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European Journal of Personality

Eur. J. Pers. 19: 1–15 (2005)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/per.565

Incremental Validity of the Structured Interview for theFive Factor Model of Personality (SIFFM)

STEPHANIE D. STEPP, TIMOTHY J. TRULL*, RACHEL M. BURR,MIMI WOLFENSTEIN and ANGELA Z. VIETH

University of Missouri—Columbia, USA

Abstract

This study examined the incremental validity of the Structured Interview for the Five-

Factor Model (SIFFM; Trull & Widiger, 1997) scores in the prediction of borderline,

antisocial, and histrionic personality disorder symptoms above and beyond variance

accounted for by scores from the Schedule for Nonadaptive and Adaptive Personality

(SNAP; Clark, 1993), a self-report questionnaire that includes items relevant to both

normal (i.e. Big Three) and abnormal personality traits. Approximately 200 participants

(52 clinical outpatients, and 149 nonclinical individuals from a borderline-features-

enriched sample) completed the SIFFM, the SNAP, and select sections of the Personality

Disorder Interview—IV (PDI-IV; Widiger, Mangine, Corbitt, Ellis, & Thomas, 1995). We

found support for the incremental validity of SIFFM scores, further indicating the clinical

utility of this instrument. However, results also supported the incremental validity of SNAP

scores in many cases. We discuss the implications of the findings in terms of dimensional

approaches to personality disorder assessment. Copyright # 2005 John Wiley & Sons, Ltd.

INTRODUCTION

Since the introduction of the personality disorder section in the DSM-III (APA, 1980),

researchers have attempted to devise measures for the reliable and valid assessment of

these conditions (see Zimmerman, 1994). The majority of personality disorder measures

are questionnaires and interviews that operationalize the DSM-IV criteria and provide cut-

offs to identify the presence or absence of a diagnosis. However, the categorical model of

personality disorder embodied in the versions of the diagnostic manual starting with DSM-

III has been subject to a number of criticisms (Clark, 1999; Trull & Durrett, in pressQ1).

Over the years, various individuals have advocated the adoption of a dimensional

Received 7 January 2005

Copyright # 2005 John Wiley & Sons, Ltd. Accepted 30 March 2005

*Correspondence to: Timothy J. Trull, 210 McAlester Hall, Department of Psychology, University of Missouri,Columbia, MO 65211, USA. E-mail TrullT@missouri.edu

Contract/grant sponsor: National Institute of Mental Health; contract/grant number: MH52695.

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classification for personality disorders (Frances, 1982; Widiger, 1993; Widiger & Frances,

1994). Several personality researchers (e.g. McCrae, 1994; Trull & Widiger, 1997;

Widiger, 1993) have suggested that the Five-Factor Model of personality (FFM) may be

particularly attractive as an alternative dimensional classification of Axis II disorders. The

five higher-order factors or domains of the FFM include Neuroticism, Extraversion,

Openness to Experience, Agreeableness, and Conscientiousness, and each one of these

higher-order traits includes first-order personality trait facets. A substantial literature has

begun to accrue on the relationship between various personality disorders and the domains

and facets of the FFM (e.g. Widiger & Costa, 2002). Overall, this literature suggests that

the FFM may be a valuable tool for the dimensional characterization of Axis II disorders.

One potential limitation of most instruments that assess the FFM is that they tend to

focus on ‘normal’ variations of these traits. To address this and other concerns, the

Structured Interview for the Five-Factor Model (SIFFM; Trull & Widiger, 1997, 2002)

was developed. The SIFFM is an alternative measure of the FFM, one that may have

several advantages over the paper-and-pencil measures of the FFM. First, due to its semi-

structured interview format, data obtained via the SIFFM may have greater validity than

the data typically obtained through questionnaires. This is because it is possible to ask

additional probes, clarify answers, and ask for specific examples of each trait. Second, and

most relevant to the present study, the SIFFM was developed to tap maladaptive aspects of

each FFM trait. Therefore, its scores may be more relevant to personality pathology.

Finally, SIFFM scores not only indicate the level of a trait, but also suggest degrees of

dysfunction. This provides more information as to the clinical significance of the

maladaptive trait.

Although many personality researchers acknowledge that the FFM shows promise for

personality disorder diagnosis, other investigators have advocated alternative personality

models for the dimensional diagnosis of personality disorder. For example, a Big Three

model of personality (three higher-order factors of positive emotionality, negative

emotionality, and constraint) has been cited as a dimensional model that might aid in the

definition and measurement of personality pathology and disorder (see e.g. DiLalla,

Gottesman, Carey, & Volger, 1993).

Building on the conceptual personality framework of Tellegen and Waller (in pressQ1),

which focuses on these higher-order personality dimensions, Clark (1993) developed a

questionnaire to assess a Big Three model of personality as well as other maladaptive

personality traits: the Schedule for Nonadaptive and Adaptive Personality (SNAP; Clark,

1993). Although conceptually similar to other Big Three measures, Clark (1993) labelled

the three higher-order dimensions of the SNAP Negative Temperament (NT), Positive

Temperament (PT), and Disinhibition (DIS; versus Constraint). It is important to note that

researchers have found a strong relationship between Eysenck’s Super Three model and

the Big Three model of personality of Tellegen, Waller, and Clark (Markon, Krueger, &

Watson, 2005).

