Principal Components Analyses of the mmp2 psy5 scales

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ASSESSMENT
Arnau
10.1177/1073191105274750
et al. / PSY-5 FACETS
Principal Components Analyses of the
MMPI-2 PSY-5 Scales
Identification of Facet Subscales
Randolph C. Arnau
University of Southern Mississippi
Richard W. Handel
Robert P. Archer
Eastern Virginia Medical School
The Personality Psychopathology Five (PSY-5) is a five-factor personality trait model
designed for assessing personality pathology using quantitative dimensions. Harkness,
McNulty, and Ben-Porath developed Minnesota Multiphasic Personality Inventory–2
(MMPI-2) scales based on the PSY-5 model, and these scales were recently added to the
standard MMPI-2 profile. Although the PSY-5 constructs are multidimensional in definition,
explicit subscales for the broader PSY-5 scales have not been developed. The primary goals
of this study were to empirically derive subscales for the MMPI-2 PSY-5 scales using principal components analysis (PCA) and to replicate these subscales with an independent sample. Individual PSY-5 scales were analyzed using PCA with an initial sample of 4,325
MMPI-2 protocols, and the component structure was replicated with a second sample of
4,277 MMPI-2 protocols. A third sample of 4,327 protocols was used to further evaluate the
internal consistency reliabilities of the resulting facet subscales. Overall, replicable facet
subscales were identified with content areas that are largely congruent with Harkness and
McNulty’s model.
Keywords: personality assessment; MMPI-2; PSY-5; psychometrics; scale development;
principal components analysis
The Minnesota Multiphasic Personality Inventory–2
(MMPI-2; Butcher et al., 2001) Personality Psychopathology Five (PSY-5) scales (Harkness McNulty, &
Ben-Porath, 1995) were developed to measure the PSY-5
constructs developed by Harkness and McNulty (1994).
Harkness and McNulty described the PSY-5 as “models of
traits designed to aid in personality description and to
complement personality disorder diagnosis with quantitative dimensions” (p. 104). Therefore, the PSY-5 repre-
sents a dimensional five-factor trait model developed specifically to be applied to personality pathology. It is noteworthy that a number of researchers have called for the
application of dimensional personality models to complement, or even replace, the categorical model of personality
disorders of the fourth editon of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, American
Psychiatric Association, 1994). With growing support for
the view that the major variations in personality can be
We thank Beverly Kaemmer and Elizabeth J. Knoll of the University of Minnesota Press for permission to use the Minnesota
Multiphasic Personality Inventory–2 NCS Pearson (1989-1999) archival data set. Correspondence concerning this article should be
addressed to Randolph C. Arnau, University of Southern Mississippi, Department of Psychology, 118 College Drive, No. 5025,
Hattiesburg, MS 39406-5025; e-mail: [email protected].
Assessment, Volume 12, No. 2, June 2005 186-198
DOI: 10.1177/1073191105274750
© 2005 Sage Publications
Arnau et al. / PSY-5 FACETS 187
captured by the five broad trait domains of the five-factor
model (FFM; Digman, 1990; Goldberg, 1993; McCrae &
John, 1992), some of the alternative dimensional approaches to personality disorders offered are applications
of the FFM (e.g., Costa & Widiger, 1994).
Harkness and McNulty (1994), although acknowledging the potential usefulness of a dimensional model
applied to personality disorders, also argued that the ability of the FFM to capture the major sources of variability in
personality in normal samples does not “guarantee that
they will do the best possible job when transplanted into
the personality disorders domain” (p. 295). Thus, whereas
a number of the current conceptualizations of the FFM
originated from descriptors taken from a dictionary,
Harkness and McNulty developed a model using both normal personality terms and also descriptors of abnormal
personality taken from the DSM-III-R (American Psychiatric Association, 1987) in order for their model to be more
applicable to personality pathology.
IDENTIFICATION OF THE
PSY-5 CONSTRUCTS
Harkness (1992) laid the groundwork for the development of the PSY-5 model by examining lower regions of
the personality disorder hierarchy. Harkness first translated a pool of 271 items, including individual diagnostic criteria from the DSM-III-R personality disorders and
Cleckley’s (1982) descriptors of psychopathy, into lay language. Three judges determined which items were
unlikely to be understood by an average person, and these
items were targeted for translation. A panel of judges subsequently rated translations on 7-point scales for ease of
use and translation fidelity. A subsample of 40 items representing the range of translations yielded alpha coefficients of .82 for ease of use and .91 for translation fidelity.
Harkness (1992) then explored how laypeople organized
136 translated personality disorder items into fundamental
topics. Participants were asked to sort the 136 personality
disorder items by grouping them into styles or ways of acting that seemed to them to be similar. Harkness analyzed
the resulting joint probability matrix using principal components analysis (PCA) and extracted 39 components.
Four psychologists provided names for the components,
and Harkness (1992) subsequently demonstrated that lay
raters could accurately match item groupings to correct
component names.
Building on the work of Harkness (1992), Harkness
and McNulty (1994) used the 39 fundamental topic areas
identified by Harkness (1992) and additional normal personality markers to develop 60 descriptor card sets. Lay
raters were asked to sort the cards into 10 or more groups
of highly related descriptors. Participants were also asked
to identify descriptors that were opposite or nearly opposite every descriptor under consideration. Then, at a second session, participants re-sorted the 60 descriptors into
coarse groups (fewer than 10 total groups). Harkness and
McNulty (1994) then constructed a psychological distance matrix for each subject that “quantifies short (very
similar), medium (somewhat similar) and very long (opposite or near opposite) psychological distances” (p. 297).
These summed matrices constituted a consensus distance
matrix. Finally, Harkness and McNulty examined multiple
PCA solutions at various levels of extraction to examine
the overall structure. Harkness and McNulty argued that
the “interpretation of a single level (in this case the five
component level) is enhanced by examining the full hierarchical structure” (p. 305). The resulting PSY-5 constructs were labeled as follows: Aggressiveness (AGGR),
Psychoticism (PSYC), Constraint, Negative Emotionality/
Neuroticism (NEGE), and Positive Emotionality/
Extraversion. Constraint and Positive Emotionality/
Extraversion were subsequently renamed Disconstraint
(DISC) and Introversion/Low Positive Emotionality
(INTR) to emphasize the psychopathological direction.
