Miller Baby Pilkonis Technique for DSM-IV PD With the FFM

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10.1177/1073191105280987ASSESSMENT Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM

A Simplified Technique for Scoring DSM-IV Personality Disorders With the

Five-Factor Model

Joshua D. MillerUniversity of Pittsburgh Medical Center

R. Michael BagbyUniversity of Toronto

Paul A. Pilkonis

Sarah K. ReynoldsUniversity of Pittsburgh Medical Center

Donald R. LynamUniversity of Kentucky

The current study compares the use of two alternative methodologies for using the Five-

Factor 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 wellas the similarity scores that are generated by the prototype matching

technique. Optimal diagnostic thresholds for the FFM PD counts are computed for identify-

ing patients who meet diagnostic criteria for a specific PD. These threshold scores demon-

strate 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 hasbeen a highlygenerativeresearch tool in

the service of exploring the relations between personality

disorder (PD) constructs and “normal” or general person-

alityfunctioning.Much of this researchhasbeen driven by

a general dissatisfaction with the categorical approach

taken by theofficial 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 dimen-

sional models of adaptive or maladaptive personality fea-

tures provide a better representation of these phenomena

(Livesley, 2001; Widiger, 1993). In addition to the FFM,

several prominent personality theorists have put forth al-

ternative 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 Psycholo-

gists (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

shouldbe addressed to JoshuaD.Miller,Ph.D.,Departmentof Psychology, UniversityofGeorgia,Athens, GA30602;e-mail: jdmiller@

uga.edu.

Assessment , Volume 12, No. 4, December 2005 404-415DOI: 10.1177/1073191105280987© 2005 Sage Publications


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personality, such as Clark’s (1993) Schedule for Non-

Adaptive 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 alterna-

tive models of PD stem from a variety of reasons, includ-

ing 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 anyof 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 reliabil-

ity (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 ex-

treme variants of general personality traits (Costa &

Widiger, 1994, 2002) or alternative psychobiological di-

mensions, such as anxiety/inhibition, impulsivity/

aggression, affective instability, and cognitive/perceptual

organization (Siever & Davis, 1991). By deconstructing

thePDs into their underlying dimensions, a wider array of

maladaptive personality styles can be conceptualized and

assessedandissues such ascomorbiditybecomelessprob-

lematic (Lynam & Widiger, 2001).

Although a number of trait models have been success-

fully 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 in-

volved 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 prototype-

matching technique in which FFM PD prototypes aregen-

erated through the use of expert ratings for both DSM-IV –

recognized PDs (Lynam & Widiger, 2001) and non- DSM-

IV –recognized forms of personality psychopathology,

such as psychopathy (Miller & Lynam, 2003; Miller,

Lynam, Widiger, & Leukefeld, 2001). These expert-generated 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 correla-

tion, which takes into account profile agreement with re-

gard 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 particularlyvirulent form of PD character-

ized 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 border-

line PD converged with other well-validated measures of

this PD as well as importantcriterionconstructs.Recently,

Miller, Pilkonis,& Morse, (2004) andMiller, Reynolds, &

Pilkonis (2004) have examined all 10 of the Lynam and

Widiger (2001) prototypes across clinical samples and in-

formant methodologies. Miller, Reynolds et al. (2004)

found support for the convergent, discriminant, and pre-

dictive validity and temporal stability of the FFM PD pro-

totypes. Two studies have also demonstrated the

“resilience” of this technique to information source;

Miller, Pilkoniset al.(2004) demonstratedthat FFMinfor-

mation 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 theempirical success of theprototype-matching

techniqueacross PDs anddata source, researchers andcli-

nicians may be reluctant to use this approach. The scoring

methodology is complexand requires a statisticalprogramto create the PD similarity scores.1 In addition, the scores

are not intuitively meaningful. One possible alternative is

to usesimple additivecounts to score individualson DSM-

IV PDs, which would stilluse information from theLynam

and Widiger (2001) FFM prototypes.2 To do this, one

would first have to identify which facets were considered

prototypically low or prototypically high foreach PD (i.e.,

a facet witha score≥ 4 or ≤2 on the Lynam& Widiger pro-

Miller et al. / SCORING PERSONALITY DISORDERS WITH FFM 405


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totypes), reversekey thefacets with a scoreof ≤ 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 individ-

ual’s scores across relevantfacets. For example,according

to this strategy, theFFMPD count for histrionic PD would

involve adding together the following facets: self-consciousness (a facetof neuroticism [N], which wouldbe

reversescored), 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). Thesecounts, 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

