ASSESSMENT
10.1177/1073191105280987
Miller
et al. / SCORING PERSONALITY DISORDERS WITH FFM
A Simplified Technique for Scoring DSM-IV
Personality Disorders With the
Five-Factor Model
Joshua D. Miller
University of Pittsburgh Medical Center
R. Michael Bagby
University of Toronto
Paul A. Pilkonis
Sarah K. Reynolds
University of Pittsburgh Medical Center
Donald R. Lynam
University of Kentucky
The current study compares the use of two alternative methodologies for using the FiveFactor Model (FFM) to assess personality disorders (PDs). Across two clinical samples, a
technique using the simple sum of selected FFM facets is compared with a previously used
prototype matching technique. The results demonstrate that the more easily calculated
counts perform as well as the similarity scores that are generated by the prototype matching
technique. Optimal diagnostic thresholds for the FFM PD counts are computed for identifying patients who meet diagnostic criteria for a specific PD. These threshold scores demonstrate good sensitivity in receiver operating characteristics analyses, suggesting their
usefulness for screening purposes. Given the ease of this scoring procedure, the FFM count
technique has obvious clinical utility.
Keywords: Five-Factor Model; personality disorders; prototypes
Costa and McCrae’s (1992) Five-Factor Model (FFM)
of personality has been a highly generative research tool in
the service of exploring the relations between personality
disorder (PD) constructs and “normal” or general personality functioning. Much of this research has been driven by
a general dissatisfaction with the categorical approach
taken by the official classification manual used throughout
psychiatry and psychology—Diagnostic and Statistical
Manual of Mental Disorders (4th ed.; DSM-IV; American
Psychiatric Association, 1994)—and a belief that dimensional models of adaptive or maladaptive personality features provide a better representation of these phenomena
(Livesley, 2001; Widiger, 1993). In addition to the FFM,
several prominent personality theorists have put forth alternative personality frameworks and assessment tools
that can be used to examine pathological variants of
This research was supported by National Institute of Mental Health Grant T32 MH18269, Clinical Research Training for Psychologists (principal investigator P. A. Pilkonis), which provided postdoctoral fellowship support to Joshua D. Miller. Please note that
Joshua D. Miller, Ph.D., is now in the Department of Psychology at the University of Georgia. Correspondence concerning this article
should be addressed to Joshua D. Miller, Ph.D., Department of Psychology, University of Georgia, Athens, GA 30602; e-mail: jdmiller@
uga.edu.
Assessment, Volume 12, No. 4, December 2005 404-415
DOI: 10.1177/1073191105280987
© 2005 Sage Publications
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 405
personality, such as Clark’s (1993) Schedule for NonAdaptive and Adaptive Personality, Livesley’s (1986,
1987; Livesley, Jackson, & Schroeder, 1989) Dimensional
Assessment of Personality Pathology, Cloninger’s
(Cloninger, Svrakic, Bayon, & Przybeck, 1999; Cloninger,
Svrakic, & Przybeck, 1993) seven-factor temperament
and character model, and Harkness and McNulty’s
(Harkness, McNulty, & Ben-Porath, 1995) Minnesota
Multiphasic Personality Inventory-2-based Personality
Psychopathology Five Scales.
The dissatisfaction and subsequent proposal of alternative models of PD stem from a variety of reasons, including the inability of the DSM-IV PD categories to account
for a range of clinically significant, personality-related
problems that either (a) do not fit with any of the currently
measured constructs or (b) are not severe enough to meet
DSM-IV criteria (Westen & Arkowitz-Westen, 1998).
Others have commented on the generally limited reliability (Cacciola, Rutherford, Alterman, McKay, &
Mulvaney, 1998; Klonsky, Oltmanns, & Turkheimer,
2002) and validity (Clark, Livesley, & Morey, 1997) of
DSM-IV PDs. It is the belief of many personality theorists
that PDs are best conceptualized as comprising either extreme variants of general personality traits (Costa &
Widiger, 1994, 2002) or alternative psychobiological dimensions, such as anxiety/inhibition, impulsivity/
aggression, affective instability, and cognitive/perceptual
organization (Siever & Davis, 1991). By deconstructing
the PDs into their underlying dimensions, a wider array of
maladaptive personality styles can be conceptualized and
assessed and issues such as comorbidity become less problematic (Lynam & Widiger, 2001).
Although a number of trait models have been successfully used in the service of understanding PDs, the most
frequently used has been the FFM. However, the manner
in which the FFM has been used to understand PDs has
evolved during the past decade. Widiger, Trull, Clarkin,
Sanderson, and Costa (1994) laid the groundwork for
much of this research by articulating specific hypotheses
regarding how each DSM-IV PD would be conceptualized
via the 30 specific personality traits (facets) of the FFM.
Numerous studies have since tested the success of the
FFM in capturing the PDs in general and the Widiger et al.
