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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.

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.

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

What is reliability in psychology?

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

What is test retest reliability?

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

How to improve reliability of an experiment?

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

What is the difference between reliability and validity?

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

Are interviews reliable and valid?

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

Are IQ tests valid and reliable?

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

Are questionnaires reliable and valid?

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

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

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Please note you do not have access to teaching notes, comprehensive criteria to judge validity and reliability of qualitative research within the realism paradigm.

Qualitative Market Research

ISSN : 1352-2752

Article publication date: 1 September 2000

Aims to address a gap in the literature about quality criteria for validity and reliability in qualitative research within the realism scientific paradigm. Six comprehensive and explicit criteria for judging realism research are developed, drawing on the three elements of a scientific paradigm of ontology, epistemology and methodology. The first two criteria concern ontology, that is, ontological appropriateness and contingent validity. The third criterion concerns epistemology: multiple perceptions of participants and of peer researchers. The final three criteria concern methodology: methodological trustworthiness, analytic generalisation and construct validity. Comparisons are made with criteria in other paradigms, particularly positivism and constructivism. An example of the use of the criteria is given. In conclusion, this paper’s set of six criteria will facilitate the further adoption of the realism paradigm and its evaluation in marketing research about, for instance, networks and relationship marketing.

• Marketing research
• Qualitative techniques
• Case studies

Healy, M. and Perry, C. (2000), "Comprehensive criteria to judge validity and reliability of qualitative research within the realism paradigm", Qualitative Market Research , Vol. 3 No. 3, pp. 118-126. https://doi.org/10.1108/13522750010333861

Copyright © 2000, MCB UP Limited

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The Qualitative Report Validity in Qualitative Research: A Processual Approach

• January 2019
• The Qualitative Report 24(1):98-112
• 24(1):98-112

• University of Campinas

• Universidade Federal do Paraná

• Universidade do Vale do Rio dos Sinos

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Ana Trujillo Zapata

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Vu Thi Thanh Nha

Educational constructs change over time to reflect developments in research and educational approaches. To illustrate the process, this article aims to examine validity and reliability, which are important concepts to justify research quality. Originally, validity and reliability were applied to quantitative research. However, these criteria can not be equally applied to qualitative research studies which differ in terms of their theoretical foundations and research aims. The unclear use of these concepts might lead to inappropriate research design or evaluation. This paper, therefore, first examines two different theoretical foundations underlying these two research traditions. It then analyses the subtle variations to clarify the notions of reliability and validity. Some implications are made for researchers to flexibly employ these criteria to enhance their research rigor.

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In general practice, qualitative research contributes as significantly as quantitative research and both try to find the same result; the truth. Qualitative research, also known as naturalistic inquiry, evolved inside the social and human sciences refers to theories on interpretation and human experience. The use of validity and reliability are common in quantitative research and currently, there are ongoing debates regarding whether the terms are appropriate to evaluate qualitative studies. Although there is no universally typical terminology and standards used to measure qualitative studies, all qualitative researchers comprise strategies to enhance the credibility of a study throughout the research design and implementation. The main aim of this article is to provide the concepts of validity and reliability and to ascertain that it is possible for qualitative research to be properly valid or reliable.

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The Validity and Reliability of the scales used in research are essential factors that enable the research to yield beneficial results. For this reason, it is useful to understand how the Reliability and Validity of the scales are measured correctly by researchers. The primary purpose of this study is to provide information on how the researchers test the Validity and Reliability of the scales used in their empirical studies and to provide resources for future research. For this purpose, the concepts of Validity and Reliability are introduced, and detailed explanations have been provided regarding the main methods used in the evaluation of Validity and Reliability with examples taken from the literature. It is considered that this study, which is a review, will attract the attention of researchers.

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With reference to definitions of validity and reliability, and drawing extensively on conceptualisations of qualitative research, this essay examines the correlation between the reliability of effort to find answers to questions about the social world, and the validity of conclusions drawn from such attempts. This is to point out the fundamental position to the role of theory in relation to research; as an inductivist strategy qualitative research tries to confer the correspondence between reality and representation. The problem of validity and reliability in qualitative research is entwined with the definition of qualitative research and the possibility to mirror this in practice to make a qualitative research properly valid and reliable. That presents both challenges and chances to qualitative researchers; yet, with taking into consideration qualitative criteria in social research, achieving validity and as well as reliability in qualitative research is not impossible.

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What is qualitative data? How to understand, collect, and analyze it

A comprehensive guide to qualitative data, how it differs from quantitative data, and why it's a valuable tool for solving problems.

What is qualitative research?

Importance of qualitative data.

• Differences between qualitative and quantitative data

Characteristics of qualitative data

Types of qualitative data.

• Pros and cons
• Collection methods

Everything that’s done digitally—from surfing the web to conducting a transaction—creates a data trail. And data analysts are constantly exploring and examining that trail, trying to find out ways to use data to make better decisions.

Different types of data define more and more of our interactions online—one of the most common and well-known being qualitative data or data that can be expressed in descriptions and feelings. 

This guide takes a deep look at what qualitative data is, what it can be used for, how it’s collected, and how it’s important to you. 

Key takeaways: 

Qualitative data gives insights into people's thoughts and feelings through detailed descriptions from interviews, observations, and visual materials.

The three main types of qualitative data are binary, nominal, and ordinal.

There are many different types of qualitative data, like data in research, work, and statistics. 

Both qualitative and quantitative research are conducted through surveys and interviews, among other methods. 

What is qualitative data?

Qualitative data is descriptive information that captures observable qualities and characteristics not quantifiable by numbers. It is collected from interviews, focus groups, observations, and documents offering insights into experiences, perceptions, and behaviors.

Qualitative data analysis cannot be counted or measured because it describes the data. It refers to the words or labels used to describe certain characteristics or traits.

This type of data answers the "why" or "how" behind the analysis . It’s often used to conduct open-ended studies, allowing those partaking to show their true feelings and actions without direction.

Think of qualitative data as the type of data you’d get if you were to ask someone why they did something—what was their reasoning? 

Qualitative research not only helps to collect data, it also gives the researcher a chance to understand the trends and meanings of natural actions. 

This type of data research focuses on the qualities of users—the actions behind the numbers. Qualitative research is the descriptive and subjective research that helps bring context to quantitative data. 

It’s flexible and iterative. For example: 

The music had a light tone that filled the kitchen.

Every blue button had white lettering, while the red buttons had yellow. 

The little girl had red hair with a white hat.

Qualitative data is important in determining the frequency of traits or characteristics. 

Understanding your data can help you understand your customers, users, or visitors better. And, when you understand your audience better, you can make them happier.  First-party data , which is collected directly from your own audience, is especially valuable as it provides the most accurate and relevant insights for your specific needs.

Qualitative data helps the market researcher answer questions like what issues or problems they are facing, what motivates them, and what improvements can be made.

Examples of qualitative data

You’ve most likely used qualitative data today. This type of data is found in your everyday work and in statistics all over the web. Here are some examples of qualitative data in descriptions, research, work, and statistics. 

Qualitative data in descriptions

Analysis of qualitative data requires descriptive context in order to support its theories and hypothesis. Here are some core examples of descriptive qualitative data:

The extremely short woman has curly hair and brilliant blue eyes.