FFMprofile (the numberof facetsused in thecounts 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 theconver-

gent validityof theFFMcounts in relation to PD symptom

counts generated by well-known PD measures and com-

pare their performance to the FFM similarity scores. Fi-

nally, 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 Ser-

viceat a large tertiary care, medical school–affiliated,psy-chiatric facility located in a large, primarily English-

speaking, North American metropolis. Ethnic status was

reported for 94 patients; 90 were of European descent, 2

wereof African descent,1 was ofAsian descent, and 1 was

of Hispanic descent. Most of these referrals were outpa-

tients (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) andcompleted theStructured Clinical Interview for

DSM-IV Personality Disorders–Personality Question-

naire (SCID-II/PQ; First, Gibbon, Spitzer, Williams, &

Benjamin, 1997) and NEO PI-R. Advanced clinical psy-chology interns (n = 5), two M.A.-level clinical psycholo-

gists, and a postdoctoral clinical fellow conducted the

interviews. Although interrater agreement was not for-

mally determined, all interviewers were trained exten-

sively 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 itemsareanswered using a yes-noresponse

format. Each of the119 questions corresponds to thediag-

nostic 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 mea-

sures areprone tooverestimatingPDs, a numberof 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 person-

ality 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 corre-

lated 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

406 ASSESSMENT


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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’

FFMprofilesand the10 FFMPD prototypes areenteredas

columns) wasusedbecause it considersboth theshapeand

elevation of individual scores (in comparison to theexpert

prototypes) rather than the shape alone, as is the case witha Pearson correlation. As such, it is a more stringent

measure of agreement.

FFM PD counts. The FFM PD counts represent an al-

ternative 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., straightfor-

wardness in antisocial PD) are reverse scored so that all

facets are scored in the direction of maladaptivity for thatspecific PD.

Sample 2

Participants and Procedures

Participantswereeither inpatients or outpatients under-

going assessment or treatment at one of several facilities

affiliatedwith theUniversity of Iowa. Outpatientswere re-

cruited from either the university medical center psychia-

try clinic or the university-based psychology clinic staffed

by graduate students andfaculty of thepsychology depart-

ment. Inpatients were recruited from the university medi-cal center psychiatric units, which serve a general

psychiatric population, with a small minority of partici-

pants (10%) recruited from the eating disorder specialty

unit. Individuals with personality pathology were not se-

lectively recruited for participation. Rather, thegoal 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 partici-

pate: 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

69women (73%) and 25men. Meanage was 34.6 (range =

18to76, SD = 10.5).Themodalparticipant wasCaucasian

(96%), unmarried (71%), andemployed (72%). Themean

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 partici-

pants received an Axis I diagnosis (88%), with the mostfrequent diagnosis a mood disorder (53%).

Measures

Structured Interview for DSM-IV Personality (SIDP-

IV). The SIDP-IV (Pfohl, Blum, & Zimmerman, 1997) isa

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 sig-

nificant; 3 = prominent symptom). Dimensional scores

were calculated for each diagnosis by summing the com-

ponentcriterion scores(0 to4).Diagnoseswere scoredin a

manner consistent with the SIDP manual and DSM-IV . In-

terviews were conductedby twoclinical psychology grad-

uate 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 themean

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 PI-

R; 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 =



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133.81 [Sample 1] and 131.47 [Sample 2]), with a mean

difference of 5.53. Because thecounts usea differentnum-

ber of facets (ranging from 7 to 17), we also provide mean

count scoresthat take this into account (thus makingit pos-

sible to compare scores across the FFM counts).

Correlations Between FFM PDCounts 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 medianr of .91. There

was one case of a gender difference: The correlation be-

tween the similarity score and count was significantly dif-

ferent 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 significantgenderdifferences in thesizeof thecorrelations; thecorre-

lation for narcissism was .94 for men and .77 for women,

whereas thecorrelation fordependentwas .93 formenand

.80 for women.