(1994) hypotheses specifically (see Saulsman & Page,
2004, for meta-analysis of FFM domains and PDs; e.g.,
Axelrod, Widiger, Trull, & Corbitt, 1997; Bagby, Costa,
Widiger, Ryder, & Marshall, 2005; Blais, 1997; Dyce &
O’Connor, 1998; Huprich, 2003; Reynolds & Clark, 2001;
Trull, 1992). The majority of this empirical work has involved an examination of the relations between the FFM
domains and facets and PD symptomatology using
bivariate correlations and multiple regression.
More recently, Lynam et al. developed a prototypematching technique in which FFM PD prototypes are generated through the use of expert ratings for both DSM-IV–
recognized PDs (Lynam & Widiger, 2001) and non-DSMIV–recognized forms of personality psychopathology,
such as psychopathy (Miller & Lynam, 2003; Miller,
Lynam, Widiger, & Leukefeld, 2001). These expertgenerated prototypes, which use all 30 FFM facets, can
then be matched to individuals’ FFM profiles (as assessed
by the Revised NEO Personality Inventory [NEO PI-R])
through the use of an intraclass correlation. This correlation, which takes into account profile agreement with regard to shape and absolute magnitude, can then be used as
an index of similarity to the pertinent PD constructs. This
technique was first successfully applied by Miller et al.
(2001) and Miller & Lynam (2003) to demonstrate that
psychopathy, a particularly virulent form of PD characterized by traits such as callousness, manipulativeness, lack
of remorse or empathy, egocentricity, and impulsivity,
could be captured by the FFM.
Following this, Lynam and Widiger (2001) solicited
expert ratings to develop FFM PD prototypes for all 10
DSM-IV PDs. Subsequently, these prototypes have been
tested in four studies. Trull, Widiger, Lynam, and Costa
(2003) demonstrated that the FFM prototype for borderline PD converged with other well-validated measures of
this PD as well as important criterion constructs. Recently,
Miller, Pilkonis, & Morse, (2004) and Miller, Reynolds, &
Pilkonis (2004) have examined all 10 of the Lynam and
Widiger (2001) prototypes across clinical samples and informant methodologies. Miller, Reynolds et al. (2004)
found support for the convergent, discriminant, and predictive validity and temporal stability of the FFM PD prototypes. Two studies have also demonstrated the
“resilience” of this technique to information source;
Miller, Pilkonis et al. (2004) demonstrated that FFM information derived from an informant could be used to score
the prototypes with equal validity, whereas Miller, Bagby,
and Pilkonis (in press) showed that data from a
semistructured interview of the FFM could also be
successfully used.
Despite the empirical success of the prototype-matching
technique across PDs and data source, researchers and clinicians may be reluctant to use this approach. The scoring
methodology is complex and requires a statistical program
to create the PD similarity scores.1 In addition, the scores
are not intuitively meaningful. One possible alternative is
to use simple additive counts to score individuals on DSMIV PDs, which would still use information from the Lynam
and Widiger (2001) FFM prototypes.2 To do this, one
would first have to identify which facets were considered
prototypically low or prototypically high for each PD (i.e.,
a facet with a score ≥ 4 or ≤ 2 on the Lynam & Widiger pro-
406
ASSESSMENT
totypes), reverse key the facets with a score of ≤ 2, and sum
the scores in the same (high) maladaptive direction (see
the appendix for count syntax and coding information). A
clinician or researcher would then simply add an individual’s scores across relevant facets. For example, according
to this strategy, the FFM PD count for histrionic PD would
involve adding together the following facets: selfconsciousness (a facet of neuroticism [N], which would be
reverse scored), impulsivity (N), gregariousness (a facet of
extraversion [E]), activity (E), excitement seeking (E),
positive emotions (E), openness to fantasy (a facet of
openness to experience [O]), openness to feelings (O),
openness to actions (O), trust (a facet of agreeableness
[A]), self-discipline (a facet of conscientiousness [C],
which would be reverse scored), and deliberation (C,
which would be reverse scored). These counts, which have
not been tested, would have greater clinical utility if they
work as well as the overall prototype-matching technique.
However, because they do not take into account the full
FFM profile (the number of facets used in the counts range
from 7 to 17), the counts may not perform as well as the
similarity scores.
In the current study, we examined the success of these
counts in comparison to the FFM PD similarity scores in
two samples, both of which have been previously used to
demonstrate the success of the FFM similarity scores.3 In
particular, we provide descriptive statistics for the FFM
counts across both samples. Next, we examine the convergent validity of the FFM counts in relation to PD symptom
counts generated by well-known PD measures and compare their performance to the FFM similarity scores. Finally, we present data from ROC analyses using FFM
counts and similarity scores to identify patients who met
criteria for the PD diagnoses.
METHOD
Sample 1
Participants and Procedures
The sample consisted of 115 patients (53 men, 62
women) assessed at the Psychological Assessment Service at a large tertiary care, medical school–affiliated, psychiatric facility located in a large, primarily Englishspeaking, North American metropolis. Ethnic status was
reported for 94 patients; 90 were of European descent, 2
were of African descent, 1 was of Asian descent, and 1 was
of Hispanic descent. Most of these referrals were outpatients (n = 100). Mood (n = 91, 79%) and anxiety (n = 9,
8%) disorders were the most common diagnoses. The
mean age of this sample was 41.4 (SD = 11.26).