There have been relatively few studies published that have directly examined the

relations between SNAP scores and the DSM-IV personality disorders. The reason for this

probably stems from the fact that many of the SNAP items are ‘criterial’ in nature (Clark &

Livesley, 2002); items were generated from the DSM criteria for personality disorders. In

fact, there are supplemental scales that can be calculated from the SNAP that represent

each of the DSM personality disorders. Therefore, assessing the concordance between

SNAP scores and the DSM personality disorders has not been a primary focus of SNAP

research.

Q1

2 S. D. Stepp et al.

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Nevertheless, Clark (1993) has presented hypotheses regarding SNAP scale correlates

of the DSM personality disorders. Existing evidence supports these propositions and

suggest that the SNAP may be particularly adept (versus general personality measures) at

tapping into maladaptive variants of personality traits that are characteristic of the DSM

personality disorders (see e.g. Reynolds & Clark, 2001).

Just as each personality disorder is characterized by certain criteria in the DSM-IV

(APA, 1994), each disorder can also be associated with a trait profile from the perspective

of both the FFM and Big Three models of personality. The current study compares the

ability of the FFM scores to predict borderline (BPD), antisocial (APD), and histrionic

(HPD) personality disorder pathology above and beyond SNAP scores, which represent

both normal and abnormal personality traits. In this way, the relevance of the FFM and Big

Three model of personality to select personality disorders can be compared, and, in

addition, the relative relevance of select SIFFM and SNAP trait scores that target these

disorders can be evaluated. Although several sets of predictions exist regarding asso-

ciations between the FFM trait profiles and personality disorders (Lynam & Widiger,

2001; Trull & Widiger, 1997, 2002; Widiger, Trull, Clarkin, Sanderson, & Costa, 1994,

2002), the present study’s predictions are based on the hypotheses offered by Trull and

Widiger (1997, 2002).

Personality trait and personality disorder relations

From the perspective of the FFM (Trull & Widiger, 1997), borderline personality disorder

(BPD) is predicted to be highly positively related to Neuroticism scores, including the five

facets of Anxiety, Hostility, Depression, Impulsivity, and Vulnerability. BPD is also

believed to be positively related to the Extraversion facets of Warmth, Assertiveness, and

Gregariousness as well as the Openness facets of Fantasy and Feelings. On the other hand,

BPD appears to be negatively related to Agreeableness scores, including the Trust,

Compliance, and Straightforwardness facet scores, as well as negatively related to the

Conscientiousness facets of Achievement Striving and Deliberation. Regarding the SNAP

model of personality, BPD is believed to be significantly related to the higher-order trait of

Negative Temperament (NT; Clark 1993). In addition, BPD is predicted to be positively

correlated with Aggression, Self-Harm, and Impulsivity (Clark, 1993).1

Antisocial personality disorder (APD) appears to be negatively related to the FFM

dimensions of Agreeableness and Conscientiousness (Trull & Widiger, 1997). APD scores

are believed to be negatively related to all facets of Agreeableness: Trust, Straightfor-

wardness, Altruism, Compliance, Modesty, and Tender-Mindedness. Negative relations

are also predicted between APD scores and the Conscientiousness facets Order,

Dutifulness, Achievement-Striving, Self-Discipline, and Deliberation (Trull & Widiger,

1997). These relations are consistent with the conceptualization of APD as oppositional,

dishonest, cruel, and manipulative (Trull & Widiger, 1997; Widiger & Costa, 2002). APD

scores are predicted to be positively related to the Neuroticism facets Hostility and

Impulsiveness. However, APD is expected to be negatively related to the Neuroticism

facets Anxiety, Depression, Self-Consciousness, and Vulnerability. Finally, APD is also

expected to be positively related to the Excitement-Seeking facet of Extraversion (Trull &

Widiger, 1997; Widiger & Costa, 2002). From the perspective of the SNAP, a positive

relationship between APD scores and the higher-order SNAP factor Disinhibition as well

1It is important to note that the SNAP items from the higher-order temperament scales (NT, PT, DIS) do notoverlap with those from the 12 lower-order trait scales.

Incremental validity of the SIFFM 3

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as positive relations between APD and scores on Manipulativeness, Aggression,

Entitlement, and Impulsivity are expected (Clark, 1993).

Histrionic personality disorder (HPD) appears to be a manifestation of extreme

Extraversion within the FFM (Trull & Widiger, 1997; Widiger & Costa, 2002). HPD is

characterized by the Extraversion facets of high Warmth, Gregariousness, extreme

Activity, being prone to Excitement-Seeking, and an elevated score on Positive Emotions.

HPD is also related to Agreeableness, correlating negatively with the facets of

Straightforwardness and Altruism (Trull & Widiger, 1997; Widiger & Costa, 2002), but

correlating positively with the facet Trust and Tender-Mindedness. Individuals with HPD

may also score high on the facets Fantasy, Feelings, and Actions, but low on Openness to

Ideas. Those with HPD are expected to also be low in Self-Discipline and Deliberation.

Finally, individuals with HPD may also have elevations on the Neuroticism facets

Hostility, Self-Consciousness, and Vulnerability (Trull & Widiger, 1997; Widiger &

Costa, 2002). According to Clark (1993), HPD is related to SNAP Positive Temperament,

and exhibits positive relations with the scales Exhibitionism and Entitlement. Finally, HPD

also appears to be positively associated with SNAP Impulsivity.

