Exercise 10: Comparing Behavior Across Situations
This exercise illustrates correlation, aggregation, and 2-Way Mixed Model
(Between/Within) ANOVA. It assumes prior familiarity with the basics of Factorial
ANOVA. 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.
If you could measure the same behaviors for a group of people in two different
situations, how consistent do you think it would be? Do you think that your results would
be different if you repeated the measurements again the following week? How much do
you think human behavior is a reflection of personality characteristics, situational
constraints, or variations over time?
This exercise illustrates the comparison of the same behaviors across two different
situations, using both a correlational and ANOVA approach. The overarching question
addressed by the PERS data is “How consistent is human behavior, and is the
consistency predictable from human personality?” Over the course of the prior
exercises, this question has been examined by intercorrelating different behaviors which
presumably are products of the same personality trait, and by using personality trait
measures to predict various relevant behaviors. The inherent unreliability of behaviors
measured on a single occasion was remedied by aggregating across time (see
Exercises 2, 3, 4). The narrow specificity of individual behaviors was broadened by
aggregation across behaviors (see Exercises 5, 6), creating a behavioral measure
comparable in generality to a personality trait scale. This current exercise takes a
different approach to the consistency question, while incorporating the processes of
temporal and content aggregation employed successfully in the previous exercises. This
exercise examines directly the question “Is behavior consistent across different
situational contexts?” This question has been historically at the very heart of a
psychological debate known as the “person/situation controversy,” (see Epstein &
O’Brien, 1985, for a brief history) which seeks to understand how much of our behavior
is a function of internal factors (e.g., personality), and how much is a function of external
factors (e.g., the particular situational context at the time). To examine this requires
measuring the same behaviors in at least two different situational contexts. Further,
given the unreliability of single-occasion measures of behavior, the measures would
need to be taken over several occasions.
The PERS dataset includes cross-situational measures as described above: the same
several behaviors measured in each of two different, specific situations, repeated on the
same day each week for three weeks. For outgoingness, several behaviors related to
(a) an in-person conversation and (b) a telephone conversation, were measured each
week. For conscientiousness, several behaviors related to (a) the participant’s most
important class and (b) the participant’s least important class, were measured each
week.
Several methodological and definitional questions may be raised about these measures.
For example, it surely is not the same conversations which are being reported on each
week, so how do these represent the same situations measured repeatedly over several
occasions? When viewed in this manner, of course they are not. But then, every
situation in our lives from moment to moment would be treated as different, and the
issue of cross-situational consistency would be meaningless. Insofar as there is some
continuity to our behavior when talking in person or on the telephone, we can speak of
these as similar “situations.” We would certainly expect variation over occasions on
each of these, and that is why they are measured repeatedly over time. The purist can
avoid the issue by only using the data for a single-week’s measurements. This is not
advised, however (see Exercises 2, 3, 4). Another obvious question about the measures
concerns the comparison of the “most important” versus “least important” classes. This
is not the same as comparing “Psychology 1A” with “English Literature 5;” in fact, it
means that most participants are measured in different classes from each other. Aside
from the logistical difficulty (impossibility?) of finding two courses in common for all the
participants to compare, the problem is one of psychological comparability. It seemed a
clearer and more definitive situational contrast to use each student’s own highest
priority and lowest priority courses, rather than specific courses which might not be
perceived much differently by some or many students.
There are two analytic strategies to use in comparing the behavior across situations:
correlational, or ANOVA. In either case, we will use the 3-week average measures
(aggregates), and we will analyze both the individual behaviors and a behavioral
aggregate (see Exercise 6). Examine the measures in the codebook in the section
“Cross-situational Measures (3-Week Average).” We will use the outgoingness
measures in this exercise (OSP1-OSP6; OST1-OST6). None of these variables need to
be recoded, though the OSP6 and OST6 may be too different from the others, and so
we will not use them. Create two new aggregate measures across the five behaviors:
osp5tot
sum(osp1 to osp5)
ost5tot
sum(ost1 to ost5)
The correlational approach to comparing behavior cross-situationally is simple: correlate
osp1 with ost1, osp2 with ost2, etc., including osp5tot with ost5tot. Look at your results
and explain them, given what you know about aggregation and the kinds of correlations
obtained in other exercises between behaviors and with personality traits. You should
find them to be lower than many of the other correlations we examined in previous
exercises. Why do you think that is? What do you think would be the results if you used
the measures for only Week#1 instead of the 3-week averages?
The ANOVA approach is more complex, but it yields some interesting information about
the interaction between personality and situation influences, so it is favored by some
researchers. Recall from Exercise 9 that ANOVA compares conditions or groups, so we
must divide our participants into groups according to their personality characteristics.
This was done in Exercise 6; if you don’t still have the “extgrp” variable on your datafile,
go back to that exercise and recreate it. Our situations already represent two
“conditions” under which measurements were taken.
We will use a “2-Way ANOVA.” There are two “factors” in our analysis of variance:
outgoingness level (low/average/high) and situation (in-person/telephone conversation),
and so we have a 3x2 (or 2x3, if you prefer the reverse order) ANOVA. Since
participants are classified in only 1 of the 3 outgoingness groups, this is a “betweensubjects” factor. Since participants were measured in both situations, this is a “withinsubjects” factor. This particular analysis, then, is called a “mixed-model,” or
“between/within” ANOVA. We need to perform separate ANOVAs on each variable; we
will use the 5-item aggregate as an example here.
Since there are repeated measures (situations; our “within-subjects” factor), we will use:
Analyze>General Linear Model>Repeated Measures
Within-Subject Factor Name: converse
Number of Levels: 2
(click on Add, then on Define; you will get a new, larger dialog box)
Within-Subjects Variable (converse): osp5tot, ost5tot
Between-Subjects Factor(s): extgrp
Click on OK to run the ANOVA
The output is daunting, because the design is complex and SPSS provides many
multivariate statistics even when they are not needed. There are several tables of
output; we will look at the third table, called “Tests of Within-Subjects Effects.” This table
gives 4 different computations for each of 3 different effects. We will only examine the
lines labeled “Sphericity Assumed.” The F-ratio for Converse is 12.945, and is highly
statistically significant (.001 in the “Sig.” Column means less than a .001 probability of
occuring by chance). The interaction effect (“Converse*Extgrp”) is not statistically
significant. Now move down to the last table, “Tests of Between-Subjects Effects.” The
line for “Extgrp” shows that it is statistically significant (F=8.034; probability less than
.001 of occuring by chance). So the analysis shows that there is significant variability in
the aggregated behaviors as a function of situational context (in-person versus
telephone conversation), and as a function of personality trait level (low versus average
versus high outgoingness). There does not appear to be any significant interaction
between the personality factors and the situational factors in influencing behavior. Since
significant effects were found, you would want to re-run the analysis and include
Options for descriptive statistics (giving you means for the different conditions) and
Post-hocs (giving you specific statistical comparisons between conditions). Also, since
this only analyzed the aggregated measures (osp5tot & ost5tot), you will need to repeat
the analysis for each of the separate behaviors (e.g., osp1 & ost1) to compare to the
correlations from the first analysis.
Compare the two analyses (correlational and ANOVA). Which do you prefer and why?
You can try the same analyses with the conscientiousness measures on your own.
References
Epstein, S. & O’Brien, E. J. (1985). The person-situation debate in historical and
current perspective. Psychological Bulletin, 98, 513-537.