The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Res...

The circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time seri...

Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2023)

The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is import...

Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an...

This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality,...

Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2023)

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scor...

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth an...

Oblique bifactor models, where group factors are allowed to correlate with one another, are commonly used. However, the lack of research on the statistical properties of oblique bifactor models ren...

Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study...

Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in mult...

Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of ...

Abstract Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation analysis for an ordinal outcome are still underdeveloped

Abstract A standard assumption of latent class (LC) analysis is conditional independence, that is the items of the LC are independent of the covariates given the LCs. Several approaches have been proposed for identifying violations of this assumption. The recently proposed likelihood ratio approach is compared to residual statistics (bivariate residuals [BVR] and expected parameter change [EPC] statistics)

Abstract Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related

Abstract Numerous methods exist to determine the optimal number of classes when using latent profile analysis (LPA), but none are consistently correct. Recently, the likelihood incremental percentage per parameter (LI3P) was proposed as a model effect-size measure. To evaluate the LI3P more thoroughly, we simulated 50,000 datasets, manipulating factors including sample size, class distance, number

Abstract Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that helps to find the optimal number of persons (N)

Abstract We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the

Abstract Latent class analysis (LCA) is a widely used technique for detecting unobserved population heterogeneity in cross-sectional data. Despite its popularity, the performance of LCA is not well understood. In this study, we evaluate the performance of LCA with binary data by examining classification accuracy, parameter estimation accuracy, and coverage rates of confidence intervals (CIs) through

Abstract Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation

Abstract A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models. Through simulation, we show that traditional benchmarks

Abstract Cross-loadings are common in multiple-factor confirmatory factor analysis (CFA) but often ignored in measurement invariance testing. This study examined the impact of ignoring cross-loadings on the sensitivity of fit measures (CFI, RMSEA, SRMR, SRMRu, AIC, BIC, SaBIC, LRT) to measurement noninvariance . The manipulated design factors included the magnitude and percentage of cross-loadings

Abstract To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals

Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 6, 2023)

Abstract In Savalei’s (2011 Savalei, V. (2011). What to do about zero frequency cells when estimating polychoric correlations. Structural Equation Modeling, 18, 253–273. https://doi.org/10.1080/10705511.2011.557339[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) simulation that evaluated the performance of polychoric correlation estimates in small samples, two methods for treating

Abstract The logistic and confined exponential curves are frequently used in studies of growth and learning. These models, which are nonlinear in their parameters, can be estimated using structural equation modeling software. This paper proposes a single combined model, a weighted combination of both models. Mplus, Proc Calis, and lavaan code for the model are provided. Monte Carlo simulations varying

Abstract Within the frequentist structural equation modeling (SEM) framework, adjudicating model quality through measures of fit has been an active area of methodological research. Complicating this conversation is research revealing that a higher quality measurement portion of a SEM can result in poorer estimates of overall model fit than lower quality measurement models, given the same structural

In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we comp...

Abstract Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact of temporal misalignment on parameter estimation

Abstract Several variants of the autoregressive structural equation model were suggested over the past years, including, for example, the random intercept autoregressive panel model, the latent curve model with structured residuals, and the STARTS model. The present work shows how to place these models into a mixed-effects model framework and how to estimate them in mixed-effects model software, namely

Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 5, 2023)

Abstract The latent growth model (LGM) is a popular tool in the social and behavioral sciences to study development processes of continuous and discrete outcome variables. A special case are frequency measurements of behaviors or events, such as doctor visits per month or crimes committed per year. Probability distributions for such outcomes include the Poisson or negative binomial distribution and

Abstract In the past decade, moderated mediation analysis has been extensively and increasingly employed in social and behavioral sciences. With its widespread use, it is particularly important to ensure the moderated mediation analysis will not bring spurious results. Spurious effects have been studied in both mediation and moderation analysis, but this issue remains unexplored in moderated mediation

Abstract Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the relationships among treatment, mediator, and

Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutor...

Regularized structural equation models have gained considerable traction in the social sciences. They promise to reduce overfitting by focusing on out-of-sample predictions and sparsity. To this en...

