Transcript Class Exercise • The first major assignment involves collecting data for the purposes of class demonstrations. • The assignment is explained in-depth at: •

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.
Correct classifications
Lost a parent before the age of 10
10%
Parent or grandparent had
schizophrenia
50%
Mother was cold and aloof to the
person when he or she was a child
15%
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.
Measure: Schizophrenic
Reality: Schizophrenic
No
Yes
No
Yes
40
10
10
40
80% ( [40 + 40] / 100) people were correctly classified
Measure: Schizophrenic
Reality: Schizophrenic
No
Yes
No
Yes
10
10
40
40
50% ( [40 + 10] / 100) people were correctly classified
Measure: Schizophrenic
Reality: Schizophrenic
No
Yes
No
Yes
98
0
1
1
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 selfesteem 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
+
+
selfesteem
distrust
friends
ability to
cope
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 discriminant
validity.
achieve
in school
+
+
ability to
cope
selfesteem
distrust
friends
like
coffee
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 selfesteem sociability that reads “I have high selfesteem” 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.