A bright white light pierced the small dark space. 

The plump fish jumped out of crystal-clear waters. 

The fluffy brown dog jumped over the tall white fence. 

A soft cloud floated by an otherwise bright blue sky.

Qualitative data in research

Qualitative data research methods allow analysts to use contextual information to create theories and models. These open- and closed-ended questions can be helpful to understand the reasoning behind motivations, frustrations, and actions —in any type of case. 

Some examples of qualitative data collection in research:

What country do you work in? 

What is your most recent job title? 

How do you rank in the search engines? 

How do you rate your purchase: good, bad, or exceptional?

Qualitative data at work

Professionals in various industries use qualitative observations in their work and research. Examples of this type of data in the workforce include:

A manager gives an employee constructive criticism on their skills. "Your efforts are solid and you understand the product knowledge well, just have patience."

A judge shares the verdict with the courtroom. "The man was found not guilty and is free to go."

A sales associate collects feedback from customers. "The customer said the check-out button did not work.”

A teacher gives feedback to their student. "I gave you an A on this project because of your dedication and commitment to the cause."

A digital marketer watches a session replay to get an understand of how users use their platform.

Qualitative data in statistics

Qualitative data can provide important statistics about any industry, any group of users, and any products. Here are some examples of qualitative data set collections in statistics:

The age, weight, and height of a group of body types to determine clothing size charts. 

The origin, gender, and location for a census reading.

The name, title, and profession of people attending a conference to aid in follow-up emails.

Difference between qualitative and quantitative data

Qualitative and quantitative data are much different, but bring equal value to any data analysis. When it comes to understanding data research, there are different analysis methods, collection types and uses. 

Here are the differences between qualitative and quantitative data :

Qualitative data is individualized, descriptive, and relating to emotions.

Quantitative data is countable, measurable and relating to numbers.

Qualitative data helps us understand why, or how something occurred behind certain behaviors .

Quantitative data helps us understand how many, how much, or how often something occurred. 

Qualitative data is subjective and personalized.

Quantitative data is fixed and ubiquitous.

Qualitative research methods are conducted through observations or in-depth interviews.

Quantitative research methods are conducted through surveys and factual measuring. 

Qualitative data is analyzed by grouping the data into classifications and topics. 

Quantitative data is analyzed using statistical analysis.

Both provide a ton of value for any data collection and are key to truly understanding trending use cases and patterns in behavior . Dig deeper into quantitative data examples .

The characteristics of qualitative data are vast. There are a few traits that stand out amongst other data that should be understood for successful data analysis. 

Descriptive : describing or classifying in an objective and nonjudgmental way.

Detailed : to give an account in words with full particulars.

Open-ended : having no determined limit or boundary.

Non-numerical : not containing numbers. 

Subjective : based on or influenced by personal feelings, tastes, or opinions.

With qualitative data samples, these traits can help you understand the meaning behind the equation—or for lack of a better term, what’s behind the results. 

As we narrow down the importance of qualitative data, you should understand that there are different data types. Data analysts often categorize qualitative data into three types:

1. Binary data

Binary data is numerically represented by a combination of zeros and ones. Binary data is the only category of data that can be directly understood and executed by a computer.

Data analysts use binary data to create statistical models that predict how often the study subject is likely to be positive or negative, up or down, right or wrong—based on a zero scale.

2. Nominal data

Nominal data , also referred to as “named, labeled data” or “nominal scaled data,” is any type of data used to label something without giving it a numerical value. 

Data analysts use nominal data to determine statistically significant differences between sets of qualitative data. 

For example, a multiple-choice test to profile participants’ skills in a study.

3. Ordinal data

Ordinal data is qualitative data categorized in a particular order or on a ranging scale. When researchers use ordinal data, the order of the qualitative information matters more than the difference between each category. Data analysts might use ordinal data when creating charts, while researchers might use it to classify groups, such as age, gender, or class.

For example, a Net Promoter Score ( NPS ) survey has results that are on a 0-10 satisfaction scale. 

When should you use qualitative research?

One of the important things to learn about qualitative data is when to use it. 

Qualitative data is used when you need to determine the particular trends of traits or characteristics or to form parameters for larger data sets to be observed. Qualitative data provides the means by which analysts can quantify the world around them.

You would use qualitative data to help answer questions like who your customers are, what issues or problems they’re facing, and where they need to focus their attention, so you can better solve those issues.

Qualitative data is widely used to understand language consumers speak—so apply it where necessary. 

Pros and cons of qualitative data

Qualitative data is a detailed, deep understanding of a topic through observing and interviewing a sample of people. There are both benefits and drawbacks to this type of data. 

Pros of qualitative data

Qualitative research is affordable and requires a small sample size.

Qualitative data provides a predictive element and provides specific insight into development.

Qualitative research focuses on the details of personal choice and uses these individual choices as workable data.

Qualitative research works to remove bias from its collected data by using an open-ended response process.

Qualitative data research provides useful content in any thematic analysis.

Cons of qualitative data 

Qualitative data can be time-consuming to collect and can be difficult to scale out to a larger population.

Qualitative research creates subjective information points.

Qualitative research can involve significant levels of repetition and is often difficult to replicate.

Qualitative research relies on the knowledge of the researchers.

Qualitative research does not offer statistical analysis, for that, you have to turn to quantitative data.

Qualitative data collection methods

Here are the main approaches and collection methods of qualitative studies and data: 

1. Interviews

Personal interviews are one of the most commonly used deductive data collection methods for qualitative research, because of its personal approach.

The interview may be informal and unstructured and is often conversational in nature. The interviewer or the researcher collects data directly from the interviewee one-to-one. Mostly the open-ended questions are asked spontaneously, with the interviewer allowing the flow of the interview to dictate the questions and answers.

The point of the interview is to obtain how the interviewee feels about the subject. 

2. Focus groups

Focus groups are held in a discussion-style setting with 6 to 10 people. The moderator is assigned to monitor and dictate the discussion based on focus questions.

Depending on the qualitative data that is needed, the members of the group may have something in common. For example, a researcher conducting a study on dog sled runners understands dogs, sleds, and snow and would have sufficient knowledge of the subject matter.

3. Data records 

Data doesn’t start with your collection, it has most likely been obtained in the past. 

Using already existing reliable data and similar sources of information as the data source is a surefire way to obtain qualitative research. Much like going to a library, you can review books and other reference material to collect relevant data that can be used in the research.

For example, if you were to study the trends of dictionaries, you would want to know the past history of every dictionary made, starting with the very first one. 

4. Observation

Observation is a longstanding qualitative data collection method, where the researcher simply observes behaviors in a participant's natural setting. They keep a keen eye on the participants and take down transcript notes to find out innate responses and reactions without prompting. 

Typically observation is an inductive approach, which is used when a researcher has very little or no idea of the research phenomenon. 

Other documentation methods, such as video recordings, audio recordings, and photo imagery, may be used to obtain qualitative data.

Further reading: Site observations through heatmaps

5. Case studies

Case studies are an intensive analysis of an individual person or community with a stress on developmental factors in relation to the environment. 