The PSY-5 constructs identified by Harkness and
McNulty (1994) are multifaceted in definition. For example, the PSY-5 AGGR construct primarily taps individual
differences in offensive and instrumental aggression
(Harkness, McNulty, Ben-Porath, & Graham, 2002). In
other words, this construct encompasses enjoyment of
aggression against others as well as the personal preference to employ aggression as a means or tool to achieve
goals. In addition, the AGGR construct, although it does
not capture defensive aggression, does encompass tendencies toward dominance and a sense of grandiosity, both of
which can be thought to increase the probability of acting aggressively (Harkness et al., 1995). PSY-5 PSYC is
considered to tap individual differences in reality testing
(Harkness et al., 2002). The construct contains elements of
unusual beliefs, hypervigilance, and unrealistic expectations of harm. In addition, Harkness and McNulty (1994)
noted that the construct contains elements very similar to
perceptual aberration and magical ideation (see Chapman
& Chapman, 1987) and absorption (see Tellegen, 1982).
The PSY-5 DISC construct is derived directly from
Tellegen’s (1982) construct of Constraint. DISC contains
elements of individual differences in risk taking, impulsivity, and the degree to which one’s behavior tends to
be less governed by traditional morals (Harkness et al.,
2002). PSY-5 NEGE is essentially identical to the negative emotionality construct as defined in the FFM (see
Tellegen, 1982; Watson & Clark, 1984) and measures individual differences in the disposition to experience negative emotions such as sadness, anxiety, worrying, and guilt
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ASSESSMENT
(Harkness et al., 2002). Finally, Harkness et al. (2002)
indicated that the PSY-5 INTR construct reflects limited
capacity to experience joy and positive engagement and
is also linked with the dimension of Introversion versus
Extraversion.
DEVELOPMENT OF THE
MMPI-2 PSY-5 SCALES
Harkness et al. (1995) constructed MMPI-2 scales to
measure the PSY-5 constructs using a procedure they
termed “replicated rational selection.” Briefly, lay item
selectors were trained in various aspects of the PSY-5
constructs and asked to select MMPI-2 items that corresponded to a given construct. Preliminary scales were
formed using items that were selected by at least 51% of
the lay raters trained for the target PSY-5 construct. Next,
the first two authors screened problematic items (e.g.,
items that did not rationally reflect a given construct) and
selectively deleted these items. The screeners could not
add new items during this phase of scale development.
Finally, additional items were deleted based on psychometric performance, resulting in the final PSY-5 scales.
A number of studies have provided data on the psychometric properties and correlates of the PSY-5 scales. In
their initial article on PSY-5 scale development, Harkness
et al. (1995) reported psychometric data from a variety of
samples. These samples included individuals from the
MMPI-2 normative sample, psychiatric patients, college
students, and chemically dependent individuals. In their
summary of these six independent samples (combined n >
9,000), internal consistency reliabilities for the MMPI-2
PSY-5 scales ranged from .65 to .88. Furthermore, testretest stability of MMPI-2 PSY-5 scores have ranged from
.78 to .88 during a 1-week period (Harkness et al., 2002),
from .62 to .86 during a 6-month period (Trull, Useda,
Costa, & McCrae, 1995), and from .69 to .82 during a 5year period (Harkness, Spiro, Butcher, & Ben-Porath,
1995, as cited in Harkness et al., 2002).
Petroskey, Ben-Porath, and Stafford (2003) replicated
a number of statistically significant and clinically meaningful (r ≥ .20) external correlates of the PSY-5 scales in a
forensic sample using a review of archival records of criminal defendants who underwent court-ordered assessments. For example, AGGR scale scores were related to a
history of violence, school suspensions, juvenile offenses,
and the diagnosis of antisocial personality disorder. The
DISC scale showed a similar correlate pattern but was also
related to current drug dependency and a diagnosis of
chemical dependency. PSYC was found to be related to
history of suicide attempts, juvenile offenses, and violence
but was also related to unemployment and unstable work
histories. NEGE was related to a number of theoretically
relevant variables, such as a history of suicide attempts,
physical and sexual abuse, current chemical dependence,
and depressed mood at the intake interview. INTR was
also related to a history of suicide attempts and sexual
abuse and depression at the intake interview but was also
related to increased episodes of outpatient treatment and
incidence of a depressive disorder diagnosis. Similar correlation patterns for the PSY-5 scales have also been reported in clinical settings (see Harkness et al., 2002).
PREDICTION OF PERSONALITY
DYSFUNCTION USING NARROW- VERSUS
BROAD-BANDWIDTH CONSTRUCTS
Although there is support for five broad factors capturing most of the variance in personality, it is also well
accepted that broad personality traits can be mapped onto
different levels of a hierarchy (e.g., see Eysenck, 1967).
Traits higher in the hierarchy are generally more heterogeneous and have a very broad bandwidth (such as the FFM
traits), and traits lower on the hierarchy are typically more
homogeneous and narrow in bandwidth. In the FFM, these
lower and narrower bandwidth traits are often referred to
as facets, such as those facets associated with the NEO-PIR (Costa & McCrae, 1992). For example, the NEO-PI-R
conceptualizes the FFM trait of Extraversion to be composed of a number of narrow-bandwidth facets, including
Warmth, Gregariousness, and Positive Emotions.
Recent evidence suggests that the lower level facets of
the FFM may offer better predictive accuracy than the
broader FFM domains in the area of personality pathology
(cf., Miller, Lynam, Widiger, & Leukefeld, 2001; Morey,
Gunderson, Quigley, & Lyons, 2000; Reynolds & Clark,
2001; Trull & Sher, 1994; Trull, Widiger, & Burr, 2001).
For example, Reynolds and Clark (2001) examined the
accuracy of the FFM domains and facets, as measured by
the NEO-PI-R, for predicting personality disorder ratings
from a structured interview. They found that the facet
scales outperformed the domain scales for predicting 11 of
the 13 personality disorder ratings. Furthermore, there is
evidence suggesting that the superiority of the facets versus the domain scores in predicting psychopathology as
reflected in personality disorders may also extend to the
prediction of a number of Axis I disorders (Quirk,
Christiansen, Wagner, & McNulty, 2003).