Correlations Between FFM PDCounts and PD Symptom Counts

Next, we examined the convergent validity of the FFM

counts with PD symptom counts from well-known mea-

sures of PD symptoms (see Table 2). In Sample 1, the cor-

relations between the FFM PD counts and the PDsymptom countsranged from –.02 (OCPD) to .64 (border-

line), 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 medianr of .45. As noted, there was one

case in which the correlation between the FFM count and

the PD symptoms was significantly different across gen-

der; in Sample 2, the correlation between the FFM histri-

onic count and a histrionic PD diagnosis was significantly

larger (and positive) for women.

We next tested, in each sample, whether thecorrelation

foreachFFMPD count wassignificantlydifferent than the

correlation previously reported (Miller et al., in press;Miller, Reynolds, et al., 2004) for itsrespectiveFFM simi-

larity score. Of the 21 comparisons, only 1 was signifi-

cantly different. The correlation between the FFM

dependent count and the dependent symptom count (r =

.34) in Sample 1 was significantly larger than the correla-

tion forthe FFMdependent similarity scoreanddependent

PD count (r = .24). These findings suggest that the differ-

408 ASSESSMENT

TABLE 1Descriptive Characteristics of FFM PD Counts

FFM Counts Sample Min. Max. M SD M (by Facets)

PAR 1 64 219 133.81 30.90 13.38

PAR 2 56 201 131.47 26.57 13.15

SZD 1 56 194 131.60 26.97 16.45

SZD 2 60 191 119.46 28.07 14.93

SCT 1 59 177 125.57 23.44 17.94

SCT 2 53 166 120.10 20.72 17.16

APD 1 131 342 226.71 33.97 13.34

APD 2 133 354 233.47 35.02 13.73

BPD 1 57 249 159.69 28.58 17.74

BPD 2 97 225 164.55 26.86 18.28

HST 1 122 248 190.77 25.95 15.90

HST 2 125 266 200.24 31.94 16.69

NAR 1 94 251 163.63 29.04 12.59

NAR 2 102 282 164.67 27.58 12.67

AVD 1 98 247 185.39 30.98 18.54

AVD 2 90 252 175.88 31.24 17.59

DEP 1 84 185 135.94 20.05 19.42

DEP 2 66 183 132.68 21.82 18.95

OC 1 93 287 204.03 29.95 15.69OC 2 138 271 204.48 30.82 15.73

NOTE:FFM = Five-Factor Model;PD = personalitydisorder; PAR = paranoid;SZD = schizoid;SCT = schizotypal; APD= antisocial; BPD= borderline;HST= histrionic;NAR = narcissistic; AVD = avoidant; DEP = dependent; OC = obsessive-compulsive.Because thecounts have a different number of fac-ets (ranging from 7 to 17), we provide the mean score taking into account the number of facets.


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ences with regard to convergent validityarequite minimal

between the FFM similarity scores and the counts.

As can be seen in Table 2, we calculated weighted ef-

fect sizes using meta-analytic techniques (i.e., Fisher z-

transformed r s were combined, taking into account

sample size, to obtain a mean effect size and then were

transformed back to r s) for the FFM counts. Overall, theeffect sizes ranged from .02 to .63, with one small effect

size (OCPD), six medium effect sizes (paranoid,

schizotypal, antisocial, histrionic, narcissistic, and de-

pendent), and threelarge effect sizes (schizoid, borderline,

and avoidant).

We also examinedthedivergent validityof theFFM 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 mediancorrela-

tion of .21. In Sample 2, the discriminant correlations

ranged from –.53 (FFM histrionic and schizoid PD symp-

toms) to .63 (FFM schizotypal and avoidant PD symp-

toms), with an absolute median correlation of .21.

Receiver Operating Characteristics (ROCs)

Finally, in theinterest of clinicalutilityandour desireto

provide a basis for initial decision making regarding the

use of the FFM counts and similarity scores to identify

PDs, we conducteda seriesof ROC analyses.These analy-

ses provide importantdiagnostic efficiencystatistics, such

as sensitivity, specificity, and positive and negative predic-

tive power, associated with the raw scores. Because theseanalyses require that a certain number of individuals re-

ceivea PD diagnosis, we limited ouranalyses in each sam-

ple to those PDs that had a sufficient prevalence. This,

coupled with thepoor performance of theFFMcountsand

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. Ta-

ble3 provides information regarding thePD 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

TheAUC wassignificant for10 of 11 similarity scoresand

for 11 of 11 counts across the two samples. The medianAUCs accounted for by the similarity scores and counts,

across samples, was .77 and .78, respectively. We also cal-

culated median sensitivities, specificities, positive predic-

tive 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 under-

stand and assess constructs has been primarily a matter of

theoretical interest aimed at demonstrating that PDs are

extensions or variants of general personality traits. Recentstudies have put forth a new technique by which an indi-

vidual’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 techniquebeingused inclinical settings seemslow. 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 expert-generatedprototypes havebeen quite successful in captur-

ingPD constructs, includingthose in DSM-IV , suchas bor-

derline 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.