In this method, data is gathered by an in-depth analysis and is used to understand both simple and complex subjects. The goal of a case study is to see how using a product or service has positively impacted the subject, showcasing a solution to a problem or the like. 

6. Longitudinal studies

A longitudinal study is where people who share a single characteristic are studied over a period of time. 

This data collection method is performed on the same subject repeatedly over an extended period. It is an observational research method that goes on for a few years and, in some cases, decades. The goal is to find correlations of subjects with common traits.

For example, medical researchers conduct longitudinal studies to ascertain the effects of a drug or the symptoms related.

Qualitative data analysis tools

And, as with anything—you aren’t able to be successful without the right tools. Here are a few qualitative data analysis tools to have in your toolbox: 

MAXQDA —A qualitative and mixed-method data analysis software 

Fullstory —A behavioral data and analysis platform

ATLAS.ti —A powerful qualitative data tool that offers AI-based functions 

Quirkos —Qualitative data analysis software for the simple learner

Dedoose —A project management and analysis tool for collaboration and teamwork

Taguette —A free, open-source, data analysis and organization platform 

MonkeyLearn —AI-powered, qualitative text analysis, and visualization tool 

Qualtrics —Experience management software

Frequently asked questions about qualitative data

Is qualitative data subjective.

Yes, categorical data or qualitative data is information that cannot generally be proven. For instance, the statement “the chair is too small” depends on what it is used for and by whom it is being used.

Who uses qualitative data?

If you’re interested in the following, you should use qualitative data:

Understand emotional connections to your brand

Identify obstacles in any funnel, for example with session replay

Uncover confusion about your messaging

Locate product feature gaps 

Improve usability of your website, app, or experience

Observe how people talk, think, and feel about your brand

Learn how an organization selects vendors and partners

What are the steps for qualitative data?

1. Transcribe your data : Once you’ve collected all the data, you need to transcribe it. The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format. 

2. Organize your data : Go back to your research objectives and organize the data based on the questions asked. Arrange your research objective in a table, so it appears visually clear. Avoid working with unorganized data, there will be no conclusive results obtained.

3. Categorize and assign the data : The coding process of qualitative data means categorizing and assigning variables, properties, and patterns. Coding is an important step in qualitative data analysis, as you can derive theories from relevant research findings. You can then begin to gain in-depth insight into the data that help make informed decisions.

4. Validate your data : Data validation is a recurring step that should be followed throughout the research process. There are two sides to validating data: the accuracy and reliability of your research methods, which is the extent to which the methods produce accurate data consistently. 

5. Conclude the data analysis : Present your data in a report that shares the method used to conduct the research studies, the outcomes, and the projected hypothesis of your findings in any related areas.

Is qualitative data better than quantitative data?

One is not better than the other, rather they work cohesively to create a better overall data analysis experience. Understanding the importance of both qualitative and quantitative data is going to produce the best possible data content analysis outcome for any study. 

Further reading : Qualitative vs. quantitative data — what's the difference?

Related posts

Learn how to analyze qualitative data. We show examples of how to collect, organize, and analyze qualitative data to gain insights.

Here's how you can quantitatively analyze your qualitative digital experience data to unlock an entirely new workflow.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

• Open access
• Published: 18 July 2024

Parent version of the Eating Disorder Examination: Reliability and validity in a treatment-seeking sample

• Lisa Hail 1 ,
• Catherine R. Drury 1 , 2 ,
• Robert E. McGrath 2 ,
• Stuart B. Murray 3 ,
• Elizabeth K. Hughes 4 , 5 ,
• Susan M. Sawyer 5 ,
• Daniel Le Grange 1 , 6 &
• Katharine L. Loeb 2 , 7  

Journal of Eating Disorders volume  12 , Article number:  101 ( 2024 ) Cite this article

15 Accesses

1 Altmetric

Metrics details

Assessment of eating disorders (ED) in youth relies heavily on self-report, yet persistent lack of recognition of the presence and/or seriousness of symptoms can be intrinsic to ED. This study examines the psychometric properties of a semi-structured interview, the parent version of the Eating Disorder Examination (PEDE), developed to systematically assess caregiver report of symptoms.

A multi-site, clinical sample of youth ( N  = 522; age range: 12 to 18 years) seeking treatment for anorexia nervosa (AN) and subsyndromal AN were assessed using the Eating Disorder Examination (EDE) for youth and the PEDE for collateral caregiver report.

Internal consistencies of the four PEDE subscales were on par with established ranges for the EDE. Significant medium-sized correlations and poor to moderate levels of agreement were found between the corresponding subscales on each measure. For the PEDE, confirmatory factor analysis of the EDE four-factor model provided a poor fit; an exploratory factor analysis indicated that a 3-factor model better fits the PEDE.

Conclusions

Findings suggest that the PEDE has psychometric properties on par with the original EDE. The addition of the caregiver perspective may provide incremental information that can aid in the assessment of AN in youth. Future research is warranted to establish psychometric properties of the PEDE in broader transdiagnostic ED samples.

Plain English summary

Assessments for eating disorders rely primarily on self-report; yet, the denial of symptoms or symptom severity among adolescents with anorexia nervosa can complicate assessment and delay treatment in this population. The Parent Eating Disorder Examination (PEDE) is the first semi-structured interview formally developed to improve childhood eating disorder assessment by including caregiver perspectives. In this study, a large sample of adolescents with anorexia nervosa completed a self-report interview (the Eating Disorder Examination or EDE) and their parents completed the PEDE. The PEDE appeared to measure parents’ report of their child’s eating disorder symptoms consistently. Results from both interviews were related to one another but did not completely agree. This suggests that in an eating disorder assessment, the PEDE can provide additional information from caregivers that might reduce diagnostic confusion and lead to earlier intervention for youth with anorexia nervosa.

With the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders ( DSM-5 ) [ 1 ], the criteria for diagnosing eating disorders (ED) were revised to reflect greater developmental sensitivity for youth. These modifications were particularly important as the onset of ED is most common in adolescence [ 2 ]. However, there remain many challenges to diagnosing restrictive ED, such as anorexia nervosa (AN), in children and adolescents, which could delay treatment of a pernicious, often refractory disorder.

One of the most significant challenges in relying on self-report in ED assessment and case identification is the persistent lack of recognition of the seriousness of symptoms, a core diagnostic feature of AN, which renders history of illness and present symptoms vulnerable to inaccuracies [ 3 , 4 , 5 ]. However, typical assessment methods for ED rely primarily on self-report, and may therefore be insufficient, particularly for younger individuals [ 6 , 7 , 8 , 9 ]. Compared to adults, adolescents generally score lower on measures of ED pathology despite similar levels of malnutrition [ 10 ], and appear to experience ED symptoms differently [ 11 ]. Minimization might be intrinsic to a developmentally normative limitation in recognizing the potential consequences of risky behaviors such as those associated with ED [ 5 , 10 , 12 , 13 ]. Shorter duration of illness could compound this, further limiting adolescents’ appreciation of the current and future impact of what could in fact become a severe and enduring disorder [ 14 , 15 ]. Relatedly, adolescents are unlikely to independently seek help for their ED, and may even engage in strategic minimization of symptoms, to avoid the implications of symptom endorsement (e.g., intervention efforts on the part of adults).

