What is the relationship between alexithymia and experiential avoidance? A latent analysis using three alexithymia questionnaires.

Alexithymia is a clinically relevant personality trait characterized by poor emotional awareness and associated with several psychological and physical health concerns. Individuals with high alexithymia tend to engage in experiential avoidance and this may mediate psychological distress. However, little is known about what specific processes of experiential avoidance are involved, and the nature of the relation between alexithymia, experiential avoidance, and psychological distress remains unclear at a latent construct level.

Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification.

Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published study is used to illustrate how strongly related an omitted variable must be to model variables for the conclusions of an analysis to change.

Identifying Correlates of Peer and Faculty/Staff Sexual Harassment in US Students.

Sexual harassment and its negative consequences continue to affect a large percentage of higher education students in the US. Previous research has identified a limited number of harassment risk factors, and has generally not examined them in combination. In this study, an expanded set of individual, relationship, and community-level risk factors were examined using hurdle models and classification and regression tree (CART) analyses to identify key risk factors for peer and faculty/staff sexual harassment.

Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study.

Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for determining the number of classes in growth mixture models.

Smoothed Quantiles for Type Test Statistics with Applications.

Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic.

A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development.

This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables.

A Two-Level Alternating Direction Model for Polytomous Items With Local Dependence.

The chiropractic clinical competency examination uses groups of items that are integrated by a common case vignette. The nature of the vignette items violates the assumption of local independence for items nested within a vignette. This study examines via simulation a new algorithmic approach for addressing the local independence violation problem using a two-level alternating directions testlet model.

Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods.

A procedure that can be used to evaluate the variance inflation factors and tolerance indices in linear regression models is discussed. The method permits both point and interval estimation of these factors and indices associated with explanatory variables considered for inclusion in a regression model. The approach makes use of popular latent variable modeling software to obtain these point and interval estimates.

Data Integration Approaches to Longitudinal Growth Modeling.

Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies.

Structural Equation Modeling With Many Variables: A Systematic Review of Issues and Developments.

Survey data in social, behavioral, and health sciences often contain many variables (). Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (), SEM enables researchers to easily set up and reliably test hypothetical relationships among theoretical constructs as well as those between the constructs and their observed indicators.

Using an Acculturation-Stress-Resilience Framework to Explore Latent Profiles of Latina/o Language Brokers.

With survey data from 243 Latina/o early adolescent language brokers, latent profile analyses were conducted to identify different types (i.e., profiles) of brokers.