TABLE 2Correlations Between FFM PD

Counts and PD Symptom Counts

FFM PD

Weighted

PD Sample 1 Sample 2 Effect Size

Paranoid PD count .41** .44** .42

Schizoid PD count .40** .60** .50

Schizotypal PD count .40** .28** .35

Antisocial PD count .36** .51** .43

Borderline PD count .64** .56** .61

Histrionic PD count .33** –.15/.41** .31

Narcissistic PD count .45** .45** .45

Avoidant PD count .63** .64** .63

Dependent PD count .34a** .46** .40

OCPD PD count –.02 .08 .02

NOTE: FFM = Five-Factor Model; PD = personality disorder; OCPD =obsessive-compulsivePD./ = a significantgenderdifference in thesize 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 PDsymptomsand theFFM similarity scoreand PDsymptomsfromMiller etal. (in press), which was .24.* p ≤ .05. ** p ≤ .01.


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410

T A B L E 3

R e c e i v e r O p e r a t i n g C h a r a

c t e r i s t i c s o f F F M P

D S i m i l a r i t y S

c o r e s a n d C o u n t s

P D B a s e R a t e /

P e r c e n t a g e

A r e a

R a w S c o r e s

S e n s i t i v i t y

S p e c i f i c i t y

P P P

N P P

P D s

S

a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

S a m p l e 1

S a m p l e 2

P a r a n o i d s i m i l a r i t y

2 9 / 2 5 %

5 / 5 %

. 6 9 * *

– . 3

2

. 8 3

. 3 7

. 3 1

. 8 6

P a r a n o i d c o u n t

2 9 / 2 5 %

5 / 5 %

. 6 9 * *

1 1 6 . 5

. 8 3

. 3 4

. 3 0

. 8 5

S c h i z o i d s i m i l a r i t y

2 1 / 1 8 %

0 / 0 %

. 7 7 * *

– . 1

1

. 8 1

. 5 4

. 2 8

. 9 3

S c h i z o i d c o u n t

2 1 / 1 8 %

0 / 0 %

. 7 9 * *

1 3 1 . 0

. 9 1

. 5 4

. 3 1

. 9 6

S c h i z o t y p a l s i m i l a r i t y

9 / 8 %

0 / 0 %

. 8 0 * *

. 2 8

. 8 9

. 7 0

. 2 0

. 9 9

S c h i z o t y p a l c o u n t

9 / 8 %

0 / 0 %

. 7 8 * *

1 3 1 . 5

. 8 9

. 6 4

. 1 7

. 9 9

A n t i s o c i a l s i m i l a r i t y

1 9 / 1 7 %

2 / 2 %

. 6 7 *

– . 4

5

. 8 4

. 4 6

. 2 4

. 9 4

A n t i s o c i a l c o u n t

1 9 / 1 7 %

2 / 2 %

. 6 9 * *

2 2 6 . 0

. 8 4

. 5 5

. 2 7

. 9 5

B o r d e r l i n e s i m i l a r i t y

5 2 / 4 5 %

2 2 / 2 3 %

. 7 8 * *

. 7 8 * *

– . 0

9

– . 0

3

. 8 1

. 8 6

. 6 7

. 6 3

. 6 7

. 4 1

. 8 1

. 9 4

B o r d e r l i n e c o u n t

5 2 / 4 5 %

2 2 / 2 3 %

. 8 0 * *

. 7 5 * *

1 5 6 . 0

1 4 8 . 5

. 8 1

. 8 2

. 6 5

. 5 7

. 6 6

. 3 7

. 8 0

. 9 1

N a r c i s s i s t i c s i m i l a r i t y

2 5 / 2 2 %

3 / 3 %

. 8 3 * *

– . 4

1

. 8 0

. 6 6

. 3 9

. 9 2

N a r c i s s i s t i c c o u n t

2 5 / 2 2 %

3 / 3 %

. 8 0 * *

1 7 0 . 5

. 8 0

. 7 2

. 4 4

. 9 3

A v o i d a n t s i m i l a r i t y

5 8 / 5 0 %

2 2 / 2 3 %

. 7 5 * *

. 8 7 * *

. 2 0

. 3 4

. 8 1

. 8 2

. 6 1

. 8 6

. 6 8

. 6 4

. 7 6

. 9 4

A v o i d a n t c o u n t

5 8 / 5 0 %

2 2 / 2 3 %

. 7 5 * *

. 8 5 * *

1 7 9 . 5

1 9 2 . 0

. 8 1

. 8 2

. 6 3

. 8 5

. 6 9

. 6 2

. 7 7

. 9 4

D e p e n d e n t s i m i l a r i t y

9 / 8 %

1 0 / 1 1 %

. 5 9

. 7 3 *

. 1 3

. 2 3

. 8 9

. 8 0

. 3 0

. 5 8

. 1 0

. 1 9

. 9 7

. 9 6

D e p e n d e n t c o u n t

9 / 8 %

1 0 / 1 1 %

. 7 2 *

. 7 8 * *

1 2 9 . 5

1 4 3 . 5

. 8 9

. 8 0

. 3 8

. 7 5

. 1 1

. 2 8

. 9 8

. 9 7

N O T E : F F M =

F i v e - F a c t o r M o d e l ; P D = p e r s o n a l i t y d i s o r d e r ; P P P = p o s i t i v e p r e d i c t i v e p o w e r ; N P P = n e g a t i v e p r e d i c t i v e p o w e r .

* p ≤

. 0 5 .

* * p ≤

. 0 1 .


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To test the comparability of the FFM PD counts with

theFFM PD similarity scores, we firstexamined thecorre-

lations between these twooverlappingscoring methodolo-

gies across the two samples. The FFM PD counts were

highly correlated with FFM PD similarity scores; the me-