In addition, there are cognitive and emotional obstacles to evaluating symptoms of AN in youth, as several of the criteria are psychological in nature. For example, the ability to report a fear of weight gain requires that the young person be able to recognize and label their affective state correctly, and to identify the motivation behind their behavior [ 15 , 16 ]. Other criteria are more abstract in nature (e.g., disturbance in the experience of shape and weight, undue influence of shape and weight on self-evaluation), and require the developmental maturation of abstract reasoning to recognize and endorse ED symptoms [ 2 , 17 , 18 , 19 ].

The utility of multi-informant methods of assessing child psychopathology is long-established, and approaches have advanced over time [ 6 , 20 , 21 ]. However, most measures used for youth with ED – with notable exceptions [ 3 ] – rely exclusively on direct patient report [ 7 ] despite the unique risks posed by false negatives in case identification, particularly of AN. Two studies have examined parent-child concordance on the Eating Disorder Examination (EDE) [ 22 ] by administering the interview to parents with minimal modifications to the measure [ 18 , 23 ]. For example, Couturier and colleagues [ 23 ] simply changed wording of questions from you to your child and retained items reflecting the internal experience of the child without prompting parents for data on why and how these experiences can be inferred through the child’s behavior. They found that youth with AN scored lower than their parents on two EDE subscales (Restraint and Weight Concern), while Mariano and colleagues [ 18 ] found good concordance between youth and parent scores. Mariano and colleagues [ 18 ] proposed that adolescents in their study were less likely to minimize their symptoms due to the timing of EDE administration (i.e., at the end of a two-day psychological assessment). It is also possible that more extensive adaptations to the EDE are needed to assist parents in consistently providing a comprehensive report of symptoms.

To address the need for a standardized method for including parental report in the assessment of ED, we developed a parent version of the EDE (PEDE) [ 7 , 24 , 36 ], with permission and input from the first author of the original measure [ 22 ], that mirrors the EDE but includes detailed questions to assess for observable indicators of ED. Although the EDE can be administered as young as 14 years and has been adapted for use in children aged 8 years and older [ 25 ], these assessments do not incorporate caregiver perspectives. Thus, the overall objective of the current study was to evaluate the psychometric properties of the PEDE in a large, multi-site sample of children and adolescents seeking treatment for AN and subsyndromal AN (SAN). Specifically, we examined the internal consistency of the PEDE subscales and the PEDE’s convergent and construct validity in relation to the EDE. We also aimed to compare PEDE and EDE rates of AN diagnosis. We hypothesized that:

Internal consistency of the four PEDE subscales (Restraint, Eating Concern, Weight Concern, Shape Concern) and global score as measured by Cronbach’s alpha reliability coefficients would be similar to those previously established for the EDE, which found values in the range from .44 to .85 [ 26 ].

Convergent validity would be demonstrated by small, positive correlations and low to moderate levels of agreement between the established EDE subscales and the corresponding subscales on the PEDE. These positive relationships would indicate that the subscales are tapping into similar constructs. The small effect sizes and low to moderate levels of agreement (as opposed to good or excellent) would suggest that both youth and parent perspectives offer incremental information to an ED assessment [ 23 ].

Both the EDE and PEDE would have a different factor structure than the four original subscales presented by Cooper and colleagues [ 27 ] as no studies evaluating factor structure in the EDE have confirmed this model. No specific hypotheses were possible for the expected factor structure given variations in the results of three prior studies [ 28 , 29 , 30 ], only one of which included adolescents [ 28 ].

The PEDE would yield a diagnosis of AN more frequently than the EDE among participants with both AN and SAN.

Participants

Participants were youth and guardian informants who presented to two research-based ED treatment programs in the United States (US; New York and Chicago) and one in Melbourne, Australia. Researchers at these sites received training on the EDE and PEDE, administered both interviews to youth and their caregivers presenting to clinical research centers for treatment of a suspected ED, and contributed deidentified baseline data as part of this multisite collaboration to establish the PEDE’s psychometric properties. Any larger studies [ 31 , 32 ] from which these deidentified data were derived for secondary analyses were approved by the respective institutions’ institutional review boards; the present study was designated exempt from board review.

In order to assess the reliability and validity of the PEDE in a relatively homogenous sample, this study focused specifically on youth presenting to these sites with probable AN or SAN [ 32 ], a site-specific research category that would fall under other specified feeding and eating disorder (OSFED) in DSM-5-TR nomenclature. The original inspiration for developing the PEDE was to help identify true caseness in the context of underweight ED where denial and minimization are prominent and therefore parental report may be most useful [ 23 ]. Thus, submitted cases ( n =  833) were excluded from analysis if one or more of the following were met: (a) percent expected body weight (EBW) based on median body mass index (mBMI) was greater than 100% ( n  = 232, 27.85%), (b) criteria for bulimia nervosa or binge eating disorder might be met by virtue of 12 or greater EDE objective bulimic episodes in the past three months and weight > 85% of EBW ( n  = 0), (c) age was younger than 12 years ( n  = 83, 9.96% of the full sample), or (d) there was insufficient information to accurately determine EBW ( n  = 1). Although low weight is a relative, personalized construct and population norms are not a valid benchmark against which to determine individual-level weight status, these weight criteria were used to reduce the likelihood of false positives and because not all sites recorded a more individualized measure of EBW and all reported percent of mBMI. The resulting sample included 522 youth paired with guardian informants, ranging in age from 12 to 18 years ( M  = 15.4; SD  = 1.7), 89.7% parent- or self-identified as female, who were at 54–99% of mBMI ( M  = 84.3%; SD  = 8.5). Further demographic data (including caregiver gender identity) were not reported consistently across all sites. The majority of participants were recruited from sites in Chicago ( n  = 219; 42.0%) and Melbourne ( n  = 260; 49.8%); 8.2% of participants ( n  = 43) were recruited from the New York-based site. There was a significant difference in PEDE global scores across sites ( F (2,6) = 7.49, p  = .002, η 2  = 0.03), with guardians in New York reporting higher levels of ED pathology than those in Chicago or Melbourne ( p  = .002). EDE global scores did not significantly differ across sites ( p  = .725).

Eating Disorder Examination (EDE) Version 16.0

The EDE [ 22 ] is a semi-structured clinical interview that was originally developed for use with adults but is also used, and has been found psychometrically acceptable, as a diagnostic and predictive tool with younger populations [ 33 , 34 ]. The EDE is comprised of 33 items and uses a 7-point scale to measure the frequency (0 = “absence of the feature”; 6 = “feature present every day”) and severity (0 = “absence of the feature;” 6 = “feature present to an extreme degree”) of ED attitudes and behaviors. Most of the questions capture data from the past 28 days only, with exception of the ten diagnostic items that extend to the previous three months to reflect the time frame evaluated to make the DSM ED diagnoses. The EDE includes four subscales: Restraint (5 items), Eating Concern (5 items), Shape Concern (8 items), and Weight Concern (5 items). The subscales are averaged to give a rating of global severity. Although these subscales have not been supported in a prior factor analysis, they remain widely used in both research and clinical practice [ 18 ].

