Roy Levy

Evaluation of Parallel Analysis Methods for Determining the Number of Factors.

Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria were examined. The 95th percentile criterion was preferable for assessing the first eigenvalue using either extraction method. In assessing subsequent eigenvalues, PA-PCA tended to perform as well as or better than PA-PAF for models with one factor or multiple minimally correlated factors; the relative performance of the mean eigenvalue and the 95th percentile eigenvalue criteria depended on the number of variables per factor. PA-PAF using the mean eigenvalue criterion generally performed best if factors were more than minimally correlated or if one or more strong general factors as well as group factors were present.

Randomized controlled trial and uncontrolled 9-month follow-up of an adjunctive emotion regulation group therapy for deliberate self-harm among women with borderline personality disorder

BACKGROUND Despite the clinical importance of deliberate self-harm (DSH; also referred to as non-suicidal self-injury) within borderline personality disorder (BPD), empirically supported treatments for this behavior among individuals with BPD are difficult to implement in many clinical settings. To address this limitation, a 14-week, adjunctive emotion regulation group therapy (ERGT) for DSH among women with BPD was developed. The current study examined the efficacy of this ERGT in a randomized controlled trial (RCT) and the durability of treatment gains over a 9-month uncontrolled follow-up period. METHOD Female out-patients with BPD and recent recurrent DSH were randomly assigned to receive this ERGT in addition to their ongoing out-patient therapy immediately (n = 31) or after 14 weeks (n = 30). Measures of DSH and other self-destructive behaviors, psychiatric symptoms, adaptive functioning and the proposed mechanisms of change (emotion dysregulation/avoidance) were administered pre- and post-treatment or -waitlist (to assess treatment efficacy), and 3 and 9 months post-treatment (to assess durability of treatment gains). RESULTS Intent-to-treat (ITT) analyses (n = 61) revealed significant effects of this ERGT on DSH and other self-destructive behaviors, emotion dysregulation, BPD symptoms, depression and stress symptoms, and quality of life. Analyses of all participants who began ERGT (across treatment and waitlist conditions; n = 51) revealed significant improvements from pre- to post-treatment on all outcomes, additional significant improvements from post-treatment to 9-month follow-up for DSH, emotion dysregulation/avoidance, BPD symptoms and quality of life, and no significant changes from post-treatment to 9-month follow-up on the other measures. CONCLUSIONS The results support the efficacy of this ERGT and the durability of treatment gains.

Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors hypothesized to influence dimensionality and dimensionality assessment are couched in conditional covariance theory and conveyed via geometric representations of multidimensionality. A simulation study investigates the performance of the model-checking tools for dichotomous observables. Key findings include support for the hypothesized effects of the manipulated factors with regard to their influence on dimensionality assessment and the superiority of certain discrepancy measures for conducting posterior predictive model checking for dimensionality assessment.

Introduction to Evidence Centered Design and Lessons Learned From Its Application in a Global E-Learning Program

This articles introduces the assessment and deployment contexts of the Networking Performance Skill System (NetPASS) project and the articles in this section that report on findings from this endeavor. First, the educational context of the Cisco Networking Academy Program is described. Second, the basic outline of Evidence Centered Design is described. In the third section, the intersection of these two activities in the NetPASS project is described and the subsequent articles introduced.

Understanding the Heterogeneity of BPD Symptoms Through Latent Class Analysis: Initial Results and Clinical Correlates Among Inner-City Substance Users

The current study investigated the heterogeneity of borderline personality disorder (BPD) symptoms in a sample of 382 inner-city, predominantly African American male substance users through the use of latent class analysis. A 4-class model was statistically preferred, with 1 class interpreted to be a baseline class, 1 class interpreted to be a high-BPD class, and 2 classes interpreted as intermediate classes. As a secondary goal, we examined the resulting BPD classes with respect to relevant clinical correlates, including temperamental vulnerabilities (affective instability, impulsivity, and interpersonal instability), childhood emotional abuse, drug choice, and co-occurring mood and anxiety disorders. The high-BPD class evidenced the highest levels of the temperamental vulnerabilities and environmental stressors, the baseline class evidenced the lowest levels, and the 2 intermediate classes fell in between. In addition, the high-BPD class had a higher probability of cocaine and alcohol dependence, as well as mood and anxiety disorders, than did the baseline class. Rates of alcohol use and mood disorders for the intermediate classes fell in between the high-BPD and the baseline classes. Results are discussed in relation to the current diagnostic conceptualization of BPD.

Emotion regulation as a mechanism of change in an acceptance-based emotion regulation group therapy for deliberate self-harm among women with borderline personality pathology

Despite the clinical importance of deliberate self-harm (DSH) within borderline personality disorder (BPD), there are few empirically supported treatments for this behavior among individuals with BPD; and those that do exist are difficult to implement in many clinical settings. Thus, Gratz and colleagues developed an adjunctive emotion regulation group therapy (ERGT) for women with BPD that directly targets both DSH and its proposed underlying mechanism of emotion dysregulation. Although previous studies support the use of this ERGT in reducing DSH, no studies have examined emotion regulation as a mechanism of change in this treatment. Therefore, this study examined the mediating role of changes in emotion dysregulation in DSH improvement across two separate trials of this ERGT. As hypothesized, changes in emotion dysregulation mediated the observed reductions in DSH frequency. Results provide support for the theoretical model underlying this ERGT and highlight the importance of targeting emotion dysregulation in treatments for DSH.

Testing Gender Effects on the Mechanisms Explaining the Association between Post-Traumatic Stress Symptoms and Substance Use Frequency

The present study examines gender differences in the mechanisms that explain the association between PTSD symptoms and substance use frequency in a sample of 182 urban substance users. Specifically, the current study examined gender differences in the role of two potential explanatory variables, namely, difficulties controlling impulsive behavior when distressed (IMP), and a lack of emotional awareness and clarity (AW/CLAR). Multiple-group path modeling (across males and females) was used to examine gender differences in the path coefficients from PTSD symptoms to IMP and AW/CLAR, and from these latter variables to drug use frequency. Results indicated that PTSD symptoms were associated with IMP and AW/CLAR, and these path coefficients did not vary by gender. However, gender differences emerged when considering the path coefficients from AW/CLAR and IMP to substance use frequency. Specifically, for women, the association between PTSD and substance use was partially explained by IMP, whereas for men, the association between PTSD and substance use was partially explained by AW/CLAR. The current study is the first to examine gender differences in mechanisms accounting for the association between PTSD and substance use frequency, and these results also support the value and importance of examining gender differences in mechanisms underlying PTSD-SUD comorbidity.

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes and illustrates key features of Bayesian approaches to model diagnostics and assessing data–model fit of structural equation models, discussing their merits relative to traditional procedures.

A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis

A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to compare revised and traditional parallel analysis approaches. Five dimensions are manipulated in the study: number of observations, number of factors, number of measured variables, size of the factor loadings, and degree of correlation between factors. Based on the results, the revised parallel analysis method, using principal axis factoring and the 95th percentile eigenvalue rule, offers promise.

A generalized dimensionality discrepancy measure for dimensionality assessment in multidimensional item response theory.

A generalized dimensionality discrepancy measure is introduced to facilitate a critique of dimensionality assumptions in multidimensional item response models. Connections between dimensionality and local independence motivate the development of the discrepancy measure from a conditional covariance theory perspective. A simulation study and a real-data analysis demonstrate the utility of the discrepancy measures application at multiple levels of analysis in a posterior predictive model checking framework.