience)  Employee group: Casual / On Call  Schedule: Hours Vary  Exempt Status: Non-Exempt  Location: Remote  Department name: HBS - Faculty Centers & Academic Program   Personnel area: School of Public Health  Total Rewards The referenced base salary range represents the low and high end of Johns Hopkins University’s salary range for this position. Not all candidates will be eligible for the upper end of the salary range. Exact salary will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, market conditions, education/training and other qualifications. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/ . Education and Experience Equivalency Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non-related college course work may be applied towards the total minimum education/experience required for the respective job. Applicants Completing Studies Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date. Background Checks The successful candidate(s) for this position will be subject to a pre-employment background check. Johns Hopkins is committed to hiring individuals with a justice-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job-related qualification or function. Diversity and Inclusion The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion . Equal Opportunity Employer All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. EEO is the Law https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf Accommodation Information If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre-employment process, please contact the Talent Acquisition Office at [email protected] . For TTY users, call via Maryland Relay or dial 711. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit https://accessibility.jhu.edu/ . Vaccine Requirements Johns Hopkins University strongly encourages, but no longer requires, at least one dose of the COVID-19 vaccine. The COVID-19 vaccine does not apply to positions located in the State of Florida. We still require all faculty, staff, and students to receive the seasonal flu vaccine . Exceptions to the COVID and flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry.  This change does not apply to the School of Medicine (SOM). SOM hires must be fully vaccinated with an FDA COVID-19 vaccination and provide proof of vaccination status. For additional information, applicants for SOM positions should visit https://www.hopkinsmedicine.org/coronavirus/covid-19-vaccine/  and all other JHU applicants should visit https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/ . The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly. The pre-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2) MMR vaccines; two (2) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office. Careers Home Current Employees Clery Notice Sustainability © Copyright 2024 Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Methodology What Is Criterion Validity? | Definition & Examples What Is Criterion Validity? | Definition & Examples Published on September 2, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023. Criterion validity (or criterion-related validity ) evaluates how accurately a test measures the outcome it was designed to measure. An outcome can be a disease, behavior, or performance. Concurrent validity measures tests and criterion variables in the present, while predictive validity measures those in the future. To establish criterion validity, you need to compare your test results to criterion variables . Criterion variables are often referred to as a “gold standard” measurement. They comprise other tests that are widely accepted as valid measures of a construct . The researcher can then compare the college entry exam scores of 100 students to their GPA after one semester in college. If the scores of the two tests are close, then the college entry exam has criterion validity. When your test agrees with the criterion variable, it has high criterion validity. However, criterion variables can be difficult to find. Table of contents What is criterion validity, types of criterion validity, criterion validity example, how to measure criterion validity, other interesting articles, frequently asked questions about criterion validity. Criterion validity shows you how well a test correlates with an established standard of comparison called a criterion. A measurement instrument, like a questionnaire , has criterion validity if its results converge with those of some other, accepted instrument, commonly called a “gold standard.” A gold standard (or criterion variable) measures: The same construct Conceptually relevant constructs Conceptually relevant behavior or performance When a gold standard exists, evaluating criterion validity is a straightforward process. For example, you can compare a new questionnaire with an established one. In medical research, you can compare test scores with clinical assessments. However, in many cases, there is no existing gold standard. If you want to measure pain, for example, there is no objective standard to do so. You must rely on what respondents tell you. In such cases, you can’t achieve criterion validity. It’s important to keep in mind that criterion validity is only as good as the validity of the gold standard or reference measure. If the reference measure suffers from some sort of research bias , it can impact an otherwise valid measure. In other words, a valid measure tested against a biased gold standard may fail to achieve criterion validity. Similarly, two biased measures will confirm one another. Thus, criterion validity is no guarantee that a measure