Parent Eating Disorder Examination (PEDE)

The PEDE version 1.4 [ 24 ] includes items that directly mirror the content and 7-point scoring scheme of the EDE. While the term “parent” is used, this measure is appropriate to use with any adult who is in the primary caretaking role. In the parent version, endorsement or denial (depending on the item) of a stem question triggers additional queries about behavioral observations and indicators of intent that are not present in the patient-directed EDE. Two additional items were added to the PEDE to assess for refusal to maintain a normal body weight and denial of the seriousness of low body weight , diagnostic features of AN that are not explicitly asked in the EDE. The item reaction to prescribed weighing from the EDE Weight Concern subscale was excluded because the item proved confusing when piloted. In total, the PEDE has 41 scored items. A symptom is rated as present if the parent has directly observed the phenomenon; heard the child report it; or heard reports from a reliable third party such as other family members, friends, or school personnel.

The PEDE requires that parents use their best judgment, including all available sources of information, in responding to the items. For example, in assessing fear of weight gain , there is not only an item evaluating verbal expression of this fear but also subsequent items assessing for indications that the young person is refusing attempts to increase their weight “by passive resistance (e.g., refusing to eat) and/or active resistance, such as yelling, throwing a tantrum, throwing food or dishes, running away, threatening to hurt themself if made to eat,” or other means. Other examples include specific questions that evaluate evidence of purging behaviors (e.g., “Have you noticed any vomit residue or odor in the bathroom or on your child’s clothes?; “Has your child rushed to the bathroom during a meal or immediately after eating?”).

The PEDE version 1.4 was developed from the EDE version 16.0 [ 22 ] and contains diagnostic items consistent with DSM-IV-TR [ 35 ] diagnostic criteria. Additionally, the PEDE items that assess for behavioral indicators allow for the evaluation of the revised DSM-5 criteria, including those criteria that are not explicitly assessed by the EDE version 16.0 or 17.0 (i.e., refusal to maintain a normal body weight and denial of the seriousness of low body weight ). The PEDE version 2.0 has since been revised aligning the measure with DSM-5 diagnostic criteria and incorporating gender-neutral language, and is publicly available [ 36 ].

Statistical Analyses

Cronbach’s alpha coefficients were calculated to evaluate the internal consistency of the EDE and PEDE subscales and global scores using IBM SPSS Statistics v.24.0, with values less than .5 considered to be unacceptable, greater than or equal to .5 poor, greater than or equal to .6 questionable, greater than or equal to .7 acceptable, greater than or equal to .8 good, and greater than or equal to .9 excellent [ 37 ].

Convergent validity was assessed through the correlation and level of agreement between the EDE and PEDE subscales. Specifically, bivariate Pearson correlations were calculated using IBM SPSS Statistics v.24.0; as suggested by Cohen [ 38 ], .10 was considered a weak or small correlation, .30 medium, and .50 or larger strong or large. Additionally, the level of agreement between the EDE and PEDE subscales and global scores was measured using a two-way random effects model (absolute agreement, average measures) intraclass correlation coefficient (ICC). In accordance with the 95% confidence interval of the ICC estimate, values less than .50 were considered evidence of poor agreement, between .50 and .75 moderate agreement, between .75 and .90 good agreement, and greater than .90 excellent agreement [ 39 ].

To assess the goodness of fit of the original four-factor structure of the traditional EDE subscales developed by Fairburn and colleagues [ 27 ], confirmatory factor analysis (CFA) was conducted with Mplus (version 8.0) [ 40 ]. Model fit was evaluated using incremental fit tests of a “good fit” [ 41 , 42 ], including the Tucker-Lewis index (TLI) ≥ .90 and comparative fit index (CFI) ≥ .90. Two absolute measures of fit were also used: the standard root mean square residual (SRMR) ≤ .08 and root mean square error of approximation (RMSEA) ≤ .10 (< .05 preferred). The same procedure was repeated with the PEDE. Given the results of the CFA, an exploratory factor analysis (EFA) was conducted using IBM SPSS Statistics v.24.0 to determine if an alternate model was a better fit for the PEDE.

Planned analyses for diagnostic agreement between the PEDE and EDE included chi-squared tests and Cohen’s kappa to compare each measure’s diagnostic items.

Internal Consistency

The coefficient alpha values for the four established subscales and global score of the EDE in the present sample ranged from acceptable to excellent: .86 for the Restraint scale, .75 for Eating Concern, .93 for Shape Concern, .83 for Weight Concern, and .93 for the global score. While the PEDE reliability coefficients for the Shape Concern and Weight Concern subscales (.85 and .74, respectively) and the global score (.80) fell in the acceptable to good ranges, alpha coefficients were poor (.59) for the Restraint subscale and unacceptable (.44) for the Eating Concern subscale.

Construct validity

Table  1 shows the results of the Pearson correlations. There were significant medium-sized positive correlations between the corresponding subscales and global scores (all p values < .001) ranging from .36 to .49. In each case, the correlation with the corresponding scale of the other instrument was higher than that with any other scale. Estimates of inter-rater agreement between the EDE and PEDE subscale and global scores are shown in Table  2 . There was moderate agreement between the PEDE and EDE global scores and the Restraint, Shape Concern, and Weight Concern subscale scores, and poor agreement between the Eating Concern subscales.

The CFA for the EDE four-factor model, based on established subscales, approached an acceptable fit after removing the preoccupation with shape or weight item from the Weight Concern factor because of a negative loading (see Table  3 for standardized factor loadings): CFI = .90, TLI = .88, RMSEA = .09, and SRMR = .05. The CFA of the four-factor model for the PEDE provided a poor fit to the data: CFI = .70, TLI = .66, RMSEA = .11 ( SE  = .10, .11); and SRMR = .10.

For the EFA, the scree plot, parallel analysis, and Velicer’s minimum average partial (MAP) tests were conducted, with the latter two based on SPSS macros developed by O’connor [ 43 ]. All three tests supported retaining a three-factor model for the PEDE. Principal axis factoring (PAF) and promax rotation (power = 4) were used to extract the three factors. Loadings above .30 were used as evidence of a meaningful relationship between an item and a factor [ 44 ]. These three factors accounted for 47.7% of the total variance of the items; see Table  3 . One item, avoidance of eating , was not associated with any scale due to insufficient loading. Looking at the items within each factor, they could be labeled as affective preoccupation with shape , weight , and eating (10 items, α  = 0.87, 30.6% of total variance), importance of shape , weight , and restriction (7 items, α  = 0.75, 9.1% of total variance), and discomfort with eating and body display (4 items, α  = 0.58, 8.0% of total variance).

Diagnostic Agreement

We initially planned to assess diagnostic agreement between the PEDE and EDE using chi-squared tests and Cohen’s kappa to compare each measure’s diagnostic items. However, of those participants who were not missing any EDE diagnostic items ( n  = 361), only 237 had no missing PEDE diagnostic items. A t -test comparison of those with and without missing PEDE diagnostic items found that participants without missing PEDE data had significantly higher PEDE global scores ( p  = .002) and significantly lower BMIs ( p  = .013) than participants who were missing PEDE diagnostic items. As the patients who could be included in this analysis appeared to have a more severe ED presentation than the remainder of the sample, results from a PEDE-EDE diagnostic comparison would be difficult to interpret. This confound precluded our conducting the planned analyses to assess diagnostic agreement.

