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Class Exercise. The first major assignment involves collecting data for the purposes of class demonstrations. The assignment is explained in-depth at: http://p034.psch.uic.edu/class-intro.htm This particular assignment will account for 8% of your grade. Different ways to think about validity.
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Class Exercise • The first major assignment involves collecting data for the purposes of class demonstrations. • The assignment is explained in-depth at: • http://p034.psch.uic.edu/class-intro.htm • This particular assignment will account for 8% of your grade.
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.
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.
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.
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 can assess validity as the degree to which those predictions are accurate or useful.
No 40 10 80% ( [40 + 40] / 100) people were correctly classified Reality: Schizophrenic Yes 10 No Measure: Schizophrenic 40 Yes
Reality: Schizophrenic No Yes 10 10 No Measure: Schizophrenic 40 40 Yes 50% ( [40 + 10] / 100) people were correctly classified
Reality: Schizophrenic No Yes 98 0 No Measure: Schizophrenic 1 1 Yes 99% ( [98 + 1] / 100) people were correctly classified, but note the base rate problem. Cohen’s kappa is used to account for this problem.
Construct Validity • Sometimes were not interested in measuring something just for “technological” purposes. • 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.
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.
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.
Nomological Network • The nomological network is the interrelations among constructs involving the construct of interest. achieve in school ability to cope + + self- esteem - distrust friends
Nomological Network & Validity • The process of assessing construct validity basically involves determining the degree to which our measure behaves in the way posited 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.
Construct Validity • Notice here that establishing construct validity involves prediction. The difference between this and what we discussed before is that we are not 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.
Discriminant Validity • The measure should also fail to be related to variables that, theoretically, self-esteem should not be related to. • This is referred to as the measure’s discriminantvalidity. achieve in school ability to cope + + self- esteem - like coffee distrust friends
Validity: Assessing validity • Finally, it is useful if the 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 sociability that reads “I have high self-esteem” has face validity. • In the context of prediction, face validity doesn’t matter.
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 study 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.