All patients were assessed with the Structured Clinical
Interview for DSM-IV (SCID), Axis I Disorders (Version
2.0/Patient Form; First, Spitzer, Gibbon, & Williams,
1995) and completed the Structured Clinical Interview for
DSM-IV Personality Disorders–Personality Questionnaire (SCID-II/PQ; First, Gibbon, Spitzer, Williams, &
Benjamin, 1997) and NEO PI-R. Advanced clinical psychology interns (n = 5), two M.A.-level clinical psychologists, and a postdoctoral clinical fellow conducted the
interviews. Although interrater agreement was not formally determined, all interviewers were trained extensively in the interview procedures and carefully observed
and approved by a Ph.D.-level clinical psychologist prior
to conducting any interview.
Measures
SCID-II Personality Questionnaire (SCID-II/PQ). PD
symptomatology was assessed via a two-tiered approach.
First, all participants were assessed using the 119-item
self-report questionnaire version of the SCID-II (SCID-II/
PQ), on which items are answered using a yes-no response
format. Each of the 119 questions corresponds to the diagnostic criteria for the 10 different PDs in the main text of
DSM-IV and the two additional PDs listed in Appendix B
of DSM-IV. Following this, the SCID-II interview items
were asked for those disorders where full DSM-IV criteria
were met on the self-report measure. In the current study,
we used both dimensionalized sum scores (a sum of each
PD’s items) derived from the self-report report ratings for
each of the PDs and the actual no-yes diagnoses that use
self-report and interview data. Although self-report measures are prone to overestimating PDs, a number of studies
have shown that the dimensional self-report scales have
reasonable validity (e.g., Carey, 1994; Huprich, 2003).
The coefficient alphas for the self-report items ranged
from .32 (OCPD) to .84 (borderline PD), with a median
alpha of .69.
NEO PI-R. The NEO PI-R (Costa & McCrae, 1992)
was specifically designed to measure the FFM of personality and provides domain scores corresponding to N, E,
O, A, and C. The NEO PI-R consists of 240 self-report
items answered on a 5-point scale, with separate scales for
each of the five domains. Each scale consists of six correlated facets or subscales with eight items, for a total of 48
items for each scale. Internal consistency reliabilities for
the five domains ranged from .89 (A) to .94 (N), whereas
the internal consistency reliabilities of the facet scales
ranged from .56 to .89 (median coefficient alpha = .79).
FFM PD similarity scores. We calculated similarity
scores for each of the 10 DSM-IV PDs by using intraclass
correlations between participants’ obtained NEO-PI-R
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 407
facets scale scores and the expert-generated facet profiles
of the PD prototypes as described in Lynam and Widiger
(2001). An intraclass Q correlation (in which individuals’
FFM profiles and the 10 FFM PD prototypes are entered as
columns) was used because it considers both the shape and
elevation of individual scores (in comparison to the expert
prototypes) rather than the shape alone, as is the case with
a Pearson correlation. As such, it is a more stringent
measure of agreement.
FFM PD counts. The FFM PD counts represent an alternative method for scoring the Lynam and Widiger
(2001) prototypes. Rather than using a prototype-matching
technique as discussed earlier, a simple count is used in
which facets that were rated as being prototypically high
(≥ 4) or prototypically low (≤ 2) are summed together (see
the appendix for the 10 PD facet counts). However, facets
that are considered prototypically low (e.g., straightforwardness in antisocial PD) are reverse scored so that all
facets are scored in the direction of maladaptivity for that
specific PD.
Sample 2
Participants and Procedures
Participants were either inpatients or outpatients undergoing assessment or treatment at one of several facilities
affiliated with the University of Iowa. Outpatients were recruited from either the university medical center psychiatry clinic or the university-based psychology clinic staffed
by graduate students and faculty of the psychology department. Inpatients were recruited from the university medical center psychiatric units, which serve a general
psychiatric population, with a small minority of participants (10%) recruited from the eating disorder specialty
unit. Individuals with personality pathology were not selectively recruited for participation. Rather, the goal of the
sampling strategy was to approximate a general clinical
sample that included a variety of clinical problems and a
wide range of severity of psychopathology. Patients who
met the following inclusion criteria were asked to participate: age of 18 years or older, high school diploma or
GED, and absence of active psychosis, organic brain
syndrome, or mental retardation (per available chart
information).
The data presented here are from 94 participants: 58
outpatients (62%) and 36 inpatients. The sample included
69 women (73%) and 25 men. Mean age was 34.6 (range =
18 to 76, SD = 10.5). The modal participant was Caucasian
(96%), unmarried (71%), and employed (72%). The mean
of self-reported age of first psychiatric contact was 24.4
(range = 5 to 59, SD = 10.5), and 55% of the sample had
had at least one prior psychiatric hospitalization (M = 3.0,
SD = 4.6). Axis I disorders were not formally assessed;
however, available Axis I chart diagnoses made as part of
routine clinical care were noted. These diagnoses often
had been made years prior to the present study and may
have limited validity. Nonetheless, the majority of participants received an Axis I diagnosis (88%), with the most
frequent diagnosis a mood disorder (53%).
