The factorial structure and construct validity of the Psychopathy Checklist‐Revised (PCL‐R) among alcoholic inpatients

Simultaneous group confirmatory factor analyses of the Psychopathy Checklist‐Revised (PCL‐R; Hare, 1991) were conducted with an alcoholic inpatient sample (N= 740). Invariance of the item‐factor relations for the 2 highly correlated factors of Personality and Behavioral Features were supported across 3 racial/ethnic groups (African American, Puerto Rican, and White) and across gender groups. Moment structure analysis indicated no significant differences in the latent means across men and women. Alternative covariance structure models were specified within a multitrait‐multimethod framework to evaluate convergent and discriminant validity across different methods of measuring antisociality. A correlated trait‐correlated method model was supported. A factor intercorrelation of .68 was indicated for Personality and Behavioral Features, and a significant correlation (.40) among method factors emerged for the PCL‐R scores across different raters (interviewers and therapists). General support was provided for the use of the PCL‐R with alcoholic inpatients, though there was a substantial intercorrelation for the factors of Personality and Behavioral Features.     

The Partial Credit Model and Generalized Partial Credit Model as Constrained Nominal Response Models,With Applications in Mplus

The purpose of this article is to demonstrate constraining the nominal response model in Mplus software to calibrate data under the partial credit model (PCM) and generalized partial credit model (GPCM). Currently, many researchers are uncertain if the PCM and GPCM can be estimated within Mplus. Through model constraint commands in Mplus, we demonstrate that both models can be estimated in recent versions of this software. We present an example of this approach with data from 522 respondents on a subset of items from the Math Self-Efficacy Scale (Betz & Hackett, 1983). It is demonstrated that the presented model code is a viable way of estimating the models in Mplus.     

Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches

Little, Bovaird and Widaman (2006) proposed an unconstrained approach with residual centering for estimating latent interaction effects as an alternative to the mean-centered approach proposed by Marsh, Wen, and Hau (2004, 2006). Little et al. also differed from Marsh et al. in the number of indicators used to infer the latent interaction factor and how they were represented, but this issue is separate from the mean versus residual centering distinction that was their primary focus. However, their implementation of the Marsh et al. mean-centered approach failed to incorporate the mean structure that Marsh et al. argued was necessary to obtain unbiased estimates. One might suppose that their new approach would suffer this same problem, an issue not addressed by Little et al. However, we demonstrate here why the Little et al. approach obviates this requirement that heretofore was thought to be necessary for all constrained, partially constrained, and unconstrained approaches. Both the Marsh et al. and Little et al. unconstrained approaches typically result in similar results and are much easier to implement than traditional constrained approaches. They differ primarily in that the Little et al. approach is a 2-step approach involving a potentially large number of separate analyses prior to estimating the structural equation model that apparently does not require the estimation of a mean structure, whereas the Marsh et al. approach is a 1-step approach that includes a mean structure.     

Modeling Unobserved Heterogeneity Using Latent Profile Analysis: A Monte Carlo Simulation

Latent profile analysis (LPA) has become a popular statistical method for modeling unobserved population heterogeneity in cross-sectionally sampled data, but very few empirical studies have examined the question of how well enumeration indexes accurately identify the correct number of latent profiles present. This Monte Carlo simulation study examined the ability of several classes of enumeration indexes to correctly identify the number of latent population profiles present under 3 different research design conditions: sample size, the number of observed variables used for LPA, and the separation distance among the latent profiles measured in Mahalanobis D units. Results showed that, for the homogeneous population (i.e., the population has k = 1 latent profile) conditions, many of the enumeration indexes used in LPA were able to correctly identify the single latent profile if variances and covariances were freely estimated. However, for a heterogeneous population (i.e., the population has k = 3 distinct latent profiles), the correct identification rate for the enumeration indexes in the k = 3 latent profile conditions was typically very low. These results are compared with the previous cross-sectional mixture modeling studies, and the limitations of this study, as well as future cross-sectional mixture modeling and enumeration index research possibilities, are discussed.     

The Impact of an Early Literacy Initiative on the Long Term Academic Success of Diverse Students

The primary goal of the Words Work (WW) early literacy initiative was to prepare diverse students to become successful learners in school. Four groups of students were followed over 6 years: students from traditional Head Start (HS) centers, students from augmented HS centers (WW students), students eligible for HS but not enrolled (waitlist or WL), and a random sample of non-Head Start students (NHS). Students received school readiness instruction at approximately 4 years of age, and their performance on standardized tests was followed from 1st grade through 5th grade. WW students significantly outscored HS, WL, and NHS students of similar backgrounds in reading and outscored WL and NHS students in mathematics on 2nd-grade standardized assessments. In addition, WW students were able to maintain their lead through 5th grade in both reading and mathematics. Based on these results, WW is an effective HS enhancement for minimizing the achievement gap often observed between students who may be at risk for academic difficulty and students who are at low risk.     

Creating a Comprehensive School Reform Model: The Talent Development High School With Career Academies

This article discusses the need for comprehensive reforms in school organization, curriculum and instruction, and professional development in order to address the typical, serious problems of large, urban, nonselective high schools. The article describes the design, implementation, and effectiveness of the Talent Development High School with Career Academies, a comprehensive school reform model developed to meet these schools' needs. A plan is outlined for additional research, development, and evaluation activities to complete this model for the remaining core high school academic subjects and grades, and to provide technical assistance materials and support for further replicating the model in high schools across the nation.