Open Access Articles- Top Results for Construct validity

Construct validity

Construct validity is “the degree to which a test measures what it claims, or purports, to be measuring.”[1][2][3] In the classical model of validity, construct validity is one of three main types of validity evidence, alongside content validity and criterion validity.[4][5] Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence.[6][7]

Construct validity is the appropriateness of inferences made on the basis of observations or measurements (often test scores), specifically whether a test measures the intended construct. Constructs are abstractions that are deliberately created by researchers in order to conceptualize the latent variable, which is the cause of scores on a given measure (although it is not directly observable). Construct validity examines the question: Does the measure behave like the theory says a measure of that construct should behave?

Construct validity is essential to the perceived overall validity of the test. Construct validity is particularly important in the social sciences, psychology, psychometrics and language studies.

Psychologists such as Samuel Messick (1989) have pushed for a unified view of construct validity “…as an integrated evaluative judgment of the degree to which empirical evidence and theoretical rationales support the adequacy and appropriateness of inferences and actions based on test scores…”[8] Key to construct validity are the theoretical ideas behind the trait under consideration, i.e. the concepts that organize how aspects of personality, intelligence, etc. are viewed.[9] Paul Meehl states that "The best construct is the one around which we can build the greatest number of inferences, in the most direct fashion." [2]


Throughout the 1940s scientists had been trying to come up with ways to validate experiments prior to publishing them. The result of this was a myriad of different validities (intrinsic validity, face validity, logical validity, empirical validity, etc.). This made it difficult to tell which ones were actually the same and which ones were not useful at all. Until the middle of the 1950s there were very few universally accepted methods to validate psychological experiments. The main reason for this was because no one had figured out exactly which qualities of the experiments should be looked at before publishing. Between 1950 and 1954 the APA Committee on Psychological Tests met and discussed the issues surrounding the validation of psychological experiments.[2]

Around this time the term construct validity was first coined by Paul Meehl and Lee Cronbach in their seminal article Construct Validity In Psychological Tests. They noted the idea of construct validly was not new at that point. Rather, it was a combinations of many different types of validity dealing with theoretical concepts. They proposed the following three steps to evaluate construct validity:

  1. articulating a set of theoretical concepts and their interrelations
  2. developing ways to measure the hypothetical constructs proposed by the theory
  3. empirically testing the hypothesized relations[2]

Many psychologists note that an important role of construct validation in psychometrics was that it place more emphasis on theory as opposed to validation. The core issue with validation was that a test could be validated, but that did not necessarily show that it measured the theoretical construct it purported to measure. Construct validity has three aspects or components: the substantive component, structural component, and external component.[10] They are related close to three stages in the test construction process: constitution of the pool of items, analysis and selection of the internal structure of the pool of items, and correlation of test scores with criteria and other variables.

In the 1970s there was growing debate between theorist who began to see construct validity as the dominant model pushing towards a more unified theory of validity and those who continued to work from multiple validity frameworks.[11] Many psychologists and education researchers saw “predictive, concurrent, and content validities as essentially ad hoc, construct validity was the whole of validity from a scientific point of view”[10] In the 1974 version of The Standards for Educational and Psychological Testing the inter-relatedness of the three different aspects of validity was recognized: "These aspects of validity can be discussed independently, but only for convenience. They are interrelated operationally and logically; only rarely is one of them alone important in a particular situation". In 1989 Messick presented a new conceptualization of construct validity as a unified and multi-faceted concept.[12] Under this framework, all forms of validity are connected to and are dependent on the quality of the construct. He noted that a unified theory was not his own idea, but rather the culmination of debate and discussion within the scientific community over the preceding decades. There are six aspects of construct validity in Messick’s Unified Theory of Construct Validity.[13] They examine six items that measure the quality of a test’s construct validity:

  1. Consequential- What are the potential risks if the scores are, in actuality, invalid or inappropriately interpreted? Is the test still worthwhile given the risks?
  2. Content- Do test items appear to be measuring the construct of interest?
  3. Substantive- Is the theoretical foundation underlying the construct of interest sound?
  4. Structural- Do the interrelationships of dimensions measured by the test correlate with the construct of interest and test scores?
  5. External- Does the test have convergent, discriminant, and predictive qualities?
  6. Generalizability- Does the test generalize across different groups, settings and tasks?

How construct validity should be properly viewed is still a subject of debate for validity theorists. The core of the difference lies in an epistemological difference between Positivist and Postpositivist theorists.