The current study compares and contrasts the variance accounted for by the FFM and

the SNAP model of personality and personality pathology in predicting borderline,

antisocial, and histrionic personality pathology. Table 1 presents the predicted relations

between each of the three personality disorders and respective lower-order scales from

the SIFFM and from the SNAP. In order to compare the predictive ability of the FFM and

the SNAP model of personality, we conducted two sets of analyses. First, we compared the

ability of five SIFFM domain scores to predict borderline, antisocial, and histrionic

personality pathology with the ability of the three temperament scores from the SNAP (i.e.

Big Three) in a combined sample of clinical outpatients and nonclinical participants.

Second, we examined the ability of select SIFFM facets scores to predict borderline,

antisocial, and histrionic personality pathology above and beyond that of the relevant

scores from the SNAP that target these disorders.

METHOD

Sampling procedure

Nonclinical sample

This sample was first described in a dissertation study (Vieth, 2000) that focused on an

alternative personality measure (Tellegen’s Multidimensional Personality Questionnaire)

that is not the focus of the present study. Nonclinical participants were screened from a

total pool of 1772 incoming, 18-year-old freshmen participating in mass testing

procedures during the academic year. In return for their participation, students received

credit toward their introductory psychology course or $5 per hour. Participants provided

their written consent to participate in a brief assessment of personality features. In

addition, individuals completed demographic information which included contact infor-

mation. The screening battery included items from the Personality Assessment

Inventory—Borderline features (PAI-BOR; Morey, 1991). The PAI-BOR is a 24-item

measure that assesses four features of BPD: Affective Instability, Identity Problems,

Negative Relationships, and Self-Harm. This measure has demonstrated reliability and

validity (see e.g. Morey, 1991; Trull, 1995). The screening battery also included validity

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items from the Personality Diagnostic Questionnaire—Revised (PDQ-R; Hyler & Rieder,

1987) to assess the veracity of the responses by identifying individuals who tended to

present themselves in an overly favorable light (Too Good subscale) or who responded in a

haphazard way (Suspect Questionnaire scale).

From the screening pool, individuals who scored above threshold on the PAI-BOR (i.e.

�38, or two or more standard deviations above the mean of the community standardization

sample) and those who scored below threshold (<38) were identified. Researchers have

reported that a score above threshold is indicative of clinically significant borderline

features (Morey, 1991; Trull, 1995). From the lists of above- and below-threshold scorers,

individuals were randomly selected to be contacted regarding the laboratory phase of the

study. An effort was made to over-sample the above-threshold individuals, and to sample

an approximately even number of men and women from each threshold group. When a

person agreed to participate in the laboratory phase, she/he was required to first complete

the PAI-BOR a second time to ensure that the individual scored in the same range (either

above or below threshold) at retest. The purpose of this procedure was to ensure that the

Table 1. Predicted associations between borderline, antisocial, and histrionic personality disordersand selected SIFFM and SNAP scales

SIFFM scales SNAP scales

Borderline Anxiety, Hostility, Depression, Negative Temperament,Impulsivity, Vulnerability, Aggression, Self-Harm,Warmth, Gregariousness, ImpulsivityAssertiveness, Fantasy, Feelings,Trust (Low),Straightforwardness (Low),Compliance (Low),Achievement Striving (Low),Deliberation (Low)

Antisocial Anxiety (Low), Hostility, Manipulativeness,Depression (Low), Aggression, Entitlement,Self-Consciousness (Low), Disinhibition, ImpulsivityImpulsivity, Vulnerability (Low),Excitement Seeking, Trust (Low),Straightforwardness (Low),Altruism (Low),Compliance (Low), Modesty (Low),Tender-Mindedness (Low),Order (Low), Dutifulness (Low),Achievement-Striving (Low),Self-Discipline (Low),Deliberation (Low)

Histrionic Hostility, Self-Consciousness, Positive Temperament,Vulnerability, Warmth, Exhibitionism, Entitlement,Gregariousness, Activity, ImpulsivityExcitement-Seeking,Positive Emotions, Fantasy,Feelings, Actions, Ideas (Low),Trust, Straightforwardness (Low),Altruism (Low),Tender-Mindedness,Self-Discipline (Low),Deliberation (Low)

Incremental validity of the SIFFM 5

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participants were not exhibiting statelike score elevations on the PAI-BOR because the

features of BPD are reputed to be traitlike. Participants scoring above threshold on both

administrations of the PAI-BOR were designated as Bþ, and those scoring below threshold

on both administrations were designated as B�. Project personnel who interviewed

participants were unaware of their borderline feature status. Of the 93 participants scoring

above threshold, 44 were male and 49 were female. Of the 57 individuals scoring below

threshold, 31 were male and 26 were female. The ages of the participants ranged from 17

to 23 (M¼ 18.41, SD¼ 0.75). The sample was predominately white (82.0%) and never

married (94.0%). Complete data were available for a total of 149 individuals.

Clinical sample

These participants were recruited from community mental health clinics and a university

clinic in a medium-sized US midwestern town. Recruitment techniques included placing

advertisements in local periodicals, leaving flyers in waiting rooms, and posting notices.

Exclusionary criteria included chronic substance abuse, brain injury, or psychotic disorder.