It is likely that the relative homogeneity of the facetlevel scales versus the domain-level scales is responsible,
at least in part, for their better utility over the broad
domains in predicting psychopathology. Indeed, as Saucier (1998) noted, although broad levels of abstraction in
personality assessment may be more parsimonious, they
Arnau et al. / PSY-5 FACETS 189
may not capture interesting details that are more distinguishable at the more narrow-bandwidth level. In terms of
personality pathology, some of the details alluded to by
Saucier may have to do with the differential patterns of
relationships between facets and personality disorders.
Widiger, Trull, Clarkin, Sanderson, and Costa (2002) provided a good example when they described low Agreeableness as characteristic of both paranoid and narcissistic
personality disorders but explained that they could be distinguished based on Agreeableness facets, with paranoid
personality disorder characterized by low Trust and low
Straightforwardness and narcissistic personality disorder
being characterized more by low Modesty, low Altruism,
and low Tendermindedness.
Given the parallels between the PSY-5 and FFM constructs, it would be reasonable to hypothesize that personality pathology may be predicted more accurately by
facet-level scales than by the broad PSY-5 domains.
Although the MMPI-2 PSY-5 scales implicitly tap a number of more narrow-bandwidth facets, the PSY-5 scales as
presently structured do not yield scores for such facets.
However, Bolinskey, Arnau, Archer, and Handel (2004)
recently demonstrated that reliable facet subscales could
be developed for the MMPI-A PSY-5 scales. If the PSY-5
scales from the MMPI-2 were also capable of yielding
reliable facet scales, the research and clinical utility of the
MMPI-2 scales may be increased. Therefore, the purpose
of this study was to empirically identify facet-level subscales for the MMPI-2 PSY-5 scales.
METHOD
Procedure and Statistical Analyses
The purpose of this study was to empirically derive
facet-level subscales for each of the PSY-5 scales. To
ensure the usefulness and replicability of the resulting
facet scales, a series of specific steps was followed in the
developmental process. The process of developing the
facet scales essentially followed that used by Saucier
(1998) in his development of facet scales for the NEO-FFI.
First, an item-level PCA for each of the five PSY-5
scales was conducted. Initially, the decision about the
number of components to retain was based on a parallel
analysis (Horn, 1965), which will be discussed later. Next,
the same number of components was extracted in a second, independent sample, and factor congruence coefficients (Harman, 1976) were computed to determine the
replicability of the component solution generated in the
first sample. Congruence coefficients were computed
using the computer program Coefficient of Congruence
(Watkins, 2002). As recommended by Saucier (1998),
components solutions were deemed sufficiently replicable
if all best matched factor congruence coefficients exceeded .90. For any component solutions that did not meet
this criterion, the number of factors was reduced and the
replication procedure repeated until all best matched factor congruences exceeded .90. In addition to all components exceeding .90 congruence, the components had to be
made up of at least three items with primary, salient factorpattern coefficients for that factor. Once the final components solutions were determined via the above procedures,
facet scales were developed, and the internal consistency
reliabilities of the facet scale scores were evaluated.
PCA. PCA was conducted separately for each of the
PSY-5 scales combined by gender. It should be noted that
although one may expect some variation of mean PSY-5
scores across gender, there is no theoretical reason to
expect a different pattern of correlations among PSY-5
item responses across gender. Therefore, we did not conduct separate PCAs for men and women.
The commonly used Pearson correlation was not appropriate as a measure of association, given that the
MMPI-2 item responses are dichotomous. Use of the phi
coefficient is less than satisfactory for factor analysis
involving dichotomous data, given that the upper bound of
this measure of association has a limit imposed by the
endorsement frequencies of the items (see Guilford, 1941;
Lord & Novick, 1968; Waller, 1999). In addition, recent
Monte Carlo evidence suggests that the tetrachoric correlation coefficient reliably produces estimates of correlation with low bias, even in situations in which the itemendorsement frequencies are relatively skewed (Greer,
Dunlap, & Beatty, 2004). Therefore, given that the MMPI2 item responses are dichotomous but are hypothesized to
be tapping dimensional constructs, tetrachoric correlations were the measures of association used in the present
PCAs. Furthermore, given that the subscales should be
indicators for the higher level PSY-5 domains to which
they belong, the facets were hypothesized to be intercorrelated. Therefore, a Promax rotation (Hendrickson &
White, 1964) was used to allow components to be correlated while also yielding a relatively simple component
structure (Gorsuch, 1983).
Parallel analysis to determine number of components.
As mentioned previously, the initial decision about the
number of components to retain was based on a parallel
analysis (Horn, 1965). Crawford and Koopman (1973)
demonstrated that parallel analysis was superior to a number of other factor retention rules for determining the optimal number of factors to retain. More recent Monte Carlo
studies have also yielded the same conclusion regarding
parallel analysis as the best factor retention rule (see
Zwick & Velicer, 1986). The basic procedure of a parallel
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ASSESSMENT
analysis is to compare eigenvalues from the factor analysis
of actual data with those from a factor analysis of random
data with the same item-response range and number of
cases. Factors are retained in the subsequent comparisons
as long as the eigenvalue from the actual data is greater
than the corresponding eigenvalue from the random data.
Therefore, factors are retained based on explaining more
variance than components obtained from random data
(O’Conner, 2000).
In a traditional parallel analysis, a number of such random data sets are generated, and either the mean or 90th
percentile of the resulting eigenvalues is used as the comparison random eigenvalue. However, given that the procedures used to generate the random data sets yield normally distributed variables, whereas the distributions of
many MMPI-2 item responses are quite skewed, the traditional approach probably would not yield simulated data
sets that would be sufficiently “parallel.” Therefore, for
the present study, we did not generate data sets from
scratch but instead created the parallel random data sets by
taking random permutations of the actual data set. Item
responses from the actual data set were randomly shuffled
across individuals but within each variable, effectively
creating a random data set with the same exact distributional properties and item endorsement frequencies for
each variable as in the actual data. Ten such random permutations of each of the PSY-5 data sets were generated
using an SPSS procedure written by O’Connor (2000).