Measures
Structured Interview for DSM-IV Personality (SIDPIV). The SIDP-IV (Pfohl, Blum, & Zimmerman, 1997) is a
semi-structured interview that contains probe questions
developed to assess each of the DSM-IV PD criteria. The
questions are grouped into 10 areas of functioning (e.g.,
close relationships, work style, perception of others)
rather than by diagnoses. Following the interview, each
criterion is rated on a 4-point scale (0 = not present; 1 =
subthreshold features; 2 = clearly present, clinically significant; 3 = prominent symptom). Dimensional scores
were calculated for each diagnosis by summing the component criterion scores (0 to 4). Diagnoses were scored in a
manner consistent with the SIDP manual and DSM-IV. Interviews were conducted by two clinical psychology graduate students who were trained in the administration and
scoring of the SIDP-IV by an author of the instrument. As
suggested by the SIDP-IV authors, chart information,
when available, was used as additional data in rating each
criterion. To examine the interjudge agreement of the PD
ratings, a second rater reviewed audiotapes of a subset of
interviews (18%) and provided independent ratings.
Intraclass correlation coefficients (ICC) were computed
for the dimensional scores of each PD scale, and the mean
ICC for the 10 PDs was .90. Schizotypal PD was the least
reliably rated criteria set (ICC = .77), whereas borderline
and avoidant were the most reliably rated (ICCs = .96). In
terms of internal consistency, coefficient alphas ranged
from .53 (schizoid) to .79 (borderline, avoidant), with a
median of .72.
FFM measures. All the FFM measures (e.g., NEO PIR; FFM PD similarity scores, FFM PD counts) were the
same as Sample 1.
RESULTS
Descriptive Statistics
Table 1 presents descriptive statistics for the FFM PD
count scores. The mean FFM counts were quite similar
across the samples (e.g., M FFM PD counts for paranoid =
408
ASSESSMENT
TABLE 1
Descriptive Characteristics of FFM PD Counts
FFM Counts
PAR
PAR
SZD
SZD
SCT
SCT
APD
APD
BPD
BPD
HST
HST
NAR
NAR
AVD
AVD
DEP
DEP
OC
OC
Sample
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
Min.
Max.
M
SD
M (by Facets)
64
56
56
60
59
53
131
133
57
97
122
125
94
102
98
90
84
66
93
138
219
201
194
191
177
166
342
354
249
225
248
266
251
282
247
252
185
183
287
271
133.81
131.47
131.60
119.46
125.57
120.10
226.71
233.47
159.69
164.55
190.77
200.24
163.63
164.67
185.39
175.88
135.94
132.68
204.03
204.48
30.90
26.57
26.97
28.07
23.44
20.72
33.97
35.02
28.58
26.86
25.95
31.94
29.04
27.58
30.98
31.24
20.05
21.82
29.95
30.82
13.38
13.15
16.45
14.93
17.94
17.16
13.34
13.73
17.74
18.28
15.90
16.69
12.59
12.67
18.54
17.59
19.42
18.95
15.69
15.73
NOTE: FFM = Five-Factor Model; PD = personality disorder; PAR = paranoid; SZD = schizoid; SCT = schizotypal; APD = antisocial; BPD = borderline;
HST = histrionic; NAR = narcissistic; AVD = avoidant; DEP = dependent; OC = obsessive-compulsive. Because the counts have a different number of facets (ranging from 7 to 17), we provide the mean score taking into account the number of facets.
133.81 [Sample 1] and 131.47 [Sample 2]), with a mean
difference of 5.53. Because the counts use a different number of facets (ranging from 7 to 17), we also provide mean
count scores that take this into account (thus making it possible to compare scores across the FFM counts).
Correlations Between FFM PD
Counts and Similarity Scores
We next examined the correlations between the FFM
PD similarity scores and the FFM PD counts across the
samples. In Sample 1, the correlations ranged from .75
(histrionic) to .97 (avoidant), with a median r of .91. There
was one case of a gender difference: The correlation between the similarity score and count was significantly different for dependent PD, with an r of .89 for men and .78
for women. In Sample 2, the correlations between the
FFM similarity scores and the FFM counts ranged from
.77 (narcissism for women only) to .98 (avoidant), with a
median r of .91. In this sample, there were two significant
gender differences in the size of the correlations; the correlation for narcissism was .94 for men and .77 for women,
whereas the correlation for dependent was .93 for men and
.80 for women.
Correlations Between FFM PD
Counts and PD Symptom Counts
Next, we examined the convergent validity of the FFM
counts with PD symptom counts from well-known measures of PD symptoms (see Table 2). In Sample 1, the correlations between the FFM PD counts and the PD
symptom counts ranged from –.02 (OCPD) to .64 (borderline), with a median r of .40. In Sample 2, the correlations
between the FFM PD counts and the PD symptom counts
ranged from –.15 (histrionic for men only) to .64
(avoidant), with a median r of .45. As noted, there was one
case in which the correlation between the FFM count and
the PD symptoms was significantly different across gender; in Sample 2, the correlation between the FFM histrionic count and a histrionic PD diagnosis was significantly
larger (and positive) for women.