In order to screen for these criteria and to ensure that participants were in psychiatric

treatment, participants were asked to sign a release allowing the researchers to request

information concerning treatment history from the participants’ mental health profes-

sionals. Information about treatment history was obtained for 83% of this sample.

The final clinical sample included 52 participants (12 males and 40 females). The mean

age was 36 years, ranging from 18 to 74. Participants had been in outpatient treatment for a

mean of 50.63 months (SD¼ 64.57). Of these participants, 22 reported having had a

previous psychiatric hospitalization (17 women and five men). The mean number of

psychiatric hospitalizations was 4.55 (SD¼ 7.01). Thirty-five participants were on

psychiatric medications (24 women and 11 men) at the time of the assessment.

Measures

All participants provided written consent, which outlined the requirements, risks, and

limits of confidentiality. In addition, the consent form allowed for the interviews to be

audiotaped for reliability purposes. The laboratory session required approximately 3 hours

and involved the completion of several self-report questionnaires and the administration of

two interviews. Participants were paid $10 per hour or were offered, in the case of

nonclincal participants, credit toward their introductory psychology class. Approximately

half of the participants were interviewed before completing the self-report questionnaires,

and the other half completed self-report measures first.

Training

Before the project began, interviewers were trained extensively by the second author (Tim

Trull), ensuring standardization in administration and scoring. In addition, he closely

supervised the interviews and met regularly with the interviewers to discuss any questions

they had about administering the SIFFM or the PDI-IV. Over the duration of the study, the

protocols of approximately one-quarter of the participants receiving each interview

administered (38 SIFFMs and 36 PDI-IVs) were selected in order to assess inter-rater

reliability. In each case, one trained interviewer reviewed (via audiotape) and

independently rated an interview conducted by another interviewer.

Personality disorder features

Three sections of a semi-structured interview, the Personality Disorder Interview-IV

(PDI-IV; Widiger et al., 1995) were administered to both samples to evaluate features of

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borderline (BPD), antisocial (APD), and histrionic (HPD) personality disorders based on

DSM-IV criteria. Most PDI-IV questions are in ‘yes/no’ format, and even though follow-up

questions are provided for some of the questions the PDI-IV manual encourages the

interviewer to follow every endorsement with further questions (Widiger et al., 1995). On

the basis of responses to the provided questions and additional probes, each criterion is

scored on a three-point scale, 0–2. A score of ‘0’ demonstrates that the individual does not

meet the DSM-IV criterion, a ‘1’ indicates that the person has met the criterion, and a ‘2’

signifies that the respondent exceeds the criterion (Widiger et al., 1995). For this study, the

inter-rater reliability of the PDI-IV for the combined sample (clinical and nonclinical),

intraclass correlation coefficients (ICC; Shrout & Fleiss, 1979) for the symptom counts,

were 0.75 (BPD symptom counts), 0.88 (APD symptom counts), and 0.65 (HPD symptom

counts).

Personality traits

The Structured Interview for the Five-Factor Model of Personality (SIFFM; Trull &

Widiger, 1997) is a 120-item interview used to assess adaptive and maladaptive

personality traits. This measure evaluates the five dimensions of personality: Neuroticism,

Extraversion, Openness, Agreeableness, and Conscientiousness. Each dimension includes

six facets, and each facet is evaluated using four questions (Trull & Widiger, 1997).

Therefore, each dimension is assessed with 24 questions. The SIFFM is scored using a

three-point scale (0–2). A score of ‘0’ indicates the absence of a trait or that the trait is only

minimally present. A score of ‘1’ illustrates the presence of a trait, but not to the degree

that it would be considered pathological or dysfunctional. Last, a score of ‘2’ demonstrates

that the trait is present and exists at such a high level that it causes the individual problems.

Thirty-two items are reverse scored (Trull & Widiger, 1997). In the present study, inter-

rater reliability checks indicated excellent agreement for all SIFFM facet and domain

scores (all ICCs> 0.90).

The SIFFM scores have been shown to be internally consistent and reliable over time,

and evidence supports the convergent validity with the NEO-PI-R (Costa & McCrae,

1992). In addition, the SIFFM domain and facet scores have demonstrated relevance to

Axis II psychopathology (see e.g. Trull, Widiger, & Burr, 2001; Trull et al., 1998). The

internal consistency for each dimension of the SIFFM in the combined sample was 0.85

(Neuroticism), 0.83 (Extraversion), 0.77 (Openness), 0.77 (Agreeableness), and 0.81

(Conscientiousness). Mean internal consistency scores for the facet scores (made up of

four items each) of the combined sample were 0.67 (Neuroticism facets), 0.65

(Extraversion facets), 0.52 (Openness facets), 0.52 (Agreeableness facets), and 0.52

(Conscientiousness facets).2

The Schedule for Nonadaptive and Adaptive Personality (SNAP; Clark, 1993) was also

administered to both samples. The SNAP is a 375-item self-report questionnaire utilizing a

true–false format that evaluates personality characteristics and general temperament. The

SNAP contains three higher-order temperament scales, 12 trait scales, and six validity

scales. In this study, the internal consistency of the higher-order temperament scales in the

combined sample was 0.67 (Negative Temperament; NT), 0.88 (Positive Temperament;

PT), and 0.84 (Disinhibition; DIS). The internal consistencies for the SNAP trait scales

ranged from 0.68 (Impulsivity) to 0.90 (Mistrust).