Evaluation of construct validity requires that the correlations of the measure be examined in regard to variables that are known to be related to the construct (purportedly measured by the instrument being evaluated or for which there are theoretical grounds for expecting it to be related). This is consistent with the multitrait-multimethod matrix (MTMM) of examining construct validity described in Campbell and Fiske's landmark paper (1959).[14] There are other method to evaluate construct validity besides MTMM. It can be evaluated through different forms of factor analysis, structural equation modeling (SEM), and other statistical evaluations.[15][16] It is important to note that a single study does not prove construct validity. Rather it is a continuous process of evaluation, reevaluation, refinement, and development. Correlations that fit the expected pattern contribute evidence of construct validity. Construct validity is a judgment based on the accumulation of correlations from numerous studies using the instrument being evaluated.[17]

Most researchers attempt to test the construct validity before the main research. To do this pilot studies may be utilized. Pilot studies are small scale preliminary studies aimed at testing the feasibility of a full-scale test. These pilot studies establish the strength of their research and allow them to make any necessary adjustments. Another method is the known-groups technique, which involves administering the measurement instrument to groups expected to differ due to known characteristics. Hypothesized relationship testing involves logical analysis based on theory or prior research.[3]Intervention studies are yet another method of evaluating construct validity. Intervention studies where a group with low scores in the construct is tested, taught the construct, and then re-measured can demonstrate a tests construct validity. If there is a significant difference pre-test and post-test, which are analyzed by statistical tests, then this may demonstrate good construct validity.[18]

Convergent and discriminant validity

Convergent and discriminant validity are the two subtypes of validity that make up construct validity. Convergent validity refers to the degree to which two measures of constructs that theoretically should be related, are in fact related. In contrast discriminant validity tests whether concepts or measurements that are supposed to be unrelated are, in fact, unrelated.[14] Take, for example, a construct of general happiness. If a measure of general happiness had convergent validity, then constructs similar to happiness (satisfaction, contentment, cheerfulness, etc.) should relate closely to the measure of general happiness. If this measure has discriminate validity, then constructs that are not supposed to be related to general happiness (sadness, depression, despair, etc.) should not relate to the measure of general happiness. Measures can have one of the subtypes of construct validity and not the other. Using the example of general happiness, a researcher could create an inventory where there is a very high correlation between general happiness and contentment, but if there is also a significant correlation between happiness and depression, then the measure's construct validity is called into question. The test has convergent validity but not discriminant validity.

Nomological network

Main article: nomological network

Paul Meehl and Lee Cronbach (1957) proposed that the development of a nomological net was essential to measurement of a tests construct validity. A nomological network defines a construct by illustrating its relation to other constructs and behaviors.[2] It is a representation of the concepts (constructs) of interest in a study, their observable manifestations and the interrelationship among them. It examines whether the relationships between similar construct are considered with relationships between the observed measures of the constructs. Thorough observation of constructs relationships to each other it can generate new constructs. For example, intelligence and working memory are considered highly related constructs. Through the observation of their underlying components psychologists developed new theoretical constructs such as: controlled attention[19] and short term loading.[20] Creating a nomological net can also make the observation and measurement of existing constructs more efficient by pinpointing errors.[2] Researchers have found that studying the bumps on the human skull (Phrenology) are not indicators of intelligence, but volume of the brain is. Removing the theory of Phrenology from the nomological net of intelligence and adding the theory of brain mass evolution, constructs of intelligence are made more efficient and more powerful. The weaving of all of these interrelated concepts and their observable traits creates a “net” that supports their theoretical concept. For example, in the nomological network for academic achievement, we would expect observable traits of academic achievement (i.e. GPA, SAT, and ACT scores) to relate to the observable traits for studiousness (hours spent studying, attentiveness in class, detail of notes). If they do not then there is a problem with measurement (of academic achievement or studiousness), or with the purported theory of achievement. If they are indicators of one another then the nomological network, and therefore the constructed theory, of academic achievement is strengthened. Although the nomological network proposed a theory of how to strengthen constructs, it doesn't tell us how we can assess the construct validity in a study.

Multitrait-multimethod matrix

The multitrait-multimethod matrix (MTMM) is an approach to examining Construct Validity developed by Campbell and Fiske (1959).[14] This model examines convergence (evidence that different measurement methods of a construct give similar results) and discriminability (ability to differentiate the construct from other related constructs). It measures six traits: the evaluation of convergent validity, the evaluation of discriminant (divergent) validity, trait-method units, multitrait-multimethods, truly different methodologies, and trait characteristics. This design allows investigators to test for: “convergence across different measures…of the same ‘thing’…and for divergence between measures…of related but conceptually distinct ‘things'.[21]

Threats to construct validity

Apparent construct validity can be misleading due to a range of problems. The normal considerations of experimental control are therefore important. In particular, in human experiments, experimenter or participant knowledge or expectations regardng the construct or hypothesis guessing [22] can alter responses in ways that create illusory support for validity (e.g. the Hawthorne effect)[citation needed]. In addition novelty, researcher expectations, contamination of the treatment conditions can alter responding. Reflecting the nomological net perspective, constructs may gain support simply by defining their predicted outcome too narrowly, excluding other relevant data. [23] For instance, using only job satisfaction as an indicator of happiness will exclude relevant information from outside the workplace. In line with the multi-method, multi-trait perspective, and stuctural equation modelling perspectives, multiple indicators are advised. Confounds (unmeasured causal variables) pose another threat.[24] Double-blind experiments indicate that researchers themselves can be threats to construct validity, and studies should attempt to control this effect.