Then tetrachoric correlation matrices of the random data
sets were computed and entered into a PCA, and the 90th
percentile of the eigenvalues from these 10 analyses was
used to compare to eigenvalues from PCAs of the actual
data sets. The use of the 90th percentile, as opposed to the
mean, of the random data eigenvalues is meant to protect
against overretention of components based on chance
fluctuations of the random data eigenvalues. Therefore,
components were retained in all cases in which the actual
data eigenvalue was larger (within the two significant digits reported) than the 90th percentile random data eigenvalue, even if the magnitude of difference was quite small.
Substantive interpretation of the rotated components
was based on the content of the items with salient pattern
coefficients, using the criterion of a pattern coefficient
magnitude of .40 or higher as indicating salience (Stevens,
1996). In cases in which items obtained salient loadings on
more than one component, interpretation was based on
which component the item had the primary or highest pattern coefficient.
Participants
Three samples of 5,000 MMPI-2 protocols each were
drawn randomly from a larger sample of 93,275 MMPI-2
protocols scored by NCS Pearson between 1989 and 1999.
Valid protocols were defined by the following inclusion
criteria for all three samples: Cannot Say Raw Score < 30,
F T score < 100, F(b) Tscore < 100, F(p)T score < 100,
VRIN T score < 80, and TRIN T score < 80. After the
removal of 675 invalid protocols, Sample 1 consisted of
4,325 MMPI-2s, with 51.5% men and 48.5% women. The
mean age of participants at the time of MMPI-2 administration was 37.2 years (SD = 11.5), and participants had a
mean of 13.6 years of education (SD = 3.0). Sample 1 participants completed the MMPI-2 in the following settings:
outpatient mental health center (53.4%), inpatient mental
health center (7.3%), general medical (5.7%), chronic
pain program (5.3%), correctional (3.1%), college
counseling (1.3%), and other (24.1%).
Sample 2 consisted of 4,277 protocols (51.9% men and
48.1% women) after the removal of 723 invalid protocols.
The mean age for Sample 2 was 37.2 years (SD = 11.6),
and the mean educational level was 13.7 years (SD = 3.1).
Sample 2 settings included outpatient mental health center (54.4%), inpatient mental health center (7.2%), general medical (5.8%), chronic pain program (5.1%), correctional (3.3%), college counseling (0.9%), and other
(23.2%).
Sample 3 consisted of 4,327 protocols (51.6% men and
48.4% women) after the removal of 673 invalid protocols.
The mean age for Sample 3 was 37.0 (SD = 11.5), and the
mean educational level was 13.7 years (SD = 3.0). Sample
3 settings included outpatient mental health center
(53.8%), inpatient mental health center (7.4%), general
medical (6.1%), chronic pain program (5.2%), correctional (3.3%) college counseling (1.1%), and other
(23.0%).
RESULTS
AGGR
For the PCA of PSY-5 AGGR items, the first five
eigenvalues from the actual data were 7.20, 5.33, 1.87,
0.68, and 0.64, and the corresponding first five 90th percentile random data eigenvalues were 1.73, 1.50, 1.42,
1.36, and 1.27. Therefore, based on the parallel analysis,
three components were retained, which accounted for
79.9% of the total variance in Sample 1 and 79.0% of the
variance in Sample 2. The rotated component pattern
matrix for the AGGR scale is presented for the initial sample (Sample 1) in the left side of Table 1 and in the replication sample (Sample 2) in the right side of Table 1.
As seen in Table 1, two items exhibited salient pattern
coefficients on more than one component. Items 85 and
414 both exhibited salient relationships with two compo-
Arnau et al. / PSY-5 FACETS 191
TABLE 1
Promax Rotated Component Pattern Matrix: Aggressiveness
Sample 1
Component
Sample 2
Component
Item
1
2
3
446
503
70
521
350
239
452
134
324
548
27
85
323
414
423
346
50
358
–.989
–.965
–.951
.950
.927
. 897
.820
–.136
.103
.343
–.133
–.407
–.365
. 403
.222
–.073
–.110
.012
–.162
.049
–.160
–.095
–.113
–.251
.147
. 909
. 881
.852
.836
.772
.756
.646
.607
–.084
–.017
.064
.101
.018
.024
–.094
–.017
.000
.099
–.055
.027
–.152
.112
–.093
–.157
.193
.272
.928
.916
.831
1
–1.020
–.937
–1.004
.933
.929
.815
.862
–.100
.015
.261
–.088
–.438
–.373
.395
.297
–.040
–.123
–.093
2
3
–.212
.055
–.187
–.111
–.093
–.281
.099
.923
.843
.852
.861
.739
.661
.586
.644
–.105
–.037
.121
.144
–.022
.087
–.101
–.035
.057
.060
–.042
.041
–.010
–.051
–.063
.144
.311
.165
.909
. 921
.859
NOTE: Pattern coefficients with an absolute value of .40 or greater are in bold. Component 1 = Assertiveness; Component 2 = Physical/Instrumental Aggression; Component 3 = Grandiosity.
nents, but both maintained their primary loading on Component 2 across both samples. As mentioned previously, in
cases of cross-loadings, the facet scale was constructed
based on the component on which the item had the primary, or largest, loading.
Based on the content of items with salient pattern coefficients, Component 1 was given the label Assertiveness,1
Component 2 was labeled Physical/Instrumental Aggression, and Component 3 was labeled Grandiosity.
PSYC
From the PCA of the PSY-5 PSYC items, the first five
eigenvalues from the actual data were 10.92, 3.33, 2.36,
1.31, and 1.27, and the corresponding 90th percentile
eigenvalues from the random data were 2.79, 2.35, 1.85,
1.70, and 1.56. Therefore, based on the parallel analysis,
three components were retained, which accounted for
66.4% of the total variance in the initial sample and 68.9%
of the variance in the replication sample. The rotated component pattern matrix for the PSYC scale is presented in
Table 2.
As seen in Table 2, there were four items (Items 42,
336, 355, and 448) with salient cross-loadings in one sample that did not replicate in the other sample, and one item
(Item 361) that had a salient cross-loading in both samples. However, for all of these items, the primary loading
was maintained across both samples.
Based on the content of items with salient pattern coefficients, Component 1 was given the label Psychotic
Experiences, Component 2 was labeled Paranoia, and
Component 3 was labeled Mistrust/Withdrawal.