To our knowledge, the PEDE is the first semi-structured interview formally developed with the aim to improve ED assessment in youth through the addition of caregiver perspectives, helping to reduce Type II error rate and under-identification of symptoms in youth with ED [ 45 ]. This study investigated the psychometric properties of the PEDE in a relatively large, international, multisite sample of families seeking treatment for AN and SAN. As predicted, the internal consistency of the PEDE was within the range of what has been published for the EDE (.44 to .85) [ 26 ], though lower than the EDE’s reliability in this sample. Regarding convergent validity, effect sizes were larger than the expected small effect size based on the meta-analytic evidence for parent-child correlation for both internalizing (.26) and externalizing (.32) disorders [ 6 ]. However, the lack of strong concordance between the EDE and PEDE subscales indicates that the information captured by the PEDE is not redundant with the EDE. This finding suggests that information from parent informants complements diagnostic and clinical information over and above that obtained by youth self-report. Specifically, the behavioral indicators and examples provided by the PEDE appear to elicit diagnostically relevant information from parents that might otherwise remain unreported. In clinical practice, such questioning can also serve to educate parents that these behaviors and beliefs are part of the ED and thereby improve their capacity to clinically monitor and intervene to support their child’s recovery.

The EDE is used with four subscales, yet none of the three studies that have examined the factor structure has replicated the four-factor model [ 28 , 29 , 30 ]. In this sample, the original factor structure approached an acceptable fit with the youth self-report data, but only after removing the preoccupation with weight and shape item from the Weight Concern subscale. Given the inconsistency of factor analysis results across studies of the EDE [ 26 ], it was not surprising that another underlying structure of three subscales seemed to provide the best fit for the PEDE. Although the PEDE has an empirically derived, three-factor structure, the original four-subscale model of the PEDE was found to measure constructs similar to those measured by the corresponding EDE scales, based on significant positive associations between corresponding subscales on the youth and parent interview. As such, it is reasonable to utilize the PEDE based on the four-subscale model to maintain consistency for research purposes. When using it for exclusively clinical purposes, the three-factor model may provide more meaningful constructs. Prior research also suggests that the EDE global score is a more useful measure of ED pathology than its subscales [ 46 ]; in light of the current study’s internal consistency and construct validity results, the PEDE global score may also provide a more valid interpretation of its findings.

Limitations of this study include a predominantly female, treatment-seeking sample with specific criteria applied, including the use of population norms (i.e., %mBMI) to determine weight eligibility instead of individualized weight status based on historical growth patterns. These limitations constrained an understanding of how the PEDE interview may perform in more diverse, transdiagnostic (including atypical AN), and non-treatment-seeking samples. Resource limitations prevented duplicate assessments by multiple interviewers to establish inter-rater reliability or compare ratings from caregivers of different genders, but this is worthy of future study, as is test-retest reliability. Furthermore, missing data precluded completion of the diagnostic agreement analyses originally proposed by this study. Although the intent of developing the PEDE was to aid in the identification of AN/SAN, future research should aim to evaluate the measure’s ability to distinguish between transdiagnostic ED cases and non-cases (i.e., criterion validity) as compared to the EDE using samples of adolescents with ED, subsyndromal ED, and no ED, and sensitivity and specificity analyses such as receiver-operator characteristic (ROC) curves. Additional work is also needed to more thoroughly assess the PEDE’s validity and predictive power, including its relationships with other measures of ED and non-ED symptoms, other parent-report measures, clinician-assigned diagnosis, and clinical outcomes. Finally, by applying more sophisticated multi-informant statistical methods [ 21 ], future research could establish how clinicians and researchers systematically integrate potentially conflicting perspectives from youth and their caregivers,.

In summary, the use of parental informants is consistent with the approach to assessment of other areas of psychopathology in youth in which collateral informants frequently aid in the evaluation and diagnosis process [ 6 , 20 , 47 ]. The introduction of the PEDE allows for a standardized way to incorporate caregiver reports to aid in the assessment of AN, potentially reducing diagnostic ambiguity and compensating for the denial and minimization inherent in the self-report of symptoms within the group. Our future research will focus on differences in diagnostic rates when parents are enlisted as informants in interview-based AN case identification efforts. Enhanced assessment approaches can theoretically make identification of clinically significant presentations more efficient and accurate, and lead to earlier intervention and improved outcomes.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The PEDE 2.0 is available at https://ccebt.com/wp-content/uploads/2024/06/PEDE-2.0_gender-neutral.pdf .

Abbreviations

Body mass index

Confirmatory factor analysis

Comparative fit index

Diagnostic and Statistical Manual of Mental Disorders

Expected body weight

Eating disorders

Eating Disorder Examination

Exploratory factor analysis

Intraclass correlation coefficient

Minimum average partial

Median body mass index

Other specified feeding and eating disorder

Principal axis factoring

Parent Eating Disorder Examination

Root mean square error of approximation

Subsyndromal anorexia nervosa

Standard root mean square residual

Tucker-Lewis index

United States

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Acknowledgements

We gratefully acknowledge Christopher Fairburn’s ongoing mentorship and support in forwarding research-based adaptations of the Eating Disorder Examination, including the parent version discussed in this paper.

This research was supported by a grant from the National Institute of Mental Health K23 MH074506 (PI: Loeb; ClinicalTrials.gov NCT00418977, Early Identification and Treatment of Anorexia Nervosa).

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Lisa Hail, Catherine R. Drury & Daniel Le Grange

School of Psychology and Counseling, Fairleigh Dickinson University, Teaneck, NJ, USA

Catherine R. Drury, Robert E. McGrath & Katharine L. Loeb

Department of Psychiatry and the Behavioral Sciences, University of Southern California, Los Angeles, CA, USA

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Department of Paediatrics, The University of Melbourne, Melbourne, Australia

Elizabeth K. Hughes

Murdoch Children’s Research Institute, Melbourne, Australia

Elizabeth K. Hughes & Susan M. Sawyer

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Contributions

KLL developed the PEDE and conceptualized the current study with LH, REM, and SBM. EKH, SMS, DLG, and KLL provided data for the study, which was curated and analyzed by LH under the supervision of REM and KLL. CRD contributed additional analyses with guidance from KLL and LH. LH wrote the initial draft of the manuscript, to which CRD, KLL, and REM also contributed. All authors read and edited subsequent iterations of the manuscript and approved the final version.

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Correspondence to Catherine R. Drury .

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This study was determined to qualify as exempt by Fairleigh Dickinson University’s Institutional Review Board (IRB). Any parent studies from which data were derived for secondary analyses included informed consent/assent and were approved by site-specific IRBs.

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Competing interests

KLL receives royalties from Cambridge University Press and Routledge, and is a faculty member of and consultant for the Training Institute for Child and Adolescent Eating Disorders. DLG receives royalties from Guilford Press and Routledge, and is co-director of the Training Institute for Child and Adolescent Eating Disorders, LLC. SBM receives royalties from Oxford University Press, Routledge, and Springer.