2The lower internal consistency values for the SIFFM facets probably reflect the fact that only four itemscomprise each scale.

Incremental validity of the SIFFM 7

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RESULTS

Descriptive analyses

The combined sample included 13 individuals with DSM-IV BPD (10 females, three

males) and six males with APD. No participants met full diagnostic criteria for HPD. In the

combined sample, the mean number of BPD symptoms was 1.29 (SD¼ 1.64), the mean

number of APD symptoms was 1.20 (SD¼ 1.54), and the mean number of HPD symptoms

was 0.42 (SD¼ 0.78). As expected, clinical participants, as a group, endorsed a higher

number of symptoms for each of the personality disorders. In the clinical sample, the mean

number of BPD symptoms was 1.85 (SD¼ 1.85), the mean number of APD symptoms was

1.23 (SD¼ 1.45), and the mean number of HPD symptoms was 0.65 (SD¼ 0.97). In the

nonclinical sample, the mean number of BPD symptoms was 1.11 (SD¼ 1.52), the mean

number of APD symptoms was 1.20 (SD¼ 1.57) and the mean number of HPD symptoms

was 0.34 (SD¼ 0.70).3

Regression analyses

In order to examine the respective relationships of the SIFFM and the SNAP scores to the

PDI-IV personality disorder symptom counts, two sets of regression analyses were per-

formed using the combined clinical and nonclinical data. To examine whether the variables

of age and biological sex covary as a function of the personality disorder symptom counts,

bivariate correlations between these variables and the personality disorder symptom

counts were computed. Because age was significantly correlated with the PDI-IV BPD

symptom counts (r¼ 0.16, p< 0.05), age was controlled for in the regressions using BPD

symptom counts as the criterion. In addition, biological sex (0¼ female, 1¼male) was

significantly correlated with the PDI-IV APD symptom counts (r¼ 0.17, p< 0.05). Thus,

age was entered as the first step in those regressions in which BPD was the dependent

variable, and sex was entered as the first step in those regressions in which APD was the

dependent variable. The PDI-IV HPD symptom count was not significantly correlated with

either age or sex.

Domains

First, the five SIFFM domain and three SNAP temperament scale scores, respectively,

were used to predict the variance of each of the PDI-IV three personality disorder symp-

tom counts. These results are presented in Table 2, and indicate the extent to which the

FFM and Big Three model, respectively, can account for variance in each of the three

personality disorder symptom counts.

In the first analysis, all five SIFFM domains were entered as a block to predict

each PDI-IV symptom count. SIFFM domains significantly predicted BPD features

when controlling for age (F�(5, 193)¼ 22.20, p< 0.0001; �R2¼ 0.34), APD features

when controlling for sex (F�(5, 193)¼ 16.72, p< 0.0001; �R2¼ 0.28), and HPD

features (F�(5, 194)¼ 12.58, p< 0.0001; �R2¼ 0.23). Likewise, when the three SNAP

higher-order temperament scales were entered as a block to predict PDI-IV symptom

counts (bottom half of Table 2), these SNAP higher-order scales significantly predicted

BPD features when controlling for age (F�(3, 195)¼ 23.23, p< 0.0001; �R2¼ 0.24),

APD features when controlling for sex (F�(3, 195)¼ 22.80, p< 0.0001; �R2¼ 0.24), and

HPD features (F�(3, 196)¼ 8.11, p< 0.0001; �R2¼ 0.10).

3Bivariate correlations matrices for the predictor variables are available from the second author.

8 S. D. Stepp et al.

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le2

.In

crem

enta

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idit

yo

fS

IFF

Md

om

ain

and

SN

AP

tem

per

amen

tsc

ale

PD

I-IV

reg

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s:co

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ined

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and

no

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inic

alsa

mp

lesa

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teri

on

sym

pto

mS

tep

/pre

dic

tors

df

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2F�

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nifi

can

tin

div

idu

alp

red

icto

rsco

un

t(a

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sted

)af

ter

fin

alst

ep

Bo

rder

lin

e(B

PD

)1

.A

ge

1,

19

80

.02

5.5

1*

*2

.S

IFF

Md

om

ain

s5

,1

93

0.3

42

2.2

0*

**

*3

.S

NA

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mp

eram

ent

scal

es3

,1

90

0.0

01

.01

(see

bel

ow

)A

nti

soci

al(A

PD

)1

.S

ex1

,1

98

0.0

26

.10

**

2.

SIF

FM

do

mai

ns

5,

19

30

.28

16

.72

**

**

3.

SN

AP

tem

per

amen

tsc

ales

3,

19

00

.02

2.9

0*

*(s

eeb

elow

)H

istr

ion

ic(H

PD

)1

.S

IFF

Md

om

ain

s5

,1

94

0.2

31

2.5

8*

**

*2

.S

NA

Pte

mp

eram

ent

scal

es3

,1

91

0.0

00

.53

(see

bel

ow

)B

ord

erli

ne

(BP

D)

1.

Ag

e1

,1

98

0.0

25

.51

**

2.

SN

AP

tem

per

amen

tsc

ales

3,

19

50

.24

23

.23

**

**

3.

SIF

FM

do

mai

ns

5,

19

00

.09

6.8

0*

**

*S

IFF

MN

euro

tici

sm(þ

þþþ

);S

IFF

MC

on

scie

nti

ou

snes

s(�

�)

An

tiso

cial

(AP

D)

1.