DISC
From the PCA of the PSY-5 DISC items, the first five
eigenvalues from the actual data were 14.12, 3.99, 1.96,
1.25, and 1.14, and the corresponding 90th percentile random data eigenvalues were 2.25, 1.69, 1.58, 1.46, and
1.44. Therefore, the parallel analysis indicated that three
components should be retained. However, upon inspection
of the rotated pattern matrix for the three-component solution, it was determined that the third component was composed of only two items with pattern coefficients that were
salient and primary to that factor. Therefore, only two
components were retained, which accounted for 62.5% of
the total variance in both the initial and replication samples. The rotated component pattern matrix for the DISC
scale is presented in Table 3.
As seen in Table 3, three items (Items 309, 351, 477)
exhibited salient cross-loadings, but the component number of the primary loading was maintained across both
samples. However, the primary loadings for two items
(Items 263 and 417) varied as a function of the sample.
Therefore, neither of these two items were used in the construction of the facet scales. One item (Item 222) did not
192
ASSESSMENT
TABLE 2
Promax Rotated Component Pattern Matrix: Psychoticism
Sample 1
Component
Sample 2
Component
Item
1
2
3
1
2
3
24
72
198
96
427
490
319
551
336
508
466
138
99
144
42
259
361
355
241
315
48
374
448
184
549
.918
.796
.792
.786
–.782
.769
.756
.658
.635
.560
.483
–.169
–.228
.175
–.221
.053
.511
.102
–.214
–.071
.263
–.064
.405
–.399
.204
.014
–.116
.084
–.163
–.141
–.065
.063
.037
.550
–.072
.029
.953
.931
.800
.722
.635
.608
.568
.191
–.010
–.191
.148
–.201
.119
.382
–.154
.163
.012
–.070
–.087
–.043
–.073
.248
–.376
.353
.201
.084
.118
–.067
.410
.394
–.239
–.180
.826
.788
.739
.704
.662
–.639
.443
.813
.767
.673
.785
–.826
.762
.731
.818
.630
.654
.553
–.133
–.210
.258
–.263
.081
.492
.525
–.285
–.029
–.044
–.019
.391
–.212
.255
–.015
–.015
–.117
–.192
–.230
–.088
–.178
–.047
.376
.084
.070
1.013
.962
.742
.817
.667
.598
.550
.346
.227
–.143
.359
–.208
.147
.420
–.003
.090
.345
–.135
.037
–.007
.285
.147
–.179
.093
.017
–.039
.063
–.169
.315
.341
–.132
–.274
.754
.618
.934
.599
.693
–.829
.426
NOTE: Pattern coefficients with an absolute value of .40 or greater are in bold. Component 1 = Psychotic Experiences; Component 2 = Paranoia; Component 3 = Mistrust/Withdrawal.
exhibit salient loadings on either factor; this item was also
not used in the construction of the facet scales.
Based on the content of items with salient pattern coefficients, Component 1 was labeled Antisocial History/
Norm Violation, and Component 2 was labeled Impulsivity/
Low Harm-Avoidance.
the primary loading was not consistent and varied as a
function of samples. Therefore, these items were excluded
from the construction of facet scales.
Based on the content of items with salient pattern coefficients, Component 1was labeled Irritability/Dysphoria,
and Component 2 was labeled Phobias.
NEGE
INTR
From the PCA of the PSY-5 NEGE items, the first five
eigenvalues from the actual data were 25.23, 1.73, 1.17,
1.02, and 0.62, and the 90th-percentile random data
eigenvalues were 1.96, 1.72, 1.63, 1.55, and 1.50. Therefore, based on the parallel analysis, two components were
retained, which explained 81.7% of the total variance in
the first sample and 81.8% of the variance in the second
sample. The rotated component pattern matrix for the
NEGE scale is presented in Table 4.
As seen in Table 4, four items (Items 196, 329, 395,
415) exhibited salient cross-loadings in at least one sample, but the primary loading for each of these items was
maintained across both samples. However, there were also
a number of items (Items 63, 223, 301, 409, 496) for which
From the PCA of the PSY-5 INTR scale, the first five
eigenvalues from the actual data were 18.01, 4.29, 2.51,
1.55, and 1.00, and the corresponding 90th-percentile random data eigenvalues were 1.84, 1.72, 1.65, 1.57, and
1.50. Therefore, based on the parallel analysis, three components were retained, which explained 69.2% of the variance in the initial sample and 73.3% of the variance in the
replication sample. The rotated component pattern matrix
for the INTR scale is presented in Table 5.
As seen in Table 5, four items (Items 49, 244, 356, 515)
exhibited salient cross-loadings but maintained consistency in the component number of the primary loading
across samples. One item (Item 131) did not exhibit a
salient loading on any component; therefore, this item was
Arnau et al. / PSY-5 FACETS 193
TABLE 3
Promax Rotated Component Pattern Matrix:
Disconstraint
Sample 1
Component
Item
412
250
35
123
121
431
266
84
126
105
418
284
362
34
100
103
209
344
417
263
497
309
385
402
154
88
351
477
222
1
.907
.905
.901
.900
–.895
.893
–.872
.857
–.852
.851
.846
.836
.819
–.782
–.732
.627
.620
.556
.528
–.430
.306
.400
.154
.118
–.075
–.042
–.506
.494
–.064
TABLE 4
Promax Rotated Component Pattern Matrix:
Negative Emotionality/Neuroticism (NEGE)
Sample 2
Component
2
.265
.000
.015
–.054
.098
–.126
–.129
.032
.236
.110
–.020
–.352
–.103
.271
–.278
–.003
.281
.229
.493
–.307
–.828
–.824
.704
–.691
–.682
.674
–.556
.535
.208
1
. 918
.830
.880
.902
–.897
.918
–.854
.850
–.791
.855
.803
.759
.825
–.856
–.676
.633
.598
.493
.467
–.242
.418
.437
.047
.113
.092
–.024
—.508
.423
.118
Sample 1
Component
2
–.246
.119
.089
–.076
.072
–.082
–.115
–.048
–.015
–.092
–.017
–.356
–.151
.342
–.342
–.038
.343
.329
.534
–.511
–.811
–.815
.771
–.688
–.746
.631
–.573
.566
.229
NOTE: Pattern coefficients with an absolute value of .40 or greater are in
bold. Component 1 = Antisocial History/Norm Violation; Component 2 =
Impulsivity/Low Harm-Avoidance.
not used in the construction of facet scales. Items 342, 460,
and 531 exhibited salient loadings on the third component
in the first sample, but the salience did not replicate in the
second sample, so these two items were not used in the
construction of facet scales. One item (Item 330) did not
maintain consistency in which component it primarily
loaded across samples and was therefore not used in facet
scale construction.