dian correlations were .91 and .91 in Samples 1 and 2, re-

spectively. We then compared the size of the correlationsbetween 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, re-

spectively. The median correlations between the similarity

scores and the PD symptom counts, across the two sam-

ples, 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 dif-

ference, however, was small (d = .11).

As hasbeen thecasewith theFFM similarity scores, the

FFM counts were not significantly related to obsessive-

compulsive PD (OCPD). This is not an uncommon find-

ing; 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 PDsthat 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 poten-

tial explanations for this failure.One explanationput 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 do-

mains. 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 manipu-

lating the NEO PI-R items to include more items repre-senting maladaptively high variants of the FFM domains

increased the size of the correlations between OCPD, de-

pendent, andschizotypal PDswith C, A, andopenness, re-

spectively. 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 be-

tween dependent PD and A and C. For example, results

from the current samples are consistent with those re-

ported 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., compe-

tence, achievement striving, self-discipline) rather than

positively, as postulated by theLynam andWidiger (2001)

prototypes. These findings are further complicated by theidea 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 re-

garding which PDs were best captured by the FFM count.

In particular, schizoid, borderline, and avoidant PDs were

well accounted for by theFFM 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

inclinical settings as a screeningmeasure forseveralof the

PDs. The data gleaned from these analyses, although

tentative, representan importantstep 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. Aswith 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 21of 22 cutscores manifested a significant

AUC, thesimilarity scoresand countsfor paranoid andan-

tisocial PD had scores that would be deemed poor (e.g., .6

to .7). The rest of the PDs testedhad, 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

theFFM asa screeningtool andnot a stand-alone,compre-

hensive PD assessment battery, we believe that sensitivity

is more important than specificity because the false

positives should be ruled out on further assessment. Giventhat we identified cut scores with sensitivities of .80 or

higher, 16 of the22 cut scores also had specificities higher

than .50. In fact, the median specificity score was .61 for

thesimilarity scoresand .63 for thecounts. TheFFMsimi-

larity scores and counts also demonstrated good negative

predictive power (medians = .94 and .94). However, the

samewas not true for PPP (medians= .31). Similar tomost


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self-report PD questionnaires, the FFM similarity scores

and counts demonstrate a clear tendency toward

overdiagnosis. The median PPP for the FFM counts is ac-

tually better than those reported for the Personality Diag-

nostic 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 re-

ported 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 af-

fects the reliability of the subsequent diagnoses. As such,

thesediagnostic scoresshould be viewedcautiously as it ispossible that they will be sample specific and fail to gener-

alize 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 PDsandthesizeof ourclini-

cal 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) areequally successfulin relatingto