Trochim.[25] includes “Inadequate Preoperational Explication of Constructs, Mono-Operation Bias, Mono-Method Bias, Interaction of Different Treatments, Interaction of Testing and Treatment, Restricted Generalizability Across Constructs, Confounding Constructs and Levels of Constructs, Hypothesis Guessing, Evaluation Apprehension, and Experimenter Expectancies", in his definitions of threats to construct validity.[25]

See also


  1. ^ Brown, J. D. (1996). Testing in language programs. Upper Saddle River, NJ: Prentice Hall Regents. 
  2. ^ a b c d e f Cronbach, L. J.; Meehl, P.E. (1955). "Construct Validity in Psychological Tests". Psychological Bulletin 52 (4): 281–302. PMID 13245896. doi:10.1037/h0040957. 
  3. ^ a b Polit DF Beck CT (2012). Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th ed. Philadelphia, USA: Wolters Klower Health, Lippincott Williams & Wilkins
  4. ^ Guion, R. M. (1980). "On trinitarian doctrines of validity". Professional Psychology 11: 385–398. doi:10.1037/0735-7028.11.3.385. 
  5. ^ Brown, J. D. (1996). Testing in language programs. Upper Saddle River, NJ: Prentice Hall Regents. 
  6. ^ Messick, S. (1995). "Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning". American Psychologist 50: 741–749. doi:10.1037/0003-066x.50.9.741. 
  7. ^ Schotte, C. K. W.; Maes, M.; Cluydts, R.; De Doncker, D.; Cosyns, P. (1997). "Construct validity of the Beck Depression Inventory in a depressive population". Journal of Affective Disorders 46 (2): 115–125. doi:10.1016/s0165-0327(97)00094-3. 
  8. ^ Messick, Samuel (1998). "Test validity: A matter of consequence". Social Indicators Research 45 (1-3): 35–44. 
  9. ^ Pennington, Donald (2003). Essential Personality. Arnold. ISBN 0-340-76118-0. 
  10. ^ a b Loevinger, J. (1957). Objective Tests As Instruments Of Psychological Theory: Monograph Supplement 9. Psychological reports, 3(3), 635-694
  11. ^ Kane, M. T. (2006). "Validation.". Educational measurement, 4: 17–64. 
  12. ^ Messick,, S. (1989). "Validity.". In R. L. Linn (Ed.),. Educational Measurement (3rd ed., pp. 13-103). New York: American Council on Education/Macmillan. 
  13. ^ Messick,, S. (1995). "Standards of validity and the validity of standards in performance assessment.". Educational Measurement: Issues and Practice, 14 (4,): 5–8. doi:10.1111/j.1745-3992.1995.tb00881.x. 
  14. ^ a b c Campbell, D. T. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin 56: pp. 81–105
  15. ^ Hammond, K. R., Hamm, R. M., & Grassia, J. (1986). Generalizing over conditions by combining the multitrait multimethod matrix and the representative design of experiments (No. CRJP-255A). Colorado University At Boulder Center For Research On Judgment And Policy.
  16. ^ Westen Drew, Rosenthal Robert (2003). "Quantifying construct validity: Two simple measures". Journal of Personality and Social Psychology 84 (3): 608–618. doi:10.1037/0022-3514.84.3.608. 
  17. ^ Peter, J. P. (1981). Construct validity: a review of basic issues and marketing practices. Journal of Marketing Research, 133-145.
  18. ^ Dimitrov, D. M., & Rumrill, Jr, P. D. (2003). Pretest-posttest designs and measurement of change. Work: A Journal of Prevention, Assessment and Rehabilitation 20(2), 159-165.
  19. ^ Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake, & P. Shah (Eds.),Models of working memory (pp. 102−134). Cambridge: Cambridge University Press.
  20. ^ Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2002). Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. Journal of Experimental Psychology-General, 131, 567−589.
  21. ^ Cook T. D., Campbell D. T. (1979). "Quasi-experimentation. Boston: Houghton Mifflin. Edgington, E. S. (1974). A new tabulation of statistical procedures used in APA journals". American Psychologist 29: 61. 
  22. ^ McCroskey, J. C., Richmond, V. P., & McCroskey, L. L. (2006). An introduction to communication in the classroom: The role of communication in teaching and training. Boston: Allyn & Bacon
  23. ^ MacKenzie, S. B. (2003). The dangers of poor construct conceptualization. Journal of the Academy of Marketing Science, 31(3), 323-326.
  24. ^ White, D., & Hultquist, R. A. (1965). Construction of confounding plans for mixed factorial designs. The Annals of Mathematical Statistics, 1256-1271.
  25. ^ a b [1], Trochim, William M. The Research Methods Knowledge Base, 2nd Edition.

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