Based on the content of items with salient pattern coefficients, Component 1 was given the label Disengagement/
Anhedonia, Component 2 was given the label Low Sociability, and Component 3 was labeled Low Diligence/
Hypomania.
Factor Congruences
As discussed previously, the replicability of the component structure derived in the first sample was evaluated
Item
52
389
407
82
372
213
564
37
513
451
542
116
444
405
166
442
390
375
93
305
415
556
196
409
301
223
63
290
496
395
435
397
329
1
1.062
1.026
.965
.956
–.913
.901
–.895
.886
.874
.856
.848
.846
.838
–.795
.779
.723
.720
.719
.705
.698
.683
.654
.648
.641
.588
–.581
–.554
.553
–.503
–.343
–.311
–.107
.420
Sample 2
Component
2
–.255
–.178
–.587
.002
–.041
–.042
–.064
.049
.129
.023
.123
.139
–.090
–.230
.091
.274
.226
.186
.137
.250
.338
.279
.392
.326
.429
–.426
–.454
.324
–.458
1.165
1.149
.782
.503
1
1.017
1.088
.786
.832
–.898
1.054
–.926
.825
.814
.739
.808
.745
.780
–.754
.627
.602
.700
.615
.626
.663
.556
.630
.535
.445
.393
–.398
–.395
.553
–.438
–.484
–.389
–.018
.137
2
–.164
–.202
–.421
.150
–.066
–.225
–.038
.132
.191
.158
.164
.259
.136
–.268
.281
.387
.248
.318
.162
.260
.459
.289
.502
.528
.622
–.605
–.607
.313
–.545
1.248
1.195
.764
.777
NOTE: Pattern coefficients with an absolute value of .40 or greater are in
bold. Component 1 = Irritability/Dysphoria; Component 2 = Phobias.
in an independent replication sample. To provide a quantitative indicator of replicability, factor-congruence
coefficients were computed for best matched factor pairs
across the two samples. These coefficients can be interpreted as correlation coefficients, ranging from a minimum of 0 (indicating no similarity) to 1.0 (indicating
perfect correspondence between components).
The coefficients of congruence across Samples 1 and 2
for all components of each of the PSY-5 analyses are presented in Table 6, in the right-most column. As seen in
Table 6, all of the congruence coefficients exceeded .90,
providing strong evidence for a high degree of replicability for the component structure derived in the first
sample.
194
ASSESSMENT
TABLE 5
Promax Rotated Component Pattern Matrix: Introversion/Low Positive Emotionality
Sample 1
Component
Item
56
534
318
95
75
9
38
233
109
517
61
78
174
343
188
330
515
49
244
86
189
359
207
340
370
231
353
342
131
226
267
356
531
460
1
1.023
–1.016
–1.013
–.994
–.993
–.991
.978
.969
.955
.910
–.870
–.856
–.838
–.775
–.698
–.639
.621
–.582
–.575
.175
.368
–.188
.335
–.057
–.274
.268
–.299
–.208
–.163
.014
.162
–.213
–.207
.390
2
–.098
.254
.266
.034
.112
.071
–.060
–.032
.102
–.028
–.014
.072
.144
–.082
–.354
–.271
.401
–.483
–.226
–1.025
–.857
–.843
–.832
–.813
–.797
–.794
–.751
–.503
–.227
–.291
–.256
.307
.200
.199
Sample 2
Component
3
–.019
.108
–.127
–.066
.040
–.018
.033
.080
–.119
.048
–.036
.269
–.007
.266
–.004
–.351
–.129
.075
–.333
.020
–.149
.044
–.049
.061
.112
–.041
.105
–.143
–.019
–.800
–.741
–.598
–.480
–.464
1
1.001
–1.032
–1.020
–.965
–.980
–1.022
.995
.955
–.937
.938
–.865
–.891
–.785
–.588
–.619
–.464
.585
–.555
–.440
.244
.246
–.073
.377
.044
–.231
.071
–.245
–.337
–.374
.118
–.009
.203
.051
–.378
2
–.052
.244
.203
.008
.072
.150
–.081
–.029
.040
–.074
–.086
–.010
.160
–.352
–.416
–.233
.484
–.538
–.256
–1.040
–.830
–.906
–.897
–.898
–.848
–.657
–.799
–.358
–.122
–.147
–.170
.403
.397
.310
3
–.011
.085
.032
–.077
.016
–.062
.046
.097
.002
.053
–.011
.279
–.054
.094
–.057
–.545
–.167
.068
–.402
–.053
–.098
–.021
–.011
–.044
.074
–.071
.047
–.027
.051
–.873
–.850
–.470
–.396
–.391
NOTE: Pattern coefficients with an absolute value of .40 or greater are in bold. Component 1 = Disengagement/Anhedonia; Component 2 = Low Sociability; Component 3 = Low Diligence/Hypomania.
Reliability Analyses
As a final step for evaluating the integrity of the derived
facet scales, the internal consistency reliabilities of the
facet score were evaluated in a third, independent sample.
An independent sample was used because, as noted by
Saucier (1998), the internal consistency reliability of scale
scores may be artificially inflated in the sample in which
the scales were derived via PCA.
The internal consistency reliabilities of the facet scale
scores from Sample 3, in the form of Chronbach’s alpha,
are presented in Table 6, along with the MMPI-2 item
numbers composing each facet. For comparison purposes,
the alphas in both Samples 1 and 2 are also presented in
Table 6. As seen in Table 6, with the exception of two fac-
ets, the internal consistencies of the facet scale scores
ranged from .41 for the Low Diligence/Hypomania facet
to .86 for the Irritability/Dysphoria facet. The internal
consistency estimates were quite stable across all three
samples.