is in fact valid. It’s best used in tandem with the other types of validity . Here's why students love Scribbr's proofreading services Discover proofreading & editing There are two types of criterion validity. Which type you use depends on the time at which the two measures (the criterion and your test) are obtained. Concurrent validity is used when the scores of a test and the criterion variables are obtained at the same time . Predictive validity is used when the criterion variables are measured after the scores of the test. Concurrent validity Concurrent validity is demonstrated when a new test correlates with another test that is already considered valid, called the criterion test. A high correlation between the new test and the criterion indicates concurrent validity. Establishing concurrent validity is particularly important when a new measure is created that claims to be better in some way than its predecessors: more objective, faster, cheaper, etc. Remember that this form of validity can only be used if another criterion or validated instrument already exists. Predictive validity Predictive validity is demonstrated when a test can predict future performance. In other words, the test must correlate with a variable that can only be assessed at some point in the future, after the test has been administered. For predictive criterion validity, researchers often examine how the results of a test predict a relevant future outcome. For example, the results of an IQ test can be used to predict future educational achievement. The outcome is, by design, assessed at some point in the future. A high correlation provides evidence of predictive validity. It indicates that a test can correctly predict something that you hypothesize it should. Criterion validity is often used when a researcher wishes to replace an established test with a different version of the same test, particularly one that is more objective, shorter, or cheaper. Although the original test is widely accepted as a valid measure of procrastination, it is very long and takes a lot of time to complete. As a result, many students fill it in without carefully considering their answers. Criterion validity is assessed in two ways: By statistically testing a new measurement technique against an independent criterion or standard to establish concurrent validity By statistically testing against a future performance to establish predictive validity The measure to be validated, such as a test, should be correlated with a measure considered to be a well-established indication of the construct under study. This is your criterion variable. Correlations between the scores on the test and the criterion variable are calculated using a correlation coefficient , such as Pearson’s r . A correlation coefficient expresses the strength of the relationship between two variables in a single value between −1 and +1. Correlation coefficient values can be interpreted as follows: r = 1: There is perfect positive correlation r = 0: There is no correlation at all. r = −1: There is perfect negative correlation You can automatically calculate Pearson’s r in Excel , R , SPSS or other statistical software. Positive correlation between a test and the criterion variable shows that the test is valid. No correlation or a negative correlation indicates that the test and criterion variable do not measure the same concept. You give the two scales to the same sample of respondents. The extent of agreement between the results of the two scales is expressed through a correlation coefficient. You calculate the correlation coefficient between the results of the two tests and find out that your scale correlates with the existing scale ( r = 0.80). This value shows that there is a strong positive correlation between the two scales. If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples. Normal distribution Degrees of freedom Null hypothesis Discourse analysis Control groups Mixed methods research Non-probability sampling Quantitative research Ecological validity Research bias Rosenthal effect Implicit bias Cognitive bias Selection bias Negativity bias Status quo bias Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something. While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something. Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity. When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research. Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity. Reliability and validity are both about how well a method measures something: Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions). Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure). If you are doing experimental research, you also have to consider the internal and external validity of your experiment. Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance. Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method. Cite this Scribbr article If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. Nikolopoulou, K. (2023, June 22). What Is Criterion Validity? | Definition & Examples. Scribbr. Retrieved July 27, 2024, from https://www.scribbr.com/methodology/criterion-validity/ Is this article helpful? Kassiani Nikolopoulou Other students also liked, what is convergent validity | definition & examples, what is content validity | definition & examples, construct validity | definition, types, & examples, what is your plagiarism score. Physical Review Research Collections Editorial Team Open Access Symmetry constraints and spectral crossing in a Mott insulator with Green's function zeros Chandan setty, shouvik sur, lei chen, fang xie, haoyu hu, silke paschen, jennifer cano, and qimiao si, phys. rev. research 6 , l032018 – published 26 july 2024. No Citing Articles Supplemental Material ACKNOWLEDGMENTS Lattice symmetries are central to the characterization of electronic topology. Recently, it was shown that Green's function eigenvectors form a representation of the space group. This formulation has allowed the identification of gapless topological states even when quasiparticles are absent. Here we demonstrate the profundity of the framework in the extreme case, when interactions lead to a Mott insulator, through a solvable model with long-range interactions. We find that both Mott poles and zeros are subject to the symmetry constraints, and relate the symmetry-enforced spectral crossings to degeneracies of the original noninteracting eigenstates. Our results lead to new understandings of topological quantum materials and highlight the utility of interacting Green's functions toward their symmetry-based design. Received 6 March 2023 Accepted 29 May 2024 DOI: https://doi.org/10.1103/PhysRevResearch.6.L032018 Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Published by the American Physical Society Physics Subject Headings (PhySH) Research Areas Physical Systems Authors & Affiliations 1 Department of Physics and Astronomy, Rice Center for Quantum Materials, Rice University , Houston, Texas 77005, USA 2 Donostia International Physics Center , P. Manuel de Lardizabal 4, 20018 Donostia-San Sebastian, Spain 3 Institute of Solid State Physics, Vienna University of Technology , Wiedner Hauptstr. 8-10, 1040, Vienna, Austria 4 Department of Physics and Astronomy, Stony Brook University , Stony Brook, New York 11794, USA 5 Center for Computational Quantum Physics, Flatiron Institute , New York, New York 10010, USA * These authors contributed equally to this work. † Contact author: [email protected] ‡ Contact author: [email protected] Article Text Vol. 6, Iss. 3 — July - September 2024 Subject Areas Condensed Matter Physics Authorization Required Other options. Buy Article » Find an Institution with the Article » Download & Share Schematic summary of symmetry constraints and spectral crossings in a Mott insulator with Green's functions zeros and poles. (a) Top: Symmetry enforced Dirac point in the noninteracting dispersion that occurs at a high symmetry point P . Bottom: Dirac points (red dots) that occur in the square net lattice at high symmetry points in the Brillouin zone. (b) Symmetry enforced spectral crossings in the Mott insulator state. They involve the upper and lower Hubbard bands (solid curves) and their associated Dirac points are separated by U and a contour of crossings of zeros (dashed curves). Both are enforced by the lattice symmetry. (c) Top: Spectral function (imaginary part of the Green's function) at a wave vector marked by the magenta line in (b) away from the Dirac point for the poles of the Green's function. Bottom: Spectral function at the wave vector P marked by the red line in (b) at the Dirac point. (d) Top: Imaginary part of the self energy at a wave vector marked by the magenta line in (b) indicating zeros of the Green's function. Bottom: Same at the wave vector P marked by the red line indicating degeneracies of zeros enforced by symmetry. Exact poles and zeros of the total Green's function of an interacting Dirac semimetal as β → ∞ . (a) The upper and lower Hubbard bands (red curves marked UHB and LHB, respectively) and zeros (gray) obtained from analytical diagonalization of the interacting Hamiltonian in the t S O / t ≪ 1 limit at U ′ = 0 . The blue dotted curve is the original noninteracting band structure which transforms to zeros at sufficiently strong U . The green arrow marks the chemical potential. (b) Same as (a) but for U ′ > 0 . Here the contours of zeros and poles are shifted by U ′ . (c) The spectrum of the Green's function obtained by numerical exact diagonalization of the interacting Hamiltonian with ( t , t S O , μ ) = ( 0.7 , 0.42 , 5 ) and ( U , U ′ ) = ( 10 , 0.5 ) . Sign up to receive regular email alerts from Physical Review Research Reuse & Permissions It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures. Forgot your username/password? Create an account Article Lookup Paste a citation or doi, enter a citation. Notification: View the latest site access restrictions, updates, and resources related to the coronavirus (COVID-19) » 100% Clean Electricity by 2035 Study An NREL study shows there are multiple pathways to 100% clean electricity by 2035 that would produce significant benefits exceeding the additional power system costs. For the study, funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, NREL modeled technology deployment, costs, benefits, and challenges to decarbonize the U.S. power sector by 2035, evaluating a range of future scenarios to achieve a net-zero power grid by 2035. The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 . Scenario Approach To examine what it would take to achieve a net-zero U.S. power grid by 2035, NREL leveraged decades of research on high-renewable power systems, from the Renewable Electricity Futures Study , to the Storage Futures Study , to the Los Angeles 100% Renewable Energy Study , to the Electrification Futures Study , and more. NREL used its publicly available flagship  Regional Energy Deployment System   capacity expansion model to study supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies. The scenarios apply a carbon constraint to: Achieve 100% clean electricity by 2035 under accelerated demand electrification Reduce economywide, energy-related emissions by 62% in 2035 relative to 2005 levels—a steppingstone to economywide decarbonization by 2050. For each scenario, NREL modeled the least-cost option to maintain safe and reliable power during all hours of the year. Key Findings Technology deployment must rapidly scale up. In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar. To achieve those levels would require rapid and sustained growth in installations of solar and wind generation capacity. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today’s installed capacity by 2035. Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure demand for electricity is met during all hours of the year. Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday to seasonal mismatch of variable renewable supply and demand. Across the scenarios, seasonal capacity in 2035 ranges about 100–680 gigawatts. Significant additional research is needed to understand the manufacturing and supply chain associated with the unprecedent deployment envisioned in the scenarios. Significant Additional Transmission Capacity In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today’s capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026. Climate and Health Benefits of Decarbonization Offset the Costs NREL finds in all modeled scenarios the health and climate benefits associated with fewer emissions offset the power system costs to get to 100% clean electricity. Decarbonizing the power grid by 2035 could total $330 billion to $740 billion in additional power system costs, depending on restrictions on new transmission and other infrastructure development. However, there is substantial reduction in petroleum use in transportation and natural gas in buildings and industry by 2035. As a result, up to 130,000 premature deaths are avoided by 2035, which could save between $390 billion to $400 billion in avoided mortality costs. When factoring in the avoided cost of damage from floods, drought, wildfires, and hurricanes due to climate change, the United States could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion. Necessary Actions To Achieve 100% Clean Electricity The transition to a 100% clean electricity U.S. power system will require more than reduced technology costs. Several key actions will need to take place in the coming decade: Dramatic acceleration of electrification and increased efficiency in demand New energy infrastructure installed rapidly throughout the country Expanded clean technology manufacturing and the supply chain Continued research, development, demonstration, and deployment to bring emerging technologies to the market. Failing to achieve any of the key actions could increase the difficulty of realizing the scenarios outlined in the study. Study Resources Full report, supporting materials. Download the technical report, Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 . Download the report overview infographic and a 1-slide summary brief deck or a 10-slide summary brief deck . Paul Denholm Principal Energy Analyst Energy Analysis Delivered to Your Inbox Your personal data will only be used for as long as you are subscribed. For more information, review the  NREL security and privacy policy . Cancer Drug Could Ease Cognitive Function for Some With Autism New research highlights a therapeutic target that could make thinking easier for patients with a variety of neurologic disorders Erin Prater Media contact: Danielle Lewis - [email protected] Published Date Share this:, article content. An experimental cancer drug could make thinking easier for individuals with Rett syndrome, a rare disorder linked to autism, according to new research from the University of California San Diego — a discovery that could lead to therapies for patients with other neurological conditions. The findings, published July 25 in Stem Cell Reports , highlight the role of microglia — a type of white blood cell found in the central nervous system — in the formation of the human brain. While such cells have been better studied in neurodegenerative disorders like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and multiple sclerosis, “very little information has existed on their role in early stages of neural development” because access to fetal tissue is limited, said Pinar Mesci, Ph.D., the study’s lead researcher. Now employed elsewhere, she completed work on the project while at the university. In a bid to better understand their function, Mesci instead used brain organoids — “mini brains,” essentially, that mimic the developing brain of an embryo — grown from skin-derived stem cells of consenting patients. Such organoids were created from individuals with Rett syndrome — a disorder primarily found in females that features loss of speech, purposeful use of hands, mobility and muscle tone, among other symptoms — as well as from neurotypical individuals. Mesci then added healthy microglia to the Rett syndrome brain organoids and found that the functioning of synapses — where neurons connect and communicate — was “rescued.” This occurred due to the restoration of phagocytosis, a process by which microglia — sometimes referred to as the “janitors” of the central nervous system — ingest and destroy foreign substances like bacteria and dead cells, keeping the brain and spinal cord tidy. The process also involves “pruning” of synapses, which optimizes brain function. Researchers also found that the synapses of typical neurons experienced impaired functioning when Rett syndrome microglia were introduced, further confirming the role of the