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  • Volume 18, Issue 3
  • Validity and reliability in quantitative studies
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  • Roberta Heale 1 ,
  • Alison Twycross 2
  • 1 School of Nursing, Laurentian University , Sudbury, Ontario , Canada
  • 2 Faculty of Health and Social Care , London South Bank University , London , UK
  • Correspondence to : Dr Roberta Heale, School of Nursing, Laurentian University, Ramsey Lake Road, Sudbury, Ontario, Canada P3E2C6; rheale{at}laurentian.ca


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Evidence-based practice includes, in part, implementation of the findings of well-conducted quality research studies. So being able to critique quantitative research is an important skill for nurses. Consideration must be given not only to the results of the study but also the rigour of the research. Rigour refers to the extent to which the researchers worked to enhance the quality of the studies. In quantitative research, this is achieved through measurement of the validity and reliability. 1

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Types of validity

The first category is content validity . This category looks at whether the instrument adequately covers all the content that it should with respect to the variable. In other words, does the instrument cover the entire domain related to the variable, or construct it was designed to measure? In an undergraduate nursing course with instruction about public health, an examination with content validity would cover all the content in the course with greater emphasis on the topics that had received greater coverage or more depth. A subset of content validity is face validity , where experts are asked their opinion about whether an instrument measures the concept intended.

Construct validity refers to whether you can draw inferences about test scores related to the concept being studied. For example, if a person has a high score on a survey that measures anxiety, does this person truly have a high degree of anxiety? In another example, a test of knowledge of medications that requires dosage calculations may instead be testing maths knowledge.

There are three types of evidence that can be used to demonstrate a research instrument has construct validity:

Homogeneity—meaning that the instrument measures one construct.

Convergence—this occurs when the instrument measures concepts similar to that of other instruments. Although if there are no similar instruments available this will not be possible to do.

Theory evidence—this is evident when behaviour is similar to theoretical propositions of the construct measured in the instrument. For example, when an instrument measures anxiety, one would expect to see that participants who score high on the instrument for anxiety also demonstrate symptoms of anxiety in their day-to-day lives. 2

The final measure of validity is criterion validity . A criterion is any other instrument that measures the same variable. Correlations can be conducted to determine the extent to which the different instruments measure the same variable. Criterion validity is measured in three ways:

Convergent validity—shows that an instrument is highly correlated with instruments measuring similar variables.

Divergent validity—shows that an instrument is poorly correlated to instruments that measure different variables. In this case, for example, there should be a low correlation between an instrument that measures motivation and one that measures self-efficacy.

Predictive validity—means that the instrument should have high correlations with future criterions. 2 For example, a score of high self-efficacy related to performing a task should predict the likelihood a participant completing the task.


Reliability relates to the consistency of a measure. A participant completing an instrument meant to measure motivation should have approximately the same responses each time the test is completed. Although it is not possible to give an exact calculation of reliability, an estimate of reliability can be achieved through different measures. The three attributes of reliability are outlined in table 2 . How each attribute is tested for is described below.

Attributes of reliability

Homogeneity (internal consistency) is assessed using item-to-total correlation, split-half reliability, Kuder-Richardson coefficient and Cronbach's α. In split-half reliability, the results of a test, or instrument, are divided in half. Correlations are calculated comparing both halves. Strong correlations indicate high reliability, while weak correlations indicate the instrument may not be reliable. The Kuder-Richardson test is a more complicated version of the split-half test. In this process the average of all possible split half combinations is determined and a correlation between 0–1 is generated. This test is more accurate than the split-half test, but can only be completed on questions with two answers (eg, yes or no, 0 or 1). 3

Cronbach's α is the most commonly used test to determine the internal consistency of an instrument. In this test, the average of all correlations in every combination of split-halves is determined. Instruments with questions that have more than two responses can be used in this test. The Cronbach's α result is a number between 0 and 1. An acceptable reliability score is one that is 0.7 and higher. 1 , 3