We next tested, in each sample, whether the correlation
for each FFM PD count was significantly different than the
correlation previously reported (Miller et al., in press;
Miller, Reynolds, et al., 2004) for its respective FFM similarity score. Of the 21 comparisons, only 1 was significantly different. The correlation between the FFM
dependent count and the dependent symptom count (r =
.34) in Sample 1 was significantly larger than the correlation for the FFM dependent similarity score and dependent
PD count (r = .24). These findings suggest that the differ-
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 409
ences with regard to convergent validity are quite minimal
between the FFM similarity scores and the counts.
As can be seen in Table 2, we calculated weighted effect sizes using meta-analytic techniques (i.e., Fisher ztransformed rs were combined, taking into account
sample size, to obtain a mean effect size and then were
transformed back to rs) for the FFM counts. Overall, the
effect sizes ranged from .02 to .63, with one small effect
size (OCPD), six medium effect sizes (paranoid,
schizotypal, antisocial, histrionic, narcissistic, and dependent), and three large effect sizes (schizoid, borderline,
and avoidant).
We also examined the divergent validity of the FFM PD
counts with the PD symptom counts. In Sample 1, the
discriminant validity correlations ranged from –.34 (FFM
histrionic and avoidant PD symptoms; FFM OCPD and
borderline PD symptoms) to .64 (FFM schizotypal and
avoidant PD symptoms), with an absolute median correlation of .21. In Sample 2, the discriminant correlations
ranged from –.53 (FFM histrionic and schizoid PD symptoms) to .63 (FFM schizotypal and avoidant PD symptoms), with an absolute median correlation of .21.
TABLE 2
Correlations Between FFM PD
Counts and PD Symptom Counts
FFM PD
PD
Sample 1
Sample 2
Paranoid PD count
Schizoid PD count
Schizotypal PD count
Antisocial PD count
Borderline PD count
Histrionic PD count
Narcissistic PD count
Avoidant PD count
Dependent PD count
OCPD PD count
.41**
.40**
.40**
.36**
.64**
.33**
.45**
.63**
a
.34 **
–.02
.44**
.60**
.28**
.51**
.56**
–.15/.41**
.45**
.64**
.46**
.08
Weighted
Effect Size
.42
.50
.35
.43
.61
.31
.45
.63
.40
.02
NOTE: FFM = Five-Factor Model; PD = personality disorder; OCPD =
obsessive-compulsive PD. / = a significant gender difference in the size of
the correlation. The relation for men is presented before the diagonal,
women after it.
a. Correlation is significantly different between the FFM count and PD
symptoms and the FFM similarity score and PD symptoms from Miller et
al. (in press), which was .24.
*p ≤ .05. **p ≤ .01.
Receiver Operating Characteristics (ROCs)
Finally, in the interest of clinical utility and our desire to
provide a basis for initial decision making regarding the
use of the FFM counts and similarity scores to identify
PDs, we conducted a series of ROC analyses. These analyses provide important diagnostic efficiency statistics, such
as sensitivity, specificity, and positive and negative predictive power, associated with the raw scores. Because these
analyses require that a certain number of individuals receive a PD diagnosis, we limited our analyses in each sample to those PDs that had a sufficient prevalence. This,
coupled with the poor performance of the FFM counts and
similarity scores to capture OCPD, limited us to testing 8
of the 10 PDs in Sample 1 and 3 of the 10 in Sample 2. Table 3 provides information regarding the PD prevalence in
each sample, the area under the curve (AUC) accounted
for by the similarity scores and counts, the first raw score
that manifested a sensitivity equaling or exceeding .80 for
each method, and other diagnostic efficiency statistics.4
The AUC was significant for 10 of 11 similarity scores and
for 11 of 11 counts across the two samples. The median
AUCs accounted for by the similarity scores and counts,
across samples, was .77 and .78, respectively. We also calculated median sensitivities, specificities, positive predictive power (PPP), and negative predictive power for these
cut scores. For the similarity scores, the medians for these
diagnostic statistics were .82, .61, .31, and .94, respectively.
For the counts, the medians for these diagnostic statistics
were .82, .63, .31, and .94, respectively.
DISCUSSION
The use of measures of general personality to understand and assess constructs has been primarily a matter of
theoretical interest aimed at demonstrating that PDs are
extensions or variants of general personality traits. Recent
studies have put forth a new technique by which an individual’s general personality profile, with regard to the
FFM, can be matched to the PDs. However, because of the
complexity of the scoring methodology, the probability of
this technique being used in clinical settings seems low. As
we have noted previously (Bagby, Schuller, Marshall, &
Ryder, 2004; Miller, Reynolds, et al., 2004), we believe
that using the FFM as an assessment tool for both adaptive
and maladaptive personality variants has real advantages.
So in conjunction with this belief, we sought to develop a
manner of scoring PDs with FFM data that also uses the
broad expertise collected in the Lynam and Widiger
(2001) prototypes. As noted earlier, these expertgenerated prototypes have been quite successful in capturing PD constructs, including those in DSM-IV, such as borderline PD, and those not included, such as psychopathy.
