The PDF of this article has been modified from its original version. The copyright statement for the MMPI-2 Psy-5 Facet scales was added to Table 6. 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 188 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 190 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. 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T., Jr. (2002). A description of the DSM-IV personality disorders with the five-factor model of personality. In P. T. Costa & T. A. Widiger (Eds.), Personality disorders and the five-factor model of personality (2nd ed.). Washington, DC: American Psychological Association. Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442. 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.