DISCUSSION
The primary goal of the study was to empirically derive
facet-level subscales for the MMPI-2 PSY-5 scales using
PCA, in a manner similar to the methodology recently
employed by Bolinskey et al. (2004) to develop the Minnesota Multiphasic Personality Inventory–Adolescent
(MMPI-A) PSY-5 facet subscales. Current results indicate
Arnau et al. / PSY-5 FACETS 195
TABLE 6
Facet Scale Item Composition and Reliabilities of Minnesota Multiphasic Personality Inventory–2
(MMPI-2) Personality Psychopathology Five (PSY-5) Domain and Facet Scales
Cronbach’s Alpha
Facet
AGGR
Assertiveness
Physical/Instrumental Aggression
Grandiosity
PSYC
Psychotic Experiences
Paranoia
Mistrust/Withdrawal
DISC
Antisocial History/ Norm Violation
Impulsivity/Low Harm Avoidance
NEGE
Irritability/Dysphoria
Phobias
INTR
Disengagement/Anhedonia
Low Sociability
Low Diligence/Hypomania
Items
Sample 1
(70), 239, 350, (446), 452, (503), 521
27, 85, 134, 323, 324, 414, 423, 548
50, 346, 358
24, 72, 96, 198, 319, 336, (427), 466, 490, 508, 551
42, 99, 138, 144, 259, 355, 361
48, (184), 241, 315, 374, 448, 549
(34), 35, 84, (100), 103, 105, (121), 123, (126),
209, 250, (266), 284, 344, 362, 412, 418, 431
88, (154), (309), (351), 385, (402), 477, (497)
37, 52, 82, 93, 116, 166, 196, 213, 290, 305, (372),
375, 389, 390, (405), 407, 415, 442, 444, 451,
513, 542, 556, (564)
329, 397, 395, 435
(9), 38, (49), 56, (61), (75), (78), (95), (109),
(174), (188), 233, (244), (318), (343), 517, 515,
(534)
(86), (189), (207), (231), (340), (353), (359), (370)
(226), (267), (356)
Sample 2
Sample 3
Rc
.68
.68
.65
.50
.74
.57
.60
.59
.71
.66
.68
.63
.50
.75
.57
.63
.60
.71
.66
.67
.62
.49
.74
.58
.63
.57
.71
.74
.48
.88
.74
.47
.88
.74
.48
.88
.994
.984
.87
.53
.80
.87
.55
.81
.86
.58
.81
.991
.966
.78
.72
.44
.79
.73
.39
.80
.72
.41
.990
.982
.958
.996
.997
.966
.975
.968
.936
NOTE: Rc = factor congruence coefficient across Samples 1 and 2. Items in parentheses are scored in the false direction. Facet items are listed in descending
order of component pattern coefficient magnitude. AGGR = Aggressiveness; DISC = Disconstraint, INTR = Introversion/Low Positive Emotionality;
PSYC = Psychoticism; NEGE = Neuroticism/Negative Emotionality.
we were able to identify conceptually meaningful and
replicable PSY-5 subscales with potentially important
clinical and research utility.
For the AGGR scale, three facets were identified,
Assertiveness, Physical/Instrumental Aggression, and
Grandiosity, with alpha coefficients ranging from .50 to
.68. All three of the facet names are concordant with the
more narrow bandwidth constructs described by Harkness
and McNulty (1994) as the theoretical content of the PSY5 AGGR construct. Two of the facets, Physical/Instrumental Aggression and Grandiosity, closely parallel the two
facets for the MMPI-A version of this scale, Hostility and
Grandiosity/Indignation, identified by Bolinskey et al.
(2004), although a third facet did not emerge for the
MMPI-A. Rather, all three MMPI-A items that correspond
to the MMPI-2 Grandiosity items loaded on the MMPI-A
Grandiosity/Indignation component. Furthermore, for the
MMPI-2, the emergence of the Assertiveness component
can probably be explained by the observation that items
illustrative of this facet (e.g., Items 350, 446, and 503) are
not represented in the item pool of the MMPI-A AGGR
scale.
Three facets of the PSYC scale, Psychotic Experiences,
Paranoia, and Mistrust/Withdrawal, were identified by the
present study. This was a slightly different structure than
that found by Bolinskey at al. (2004) for the MMPI-A, in
which two facets, Psychotic Beliefs/Experiences and Odd
Mentation, were found for the PSYC scale. One possible
explanation for the emergence of the Mistrust/Withdrawal
component on the MMPI-2 is that the MMPI-A PSYC
scale appears to contain a smaller percentage of items that
are not overtly psychotic in nature in comparison to the
MMPI-2 PSYC scale. The reliabilities of PSYC facet
scales identified in the study ranged from .57 to .60. The
content areas of the three PSYC facet subscales are consistent with Harkness and McNulty’s (1994) description of
the PSYC construct.
The DISC construct includes elements of behavioral
disinhibition, sensation seeking, and nontraditional values
(Harkness et al., 2002). These lower order features are
illustrated by our empirical identification of facets that tap
these content areas, that is, Antisocial History/Norm Violation and Impulsivity/Low Harm Avoidance. Similarly,
Bolinskey at al. (2004) found two facets for the MMPI-A
196
ASSESSMENT
version of the DISC scale. However, for the MMPI-A, an
impulsivity facet did not emerge, and antisocial behaviors
and violation of societal norms emerged as two distinct
facets, Delinquent Behaviors and Attitudes and Norm Violation. In the present study, although the Antisocial History/Norm Violation facet scores demonstrated good reliability, as evidenced by an alpha of .74, the reliability of
the Impulsivity/Low Harm Avoidance facet was less than
acceptable, with an alpha of only .48.
For NEGE, two facets were identified, Irritability/
Dysphoria and Phobias, which is in contrast to the finding
that the MMPI-A version of this scale was unidimensional
(Bolinskey et al., 2004). In the present study, the Irritability/Dysphoria facet displayed excellent reliabilities, with
alphas between .86 and .87 across all three samples, but
the reliabilities of the Phobias facet were less acceptable,
with alphas ranging from .53 to .58 across the three samples. Future research should focus on the psychometric
utility of this facet subscale. Given the relatively short
length of the Phobias facet, and its relatively low reliability, it may ultimately prove to be the case that facet subscales for NEGE are not psychometrically warranted.