Sex

1,

19

80

.02

6.1

0*

*2

.S

NA

Pte

mp

eram

ent

scal

es3

,1

95

0.2

42

2.8

0*

**

*3

.S

IFF

Md

om

ain

s5

,1

90

0.0

64

.17

**

*S

IFF

MN

euro

tici

sm(þ

þ);

SIF

FM

Co

nsc

ien

tio

usn

ess

(��

);S

NA

PD

isin

hib

itio

n(þ

þ)

His

trio

nic

(HP

D)

1.

SN

AP

tem

per

amen

tsc

ales

3,

19

60

.10

8.1

1*

**

*2

.S

IFF

Md

om

ain

s5

,1

90

0.1

27

.18

**

**

SIF

FM

Neu

roti

cism

(þþþ

);S

IFF

MO

pen

nes

s(þ

þ)

an¼

20

2.*

*p<

0.0

5,*

**p<

0.0

05

,*

**

*p<

0.0

00

1;þþ

po

siti

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late

d,p<

0.0

5;þþþ

posi

tivel

yre

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d,p<

0.0

05

;þþþþ

posi

tivel

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d,p<

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;��

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ativ

ely

rela

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,

p<

0.0

5;���

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ativ

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rela

ted

,p<

0.0

05

.

Incremental validity of the SIFFM 9

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Table 2 also presents the results of the hierarchical analyses that ascertained whether or

not the SIFFM domain scores predicted variance in symptom counts above and beyond

that accounted for by the SNAP temperament scores (or vice versa). In the first set of

hierarchical regressions (top of table, last step), the SNAP temperament scales accounted

for a significant amount of the variance above and beyond the SIFFM domains only for the

APD symptom count (F�(3, 190)¼ 2.90, p< 0.001; �R2¼ 0.02). An examination of the

last column (Significant Individual Predictors after Final Step) reveals that this is probably

due to the unique contribution of SNAP Disinhibition scores in the prediction of APD

symptoms. In contrast, the SIFFM domain scores accounted for a significant amount of

variance beyond that accounted for by the SNAP temperament scores in the case of all

three criterion symptom counts (Table 2, bottom half). An examination of the significant

predictors after the last step (last column) reveals that high SIFFM Neuroticism and low

SIFFM Conscientiousness scores were significantly related to BPD symptoms; high

SIFFM Neuroticism, high SNAP Disinhibition, and low SIFFM Conscientiousness scores

were significantly related to APD symptoms; and high SIFFM Neuroticism and high

SIFFM Openness scores were significantly related to HPD symptoms.

FFM lower-order traits and SNAP scales

Next, selected SIFFM facet scores and SNAP scale scores targeting each of the three

personality disorders (see Table 1) were included in prediction models of the three PDI-IV

symptom counts. The results of these regression analyses are presented in Table 3.4

As indicated in the top half of Table 3, the selected SIFFM facet scores significantly

predicted BPD symptoms when controlling for age (F�(15, 182)¼ 9.97, p< 0.0001;

�R2¼ 0.44), APD features when controlling for sex (F�(18, 178)¼ 6.25, p< 0.0001;

�R2¼ 0.32), and HPD features (F�(18, 180)¼ 3.42, p< 0.0001; �R2¼ 0.18). Focusing

on the bottom half of the table, the selected SNAP scale scores significantly predicted BPD

features when controlling for age (F�(4, 193)¼ 80.62, p< 0.0001; �R2¼ 0.28), APD

features when controlling for sex (F�(5, 191)¼ 17.77, p< 0.0001; �R2¼ 0.30), and HPD

features (F�(4, 194)¼ 6.62, p< 0.0001; �R2¼ 0.10).

Table 3 also presents the results of the hierarchical analyses that ascertained whether or

not the SIFFM facet scores targeting each personality disorder, respectively, predicted

variance above and beyond that accounted for by the selected SNAP scale scores (or vice

versa). In the first set of hierarchical regressions (top of table), the SNAP scales accounted

for variance above and beyond the SIFFM facet scores in the case of BPD symptom counts

(F�(4, 178)¼ 7.03, p< 0.0001; �R2¼ 0.02), APD symptom counts (F�(5, 173)¼ 2.65,

p< 0.05; �R2¼ 0.03), and HPD symptom counts (F�(4, 176)¼ 6.12, p< 0.0001;

�R2¼ 0.08). Similarly, the SIFFM facet scores accounted for a significant amount of

variance beyond that accounted for by the SNAP scores in the case of all three criterion

symptom counts (Table 3, bottom half).

An examination of the last column (Significant Individual Predictors after Final Step)

reveals that individual scores significantly related to BPD symptoms included SIFFM

Depression, SIFFM Assertiveness, SIFFM Achievement Striving (negative coefficient),

SIFFM Gregariousness, and SNAP Self-Harm. Scores significantly related to APD

4A few of the values presented here differ from those mentioned by Trull and Widiger (2002), who summarizedpreliminary findings from these samples. The unpublished study mentioned by Trull and Widiger (2002) usedslightly different subsets of SNAP items in the prediction models. The present study, however, includes in therespective models those SNAP scales that Clark (1993, Table 2, p. 16) reports as corresponding conceptually tothe three personality disorders of interest.