PD symptoms. With the exception of OCPD, the FFM

counts and similarity scores are relatively successful atcapturing 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

theFFM 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 touse theFFM PD prototypes (ineither

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 notas 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 broadcover-

age. Overall,webelievethat thedevelopment of these sim-

ple 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 per-

sonality configurations as well as their more maladaptive

personality styles. More broadly, we believe that theuse 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 Excelworksheetand/orSPSSsyntaxareavailable fromthe first or lastauthor.

2. We would like to thank an anonymous reviewer from a previousmanuscriptwho first suggestedthe ideaof using additive counts basedonthe FFM to assess the PDs.

3. See Miller et al. (in press) and Miller, Reynolds, et al. (2004) forspecific data on the relations between the FFM PD similarity scores andPD symptoms.

4. A number of the cut scores for the FFM similarity scores have anegative value. Because these scores take into account similarity across30 traits, many individuals who are considered a good match (e.g., meetor exceed the identified cut score) are still going to be quite dissimilar to

the overall prototype, which is reflected in these negative values.

412 ASSESSMENT


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APPENDIX

FFM PD Counts

Paranoid PD = n2 + e1r +e2r + o4r + o6r + a1r + a2r + a3r + a4r + a6r.

Schizoid PD = e1r + e2r + e3r + e4r + e5r + e6r + o3r + o4r.

Schizotypal PD = n1 + n4 + e1r + e2r + e6r + o5 + c2r.

Antisocial PD = n1r + n2 + n4r + n5 + e3 + e4 + e5 + o4 + a1r + a2r + a3r + a4r + a5r + a6r + c3r + c5r + c6r.

Borderline PD = n1 + n2 + n3 + n5 + n6 + o3 + o4 + a4r + c6r.Histrionic PD = n4r + n5 + e2 + e4 + e5 + e6 + o1 + o3 + o4 + a1 + c5r + c6r.

Narcissistic PD = n2+n4r + e1r + e3 + e5 + o3r + o4 + a1r + a2r + a3r + a4r + a5r + a6r.

Avoidant PD = n1 + n4 + n5r + n6 + e2r + e3r + e5r + e6r + o4r + a5.

Dependent PD = n1 + n4 + n6 + e3r + a1 + a4 + a5.

OCPD = 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 APDwould 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 psychol-

ogyfromtheUniversityof Kentucky andis currentlyan assistant

professor of psychology at the University of Georgia. His re-

search focuses on the role of general personality traits in under-

standing 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 professorin the Depart-

ment of Psychiatry at the University of Toronto, and is the direc-

torof theClinicalResearchDepartment,as well as thecodirector

of thePsychological Assessment Service at theCentreforAddic-

tion and Mental Health. He has a wide range of clinical and re-

search interests, including an active program of research in the

assessment of malingering and socially desirable responding.

Other interests include the relation between personality and de-

pression, and the use of the Five Factor Model of personality in

the assessment of personality pathology.

PaulA. Pilkonis, Ph.D., isa professorof psychiatry andpsychol-

ogy in the Department of Psychiatry, Western Psychiatric Insti-

tuteand Clinic,Universityof PittsburghSchool of Medicine. His

primary interest is clinical research, both research on

psychopathology, with a focus on the assessment and longitudi-nalcourse of personality disorders, andresearch onpsychosocial

treatments for personality and affective disorders.

Sarah K. Reynolds, Ph.D., is an assistant professor in the De-

partment of Psychiatry, Western Psychiatric Institute and Clinic,

University of Pittsburgh School of Medicine. Her primary re-

search interest is in the assessment and treatment of personality

414 ASSESSMENT


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disorder, with a focus on the development of psychosocial inter-

ventions for women with personality disorder and cooccurring

medical problems.

Donald R. Lynam, Ph.D., received his degree in clinical psy-

chology 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 devi-

ance, theearly identification of chronicoffenders, andpsychopa-

thy at the juvenile and adult levels.


Miller Baby Pilkonis Technique for DSM-IV PD With the FFM

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