1. Different ways to think about validity To the extent that a measure has validity, we can say that it measures what it is supposed to measure. There are different reasons for measuring psychological variables. The precise way in which we assess validity depends on the reason that we’re taking the measurements in the first place.

2. Prediction As an example, if one’s goal is to develop a way to determine who is at risk for developing schizophrenia, one’s goal is prediction.

3. Predictive Validity We may begin by obtaining a group of people who have schizophrenia and a group of people who do not. Then, we may try to figure out which kinds of antecedent variables differentiate the two groups.

4. Correct classifications Lost a parent before the age of 1010% Parent or grandparent had schizophrenia 50% Mother was cold and aloof to the person when he or she was a child 15%

5. Predictive Validity In short, some of these variables appear to be better than others at discriminating schizophrenics from non-schizophrenics The degree to which a measure can predict what it is supposed to predict is called it’s predictive validity. When we are taking measurements for the purpose of prediction, we assess validity as the degree to which those predictions are accurate or useful.

6. Yes No Yes 40 10 Reality: Schizophrenic Measure: Schizophrenic No 10 40 80% ( [40 + 40] / 100) people were correctly classified

7. YesNo Yes 40 10 Reality: Schizophrenic Measure: Schizophrenic 40 10 50% ( [40 + 10] / 100) people were correctly classified

8. YesNo Yes 1 0 Reality: Schizophrenic Measure: Schizophrenic 1 98 99% ( [98 + 1] / 100) people were correctly classified, but note the base rate problem. Cohen’s kappa is used to account for this problem.

9. Construct Validity Sometimes we’re not interested in measuring something just for “technological” purposes, such as prediction. We may be interested in measuring a construct in order to learn more about it –Example: We may be interested in measuring self- esteem not because we want to predict something with the measure per se, but because we want to know how self-esteem develops, whether it develops differently for males and females, etc.

10. Construct Validity Notice that this is much different than what we were discussing before. In our schizophrenia example, it doesn’t matter whether our measure of schizophrenia really measured schizophrenic tendencies per se. As long as the measure helps us predict schizophrenia well, we don’t really care what it measures.

11. Construct Validity When we are interested in the theoretical construct per se, however, the issue of exactly what is being measured becomes much more important. The general strategy for assessing construct validity involves (a) explicating the theoretical relations among relevant variables and (b) examining the degree to which the measure of the construct relates to things that it should and fails to relate to things that it should not.

12. Nomological Network The nomological network represents the interrelations among variables involving the construct of interest. self- esteem achieve in school distrust friends ability to cope - + +

13. Nomological Network & Validity The process of assessing construct validity basically involves determining the degree to which our measure of the construct behaves in the way assumed by the theoretical network in which it is embedded. If, theoretically, people with high self-esteem should be more likely to succeed in school, then our measure of self-esteem should be able to predict people’s grades in school.

14. Construct Validity Notice here that establishing construct validity involves prediction. The difference between prediction in this context and prediction in the previous context is that we are no longer trying to predict school performance as best as we possibly can. Our measure of self-esteem should only predict performance to the degree to which we would expect these two variables to be related theoretically.

15. Discriminant Validity The measure should also fail to be related to variables that, theoretically, are unrelated to self-esteem. The ability of a measure to fail to predict irrelevant variables is referred to as the measure’s discriminant validity. self- esteem achieve in school distrust friends ability to cope - + + like coffee

16. Validity: Assessing validity Finally, it is useful, but not necessary, if a measure has face validity. Face validity: The degree to which a measure appears to measuring what it is supposed to measure. A questionnaire item designed to measure self- esteem that reads “I have high self-esteem” has face validity. An item that reads “I like cabbage in my Frosted Flakes” does not. In the context of prediction, face validity doesn’t matter.

17. A Final Note on Construct Validity The process of establishing construct validity is one of the primary enterprises of psychological research. When we are measuring the association between two variables to assess a measure’s predictive or discriminant validity, we are evaluating both the quality of the measure and the soundness of the nomological network. It is not unusual for researchers to refine the nomological network as they learn more about how various measures are inter-related.