Given the general success of these prototypes, it seemed
particularly important to develop a scoring methodology
that used, in some form, the prototype information but did
so in a manner that might have real world applications.
410
29/25%
29/25%
21/18%
21/18%
9/8%
9/8%
19/17%
19/17%
52/45%
52/45%
25/22%
25/22%
58/50%
58/50%
9/8%
9/8%
Paranoid similarity
Paranoid count
Schizoid similarity
Schizoid count
Schizotypal similarity
Schizotypal count
Antisocial similarity
Antisocial count
Borderline similarity
Borderline count
Narcissistic similarity
Narcissistic count
Avoidant similarity
Avoidant count
Dependent similarity
Dependent count
.69**
.69**
.77**
.79**
.80**
.78**
.67*
.69**
.78**
.80**
.83**
.80**
.75**
.75**
.59
.72*
.87**
.85**
.73*
.78**
.78**
.75**
Sample 1 Sample 2
Area
–.32
116.5
–.11
131.0
.28
131.5
–.45
226.0
–.09
156.0
–.41
170.5
.20
179.5
.13
129.5
.34
192.0
.23
143.5
–.03
148.5
Sample 1 Sample 2
Raw Scores
.83
.83
.81
.91
.89
.89
.84
.84
.81
.81
.80
.80
.81
.81
.89
.89
.82
.82
.80
.80
.86
.82
Sample 1 Sample 2
Sensitivity
NOTE: FFM = Five-Factor Model; PD = personality disorder; PPP = positive predictive power; NPP = negative predictive power.
*p ≤ .05. **p ≤ .01.
5/5%
5/5%
0/0%
0/0%
0/0%
0/0%
2/2%
2/2%
22/23%
22/23%
3/3%
3/3%
22/23%
22/23%
10/11%
10/11%
Sample 1 Sample 2
PDs
PD Base Rate/
Percentage
.37
.34
.54
.54
.70
.64
.46
.55
.67
.65
.66
.72
.61
.63
.30
.38
.86
.85
.58
.75
.63
.57
Sample 1 Sample 2
Specificity
PPP
.31
.30
.28
.31
.20
.17
.24
.27
.67
.66
.39
.44
.68
.69
.10
.11
.64
.62
.19
.28
.41
.37
Sample 1 Sample 2
TABLE 3
Receiver Operating Characteristics of FFM PD Similarity Scores and Counts
.86
.85
.93
.96
.99
.99
.94
.95
.81
.80
.92
.93
.76
.77
.97
.98
.94
.94
.96
.97
.94
.91
Sample 1 Sample 2
NPP
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 411
To test the comparability of the FFM PD counts with
the FFM PD similarity scores, we first examined the correlations between these two overlapping scoring methodologies across the two samples. The FFM PD counts were
highly correlated with FFM PD similarity scores; the median correlations were .91 and .91 in Samples 1 and 2, respectively. We then compared the size of the correlations
between these two FFM measures and PD symptom
counts across two similar clinical samples. Finally, using
receiver operator characteristics, we identified cut scores
for the FFM similarity scores and counts and looked at the
generalizability of these cut score across the samples.
Across the analyses and samples, the FFM similarity
scores and counts performed in a nearly identical fashion.
The median correlations for the FFM counts with the PD
symptom counts were .40 and .45 in Samples 1 and 2, respectively. The median correlations between the similarity
scores and the PD symptom counts, across the two samples, were .39 (Miller et al., in press) and .50 (Miller,
Reynolds et al., 2004), respectively. In fact, there was only
one case in which a correlation was significantly different
between the counts and similarity scores; in Sample 1, the
correlation for the dependent count was stronger than the
respective correlation using the similarity score. This difference, however, was small (d = .11).
As has been the case with the FFM similarity scores, the
FFM counts were not significantly related to obsessivecompulsive PD (OCPD). This is not an uncommon finding; numerous studies have found that OCPD is not well
captured by the FFM (Ball, Tennen, Poling, Kranzler, &
Rounsaville, 1997; Huprich, 2003; Saulsman & Page,
2004; cf. Dyce & O’Connor, 1998). The two other PDs
that are typically more weakly represented by the FFM,
schizotypal and dependent PD, were significantly related
to their respective PD diagnoses, albeit less strongly and
consistently across the samples. There are several potential explanations for this failure. One explanation put forth
by Haigler and Widiger (2001) is that the NEO PI-R does
not include an adequate number of items written to assess
maladaptivity at both the high and low poles of the domains. So PDs hypothesized to be based, in part, on high
scores on domains such as C (OCPD), A (dependent), or
openness (schizotypal) may be more poorly assessed by
the FFM. Haigler and Widiger (2001) found that manipulating the NEO PI-R items to include more items representing maladaptively high variants of the FFM domains
increased the size of the correlations between OCPD, dependent, and schizotypal PDs with C, A, and openness, respectively. Miller, Reynolds, et al. (2004) also suggested
that it is possible that the prototypes may be mistaken in
their view of certain disorders, such as the relation between dependent PD and A and C. For example, results
from the current samples are consistent with those reported in a meta-analysis by Bornstein and Cecero (2000)
that suggest that dependent PD is negatively correlated
with trust (a facet of A) and certain C facets (e.g., competence, achievement striving, self-discipline) rather than
positively, as postulated by the Lynam and Widiger (2001)
prototypes. These findings are further complicated by the
idea that there may be different forms of dependency,
which have different FFM conceptualizations (Pincus,
2002). Further examination will be necessary to tease
apart these weaker relations and determine if they are an
artifact of the personality measure or a case of
misconceptualization with regard to the expert ratings.
