Editing Construct validity (section)

== Evaluation ==
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).<ref name="Campbell"/> There are other methods to evaluate construct validity besides MTMM. It can be evaluated through different forms of [[factor analysis]], [[structural equation modeling]] (SEM), and other statistical evaluations.<ref name="Hammond96">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.</ref><ref>{{cite journal |author1=Westen Drew |author2=Rosenthal Robert | year = 2003 | title = Quantifying construct validity: Two simple measures | url = http://nrs.harvard.edu/urn-3:HUL.InstRepos:3708469| journal = Journal of Personality and Social Psychology | volume = 84 | issue = 3| pages = 608–618 | doi=10.1037/0022-3514.84.3.608|pmid=12635920 | url-access = subscription }}</ref> 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.<ref>Peter, J. P. (1981). Construct validity: a review of basic issues and marketing practices. Journal of Marketing Research, 133-145.</ref>

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.<ref name="Polit"/> [[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 test's 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.<ref>{{cite journal |author1=Dimitrov D. M. |author2=Rumrill Jr P. D. | year = 2003 | title = Pretest-posttest designs and measurement of change | journal = Work: A Journal of Prevention, Assessment and Rehabilitation | volume = 20 | issue = 2| pages = 159–165 |pmid=12671209 }}</ref>

===Convergent and discriminant validity===
{{Main| convergent validity| 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.<ref name="Campbell">{{cite journal | author = Campbell D. T. | year = 1959 | title = Convergent and discriminant validation by the multitrait-multimethod matrix | journal = Psychological Bulletin | volume = 56 | issue = 2| pages = 81–105 | doi=10.1037/h0046016| pmid = 13634291 }}</ref> 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 positively to the measure of general happiness. If this measure has discriminant validity, then constructs that are not supposed to be related positively 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 positive correlation between general happiness and contentment, but if there is also a significant positive 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|nomological network}}
Lee Cronbach and Paul Meehl (1955)<ref name="Cronbach55"/> proposed that the development of a nomological net was essential to the measurement of a test's construct validity. A [[nomological network]] defines a construct by illustrating its relation to other constructs and behaviors. 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. A 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<ref>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.</ref> and short term loading.<ref>{{cite journal |author1=Ackerman P. L. |author2=Beier M. E. |author3=Boyle M. O. | year = 2002 | title = Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities | journal = Journal of Experimental Psychology: General | volume = 131 | issue = 4| pages = 567–589 | doi=10.1037/0096-3445.131.4.567|pmid=12500864 }}</ref> Creating a nomological net can also make the observation and measurement of existing constructs more efficient by pinpointing errors.<ref name="Cronbach55"/> 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 ===
{{Main|Multitrait-multimethod matrix}}
The [[multitrait-multimethod matrix]] (MTMM) is an approach to examining construct validity developed by Campbell and Fiske (1959).<ref name="Campbell"/> 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'.<ref name="Quasi-experimentation"/><ref>{{cite journal | author = Edgington, E. S. |date=1974 | title =  A new tabulation of statistical procedures used in APA journals | journal = American Psychologist | volume = 29 | page = 61 | doi = 10.1037/h0035846 }}</ref>