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Hail, L., Drury, C.R., McGrath, R.E. et al. Parent version of the Eating Disorder Examination: Reliability and validity in a treatment-seeking sample. J Eat Disord 12 , 101 (2024). https://doi.org/10.1186/s40337-024-01062-4

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Received : 29 March 2024

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DOI : https://doi.org/10.1186/s40337-024-01062-4

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Journal of Eating Disorders

ISSN: 2050-2974

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• About YRBSS
• YRBSS Results
• Data and Documentation
• YRBSS Methods
• YRBSS Questionnaires
• YRBSS Reports and Publications
• Data Summary & Trends
• Results Toolkit
• Communication Resources
• YRBSS Data Request Form

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• YRBS Explorer
• Adolescent and School Health

YRBSS Frequently Asked Questions

• The YRBSS FAQ page provides information to help you learn more about the surveillance system and its use.

Top 5 YRBSS FAQs

Are yrbss results available by zip code, census tract, school, local school district, or county are yrbss results available for my town, city, or local school district.

YRBSS data are not available by zip code, census tract, or school. Sample size limitations and confidentiality requirements do not support analyses at these levels.

YRBSS data are available for a small number of specifically funded local school districts or counties. CDC funds certain local school districts to conduct the YRBS. Some of those local school districts are county-based. See Participation Maps & History for more information about county-based local school districts with YRBSS data. Data are only available for local school districts or counties on the list. No other local YRBSS data are available.

County-level identifiers are not available in the National YRBS data set or in most state data sets.

When are updated YRBSS results released?

Most YRBSs are conducted during the spring of odd-numbered years and results are released in the summer of the following year. For example, results from the 2019 national, state, and local YRBSs were released in an MMWR Surveillance Summary during the summer of 2020. The specific release date for a given cycle is posted on the YRBSS home page as soon as it has been determined.

What is the suggested citation for the YRBSS questionnaire or YRBSS data in a publication?

YRBSS questionnaires should be cited as follows:

Centers for Disease Control and Prevention. [survey year] Youth Risk Behavior Survey Questionnaire. Accessed [date]. www.cdc.gov/yrbs.

Do I need permission to use YRBSS questionnaires for my study, area, local school district, or school? Is there a cost?

The YRBSS questionnaires are in the public domain, and no permission is required to use them. You may download and use the questionnaires as is or with changes at no charge.

Are the YRBSS questionnaires available in languages other than English?

Yes. Beginning with the 2021 cycle, the National YRBS questionnaire is available in Spanish. Translation of state and local YRBSS questionnaires is left to the discretion of state and local agencies.

YRBS questionnaires are designed to be administered in a school setting. It is important to consider the language used in regular classrooms and common second languages, if any, spoken by the student population. Check with school officials before deciding whether translation is needed.

YRBSS questionnaires in English and Spanish are in the public domain. Questionnaires may be translated to any language. No specific permission is required.

Uses of YRBSS results

How are the yrbss results used.

State, territorial, and tribal governments, as well as local agencies and nongovernmental organizations use YRBSS data to set and track progress toward meeting school health and health promotion program goals.

They also use YRBSS data to support modification of school health curricula or other programs, support new legislation and policies that promote health, and seek funding and other support for new initiatives.

CDC and other federal agencies routinely use YRBSS data to assess trends in priority health behaviors among high school students, monitor progress toward achieving national health objectives, and evaluate the contribution of broad prevention efforts in schools and other settings.

These activities support efforts to reduce health risk behaviors among young people.

Where can I find more information on using YRBSS data?

Uses of Youth Risk Behavior Survey and School Health Profiles Data: Applications for Improving Adolescent and School Health

Foti K, Balaji A, Shanklin S. J Sch Health . 2011;81(6):345–354.

Can student behavior changes over time be tracked using the YRBSS?

Yes. The YRBSS tracks aggregate changes in student behavior over time.

Does the YRBSS track specific students over time?

No. A new sample of schools and students is drawn for each survey cycle. Students who participated cannot be tracked because no identifying information is collected.

Is it appropriate to report prevalence estimates for any of the U.S. racial or ethnic subgroups (such as, American Indian or Alaska Native students) asked about on the National YRBS questionnaire?

Although prevalence estimates generated for students in each racial or ethnic subgroup are representative of these students nationally, caution should be used when analyzing and interpreting these data. Because of the small numbers of students in some racial or ethnic subgroups who participate in any single National YRBS, the estimates may lack precision. Precision can be improved by combining multiple years of National YRBS data.

Analyzing YRBSS data

What software should i use to analyze yrbss data.

See Software for Analysis of YRBS Data for a review of software packages suitable for analyzing YRBSS data and guidance on how to use them.

How are the national, state, territory, tribal government, and local YRBS data different? Are the national data the aggregate of the state and other YRBS data?

National, state, territorial, and tribal government data and local data come from separate scientific samples of schools and students. National YRBS data are not the aggregate of the state YRBS data sets. State, territorial, and tribal government data and local YRBS data are not subsets of the National YRBSS data set. Both national, state, territory, tribal government YRBSs and local YRBSs all follow the same survey methodology and use the same core questionnaire.

Is there a national middle school YRBS?

No. However, middle school results are available for some states, districts, territories, and tribes that have elected to conduct a middle school YRBS in their jurisdiction. Middle school YRBS results are available on Youth Online .

Can I calculate state-level estimates of a health behavior using the National YRBS data?

No. The National YRBS was not designed to produce representative estimates at the state level.

Is it possible to analyze associations between state-level characteristics and student-level behaviors using the National YRBS data?

This type of analysis has significant limitations and should be conducted with caution. A state-level characteristic, such as the presence of a state law, can be added to a regression model as an exogenous (independent) variable and will yield statistically correct estimates. However, it is important to fully consider the context of these estimates.

The National YRBS was not designed to produce representative estimates at the state level. The number of students chosen from each state varies considerably and is usually too small to generate precise or stable state-level estimates. In addition, fewer than 50 states are included in the national sample each cycle.

Researchers should fully investigate the implications and interpretations of this type of analysis and should understand the sampling design of the National YRBS and how that design might influence their results. See Methodology of the Youth Risk Behavior Surveillance System for more information about the National YRBS sampling design.

Can I calculate prevalence estimates by urban/rural status? Is an urban/rural identifier available for the National YRBS data sets?

No. The National YRBS was not designed to produce estimates by urban or rural status. In the National YRBS, primary sampling units (PSUs) are selected based on urban and non-urban definitions, but it does not necessarily follow that a non-urban area is rural. Urban status indicates only that the PSU was one of the largest 54 metropolitan statistical areas (MSAs). Non-urban indicates that the PSU was not one of the largest 54 MSAs. It could be rural but is not necessarily rural. See Methodology of the Youth Risk Behavior Surveillance System for more information about the National YRBS sampling design.

Conducting your own YRBS

Do i need permission to use yrbss questionnaires for my study, area, district, or school is there a cost.

The YRBSS questionnaires are in the public domain, and no permission is required to use them. You may download the questionnaires at no charge.

How do I conduct a YRBS in my area, district, or school?