The findings were also consistent across samples regarding which PDs were best captured by the FFM count.
In particular, schizoid, borderline, and avoidant PDs were
well accounted for by the FFM counts across both clinical
samples. Weighted effect sizes for the relations between
these three counts and PD symptoms across the samples
were large.
One innovative aspect of this study is that it provides
cut scores from one to two clinical samples that can be
used for the FFM similarity scores and counts. These
analyses are important because they provide information
that allows these two scoring techniques scores to be used
in clinical settings as a screening measure for several of the
PDs. The data gleaned from these analyses, although
tentative, represent an important step toward making these
approaches clinically useful. However, because of the
sample sizes and the use of a self-report PD measure
(Sample 1), these scores should be tested further to
see whether they replicate in other clinical samples. As
with the bivariate relations, borderline and avoidant PDs
were well accounted for by the similarity scores and
counts, as the diagnostic efficiency statistics worked quite
well. Although 21 of 22 cut scores manifested a significant
AUC, the similarity scores and counts for paranoid and antisocial PD had scores that would be deemed poor (e.g., .6
to .7). The rest of the PDs tested had, for the most part, cut
scores that resulted in either fair (e.g., .7 to .8) or good
(e.g., .8 to .9) AUCs. As we have advocated for the use of
the FFM as a screening tool and not a stand-alone, comprehensive PD assessment battery, we believe that sensitivity
is more important than specificity because the false
positives should be ruled out on further assessment. Given
that we identified cut scores with sensitivities of .80 or
higher, 16 of the 22 cut scores also had specificities higher
than .50. In fact, the median specificity score was .61 for
the similarity scores and .63 for the counts. The FFM similarity scores and counts also demonstrated good negative
predictive power (medians = .94 and .94). However, the
same was not true for PPP (medians = .31). Similar to most
412
ASSESSMENT
self-report PD questionnaires, the FFM similarity scores
and counts demonstrate a clear tendency toward
overdiagnosis. The median PPP for the FFM counts is actually better than those reported for the Personality Diagnostic Questionnaire–4+ (PDQ-4+; Hyler, 1994); median
reported PPPs for the PDQ-4+ include .16 (Yang et al.,
2000), .18 (Wilberg, Dammen, & Fries, 2000), and .19
(Fossati et al., 1998). Similarly, median PPPs from the
self-report Millon Clinical Multiaxial Inventory–III, as reported by Hsu (2002), have ranged from .18 (Millon,
1994) to .72 (Millon, Davis, & Millon, 1997; see Hsu,
2002, for possible explanations for elevated scores in this
sample). These results, including those reported here,
suggest that self-report PD measures, regardless of how
they were created, are prone to generating a high number
of false positives.
Limitations
One limitation of this study is that the data used here
were primarily limited to self-reports. As such, the size of
the relations reported may have been inflated because of
common method variance. This concern is mitigated to
some degree by previous studies that have found similar
patterns of findings using significant other reports and
interview-based data (Miller et al., in press; Miller,
Pilkonis, et al., 2004). Another limitation is that the cut
scores provided are based on one or two moderately sized
clinical samples. In addition, PD diagnoses in Sample 1
were, in part, determined by a self-report scale, which affects the reliability of the subsequent diagnoses. As such,
these diagnostic scores should be viewed cautiously as it is
possible that they will be sample specific and fail to generalize to other samples. The replication reported here for
three of the PDs may make this less likely for these PDs,
but it is a concern for the remaining ones. Because of the
low prevalence rate of certain PDs and the size of our clinical samples, we were unable to provide a comprehensive
test of the diagnostic efficiencies of these methods for all
10 PDs. Finally, given the clinical nature of both samples,
the cut scores may only be appropriate for use with
individuals of a moderate to high severity and may be less
appropriate for use in nonclinical samples.
CONCLUSION
Overall, the current results suggest that both the FFM
counts and the full prototype-matching technique (e.g.,
FFM similarity scores) are equally successful in relating to
PD symptoms. With the exception of OCPD, the FFM
counts and similarity scores are relatively successful at
capturing the various DSM-IV PD constructs. The counts
may be easier to use given the simplicity of the scoring
methodology; however, it is worth noting that scoring of
the FFM PD prototypes is now available using two readily
available software programs. The current results should
move this line of research forward by allowing clinicians
and/or researchers to use the FFM PD prototypes (in either
the count or similarity score form) in clinical settings. We
believe that it is important to consider that the counts are
still a technique that takes into account the dimensional,
multitrait model even if it is not as broad or comprehensive
as the full prototype-matching technique. In fact, 9 of the
10 FFM PD counts use facets from three or more of the
personality domains, thus ensuring relatively broad coverage. Overall, we believe that the development of these simple additive counts will make this approach more
applicable in clinical settings. A benefit of this is that it
will allow clinicians to gather data on clients’ general personality configurations as well as their more maladaptive
personality styles. More broadly, we believe that the use of
basic, dimensional models of personality in understanding
DSM Axis II diagnoses holds great promise for providing
a model for more empirically valid measures of
personality-based psychopathology.
