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Identifying Dynamic Shifts to Careless and Insufficient Effort Behavior in Questionnaire Responses; a Novel Approach and Experimental Validation Struct. Equ. Model. (IF 6.0) Pub Date : 2024-03-14 Zachary J. Roman, Patrick Schmidt, Jason M. Miller, Holger Brandt
Careless and insufficient effort responding (C/IER) is a situation where participants respond to survey instruments without considering the item content. This phenomena adds noise to data leading t...
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Bayesian Structural Equation Models of Correlation Matrices Struct. Equ. Model. (IF 6.0) Pub Date : 2024-03-12 James Ohisei Uanhoro
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector us...
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Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models Struct. Equ. Model. (IF 6.0) Pub Date : 2024-03-12 Daniel McNeish, Melissa G. Wolf
Despite the popularity of traditional fit index cutoffs like RMSEA ≤ .06 and CFI ≥ .95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been ...
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A Technique for Efficient Estimation of Dynamic Structural Equation Models: A Case Study Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-22 Leonidas Sakalauskas, Vytautas Dulskis, Darius Plikynas
Dynamic structural equation models (DSEM) are designed for time series analysis of latent structures. Inherent to the application of DSEM is model parameter estimation, which has to be addressed in...
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Fitting Cross-Lagged Panel Models with the Residual Structural Equations Approach Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-22 Ming-Chi Tseng
This study simplifies the seven different cross-lagged panel models (CLPMs) by using the RSEM model for both inter-individual and intra-individual structures. In addition, the study incorporates th...
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Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-22 Yuan Fang, Lijuan Wang
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on ...
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Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-16 Johan Lyrvall, Zsuzsa Bakk, Jennifer Oser, Roberto Di Mari
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ...
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D-Scoring Method of Measurement Classical and Latent Frameworks Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-16 Ademola B. Ajayi
Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2024)
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Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-16 Lihan Chen, Milica Miočević, Carl F. Falk
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets e...
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Measurement Invariance is Not Sufficient for Meaningful and Valid Group Comparisons: A Note on Robitzsch and Lüdtke Struct. Equ. Model. (IF 6.0) Pub Date : 2024-02-16 Tenko Raykov
This note demonstrates that measurement invariance does not guarantee meaningful and valid group comparisons in multiple-population settings. The article follows on a recent critical discussion by ...
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Mediation Analyses of Intensive Longitudinal Data with Dynamic Structural Equation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2024-01-30 Jie Fang, Zhonglin Wen, Kit-Tai Hau
Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, ...
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Quantifying Individual Personality Change More Accurately by Regression-Based Change Scores Struct. Equ. Model. (IF 6.0) Pub Date : 2024-01-30 Steffen Zitzmann, Lisa Bardach, Kai T. Horstmann, Matthias Ziegler, Martin Hecht
We investigated three different approaches for quantifying individual change and reporting it back to persons: (a) the common change score, which is obtained by first computing scale scores from tw...
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Penalized Structural Equation Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Tihomir Asparouhov, Bengt Muthén
Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with prev...
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Recovering Developmental Bivariate Trajectories in Accelerated Longitudinal Designs with Dynamic Continuous Time Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Nuria Real-Brioso, Eduardo Estrada, Pablo F. Cáncer
Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has...
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On the Performance of Horseshoe Priors for Inducing Sparsity in Structural Equation Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Kjorte Harra, David Kaplan
The present work focuses on the performance of two types of shrinkage priors—the horseshoe prior and the recently developed regularized horseshoe prior—in the context of inducing sparsity in path a...
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Under-Fitting and Over-Fitting: The Performance of Bayesian Model Selection and Fit Indices in SEM Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Sarah Depaoli, Sonja D. Winter, Haiyan Liu
We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to de...
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Latent Profile Transition Analysis with Random Intercepts (RI-LPTA) Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Ming-Chi Tseng
The primary objective of this investigation is the formulation of random intercept latent profile transition analysis (RI-LPTA). Our simulation investigation suggests that the election between LPTA...
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Review of Handbook of Structural Equation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Jam Khojasteh, Ademola Ajayi
Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2023)
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GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Heungsun Hwang, Gyeongcheol Cho, Hosung Choo
GSCA Pro is free, user-friendly software for generalized structured component analysis structural equation modeling (GSCA-SEM), which implements three statistical methods for estimating models with...