Stability is tested using test–retest and parallel or alternate-form reliability testing. Test–retest reliability is assessed when an instrument is given to the same participants more than once under similar circumstances. A statistical comparison is made between participant's test scores for each of the times they have completed it. This provides an indication of the reliability of the instrument. Parallel-form reliability (or alternate-form reliability) is similar to test–retest reliability except that a different form of the original instrument is given to participants in subsequent tests. The domain, or concepts being tested are the same in both versions of the instrument but the wording of items is different. 2 For an instrument to demonstrate stability there should be a high correlation between the scores each time a participant completes the test. Generally speaking, a correlation coefficient of less than 0.3 signifies a weak correlation, 0.3–0.5 is moderate and greater than 0.5 is strong. 4

Equivalence is assessed through inter-rater reliability. This test includes a process for qualitatively determining the level of agreement between two or more observers. A good example of the process used in assessing inter-rater reliability is the scores of judges for a skating competition. The level of consistency across all judges in the scores given to skating participants is the measure of inter-rater reliability. An example in research is when researchers are asked to give a score for the relevancy of each item on an instrument. Consistency in their scores relates to the level of inter-rater reliability of the instrument.

Determining how rigorously the issues of reliability and validity have been addressed in a study is an essential component in the critique of research as well as influencing the decision about whether to implement of the study findings into nursing practice. In quantitative studies, rigour is determined through an evaluation of the validity and reliability of the tools or instruments utilised in the study. A good quality research study will provide evidence of how all these factors have been addressed. This will help you to assess the validity and reliability of the research and help you decide whether or not you should apply the findings in your area of clinical practice.

  • Lobiondo-Wood G ,
  • Shuttleworth M
  • ↵ Laerd Statistics . Determining the correlation coefficient . 2013 . https://statistics.laerd.com/premium/pc/pearson-correlation-in-spss-8.php

Twitter Follow Roberta Heale at @robertaheale and Alison Twycross at @alitwy

Competing interests None declared.

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17.4.1   Validity of instruments

Validity has to do with whether the instrument is measuring what it is intended to measure. Empirical evidence that PROs measure the domains of interest allows strong inferences regarding validity. To provide such evidence, investigators have borrowed validation strategies from psychologists who for many years have struggled with determining whether questionnaires assessing intelligence and attitudes really measure what is intended.

Validation strategies include:

content-related: evidence that the items and domains of an instrument are appropriate and comprehensive relative to its intended measurement concept(s), population and use;

construct-related: evidence that relationships among items, domains, and concepts conform to a priori hypotheses concerning logical relationships that should exist with other measures or characteristics of patients and patient groups; and

criterion-related (for a PRO instrument used as diagnostic tool): the extent to which the scores of a PRO instrument are related to a criterion measure.

Establishing validity involves examining the logical relationships that should exist between assessment measures. For example, we would expect that patients with lower treadmill exercise capacity generally will have more shortness of breath in daily life than those with higher exercise capacity, and we would expect to see substantial correlations between a new measure of emotional function and existing emotional function questionnaires.

When we are interested in evaluating change over time, we examine correlations of change scores. For example, patients who deteriorate in their treadmill exercise capacity should, in general, show increases in dyspnoea, whereas those whose exercise capacity improves should experience less dyspnoea. Similarly, a new emotional function measure should show improvement in patients who improve on existing measures of emotional function. The technical term for this process is testing an instrument’s construct validity.

Review authors should look for, and evaluate the evidence of, the validity of PROs used in their included studies. Unfortunately, reports of randomized trials and other studies using PROs seldom review evidence of the validity of the instruments they use, but review authors can gain some reassurance from statements (backed by citations) that the questionnaires have been validated previously.

A final concern about validity arises if the measurement instrument is used with a different population, or in a culturally and linguistically different environment, than the one in which it was developed (typically, use of a non-English version of an English-language questionnaire). Ideally, one would have evidence of validity in the population enrolled in the randomized trial. Ideally PRO measures should be re-validated in each study using whatever data are available for the validation, for instance, other endpoints measured. Authors should note, in evaluating evidence of validity, when the population assessed in the trial is different from that used in validation studies.

validity of the research instrument pdf

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  • How to Determine the Validity and Reliability of an Instrument

How to Determine the Validity and Reliability of an Instrument By: Yue Li

Validity and reliability are two important factors to consider when developing and testing any instrument (e.g., content assessment test, questionnaire) for use in a study. Attention to these considerations helps to insure the quality of your measurement and of the data collected for your study.