Abstract The recently proposed continuous-time latent curve model with structured residuals (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle unequally spaced measurement occasions and describes processes

Statistical mediation analysis is used to uncover intermediate variables, known as mediators [M], that explain how a treatment [X] changes an outcome [Y]. Often, researchers examine whether baselin...

It is frequently stated in the literature that measurement invariance is a prerequisite for the comparison of group means or standard deviations of the latent variable in factor models. This articl...

Measurement invariance (MI) describes the equivalence of a construct across groups. To be able to meaningfully compare latent factor means between groups, it is crucial to establish MI. Although me...

There are three common types of confidence interval (CI) in structural equation modeling (SEM): Wald-type CI, bootstrapping CI, and likelihood-based CI (LBCI). LBCI has the following advantages: (1...

This study proposes a Bayesian variable selection approach to select mediators and quantify their respective posterior probabilities in exploratory mediation analysis. Monte Carlo simulation studie...

Abstract In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be far from optimal, making the algorithm prone

Measurement bias (MB), or differences in the measurement properties of a latent variable, is often evaluated for a single categorical background variable (e.g., gender). However, recent statistical...

Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 4, 2023)

The detection of switched class labels is required in latent class analysis (LCA) simulations involving parameter estimation. The present study proposed a class similarity (CS) algorithm to detect ...

Structural equation modeling is prone to a variety of problems when the sample size is small. One solution that attempts to solve the (non-convergence) problem of small sample SEM is found in shrin...

Abstract Although small sample sizes represent an important issue, few studies investigated the requirements in dynamic latent variable model frameworks (e.g., dynamic structural equation modeling, DSEM; dynamic latent class analysis, DLCA). We conduct a small sample performance study of Bayesian estimation for the non-linear dynamic latent class structural equation model which generalizes DSEM and

Abstract Serial dependence is present in most time series data sets collected in psychological research. This paper investigates the implications of various approaches for handling such serial dependence, when one is interested in the linear effect of a time-varying covariate on the time-varying criterion. Specifically, the serial dependence is either neglected, corrected for by specifying autocorrelated

Abstract In latent variable models, interpretational confounding occurs when the inclusion of a covariate or outcome when fitting the model alters the results for the measurement model. Commonly used estimation procedures do not preclude this possibility. Multi-stage estimation approaches preclude interpretational confounding, but most are limited in that they do not properly propagate uncertainty

Abstract The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect of a treatment on class membership even with observational data. In this article, we present an extension of the bias-adjusted three-step LCA with IPW, which allows accounting for differential item function (DIF) caused by the treatment

Abstract A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include the estimation of interactions between

A unique feature of Bayesian estimation is the inclusion of prior knowledge through prior distributions. These prior distributions can benefit or impair many components of the ensuing analysis. Pri...

Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2023)

Abstract To examine relationships between the global construct of multidimensional data and external criteria, bifactor exploratory structural equation modeling (B-ESEM) and traditional methods (e.g., unidimensional confirmatory factor analyses, CFA; parceled CFA; and bifactor models) can be used. We compared their performance in a Monte Carlo simulation study. (a) B-ESEM performed the best, followed

Abstract Accelerated longitudinal designs (ALD) allow studying developmental processes usually spanning multiple years in a much shorter time framework by including participants from different age cohorts, which are assumed to share the same population parameters. However, different cohorts may have been exposed to dissimilar contextual factors, resulting in different developmental trajectories. If

Abstract This study proposes a joint modeling approach to conduct causal mediation analysis that accommodates multivariate longitudinal data, dynamic latent mediator, and survival outcome. First, we introduce a confirmatory factor analysis model to characterize a time-varying latent mediator through multivariate longitudinal observable variables. Then, we establish a growth curve model to describe

Abstract The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the latent trajectories are affected by stochastic innovations (i.e., dynamic error). The deterministic specification of the BLCS model does not account for the

Abstract Several interesting variations on the Common Fate Model (CFM) for dyadic analysis have emerged over the past decade. For instance, the multilevel-CFM is characterized by directional paths between Person level observed X and Y variables in addition to the Dyad level direct path between the corresponding latent variables. Although this model appears to provide a decomposition of the Y on X regression