10 S. D. Stepp et al.

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Tab

le3

.In

crem

enta

lval

idit

yo

fS

IFF

Mfa

cet

and

SN

AP

scal

eP

DI-

IVre

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ssio

nan

aly

ses:

com

bin

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alan

dn

on

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sam

ple

sa

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on

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pto

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/pre

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tors

df

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div

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dju

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ter

fin

alst

ep

Bo

rder

lin

e(B

PD

)1

.A

ge

1,

19

0.0

25

.36

**

2.

SIF

FM

BP

Dfa

cets

15

,1

82

0.4

49

.97

**

**

3.

SN

AP

BP

Dsc

ales

4,

17

80

.02

7.0

3*

**

*(s

eeb

elow

)A

nti

soci

al(A

PD

)1

.S

ex1

,1

96

0.0

25

.52

**

2.

SIF

FM

AP

Dfa

cets

18

,1

78

0.3

26

.25

**

**

3.

SN

AP

AP

Dsc

ales

5,

17

30

.03

2.6

5*

*(s

eeb

elow

)H

istr

ion

ic(H

PD

)1

.S

IFF

MH

PD

face

ts1

8,

18

00

.18

3.4

2*

**

*2

.S

NA

PH

PD

scal

es4

,1

76

0.0

86

.12

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(see

bel

ow

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ord

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ne

(BP

D)

1.

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e1

,1

97

0.0

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.36

**

2.

SN

AP

BP

Dsc

ales

4,

19

30

.28

80

.62

**

**

3.

SIF

FM

BP

Dfa

cets

15

,1

78

0.1

84

.11

**

**

SIF

FM

Dep

ress

ion

(þþ

);S

IFF

MA

sser

tiven

ess

(þþ

);S

IFF

MA

chie

vem

ent-

Str

ivin

g(�

�);

SIF

FM

Gre

gar

iou

snes

s(þ

þ);

SN

AP

Sel

f-H

arm

(þþþþ

)A

nti

soci

al(A

PD

)1

.S

ex1

,1

96

0.0

25

.52

**

2.

SN

AP

AP

Dsc

ales

5,

19

10

.30

17

.77

**

**

3.

SIF

FM

AP

Dfa

cets

18

,1

73

0.0

51

.92

**

SIF

FM

Dep

ress

ion

(þþ

);S

IFF

MIm

pu

lsiv

enes

s(þ

þ);

SN

AP

Ag

gre

ssio

n(þ

þ)

His

trio

nic

(HP

D)

1.

SN

AP

HP

Dsc

ales

4,

19

40

.10

6.6

2*

**

*2

.S

IFF

MH

PD

face

ts1

8,

17

60

.16

3.3

7*

**

*S

IFF

MS

elf-

Co

nsc

iou

snes

s(þ

þ);

SIF

FM

Vuln

erab

ilit

y(þ

þ);

SN

AP

Ex

hib

itio

nis

m(þ

þþ

);S

NA

PE

nti

tlem

ent

(þþ

)

See

Tab

le1

for

sele

cted

SIF

FM

and

SN

AP

pre

dic

tors

.an¼

20

2,

**p<

0.0

5,

**

*p<

0.0

05

,*

**

*p<

0.0

00

1;þþ

posi

tivel

yre

late

d,

p<

0.0

5;þþþ

po

siti

vel

yre

late

d,

p<

0.0

05

;

þþþþ

posi

tivel

yre

late

d,

p<

0.0

01

;��

neg

ativ

ely

rela

ted,

p<

0.0

5;���

neg

ativ

ely

rela

ted

,p<

0.0

05

.

Incremental validity of the SIFFM 11

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symptoms were SIFFM Depression, SIFFM Impulsiveness, and SNAP Aggression.

Finally, scores significantly related to HPD symptoms were SIFFM Self-Consciousness,

SIFFM Vulnerability, SNAP Exhibitionism, and SNAP Entitlement.

DISCUSSION

There were several major findings from this study. First, both FFM and Big Three higher-

order traits are significantly related to borderline, antisocial, and histrionic symptoms, and

FFM domain scores demonstrated incremental validity in predicting symptoms of all three

of these personality disorders. Therefore, it appears that the FFM domains assess features

of BPD, APD, and HPD that are not tapped into by the Big Three model as measured by

the SNAP. Borderline symptoms were significantly related to SIFFM Neuroticism and

Conscientiousness scores; Antisocial symptoms were significantly related to SIFFM

Neuroticism, SIFFM Conscientiousness, and SNAP Disinhibition scores; and Histrionic

symptoms were significantly related to SIFFM Neuroticism and SIFFM Openness scores.

Second, as a group, SIFFM facet scores and SNAP scale scores, respectively, were

significantly associated with targeted personality disorders. Finally, both SIFFM facet and

SNAP scores did show incremental validity in the prediction of all three personality

disorder symptom counts.

Several findings deserve further comment. SNAP Self-Harm scores were highly

significant predictors of BPD symptoms. High scorers on this scale have low self-esteem

and deal with their disappointment and frustration by hurting themselves (Clark, 1993).

