Construct validity

7 July 2016

Most, but not all, tests are designed to measure skills, abilities, or traits that are and are not directly observable. The process of using a test score as a sample of behavior in order to draw conclusions about a larger domain of behaviors is characteristic of most educational and psychological tests (Miller, et. al., 2013). Responsible test developers and publishers must be able to demonstrate that it is possible to use the sample of behaviors measured by a test to make valid inferences about an examinee’s ability to perform tasks that represent the larger domain of interest. Construct validity pertains to the correspondence between your concepts and the actual measurements that you use (Miller, et. al., 2013). A measure with high construct validity accurately reflects the abstract concept that you are trying to study. Since we can only know about our concepts through the concrete measures that we use; you can see that construct validity is extremely important. It also becomes clear why it is so important to have very clear conceptual definitions of our variables.

Only then can we begin to assess whether our measures, in fact, correspond to these concepts. This is why it is the most important thing a test can possess. Construct validity is often established through the use of a multi-trait, multi-method matrix (Miller, et. al., 2013). At least two constructs are measured. Each construct is measured at least two different ways, and the type of measures is repeated across constructs. Typically, under conditions of high construct validity, correlations are high for the same construct across a host of different measures (Miller, et. al., 2013). Correlations are low across constructs that are different but measured using the same general technique.

Under low construct validity, the reverse holds (Miller, et. al., 2013). Correlations are high across traits using the same method but low for the same trait measured in different ways. Could a test be useful if it had reliability, and either content/ criterion validity, but lacked construct validity? The answer for me is no. Reliability is not enough; a test must also be valid for its use. If test scores are to be used to make accurate inferences about an examinee’s ability, they must be both reliable and valid. Reliability is a prerequisite for validity and refers to the ability of a test to measure a particular trait or skill consistently.

However, tests can be highly reliable and still not be valid for a particular purpose. I think that this holds true when it comes to construct validity. I believe that this needs to be as important as reliability and content/criterion validity. In order to provide evidence that your measure has construct validity, a nomological network is needed for your measure. This network includes the theoretical framework for what you are trying to measure, an empirical framework for how you are going to measure it, and specification of the linkages among and between these two frameworks (Miller, et. al., 2013). The nomological network consists of laws that relate attributes to one another and to observable properties. These laws are tested empirically as hypotheses about the relationships of the attribute of interest with the other attributes in the network using test scores to represent each but also by testing group differences with respect to test scores, score change over time, and the internal structure of the items in the test (Miller, et. al., 2013).

So when combining these terms all together, you will make for a better research paper in general. If you want to prove a point, it not only has to be reliable, but it as has to be a valid point at that. When using the construct validity within your research you must also include your nomological network in order to provide the evidence that is needed. Using each of these items within your research will not only help the research paper to be more cohesive, but it will also make it reliable and valid as well. References

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