Inventory of Interpersonal Problems Personality Disorder scales: Operatingcharacteristics and confirmatory factor analysis in nonclinical samples

Citation
Bl. Stern et al., Inventory of Interpersonal Problems Personality Disorder scales: Operatingcharacteristics and confirmatory factor analysis in nonclinical samples, J PERS ASSE, 74(3), 2000, pp. 459-471
Citations number
23
Categorie Soggetti
Psycology
Journal title
JOURNAL OF PERSONALITY ASSESSMENT
ISSN journal
00223891 → ACNP
Volume
74
Issue
3
Year of publication
2000
Pages
459 - 471
Database
ISI
SICI code
0022-3891(200006)74:3<459:IOIPPD>2.0.ZU;2-M
Abstract
Research involving clinical samples has demonstrated the utility of a 28-it em personality disorder (PD) screening measure (Inventory of Interpersonal Problems-Personality Disorder scale [IIP-PD]) culled from the IIP in the pr ediction of the presence or absence of a PD (Pilkonis, Kim, Proietti, & Bar kham, 1996). This article extends these diagnostic efficiency findings to n onclinical samples and presents additional data regarding the factor struct ure of the 28 IIP-PD items. Diagnostic efficiency statistics for the IIP-PD scale, calculated using both interview and self-report methods, support th e utility of the IIP-PD scale as a screening tool for the presence or absen ce of a PD. High specificity estimates indicate that individuals who do not exceed Diagnostic and Statistical Manual of Mental Disorders (4th ed.; Ame rican Psychiatric Association, 1994) symptom thresholds rarely exceed the I IP-PD cutoff. Furthermore, a high negative predictive power (NPP) estimate derived using an interview-based diagnostic standard suggests that the IIP- PD scale accurately screens out individuals who do not have a PD. Finally, cross-validated confirmatory factor-analytic results involving items compos ing the 5 IIP PD subscales identified in previous research (Kim, Pilkonis, & Barkham, 1997) suggest that a measurement model with a single second-orde r factor (general PD) and 5 first-order factors (one representing each PD s ubscale) provided the best fit to the observed data compared to 2 other com peting models.