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Construct validity refers to the degree to which inferences can legitimately be made from the operationalizations in your study to the theoretical constructs on which those operationalizations were based. Like external validity, construct validity is related to generalizing. But, where external validity involves generalizing from your study context to other people, places or times, construct validity involves generalizing from your program or measures to the concept of your program or measures. You might think of construct validity as a "labeling" issue. When you implement a program that you call a "Head Start" program, is your label an accurate one? When you measure what you term "self esteem" is that what you were really measuring?

I would like to tell two major stories here. The first is the more straightforward one. I'll discuss several ways of thinking about the idea of construct validity, several metaphors that might provide you with a foundation in the richness of this idea. Then, I'll discuss the major construct validity threats, the kinds of arguments your critics are likely to raise when you make a claim that your program or measure is valid. In most research methods texts, construct validity is presented in the section on measurement. And, it is typically presented as one of many different types of validity (e.g., face validity, predictive validity, concurrent validity) that you might want to be sure your measures have. I don't see it that way at all. I see construct validity as the overarching quality with all of the other measurement validity labels falling beneath it. And, I don't see construct validity as limited only to measurement. As I've already implied, I think it is as much a part of the independent variable -- the program or treatment -- as it is the dependent variable. So, I'll try to make some sense of the various measurement validity types and try to move you to think instead of the validity of any operationalization as falling within the general category of construct validity, with a variety of subcategories and subtypes.

The second story I want to tell is more historical in nature. During World War II, the U.S. government involved hundreds (and perhaps thousands) of psychologists and psychology graduate students in the development of a wide array of measures that were relevant to the war effort. They needed personality screening tests for prospective fighter pilots, personnel measures that would enable sensible assignment of people to job skills, psychophysical measures to test reaction times, and so on. After the war, these psychologists needed to find gainful employment outside of the military context, and it's not surprising that many of them moved into testing and measurement in a civilian context. During the early 1950s, the American Psychological Association began to become increasingly concerned with the quality or validity of all of the new measures that were being generated and decided to convene an effort to set standards for psychological measures. The first formal articulation of the idea of construct validity came from this effort and was couched under the somewhat grandiose idea of the nomological network. The nomological network provided a theoretical basis for the idea of construct validity, but it didn't provide practicing researchers with a way to actually establish whether their measures had construct validity. In 1959, an attempt was made to develop a method for assessing construct validity using what is called a multitrait-multimethod matrix, or MTMM for short. In order to argue that your measures had construct validity under the MTMM approach, you had to demonstrate that there was both convergent and discriminant validity in your measures. You demonstrated convergent validity when you showed that measures that are theoretically supposed to be highly interrelated are, in practice, highly interrelated. And, you showed discriminant validity when you demonstrated that measures that shouldn't be related to each other in fact were not. While the MTMM did provide a methodology for assessing construct validity, it was a difficult one to implement well, especially in applied social research contexts and, in fact, has seldom been formally attempted. When we examine carefully the thinking about construct validity that underlies both the nomological network and the MTMM, one of the key themes we can identify in both is the idea of "pattern." When we claim that our programs or measures have construct validity, we are essentially claiming that we as researchers understand how our constructs or theories of the programs and measures operate in theory and we claim that we can provide evidence that they behave in practice the way we think they should. The researcher essentially has a theory of how the programs and measures related to each other (and other theoretical terms), a theoretical pattern if you will. And, the researcher provides evidence through observation that the programs or measures actually behave that way in reality, an observed pattern. When we claim construct validity, we're essentially claiming that our observed pattern -- how things operate in reality -- corresponds with our theoretical pattern -- how we think the world works. I call this process pattern matching, and I believe that it is the heart of construct validity. It is clearly an underlying theme in both the nomological network and the MTMM ideas. And, I think that we can develop concrete and feasible methods that enable practicing researchers to assess pattern matches -- to assess the construct validity of their research. The section on pattern matching lays out my idea of how we might use this approach to assess construct validity.

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