Finally, three facets were identified for the INTR scale:
Disengagement/Anhedonia, Low Sociability, and Low
Diligence/Hypomania. Similarly, Bolinskey et al. (2004)
identified two nearly identical facets, Low Drive/
Expectations and Low Sociability, for the MMPI-A version of this scale, but a third facet for the MMPI-A version
did not emerge. Two of the three facets identified by the
present study displayed very good reliabilities. However,
the Low Diligence/Hypomania scale did not demonstrate
acceptable reliabilities, with alphas ranging between .40
and .43. Even though the item composition of this facet
was relatively small, the reliability is probably still too
poor to allow for meaningful scale interpretations. However, this scale, as well as the Impulsivity/Low Harm
Avoidance and Phobias subscales (described previously),
should be examined in future research to determine if there
are any replicable, noteworthy correlates that may make
them worthy of interpretation. In addition, even the facet
scales with relatively low reliabilities may be clinically
useful if they are used strictly to clarify the item content
when there are elevations on the parent domain scale.
The facet subscales identified in the present study have
potential clinical usefulness in a manner similar to the
Harris-Lingoes subscales (Harris & Lingoes, 1968) and
the Content Component Scales (Ben-Porath & Sherwood,
1993). The Harris-Lingoes subscales are commonly used
to “refine” or identify which content areas are contributing
to elevations on a corresponding basic clinical scale. For
example, the extent of endorsement of content areas for a
given elevation on Scale 6 (Pa) could be further investigated by examining T scores for the Harris-Lingoes
subscales Persecutory Ideas (Pa1), Poignancy (Pa2), and
Naivete (Pa3). Similarly, when attempting to identify clinically applicable correlates for an elevation on the Content
Scale Bizarre Mentation (BIZ), an investigation of the
Content Component Scales Psychotic Symptomatology
(BIZ1) and Schizotypal Characteristics (BIZ2) may
provide additional important information in terms of content areas.
Similar to the Harris-Lingoes and Content Component
scales, the PSY-5 facet subscales identified in the present
study could be used to identify important content areas
contributing to PSY-5 scale elevations. However, given the
small number of items and consequently low internal consistencies of several of the facets, it is important that the
facets not be used as stand-alone scales for clinical interpretation. Rather, the facet scales should be used only in
conjunction with their parent domain scales, for the purpose of clarifying the specific item content responsible for
elevations on the parent domains. As recommended by
Ben-Porath and Sherwood (1993) for the Content Component Scales and by Graham (2000) for the Harris-Lingoes
subscales, the PSY-5 facet scales should be used to clarify
the content of the parent PSY-5 scale only when the parent
PSY-5 scale is clinically elevated, with a T score of at least
60 or higher. This recommendation is especially important, given that the internal consistency reliability of several of the facets is quite low. In addition, facet subscale
scores may also be most useful when a particular facet
score is at least 10 T-score points greater than the other facets from that domain, as suggested by McNulty, BenPorath, Graham, and Stein (1997), as cited in Graham,
2000) for the use of the Content Component Scales. However, future research will determine whether this recommendation is borne out empirically for the PSY-5 scales.
One final note should be made about the brevity of
some of the facet subscales. Specifically, three of the facet subscales (Low Diligence/Hypomania, Phobias, and
Grandiosity) contain only three to four items. Although T
scores have not yet been developed for the facet scales,
when they are, clinicians should be cautious about interpreting clinically elevated T scores for these particular facets, because the endorsement (or nonendorsement) of simply one item may make the difference between a clinical
versus nonclinical scale elevation.
Future research on dimensional trait approaches to personality disorders may benefit from exploring the relationship between the PSY-5 facets identified by the present
study and personality disorder diagnosis. The PSY-5 facets could potentially yield a more detailed description of
personality disorders in terms of dimensional personality
traits than that offered by the overall PSY-5 scales on their
own. Therefore, this may be a potentially fruitful area for
future research.
Arnau et al. / PSY-5 FACETS 197
In sum, this study identified conceptually meaningful
and replicable facet-level subscales for the MMPI-2 PSY5 scales, with content areas largely consistent with
Harkness and McNulty’s (1994) PSY-5 model. Important
future directions include examination of the test-retest
reliabilities of the facet scales as well as the development
of gender specific T-score conversions for the PSY-5 facet
subscales based on the MMPI-2 normative sample. In addition, given the recent development of nongendered T
scores for other MMPI-2 scales (see Ben-Porath & Forbey,
2003), development of nongendered T-score conversions
would also be advised for the PSY-5 facet subscales. Uniform T scores would appear to be the most appropriate
form of transformation given the PSY-5 domain scales use
uniform T-scores. Finally, research on the external correlates of the PSY-5 facet scales is needed. Grounding the
PSY-5 facet subscales in a strong nomological network of
related external variables will be important not only to
demonstrate construct validity of scores from these scales
but also to provide a basis for clinical interpretation of the
scales. Finally, correlations between the facet scales and
external variables should also be compared with the correlate patterns of the overall PSY-5 scales. The purpose of
such a line of research would be to determine whether the
facet scales offer a differential pattern of correlates beyond
those correlates identified with the overall PSY-5 scales.
In some cases, the correlates of facets may be found to be
redundant with the parent PSY-5 scales in predicting external criteria, whereas in other cases facet scale correlates
may prove richer and more descriptive than correlates
established for the broader domains. The latter finding
would offer some evidence for the incremental predictive
validity of the facets above and beyond the overall PSY-5
scales.
NOTE
1. The items composing the final facet subscales are presented in
Table 6 by item number. The interested reader is encouraged to consult a
Minnesota Multiphasic Personality Inventory–2 item booklet to evaluate
the item content of the facets.
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Randolph C. Arnau, Ph.D., is an assistant professor and clinic
director in the Department of Psychology at the University of
Southern Mississippi. He conducts research in personality
assessment and positive psychology. He received his doctoral
degree from Texas A&M University.
Richard W. Handel, Ph.D., is an assistant professor in the Department of Psychiatry and Behavioral Sciences at Eastern Virginia Medical School, where he is active in a number of ongoing
MMPI-2 and MMPI-A research projects. He received his clinical psychology degree from Kent State University.
Robert P. Archer, Ph.D., is the Frank Harrell Redwood Distinguished Professor of Psychiatry and Behavioral Sciences at the
Easter in Virginia Medical School as well as the director of the
Division of Psychology.