Understanding and Testing Validity

Validity refers to the degree to which an instrument accurately measures what it intends to measure. Three common types of validity for researchers and evaluators to consider are content, construct, and criterion validities.

  • Content validity indicates the extent to which items adequately measure or represent the content of the property or trait that the researcher wishes to measure. Subject matter expert review is often a good first step in instrument development to assess content validity, in relation to the area or field you are studying.
  • Construct validity indicates the extent to which a measurement method accurately represents a construct (e.g., a latent variable or phenomena that can’t be measured directly, such as a person’s attitude or belief) and produces an observation, distinct from that which is produced by a measure of another construct. Common methods to assess construct validity include, but are not limited to, factor analysis, correlation tests, and item response theory models (including Rasch model).
  • Criterion-related validity indicates the extent to which the instrument’s scores correlate with an external criterion (i.e., usually another measurement from a different instrument) either at present ( concurrent validity ) or in the future ( predictive validity ). A common measurement of this type of validity is the correlation coefficient between two measures.

Often times, when developing, modifying, and interpreting the validity of a given instrument, rather than view or test each type of validity individually, researchers and evaluators test for evidence of several different forms of validity, collectively (e.g., see Samuel Messick’s work regarding validity).

Understanding and Testing Reliability

Reliability refers to the degree to which an instrument yields consistent results. Common measures of reliability include internal consistency, test-retest, and inter-rater reliabilities.

  • Internal consistency reliability looks at the consistency of the score of individual items on an instrument, with the scores of a set of items, or subscale, which typically consists of several items to measure a single construct. Cronbach’s alpha is one of the most common methods for checking internal consistency reliability. Group variability, score reliability, number of items, sample sizes, and difficulty level of the instrument also can impact the Cronbach’s alpha value.
  • Test-retest measures the correlation between scores from one administration of an instrument to another, usually within an interval of 2 to 3 weeks. Unlike pre-post tests, no treatment occurs between the first and second administrations of the instrument, in order to test-retest reliability. A similar type of reliability called alternate forms , involves using slightly different forms or versions of an instrument to see if different versions yield consistent results.
  • Inter-rater reliability checks the degree of agreement among raters (i.e., those completing items on an instrument). Common situations where more than one rater is involved may occur when more than one person conducts classroom observations, uses an observation protocol or scores an open-ended test, using a rubric or other standard protocol. Kappa statistics, correlation coefficients, and intra-class correlation (ICC) coefficient are some of the commonly reported measures of inter-rater reliability.

Developing a valid and reliable instrument usually requires multiple iterations of piloting and testing which can be resource intensive. Therefore, when available, I suggest using already established valid and reliable instruments, such as those published in peer-reviewed journal articles. However, even when using these instruments, you should re-check validity and reliability, using the methods of your study and your own participants’ data before running additional statistical analyses. This process will confirm that the instrument performs, as intended, in your study with the population you are studying, even though they are identical to the purpose and population for which the instrument was initially developed. Below are a few additional, useful readings to further inform your understanding of validity and reliability.

Resources for Understanding and Testing Reliability

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1985).  Standards for educational and psychological testing . Washington, DC: Authors.
  • Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences . Mahwah, NJ: Lawrence Erlbaum.
  • Cronbach, L. (1990).  Essentials of psychological testing .   New York, NY: Harper & Row.
  • Carmines, E., & Zeller, R. (1979).  Reliability and Validity Assessment . Beverly Hills, CA: Sage Publications.
  • Messick, S. (1987). Validity . ETS Research Report Series, 1987: i–208. doi: 10.1002/j.2330-8516.1987.tb00244.x
  • Liu, X. (2010). Using and developing measurement instruments in science education: A Rasch modeling approach . Charlotte, NC: Information Age.
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Reliability vs. Validity in Research | Difference, Types and Examples

Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.

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.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 bias and seriously affect your work.

Table of contents

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

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

What is reliability?

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

What is validity?

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

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

If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.

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

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

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validity of the research instrument pdf

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

Types of reliability

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

Types of validity

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

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

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

Ensuring validity

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

  • Choose appropriate methods of measurement

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

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

  • Use appropriate sampling methods to select your subjects

To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession).  Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .

Ensuring reliability

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

  • Apply your methods consistently

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

For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .

  • Standardize the conditions of your research

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

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.

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

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

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Measuring the Validity and Reliability of Research Instruments

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    Validity. Validity is defined as the extent to which a concept is accurately measured in a quantitative study. For example, a survey designed to explore depression but which actually measures anxiety would not be considered valid. The second measure of quality in a quantitative study is reliability, or the accuracy of an instrument.In other words, the extent to which a research instrument ...

  11. PDF Two Criteria for Good Measurements in Research: Validity and ...

    3. Research Objectives The aim of this study is to discuss the aspects of reliability and validity in research. The objectives of this research are: • To indicate the errors the researchers often face. • To show the reliability in a research. • To highlight validity in a research. 4. Methodology


    3.1 INTRODUCTION. In Chapter 2, the study's aims of exploring how objects can influence the level of construct validity of a Picture Vocabulary Test were discussed, and a review conducted of the literature on the various factors that play a role as to how the validity level can be influenced. In this chapter validity and reliability are ...

  13. PDF Validity of Measurement Instruments Used in Research

    Validity and reliability of measurement instruments used in research. Am J Health Syst Pharm. 2008 Dec 1;65(23):2276-84. doi: 10.2146/ajhp070364. PMID: 19020196. Taherdoost, Hamed. (2016). Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research. International Journal of Academic ...

  14. Measuring the Validity and Reliability of Research Instruments

    2. Research Objectives The objectives of the study are as follows: i) To analyse the reliability of the ILS, SPCD, and CMAT instruments; ii) To analyse the value of separation index in the ILS, SPCD, and CMAT instruments; iii) To distinguish the sufficiency of PTMEA and item fit in defining the terms in research instruments; and iv) To analyse ...


    The concept of instrument validity or test can be divided into three types, namely: (a) ... Research Methods 2.1. Rational Test Validity Testing Learning outcomes that have been analyzed rationally have the power to measure accuracy, called learning outcomes tests that have logical validity. Other terms for logical validity are: rational validity,

  16. 17.4.1 Validity of instruments

    17.4.1 Validity of instruments. Validity has to do with whether the instrument is measuring what it is intended to measure. Empirical evidence that PROs measure the domains of interest allows strong inferences regarding validity. To provide such evidence, investigators have borrowed validation strategies from psychologists who for many years ...

  17. (PDF) Two Criteria for Good Measurements in Research: Validity and

    2011]: i) content validity, ii) face validity, iii) construct validity, and iv) criterion- related validity (figure no. 2). Content validity: It is the extent to which the questions on the instrument

  18. PDF A Reliability and Validity of an Instrument to Evaluate the School ...

    An instrument is valid when it is measuring what is supposed to measure [20]. Or, in other words, when an instrument accurately measures any prescribed variable it is considered a valid instrument for that particular variable. There are four types of validity; face validity, criterion validity, content validity or construct validity [20],[21].

  19. How to Determine the Validity and Reliability of an Instrument

    Validity refers to the degree to which an instrument accurately measures what it intends to measure. Three common types of validity for researchers and evaluators to consider are content, construct, and criterion validities. Content validity indicates the extent to which items adequately measure or represent the content of the property or trait ...

  20. The 4 Types of Validity in Research

    Face validity. Face validity considers how suitable the content of a test seems to be on the surface. It's similar to content validity, but face validity is a more informal and subjective assessment. Example. You create a survey to measure the regularity of people's dietary habits. You review the survey items, which ask questions about ...

  21. 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 ...

  22. Measuring the Validity and Reliability of Research Instruments

    Three sets of research instruments were... | Find, read and cite all the research you need on ResearchGate. Article PDF Available. ... Measuring Validity.pdf. 56ad645408ae28588c5fc418.pdf.

  23. (PDF) Measuring the Validity and Reliability of Research Instruments

    This paper discussed how the applying of Rasch Model in validity and reliability of research instruments. Three sets of research instruments were developed in this study. The Felder-Solomon Index of Learning Styles (ILS) is essential to find out the learning style abilities of learners.