See A Guide to Conducting Your Own Youth Risk Behavior Survey for information useful to communities and groups that plan to conduct their own YRBS.

If I conduct a YRBS, can CDC help me scan, process, or tabulate my data?

CDC provides data processing assistance only to states, territories, and local school districts that it funds directly to conduct a YRBSS. However, information on how the data are processed can be found on the Methods page and in the Methodology of the Youth Risk Behavior Surveillance System .

Is funding available for conducting a YRBS?

CDC has funding available for all 50 states, a small number of territories, and large urban school districts during each 5-year funding cycle.

Validity and reliability

Do students tell the truth on the yrbs questionnaire.

Research indicates that data of this nature may be gathered as credibly from adolescents as from adults. Internal reliability checks help identify the small percentage of students who falsify their answers. To obtain truthful answers, survey administrators must ensure that students perceive the survey as important and know that procedures have been developed to protect their privacy and allow for anonymous participation.

What kinds of validation or reliability studies have been done on the YRBS questionnaire?

The Methodology of the Youth Risk Behavior Surveillance System contains a description of most of the methodological studies conducted to date on the YRBSS questionnaires or YRBSS data collection procedures. In addition, the list of YRBSS MMWR publications and journal articles contains the actual journal articles describing the results of these studies.

These methodological studies include test-retest reliability studies on the 1991 and 1999 versions of the questionnaire; a study assessing the validity of self-reported height and weight; a study assessing the effect of changing the race or ethnicity question; a study examining how varying honesty appeals, question wording, and data-editing protocols affect prevalence estimates; and a study examining how varying the mode and setting of survey administration affects prevalence estimates.

Youth Risk Behavior Surveillance System (YRBSS)

YRBSS is the largest public health surveillance system in the U.S, monitoring multiple health-related behaviors among high school students.

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 ...

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.

Validity and reliability or trustworthiness are fundamental issues in scientific research whether qualitative, quantitative, or mixed research. It is a necessity for researchers to describe which ...

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research.2-4 In the broadest context these terms are applicable, with validity referring to the integrity and ...

Validity and reliability of research and its results are important elements to provide evidence of the quality of research in the organizational field. However, validity is better evidenced in quantitative studies than in ... validity in qualitative research. Examples are the works of Aubin-Auger et al. (2008), Ergene,

Revised on 10 October 2022. Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method, technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. It's important to consider reliability and validity when you are ...

As with validity, reliability is an attribute of a measurement instrument - for example, a survey, a weight scale or even a blood pressure monitor. But while validity is concerned with whether the instrument is measuring the "thing" it's supposed to be measuring, reliability is concerned with consistency and stability.

Kirk and Miller (1986) identify three types of reliability referred to in quantitative research, which relate to: (1) the degree to which a measurement, given repeatedly, remains the same (2) the stability of a measurement over time; and (3) the similarity of measurements within. a given time period (pp. 41-42).

Validity is the extent to which a study accurately measures what it intends to measure, while reliability refers to the consistency and stability of the results (Kimberlin & Winterstein, 2008 ...

The purpose of this article is to reestablish reliability and validity as appropriate to qualitative inquiry; to identify the problems created by post hoc assessments of qualitative research; to review general verification strategies in relation to qualitative research, and to discuss the implications of returning the responsibility for the ...

Cypress B. S. (2017). Rigor or reliability and validity in qualitative research: Perspectives, strategies, reconceptualization, and recommendations. Dimensions ... Contextualizing reliability and validity in qualitative research: Toward more rigorous and trustworthy qualitative social science in leisure research. Journal of Leisure Research, 5 ...

Reliability in qualitative research refers to the stability of responses to multiple coders of data sets. It can be enhanced by detailed field notes by using recording devices and by transcribing the digital files. However, validity in qualitative research might have different terms than in quantitative research. Lincoln and Guba (1985) used "trustworthiness" of ...

To widen the spectrum of conceptualization of reliability and revealing the congruence of. reliability and validity in qualitative research, Lincoln and Guba (1985) states that: "Since there. can ...

In Quantitative research, reliability refers to consistency of certain measurements, and validity - to whether these measurements "measure what they are supposed to measure". Things are slightly different, however, in Qualitative research. Reliability in qualitative studies is mostly a matter of "being thorough, careful and honest in ...

However, the increased importance given to qualitative information in the evidence-based paradigm in health care and social policy requires a more precise conceptualization of validity criteria that goes beyond just academic reflection. After all, one can argue that policy verdicts that are based on qualitative information must be legitimized by valid research, just as quantitative effect ...

What is the difference between reliability and validity in a study? In the domain of research, whether qualitative or quantitative, two concepts often arise when discussing the quality and rigor of a study: reliability and validity.These two terms, while interconnected, have distinct meanings that hold significant weight in the world of research.

ting to understand the notion of validity in qualitative inquiry.There is a general consensus, however, that qualitat. ve inquirers need to demonstrate that their studies are credible. To this end, severa. authors iden-tify common procedures for establishing validity inJohn W. Creswell is professor of educational psychology at the University of ...

Reliability and validity are equally important to consider in qualitative research. Ways to enhance validity in qualitative research include: Building reliability can include one or more of the following: The most well-known measure of qualitative reliability in education research is inter-rater reliability and consensus coding.

least a qualitative research study, and the need to ensure practitioners can have confidence in the results, conclusions, and recommendations of such work. Validity and reliability are defined from a qualitative research perspective and various techniques described which can be utilised to help ensure investigative rigour. As one of

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 ...

the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evalu-ate qualitative research.2-4 In the broadest context these terms are applicable, with validity referring to the integ-

Aims to address a gap in the literature about quality criteria for validity and reliability in qualitative research within the realism scientific paradigm. Six comprehensive and explicit criteria for judging realism research are developed, drawing on the three elements of a scientific paradigm of ontology, epistemology and methodology.

100 The Qualitative Report 2019. Currently, quantitative researchers show a certain consensus about the use, importance. and especially the operationalization of the validity itself (Cronbach ...

Qualitative data in research. Qualitative data research methods allow analysts to use contextual information to create theories and models. These open- and closed-ended questions can be helpful to understand the reasoning behind motivations, frustrations, and actions—in any type of case. Some examples of qualitative data collection in research:

In order to assess the reliability and validity of the PEDE in a relatively homogenous sample, this study focused specifically on youth presenting to these sites with probable AN or SAN , a site-specific research category that would fall under other specified feeding and eating disorder (OSFED) in DSM-5-TR nomenclature.

Example: Research using only qualitative methods to explore patients' subjective experiences in a hospital setting. Methodological delimitations specify the research methods (qualitative) that will be employed to gather and analyze data, reflecting the study's focus on subjective experiences. 3. Sample size restrictions.

Subsequently, the research methodology (sample, data collection methods, data analysis methods) will be elucidated, along with clarification of the research findings and discussion of their implications. ... Moreover, the scale's reliability and validity meet the standards, with Cronbach's α values for various dimensions of motivation and ...

These methodological studies include test-retest reliability studies on the 1991 and 1999 versions of the questionnaire; a study assessing the validity of self-reported height and weight; a study assessing the effect of changing the race or ethnicity question; a study examining how varying honesty appeals, question wording, and data-editing ...

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