immune cell in brain function and development. “If the brain’s ‘janitors’ are not working, problems start to arise,” said UC San Diego School of Medicine professor Alysson Muotri, Ph.D., senior author and director of the university’s Sanford Stem Cell Institute's Integrated Space Stem Cell Orbital Research Center. Faulty microglia make cognition even harder for Rett syndrome patients, who already contend with fewer and impaired synapses and dysfunctional astrocytes due to a loss of function in the MECP2 gene, implicated in other types of neurodevelopmental conditions as well. Microglia with loss of MECP2 function “are not as good at pruning synapses and shaping the neural network — they don’t do a good job,” Muotri said. The team then tested a battery of existing drugs on the microglia, to see if any might restore phagocytosis. They found one: ADH-503, also known as GB1275 — an experimental oral pancreatic cancer medication that also reduces the number of immune-supressing cells that enter a tumor. The drug serves as a regulator of CD11b, a protein involved in phagocytosis, among other processes. Other studies on Rett syndrome have highlighted potential therapeutic targets. But none so far have identified a potential treatment involving human microglial cells. By the time Rett syndrome patients are diagnosed, it’s too late to repair and not currently possible to replace faulty neurons, the primary issue in the disease. “But by focusing on other cell types — and potentially finding drugs that improve how they work — we might improve the environment for those neurons and ease functioning for patients,” Mesci said. “That’s what I’m excited about.” Jonathan Kipnis, Ph.D., professor of pathology, immunology, neurology, neuroscience and neurosurgery at Washington University School of Medicine in St. Louis and director of its Brain Immunology and Glia Center, said the new research “nicely demonstrates” microglia as a potential therapeutic target in Rett syndrome. “I hope this work will ‘move the needle’ and bring the Rett community back to neuroimmunology,” Kipnis said. “Understanding neuro-immune interactions in this complex disease may not only provide new insights into the disease biology, but also develop novel approaches to attenuate its progression.” The research represents the first successful integration of human microglia into Rett syndrome brain tissues in vitro — a model that may prove superior to mouse models. The researchers hope the study “opens doors for therapies,” not only for those with Rett syndrome, but for those with other neurodevelopmental and neurodegenerative disorders in which microglia play a role. “That’s my wish,” Mesci said, “that we can improve quality of life.” Co-authors of the study include Christopher LaRock, with the Department of Pediatrics at the University California San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences; Jacob J. Jeziroski, Natalia Chermount, Tomoka Ozaki, Aurian Saleh, Cedric E. Snethlage, Sandra Sanchez, Gabriela Goldberg, Clever A. Trujillo and Kinichi Nakashima, with the University of California San Diego School of Medicine and Department of Pediatrics at Rady Children’s Hospital San Diego, and the Department of Cellular & Molecular Medicine; Hideyuki Nakashima, with the Department of Stem Cell Biology and Medicine at Kyushu University’s Graduate School of Medicine; Adriano Ferrasa, with the Experimental Multiuser Labratory at the Graduate Program in Health Sciences at the School of Medicine at Pontifícia Universidade Católica do Paraná in Curitiba, Paraná, Brazil, as well as the Department of Informatics at the Universidade Estadual de Ponta Grossa in Ponta Grossa, Parana, Brazil; Roberto H. Herai, with the Experimental Multiuser Labratory at the Graduate Program in Health Sciences at the School of Medicine at Pontifícia Universidade Católica do Paraná in Curitiba, Paraná, Brazil, and the Research Department at Lico Kaesemodel Institute in Curitiba, Paraná, Brazil; and Victor Nizet, with the Department of Pediatrics at the University California San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences. This work was made possible in part by the California Institute for Regenerative Medicine (CIRM) Major Facilities grant (FA1-00607) to the Sanford Consortium for Regenerative Medicine. Muotri is supported by the National Institutes of Health (NIH) R01MH107367, R01HD107788, R01NS105969 and R01NS123642, and a grant from the International Rett Syndrome Foundation (IRSF). This work was also partially funded by the IRSF Innovation Award granted to Mesci (grant No. 3905). Herai is funded by Fundação Araucária (grant No. FA09/2016). This work was also partially funded by AMED (JP22mg1310008), an Intramural Research Grant (3-9) for Neurological and Psychiatric Disorders of NCNP grant to Nakashima and a Japan Society for the Promotion of Science (JSPS) KAKENHI (JP22K15201) to Nakashima. This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from an NIH SIG grant (No. S10 OD026929). Disclosures: Muotri is a co-founder and has an equity interest in TISMOO, a company dedicated to genetic analysis and human brain organogenesis focusing on therapeutic applications customized for autism spectrum disorder and other neurological disorders with genetic origins. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. The authors have a patent application in the works related to this publication. About the Sanford Stem Cell Institute The UC San Diego Sanford Stem Cell Institute (SSCI) is a global leader in regenerative medicine and a hub for stem cell science and innovation in space. SSCI aims to catalyze critical basic research discoveries, translational advances and clinical progress — terrestrially and in space — to develop and deliver novel therapeutics to patients. {/exp:typographee} A representative culture from a brain organoid in which the gene MECP2 — causative in Rett syndrome — has been "knocked out," as shown through a fluorescent microscope. Because the culture was treated with experimental cancer drug ADH-503, new synapses formed. Photo credit: Muotri Lab/ UC San Diego Health Sciences You May Also Like Diatom surprise could rewrite the global carbon cycle, researchers identify brain circuits tied to the behavior of schooling fish, aacp ranks uc san diego pharmacy school in top ten nationally for research funding, new $42m grid-tech sandbox will help get more renewables on electric grids, stay in the know. Keep up with all the latest from UC San Diego. Subscribe to the newsletter today. You have been successfully subscribed to the UC San Diego Today Newsletter. Campus & Community Arts & culture, visual storytelling. Media Resources & Contacts Signup to get the latest UC San Diego newsletters delivered to your inbox. Award-winning publication highlighting the distinction, prestige and global impact of UC San Diego. Popular Searches: Covid-19   Ukraine   Campus & Community   Arts & Culture   Voices 459 COMMENTS Validity Ensuring validity in research involves several strategies: Clear Operational Definitions: Define variables clearly and precisely. Use of Reliable Instruments: Employ measurement tools that have been tested for reliability. Pilot Testing: Conduct preliminary studies to refine the research design and instruments. The 4 Types of Validity in Research The 4 Types of Validity in Research | Definitions & Examples. Published on September 6, 2019 by Fiona Middleton.Revised on June 22, 2023. Validity tells you how accurately a method measures something. If a method measures what it claims to measure, and the results closely correspond to real-world values, then it can be considered valid. Validity In Psychology Research: Types & Examples 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, including construct validity (measuring the intended abstract trait), internal validity (ensuring causal conclusions ... What Is Content Validity? Revised on June 22, 2023. Content validity evaluates how well an instrument (like a test) covers all relevant parts of the construct it aims to measure. Here, a construct is a theoretical concept, theme, or idea: in particular, one that cannot usually be measured directly. Content validity is one of the four types of measurement validity. Validity in Research and Psychology: Types & Examples In this vein, there are many different types of validity and ways of thinking about it. Let's take a look at several of the more common types. Each kind is a line of evidence that can help support or refute a test's overall validity. In this post, learn about face, content, criterion, discriminant, concurrent, predictive, and construct ... Content Validity: Definition, Examples & Measuring Content Validity Examples. Evaluating content validity is crucial for the following examples to ensure the tests assess the full range of knowledge and aspects of the psychological constructs: A test to obtain a license, such as driving or selling real estate. Standardized testing for academic purposes, such as the SAT and GRE. Content Validity in Research: Definition & Examples Olivia Guy-Evans, MSc. Content validity is a type of criterion validity that demonstrates how well a measure covers the construct it is meant to represent. It is important for researchers to establish content validity in order to ensure that their study is measuring what it intends to measure. There are several ways to establish content ... What is Content Validity? (Definition & Example) In practice, content validity is often used to assess the validity of tests that assess content knowledge. Examples include: Example 1: Statistics Final Exam. A final exam at the end of a semester for a statistics course would have content validity if it covers every topic discussed in the course and excludes all other irrelevant topics. Construct Validity The 4 Types of Validity in Research | Definitions & Examples When you measure or test something, you must make sure your method is valid. Validity is split up into construct, content, face and criterion. ... Formulas & Examples A correlation coefficient tells you the strength of the relationship between variables using a single number between ... What is Validity in Research? Validity is an important concept in establishing qualitative research rigor. At its core, validity in research speaks to the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure or understand. It's about ensuring that the study investigates what it purports to investigate. Understand Content Validity: Guide, Examples & FAQs Finding the content validity ratio. Once you've gathered this initial data from the SMEs, you'll calculate each question's content validity ratio (CVR). CVR = (Nₑ - N/2) / (N/2) In this formula, Ne e refers to the number of SMEs who have indicated a question is essential. N equals the total number of participating SMEs. Validity in Research: A Guide to Better Results Validity in research is the ability to conduct an accurate study with the right tools and conditions to yield acceptable and reliable data that can be reproduced. Researchers rely on carefully calibrated tools for precise measurements. However, collecting accurate information can be more of