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A Simple Two-Step Procedure for Fitting Fully Unrestricted Exploratory Factor Analytic Solutions with Correlated Residuals Struct. Equ. Model. (IF 6.0) Pub Date : 2023-12-19 Pere J. Ferrando, Ana Hernández-Dorado, Urbano Lorenzo-Seva
A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In th...
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How to Evaluate Causal Dominance Hypotheses in Lagged Effects Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-11-09 Chuenjai Sukpan, Rebecca M. Kuiper
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...
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Circumplex Models with Multivariate Time Series: An Idiographic Approach Struct. Equ. Model. (IF 6.0) Pub Date : 2023-11-09 Dayoung Lee, Guangjian Zhang, Shanhong Luo
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...
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Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences) Struct. Equ. Model. (IF 6.0) Pub Date : 2023-11-09 Aszani Aszani, Ruslan Anwar
Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 31, No. 1, 2024)
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Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data Struct. Equ. Model. (IF 6.0) Pub Date : 2023-11-02 Ihnwhi Heo, Fan Jia, Sarah Depaoli
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...
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Does Acquiescence Disagree with Measurement Invariance Testing? Struct. Equ. Model. (IF 6.0) Pub Date : 2023-11-02 E. Damiano D’Urso, Jesper Tijmstra, Jeroen K. Vermunt, Kim De Roover
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...
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The Sensitivity of Bayesian Fit Indices to Structural Misspecification in Structural Equation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2023-10-12 Chunhua Cao, Benjamin Lugu, Jujia Li
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,...
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Comparing Methods for Factor Score Estimation in Structural Equation Modeling: The Role of Network Analysis Struct. Equ. Model. (IF 6.0) Pub Date : 2023-10-12 Jinying Ouyang, Zhehan Jiang, Christine DiStefano, Junhao Pan, Yuting Han, Lingling Xu, Dexin Shi, Fen Cai
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...
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Review of Handbook of Structural Equation Modeling (2nd ed.) Struct. Equ. Model. (IF 6.0) Pub Date : 2023-10-12 Jam Khojasteh, Ademola Ajayi
Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 31, No. 2, 2024)
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Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM Struct. Equ. Model. (IF 6.0) Pub Date : 2023-10-09 C. J. Van Lissa, M. Garnier-Villarreal, D. Anadria
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...
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Improving the Statistical Performance of Oblique Bifactor Measurement and Predictive Models: An Augmentation Approach Struct. Equ. Model. (IF 6.0) Pub Date : 2023-10-09 Bo Zhang, Jing Luo, Susu Zhang, Tianjun Sun, Don C. Zhang
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...
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Comparing MIMIC and MIMIC-interaction to Alignment Methods for Investigating Measurement Invariance concerning a Continuous Violator Struct. Equ. Model. (IF 6.0) Pub Date : 2023-09-26 Yuanfang Liu, Mark H. C. Lai, Ben Kelcey
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 ...
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Performance of Estimation Methods in Bifactor Models with Ordered Categorical Data Struct. Equ. Model. (IF 6.0) Pub Date : 2023-09-26 Ismail Cuhadar, Ömür Kaya Kalkan
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...
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Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples Struct. Equ. Model. (IF 6.0) Pub Date : 2023-09-26 Emma Somer, Carl Falk, Milica Miočević
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...
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Causal Mediation Analysis for an Ordinal Outcome with Multiple Mediators Struct. Equ. Model. (IF 6.0) Pub Date : 2023-09-15 Yuejin Zhou, Wenwu Wang, Tao Hu, Tiejun Tong, Zhonghua Liu
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 rece...
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Finding the Optimal Number of Persons (N) and Time Points (T) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-22 Martin Hecht, Julia-Kim Walther, Manuel Arnold, Steffen Zitzmann
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)
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Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-25 Gyeongcheol Cho, Heungsun Hwang
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexib...
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Latent Class Analysis with Measurement Invariance Testing: Simulation Study to Compare Overall Likelihood Ratio vs Residual Fit Statistics Based Model Selection Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-22 Zsuzsa Bakk
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 f...
