Exercise 6: Correlating Personality & Aggregated Behavior Scales
This exercise illustrates correlation and content aggregation. It uses the PERS dataset,
consisting of 90 cases and 968 variables. The variables represent measures of traits
and relevant behaviors for the dimensions of extraversion (outgoingness) and
conscientiousness, reported each week for three weeks by a group of undergraduate
psychology students.
How well can a score on a personality test predict behavior, if several different
behaviors are added together into a single, aggregated “scale.”
This exercise will examine the prediction of behavior from personality. This exercise
continues the illustration from Exercise 5, which predicted various single behaviors
which had been aggregated over time. This exercise uses the same personality trait
measures and behaviors, but aggregates the various behaviors into a single “scale”
score, to increase the prediction of behavior by personality traits. This is desirable
because personality trait measures are more broad and general than specific behaviors.
See Epstein (1984) and Buss and Craik (1984) for further explanation.Aggregating a
range of similar, relevant behaviors into a single index or “behavioral scale” should
make the behavioral measure comparable in level of generality to the trait measure.
Behaviors are unreliable when measured at one point in time, so this exercise assumes
that the behavioral measures involved have first been averaged over several occasions
(temporal aggregation); see Exercise 5 for a demonstration of using personality traits to
predict behaviors which have been measured only once, versus those that have been
measured repeatedly and averaged over time.
Generality of personality traits and specificity of individual behaviors.
How well can personality traits predict behavior? The empirical counterpart to this
question is “How strongly do personality trait measures correlate with behavioral
measures?” In addition to being affected by the reliability over time of single behavioral
measures (see Exercise 5), personality traits are general tendencies or dispositions to
display a variety of behaviors or styles in behavior, and are not necessarily prescriptions
to enact any particular, specific behavior. Likewise, the inventories used to measure
personality traits ask general questions about likes and dislikes, “usual” patterns of
behavior, and so forth. A behavioral measure may ask “How many nights last week did
you meet with friends to socialize?” or “How many different people did you socialize with
today?” A personality test item may ask “Do you like to socialize with friends?” Thus, it
may be that any one specific behavior, even those that are very relevant to the
personality trait in question, is just too narrow to correlate very strongly with the general
trait measure. In addition to measuring behaviors repeatedly over time and averaging
them to increase reliability, we may also need to average a set of relevant behaviors,
that is to aggregate across “content,” or specific behaviors, in addition to aggregating
across time. This means of course that we are no longer predicting one identifiable
behavior, but rather we are predicting a set of behaviors, with our personality trait
measure.
If you completed Exercise 5, you have already seen the personality inventories for
outgoingness (JPISPT and CPISY) predicted outgoingness behaviors better after they
had been averaged over the three occasions of measurement, compared to one single
week’s measurement. This exercise will use the same 10 weekly behaviors for
outgoingness, aggregated across time (wbo1 to wbo10), and the JPI and CPI
outgoingness scales.
Check your results from Exercise 5. You should have found that for predicting singleoccasion measures (Week#1), the correlations were as follows:
Scale
lowest highest median
JPI
.082
.351
.228
CPI
.040
.335
.266
And for the 3-week average measures of behavior, the correlations were:
Scale
lowest highest median
JPI
.120
.510
.250
CPI
.020
.382
.270
Aggregating the behaviors over time improved their correlation with personality traits to
a modest extent. Applying the reasoning explained above about the specificity of
individual behaviors, we should expect a stronger correlation between the trait
measures and an average of the ten behaviors. Start by creating an aggregated
variable from the 10 weekly behaviors used above. Use Transform>Compute, just as
you did in Exercise 2 to create an average of the same behavior over time. The
difference here is that we are using different behaviors to create our aggregate (which
have already been averaged over time). For the Target variable, type a name like
wbo10tot (to signify that it is a total across 10 weekly behaviors). For the Numeric
Expression, type: wbo1+wbo2+wbo3+wbo4+wbo5+wbo6+wbo7+wbo8+wbo9+wbo10
(you can leave this as a sum, or get an average by enclosing the entire expression in
parentheses and dividing by 10—it won’t affect the correlations with anything else; you
can also use the Compute Functions, such as “Sum,” if you know how to do this)
Now, correlate this new aggregated variable, wbo10tot, with JPISPT and CPISY. You
should get r=.409 with JPISPT and r=.410 with CPISY. Compared to the lowest or the
median correlations (see above) with the individual behaviors, this represents a
considerable improvement. It is comparable to the highest correlations obtained when
individual, specific behaviors were used, and in fact exceeds the highest correlation for
the CPI personality inventory.
References
Buss, D. M. & Craik, K. H. (1984). Acts, dispositions, and personality. Progress in
Experimental Personality Research, 13, 241-301.
Epstein, S. (1984). The stability of behavior across time and situations. In R. A.
Zucker, J. Aronoff, & A. I. Rabin (Eds.), Personality and the prediction of behavior.
Orlando, FL: Academic Press.