a challenge. Studies must be conducted in environments ... Validity (statistics) Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a ... PDF Measurement Validity: A Shared Standard for Qualitative and In the process we seek to formulate a methodological standard that can be applied in both qualitative and quantitative research. Measurement validity is specifically concerned with whether operationalization and the scoring of cases adequately reflect the concept the researcher seeks to measure. PDF VALIDITY OF QUANTITATIVE RESEARCH Statistical conclusion validity is an issue whenever statistical tests are used to test hypotheses. The research design can address threats to validity through. considerations of statistical power. alpha reduction procedures (e.g., Bonferoni technique) when multiple tests are used. use of reliable instruments. Reliability and Validity of Measurement Reliability is consistency across time (test-retest reliability), across items (internal consistency), and across researchers (interrater reliability). Validity is the extent to which the scores actually represent the variable they are intended to. Validity is a judgment based on various types of evidence. Validity, reliability, and generalizability in qualitative research Validity. Validity in qualitative research means "appropriateness" of the tools, processes, and data. Whether the research question is valid for the desired outcome, the choice of methodology is appropriate for answering the research question, the design is valid for the methodology, the sampling and data analysis is appropriate, and ... How do we assess reliability and validity? The criterion-related validity focus on the degree to which it correlates with some chosen criterion measure of the same construct (relations to other variables). There are two broad classes of this validity form. Predictive validity: if the test information is to be used to forecast future criterion performance.; Example: Use spelling test scores to predict reading test scores, the validity ... Reliability and Validity 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 ... PDF Microsoft PowerPoint Reliability -consistency of measurement -a value that expresses the degree to which a test consistently produces the same result. Internal consistencyis typically a measure based on the correlationsbetween different items on the same test. Correlation -a statistical estimate of the strength and direction of the relationship between two ... Reliability vs. Validity in Research Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt. 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. Failing to do so can lead to several types of research ... FORMULA SCORING AND VALIDITY formula-scored test r = .90, r = .60 , and that a reduction in xx xc validity from .60 to .57 would result from abandoning formula scoring. By (3), this reduction in validity is the same as would occur if the test length were halved ( K = 0.48)! In this case, failure to use. Functional Communication Training in Schools: A Systematic Analysis of The overall evidence for ecological validity was moderate for 16.7% and low for 83.3% of the behavioral assessments. The evidence for ecological validity for all FCT procedures was low. Future research and implications related to ecological validity are discussed. Yale Research Team Investigates Difference Between Baby Formula and The team says that if the factors in human milk that drive these intestinal improvements can be identified through more research, the possibility exists to then supplement formula-fed infants, or improve formula overall, to promote intestinal health and development. That can have profound clinical implications. The state of AI in early 2024: Gen AI adoption spikes and starts to Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. ... About the research. The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries ... Research Assistant Job Details Baltimore Research Assistant - MD, 21205. Our team is seeking a Research Assistant (RA) to support Dr. Tahilin Sanchez Karver's federally-funded projects on Latina/o/x HIV prevention and care navigation and support strategies.The RA will be responsible for recruiting and enrolling study participants and actively engage in qualitative and quantitative data collection and analysis. What Is Criterion Validity? Revised on June 22, 2023. Criterion validity (or criterion-related validity) evaluates how accurately a test measures the outcome it was designed to measure. An outcome can be a disease, behavior, or performance. Concurrent validity measures tests and criterion variables in the present, while predictive validity measures those in the future. Phys. Rev. Research 6, L032018 (2024) Phys. Rev. Research 6, L032018 - Published 26 July 2024. ... Both are enforced by the lattice symmetry. (c) Top: Spectral function (imaginary part of the Green's function) at a wave vector marked by the magenta line in (b) away from the Dirac point for the poles of the Green's function. 100% Clean Electricity by 2035 Study The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035. Scenario Approach. To examine what it would take to ... Cancer Drug Could Ease Cognitive Function for Some With Autism An experimental cancer drug could ease cognition for individuals with Rett syndrome, a rare disorder linked to autism, according to new research from the Muotri Lab at the University of California San Diego — a discovery that could lead to therapies for patients with other neurologic conditions. assignment essay homework paper phd report speech thesis