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Evaluating the Performance of the LI3P in Latent Profile Analysis Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-22 Russell P. Houpt, Kevin J. Grimm, Aaron T. McLaughlin, Daryl R. Van Tongeren
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 pa...
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Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-18 Walter P. Vispoel, Hyeryung Lee, Hyeri Hong
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
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Label Switching in Latent Class Analysis: Accuracy of Classification, Parameter Estimates, and Confidence Intervals Struct. Equ. Model. (IF 6.0) Pub Date : 2023-08-14 Meng Qiu, Ke-Hai Yuan
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 understo...
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Leveraging Observation Timing Variability to Understand Intervention Effects in Panel Studies: An Empirical Illustration and Simulation Study Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-28 Andrea Hasl, Manuel Voelkle, Charles Driver, Julia Kretschmann, Martin Brunner
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,...
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Review of Handbook of Structural Equation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-28 Jam Khojasteh
Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 6, 2023)
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Bayesian Inference of Dynamic Mediation Models for Longitudinal Data Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-28 Saijun Zhao, Zhiyong Zhang, Hong Zhang
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 cor...
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Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-28 Daniel McNeish, Patrick D. Manapat
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 eme...
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The Impact of Ignoring Cross-loadings on the Sensitivity of Fit Measures in Measurement Invariance Testing Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-28 Chunhua Cao, Xinya Liang
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...
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The SEM Reliability Paradox in a Bayesian Framework Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-14 Timothy R. Konold, Elizabeth A. Sanders
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 convers...
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An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM) Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-14 Sara Dhaene, Yves Rosseel
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...
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Revisiting Savalei’s (2011) Research on Remediating Zero-Frequency Cells in Estimating Polychoric Correlations: A Data Distribution Perspective Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-14 Tong-Rong Yang, Li-Jen Weng
In Savalei’s (2011) simulation that evaluated the performance of polychoric correlation estimates in small samples, two methods for treating zero-frequency cells, adding 0.5 (ADD) and doing nothing...
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Combined Logistic and Confined Exponential Growth Models: Estimation Using SEM Software Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-14 Phillip K. Wood
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...
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Temporal Misalignment in Intensive Longitudinal Data: Consequences and Solutions Based on Dynamic Structural Equation Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-06 Xiaohui Luo, Yueqin Hu
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 consequ...
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Univariate Autoregressive Structural Equation Models as Mixed-Effects Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-06 Steffen Nestler, Sarah Humberg
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 ...
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Review of Educational and Psychological Measurement Struct. Equ. Model. (IF 6.0) Pub Date : 2023-07-06 Ademola B. Ajayi
Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 5, 2023)
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Latent Growth Models for Count Outcomes: Specification, Evaluation, and Interpretation Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-26 Daniel Seddig
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 measu...
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A Note on Evaluating the Moderated Mediation Effect Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-19 Chi Kit Jacky Ng, Lok Yin Joyce Kwan, Wai Chan
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 th...
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The Impact of Omitting Confounders in Parallel Process Latent Growth Curve Mediation Models: Three Sensitivity Analysis Approaches Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-19 Xiao Liu, Zhiyong Zhang, Kristin Valentino, Lijuan Wang
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...
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Striving for Sparsity: On Exact and Approximate Solutions in Regularized Structural Equation Models Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-11 Jannik H. Orzek, Manuel Arnold, Manuel C. Voelkle
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...
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Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-11 Walter P. Vispoel, Hyeri Hong, Hyeryung Lee
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...
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Estimating Latent Baseline-by-Treatment Interactions in Statistical Mediation Analysis Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-03 Oscar Gonzalez, Jeno R. Millechek, A. R. Georgeson
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...
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Studying Between-Subject Differences in Trends and Dynamics: Introducing the Random Coefficients Continuous-Time Latent Curve Model with Structured Residuals Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-03 Julian F. Lohmann, Steffen Zitzmann, Martin Hecht
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. Fi...
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Why Full, Partial, or Approximate Measurement Invariance Are Not a Prerequisite for Meaningful and Valid Group Comparisons Struct. Equ. Model. (IF 6.0) Pub Date : 2023-05-03 Alexander Robitzsch, Oliver Lüdtke
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...