NOTES
1. FFM PD similarity scoring programs via Microsoft Excel
worksheet and/or SPSS syntax are available from the first or last author.
2. We would like to thank an anonymous reviewer from a previous
manuscript who first suggested the idea of using additive counts based on
the FFM to assess the PDs.
3. See Miller et al. (in press) and Miller, Reynolds, et al. (2004) for
specific data on the relations between the FFM PD similarity scores and
PD symptoms.
4. A number of the cut scores for the FFM similarity scores have a
negative value. Because these scores take into account similarity across
30 traits, many individuals who are considered a good match (e.g., meet
or exceed the identified cut score) are still going to be quite dissimilar to
the overall prototype, which is reflected in these negative values.
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 413
APPENDIX
FFM PD Counts
Paranoid PD
Schizoid PD
Schizotypal PD
Antisocial PD
Borderline PD
Histrionic PD
Narcissistic PD
Avoidant PD
Dependent PD
OCPD
=
=
=
=
=
=
=
=
=
=
n2 + e1r +e2r + o4r + o6r + a1r + a2r + a3r + a4r + a6r.
e1r + e2r + e3r + e4r + e5r + e6r + o3r + o4r.
n1 + n4 + e1r + e2r + e6r + o5 + c2r.
n1r + n2 + n4r + n5 + e3 + e4 + e5 + o4 + a1r + a2r + a3r + a4r + a5r + a6r + c3r + c5r + c6r.
n1 + n2 + n3 + n5 + n6 + o3 + o4 + a4r + c6r.
n4r + n5 + e2 + e4 + e5 + e6 + o1 + o3 + o4 + a1 + c5r + c6r.
n2+n4r + e1r + e3 + e5 + o3r + o4 + a1r + a2r + a3r + a4r + a5r + a6r.
n1 + n4 + n5r + n6 + e2r + e3r + e5r + e6r + o4r + a5.
n1 + n4 + n6 + e3r + a1 + a4 + a5.
n1 + n5r + e5r + o3r + o4r + o5r + o6r + c1 + c2 + c3 + c4 + c5 + c6.
NOTE: r = indicates that this facet should be reversed scored before summing it into the count. For example, a Trust score (a1) of 31 for antisocial APD
would be scored a 1 for the count.
0 = 32
11 = 21
22 = 10
1 = 31
12 = 20
23 = 9
2 = 30
13 = 19
24 = 8
3 = 29
14 = 18
25 = 7
4 = 28
15 = 17
26 = 6
5 = 27
16 = 16
27 = 5
6 = 26
17 = 15
28 = 4
7 = 25
18 = 14
29 =3
8 = 24
19 = 13
30 = 2
9 = 23
20 = 12
31 = 1
10 = 22
21 = 11
32 = 0
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Joshua D. Miller, Ph.D., received his degree in clinical psychology from the University of Kentucky and is currently an assistant
professor of psychology at the University of Georgia. His research focuses on the role of general personality traits in understanding personality psychopathology, such as the DSM
personality disorders and psychopathy, as well as problematic,
externalizing behaviors, such as antisocial behavior, substance
use, risky sex, and aggression.
R. Michael Bagby, Ph.D., C. Psych, is a professor in the Department of Psychiatry at the University of Toronto, and is the director of the Clinical Research Department, as well as the codirector
of the Psychological Assessment Service at the Centre for Addiction and Mental Health. He has a wide range of clinical and research interests, including an active program of research in the
assessment of malingering and socially desirable responding.
Other interests include the relation between personality and depression, and the use of the Five Factor Model of personality in
the assessment of personality pathology.
Paul A. Pilkonis, Ph.D., is a professor of psychiatry and psychology in the Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine. His
primary interest is clinical research, both research on
psychopathology, with a focus on the assessment and longitudinal course of personality disorders, and research on psychosocial
treatments for personality and affective disorders.
Sarah K. Reynolds, Ph.D., is an assistant professor in the Department of Psychiatry, Western Psychiatric Institute and Clinic,
University of Pittsburgh School of Medicine. Her primary research interest is in the assessment and treatment of personality
Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 415
disorder, with a focus on the development of psychosocial interventions for women with personality disorder and cooccurring
medical problems.
Donald R. Lynam, Ph.D., received his degree in clinical psychology from the University of Wisconsin–Madison and is cur-
rently a professor of psychology at the University of Kentucky.
His primary research interests include developmental models of
antisocial behavior, the role of individual differences in deviance, the early identification of chronic offenders, and psychopathy at the juvenile and adult levels.