Item content that reflects this tendency toward self-harm is not well represented in FFM

instruments, including the SIFFM. (In fact, no SIFFM item directly assesses self-harm

behaviour or suicide-proneness.) Therefore, it is then not surprising that scores from this

SNAP scale are significant predictors of BPD symptoms, which include several features

related to self-harm. On the other hand, it might be argued that the suicide-proneness

component of SNAP Self-Harm is better conceptualized as more environmentally

influenced and less traitlike than other core BPD features such as affective instability or

impulsivity. Nevertheless, scores from this SNAP scale do provide incremental prediction

beyond that afforded by SIFFM scores and serve to complement the personality trait

description of BPD.

Neither the SIFFM facet scores nor the SNAP scales were particularly strong predictors

of histrionic symptoms. The total amount of variance accounted for in HPD symptoms was

substantially smaller than that accounted for in BPD or APD symptoms. There are several

possible explanations for this. First, the fact that no person in the sample met criteria for an

HPD diagnosis suggests that the HPD symptom count had a relatively restricted range

compared to that for BPD or APD. Second, perhaps at least some of the SIFFM facets that

were nominated to account for HPD pathology by Trull and Widiger (1997) were in error.

For example, Lynam and Widiger (2001) surveyed 19 personality disorder experts as to

which FFM facet scores would be elevated for a prototypic HPD patient. Comparing the

traits listed in Table 1 with the facet scores that were nominated reveals that the facet of

Impulsiveness was nominated by experts but not included in the predictions of Trull and

Widiger (1997) and that several facets in Table 1 were not nominated by the experts. So,

this at least raises the possibility that the wrong SIFFM facets may have been included in

the Trull–Widiger (1997) prediction models for HPD. Future research might test alterna-

tive FFM conceptualizations of HPD in other samples that include more HPD diagnoses.

12 S. D. Stepp et al.

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Like the SIFFM the SNAP also accounted for a significant portion of the variance in

each of the criterion symptom counts, and like the SIFFM the selected SNAP scales did

account for variance in the criterion measures above and beyond the SIFFM. Thus,

although the SIFFM accounts for distinct variance other than that accounted for by the

SNAP, the current study does not support the idea that the predictive ability of the SNAP

scales can be entirely accounted for by the SIFFM in the context of predicting personality

disorder features. Although our results support the incremental validity of SIFFM scores in

predicting symptoms of these three personality disorders, the results do not suggest that

the SIFFM is better than the SNAP or vice versa. Rather, our results suggest that the two

instruments complement each other and each provides some unique predictive ability not

afforded by the other.

Reynolds and Clark (2001) reported that the 15 SNAP scales outperformed an FFM

measure, the NEO PI-R facet scales, in predicting personality disorder symptoms.

However, they noted that ‘the maladaptive personality traits assessed by the SNAP were

strongly represented in the facet scales of the NEO PI-R’ (Reynolds & Clark, 2001, p.

216). They suggested that the primary reason that the SNAP outperformed the NEO PI-R

was that ‘the FFM measures assess normal-range traits [whereas] the SNAP primarily

assesses extreme variants of normal-range traits that are maladaptive and clinically

relevant’ (Reynolds & Clark, 2001, p. 218). Similarly, although the SIFFM was designed

to assess more maladaptive variants of the traits included in the FFM, the SNAP scales

contain many more items that focus on these maladaptive variants. Thus, in general, SNAP

scores may provide a richer picture of the maladaptive realms of these traits. On the other

hand, it is impressive that that SIFFM facet scores do so well in predicting personality

pathology given the limited number of items included in these scales.

If one’s goal is to arrive at a DSM-IV diagnosis of BPD, APD, or HPD, then a DSM-IV

based semi-structured interview should be used. Why then even consider using the SIFFM

or the SNAP? We believe that these instruments (as well as others) provide an alternative

means of assessing personality pathology. On the one hand, because they do account for

variance in DSM-IV personality disorder symptoms, they can help us understand the

nature of the disorders, provide a better description of the traits involved, and help us

understand why certain disorders tend to co-occur (because they share underlying traits).

On the other hand, measures of alternative dimensional models of personality pathology

can aid in the identification of forms of personality pathology or disorder that are not

represented in DSM-IV (Trull & Durrett, in pressQ1). Further, these measures, through

their connection to well established personality models and traits that have been around for

decades, can inform our aetiological theories, given the abundance of research that has

been conducted on these traits.

In conclusion, the SIFFM scores do show incremental validity in predicting three

personality disorder symptom counts. Further, specific SIFFM trait scores provided unique

diagnostic information above and beyond scores from another alternative personality

measure. These findings indicate that the SIFFM may be a useful tool for the assessment of

personality disorders and personality pathology, especially for those clinicians and

researchers who prefer interview-based assessments. Future research should assess the

incremental validity of SIFFM scores in a larger clinical sample with more and a broader

range of personality disorder diagnoses. Finally, given that the SIFFM currently is

administered in an interview format, future research may attempt to distinguish the vari-

ance accounted for by the content of the measure and possible methodological variance

specific to the interview format. For example, a study could specifically re-examine the

Q1

Incremental validity of the SIFFM 13

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predictive power of the SNAP and the SIFFM using a self-report measure of DSM-IV

personality disorder symptoms. Furthermore, a self-report format of the SIFFM could be

created. This new self-report measure could be compared not only with other self-report

measures of personality, but with the current interview version of the SIFFM itself.

ACKNOWLEDGEMENT

This research was partially supported by National Institute of Mental Health grant

MH52695 awarded to Timothy J. Trull.

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