Feist−Feist: Theories of
Personality, Seventh
EditionIV. Dispositional Theories 14. Eysenck, McCrae, and
Costa’s Trait and Factor
Theories(^412) © The McGraw−Hill
Companies, 2009
To use factor analysis, one begins by making specific observations of many in-
dividuals. These observations are then quantified in some manner; for example,
height is measured in inches; weight in pounds; aptitude in test scores; job perfor-
mance by rating scales; and so on. Assume that we have 1,000 such measures on 5,000
people. Our next step is to determine which of these variables (scores) are related to
which other variables and to what extent. To do this, we calculate the correlation co-
efficientbetween each variable and each of the other 999 scores. (A correlation co-
efficient is a mathematical procedure for expressing the degree of correspondence
between two sets of scores.) To correlate 1,000 variables with the other 999 scores
would involve 499,500 individual correlations (1,000 multiplied by 999 divided by
2). Results of these calculations would require a table of intercorrelations, or a
matrix,with 1,000 rows and 1,000 columns. Some of these correlations would be
high and positive, some near zero, and some would be negative. For example, we
might observe a high positive correlation between leg length and height, because
one is partially a measure of the other. We may also find a positive correlation
between a measure of leadership ability and ratings on social poise. This relation-
ship might exist because they are each part of a more basic underlying trait—self-
confidence.
With 1,000 separate variables, our table of intercorrelations would be quite
cumbersome. At this point, we turn to factor analysis,which can account for a large
number of variables with a smaller number of more basic dimensions. These more
basic dimensions can be called traits,that is, factors that represent a cluster of
closely related variables. For example, we may find high positive intercorrelations
among test scores in algebra, geometry, trigonometry, and calculus. We have now
identified a cluster of scores that we might call Factor M, which represents mathe-
matical ability. In similar fashion, we can identify a number of other factors,or units
of personality derived through factor analysis. The number of factors, of course, will
be smaller than the original number of observations.
Our next step is to determine the extent to which each individual score con-
tributes to the various factors. Correlations of scores with factors are called factor
loadings.For example, if scores for algebra, geometry, trigonometry, and calculus
contribute highly to Factor M but not to other factors, they will have high factor load-
ings on M. Factor loadings give us an indication of the purity of the various factors
and enable us to interpret their meanings.
Traits generated through factor analysis may be either unipolar or bipolar.
Unipolar traitsare scaled from zero to some large amount. Height, weight, and in-
tellectual ability are examples of unipolar traits. In contrast,bipolar traitsextend
from one pole to an opposite pole, with zero representing a midpoint. Introversion
versus extraversion, liberalism versus conservatism, and social ascendancy versus
timidity are examples of bipolar traits.
In order for mathematically derived factors to have psychological meaning, the
axes on which the scores are plotted are usually turned or rotatedinto a specific
mathematical relationship with each other. This rotation can be either orthogonal or
oblique, but Eysenck and advocates of the Five-Factor Theory favor the orthogonal
rotation.Figure 14.1 shows that orthogonally rotated axes are at right angles to each
other. As scores on the xvariable increase, scores on the yaxis may have any value;
that is, they are completely unrelated to scores on the xaxis.
406 Part IV Dispositional Theories