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. To examine this relationship at the latent construct level, a representative sample of 693 U.S. adults completed alexithymia (TAS-20, BVAQ, PAQ), general distress (DASS-21), multi-dimensional experiential avoidance (MEAQ), and general health (PROMIS-G-10) questionnaires. Structural equation modeling revealed that alexithymia significantly predicted experiential avoidance (ß = 0.966, t = 82.383, p < .01), experiential avoidance significantly predicted general distress (ß = 0.810, t = 2.017, p < .05), and experiential avoidance fully mediated the relationship between alexithymia and general distress (ßindirect = -0.159, t = -0.398, p > .05). Correlations between alexithymia and experiential avoidance subfactors revealed a strong relationship to the repression and denial subfactor. Experiential avoidance is a promising target for clinical interventions, though longitudinal research is necessary to elucidate how the relationship between alexithymia and experiential avoidance unfolds over time.

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. The algorithm is outlined in detail and the results stemming from the sensitivity analysis are discussed.

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. Secondary data analysis was conducted using data from a sample of 9,285 students from 18 two-year and four-year schools in Minnesota. CART analyses indicated that, for peer sexual harassment, being younger; consuming alcohol more than once a month; attending a four-year school; being transgender, genderqueer, self-identified, or a cisgender woman; and having experienced bullying were the most important risk factors for peer harassment on campus. For faculty/staff harassment, being gay, lesbian, bisexual, questioning, or having a self-identified sexual orientation was the most important risk factor. These and other risk factors were significant in the hurdle models. More research is needed to understand why these factors are associated with harassment. Limitations and implications for prevention programming at higher education institutions are discussed.

Smoothed Quantiles for χ2 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. However, the sample quantile may be a poor estimate of the population counterpart when either the sample size is small or the number of different values of the replicated statistic is limited. Using statistical learning, this article develops a method that yields more accurate quantiles for chi-square type test statistics. Formulas for smoothing the quantiles of chi-square type statistics are obtained. Combined with the bootstrap methodology, the smoothed quantiles are further used to conduct equivalence testing in mean and covariance structure analysis. Two real data examples illustrate the applications of the developed formulas in quantifying the size of model misspecification under equivalence testing. The idea developed in the article can also be used to develop formulas for smoothing the quantiles of other types of test statistics or parameter estimates.

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. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered.

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. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tabu search, and genetic algorithm to select short forms of scales, as well as a new implementation of the simulated annealing algorithm. Selection of short forms of scales with unidimensional, multidimensional, and bifactor structure were evaluated, with and without model misspecification and/or an external variable. The results showed that when the confirmatory factor analysis model of the full form of the scale was correctly specified or had only minor misspecification, the four algorithms produced short forms with good psychometric qualities that maintained the desired factor structure of the full scale. Major model misspecification resulted in worse performance for all algorithms, but including an external variable only had minor effects on results. The simulated annealing algorithm showed the best overall performance as well as robustness to model misspecification, while the genetic algorithm produced short forms with worse fit than the other algorithms under conditions with model misspecification.

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. Parameter values for item difficulty, discrimination, test-taker ability, and test-taker secondary abilities associated with a particular testlet are generated and parameter recovery through Markov Chain Monte Carlo Bayesian methods and generalized maximum likelihood estimation methods are compared. To aid with the complex computational efforts, the novel so-called TensorFlow platform is used. Both estimation methods provided satisfactory parameter recovery, although the Bayesian methods were found to be somewhat superior in recovering item discrimination parameters. The practical significance of the results are discussed in relation to obtaining accurate estimates of item, test, ability parameters, and measurement reliability information.

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. The procedure allows more informed evaluation of these quantities when addressing multicollinearity-related issues in empirical research using regression models. The method is illustrated on an empirical example using the popular software Mplus. Results of a simulation study investigating the capabilities of the procedure are also presented.

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 (p). Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (N), 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. However, SEM analyses with small N or large p have been shown to be problematic. This article reviews issues and solutions for SEM with small N, especially when p is large. The topics addressed include methods for parameter estimation, test statistics for overall model evaluation, and reliable standard errors for evaluating the significance of parameter estimates. Previous recommendations on required sample size N are also examined together with more recent developments. In particular, the requirement for N with conventional methods can be a lot more than expected, whereas new advances and developments can reduce the requirement for N substantially. The issues and developments for SEM with many variables described in this article not only let applied researchers be aware of the cutting edge methodology for SEM with big data as characterized by a large p but also highlight the challenges that methodologists need to face in further investigation.

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. Profiles were based on how often Latina/o early adolescents brokered for family members, as well as their levels of family-based acculturation stress, negative brokering beliefs, parentification, and positive brokering beliefs. Three brokering profiles emerged: (1) infrequent-ambivalents, (2) occasional-moderates, and (3) parentified-endorsers. Profile membership was significantly predicted by ethnic identification and brokering in a medical context. Respect, brokering at school, and brokering at home did not significantly predict profile membership. In addition, parentified-endorsers had more frequent perceived ethnic/racial discrimination and depressive symptoms than other profiles. In contrast, infrequent-ambivalents engaged in risky behaviors less frequently than other profiles.

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. To date, these methods are not commonly utilized in the social and behavioral sciences, even though they can be very useful for studying various complex developmental processes. This article illustrates the use of two data integration methods, the data fusion and the parallel analysis approaches. The illustration makes use of six longitudinal studies of mathematics ability in children with a goal of examining individual changes in mathematics ability and determining differences in the trajectories based on sex and socioeconomic status. The studies vary in their assessment of mathematics ability and in the timing and number of measurement occasions. The advantages of using a data fusion approach, which can allow for the fitting of more complex growth models that might not otherwise have been possible to fit in a single data set, are emphasized. The article concludes with a discussion of the limitations and benefits of these approaches for research synthesis.