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Bayesian estimation of single and multilevel models with latent variable interactions Struct. Equ. Model. (IF 3.638) Pub Date : 20200619
Tihomir Asparouhov; Bengt MuthénIn this article, we discuss single and multilevel SEM models with latent variable interactions. We describe the Bayesian estimation for these models and show through simulation studies that the Bayesian method outperforms other methods such as the maximumlikelihood method. We show that multilevel moderation models can easily be estimated with the Bayesian method.

A Monte Carlo Test for Longitudinal Structural Equation Models in Small Samples Struct. Equ. Model. (IF 3.638) Pub Date : 20200619
Sarfaraz SerangModel fit criteria for structural equation models rely on asymptotic behavior. As such, corrections are applied when fitting models to small samples (N ≤ 100). However, these corrections are based on heuristics designed for measurement models and thus are not necessarily appropriate for longitudinal models. Although extensions have been proposed for latent growth curve models, this study demonstrates

Advances in Bayesian model fit evaluation for structural equation models Struct. Equ. Model. (IF 3.638) Pub Date : 20200608
Tihomir Asparouhov; Bengt MuthénIn this article, we discuss the Posterior Predictive Pvalue (PPP) method in the presence of missing data, the Bayesian adaptation of the approximate fit indices RMSEA, CFI and TLI, as well as the Bayesian adaptation of the Wald test for nested models. Simulation studies are presented. We also illustrate how these new methods can be used to build BSEM models.

Multilevel CFA with Ordered Categorical Data: A Simulation Study Comparing Fit Indices Across Robust Estimation Methods Struct. Equ. Model. (IF 3.638) Pub Date : 20200605
R. Noah Padgett; Grant B. MorganWithin a multilevel confirmatory factor analysis framework, we investigated the ability of commonly used fit indices to discriminate between correctly specified models and misspecified models. Receiver operating characteristics (ROC) analyses were used to evaluate the performance of fit indices. Combining ROC analyses with checks of the convergence rates across Monte Carlo replications and ANOVA for

A Cautionary Note on Identification and Scaling Issues in Secondorder Latent Growth Models Struct. Equ. Model. (IF 3.638) Pub Date : 20200605
Yanyun Yang; Yachen Luo; Qian ZhangThe secondorder latent growth models (2ndorder LGMs) have been recommended to analyze longitudinal data when latent constructs are measured by multiple indicators. However, identification and scaling issues in 2ndorder LGMs have not been well understood. Using both formulas and a numerical example, we show that under strong longitudinal factor invariance estimates of growth parameters in 2ndorder

Multilevel Autoregressive Models when the Number of Time Points is Small Struct. Equ. Model. (IF 3.638) Pub Date : 20200605
Fien Gistelinck; Tom Loeys; Nele FlamantThe multilevel autoregressive model disentangles unobserved heterogeneity from statedependence. Statistically, the random intercept accounts for the dependence of all measurements at different time points on an observed underlying factor, while the lagged dependent predictor allows the outcome to depend on the outcome at the previous time point. In this paper, we consider different implementations

On the Performance of Bayesian Approaches in Small Samples: A Comment on Smid, McNeish, Miocevic, and van de Schoot (2020) Struct. Equ. Model. (IF 3.638) Pub Date : 20200529
Steffen Zitzmann; Oliver Lüdtke; Alexander Robitzsch; Martin HechtThis journal recently published a systematic review of simulation studies on the performance of Bayesian approaches for estimating latent variable models in small samples. The authors of this review highlighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distributions are not thoughtfully constructed on the basis of previous knowledge. In this comment, we question

Teacher’s Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models Struct. Equ. Model. (IF 3.638) Pub Date : 20200529
Caspar J. Van Lissa; Xin Gu; Joris Mulder; Yves Rosseel; Camiel Van Zundert; Herbert HoijtinkABSTRACT This Teacher’s Corner paper introduces Bayesian evaluation of informative hypotheses for structural equation models, using the free opensource R packages bain, for Bayesian informative hypothesis testing, and lavaan, a widely used SEM package. The introduction provides a brief nontechnical explanation of informative hypotheses, the statistical underpinnings of Bayesian hypothesis evaluation

Effects of crossloadings on determining the number of factors to retain Struct. Equ. Model. (IF 3.638) Pub Date : 20200515
Yujun Li; Zhonglin Wen; KitTai Hau; KeHai Yuan; Yafeng PengIn exploratory factor analysis (EFA), crossloadings frequently occur in empirical research, but its effects on determining the number of factors to retain are seldom known. In this paper, we analyzed whether and how crossloadings affected the performance of the parallel analysis (PA), the empirical Kaiser criterion (EKC), the likelihood ratio test (LRT), the comparative fit index (CFI), the TuckerLewis

Probing Twoway Moderation Effects: A Review of Software to Easily Plot JohnsonNeyman Figures Struct. Equ. Model. (IF 3.638) Pub Date : 20200512
Hua LinThis review explores the possibility of generating JohnsonNeyman’s interaction plot merely using the functions from software or packages without involving complex calculations. Three different programs were compared: Mplus version 8.3, the JohnsonNeyman() function in the interaction package for R version 3.6.2, and PROCESS Macro version 3.4 for IBM SPSS Statistics version 25. These three functions/software

Review of Data Visualisation: A Handbook for Data Driven Design Struct. Equ. Model. (IF 3.638) Pub Date : 20200109
Paola Sainz Sujet(2020). Review of Data Visualisation: A Handbook for Data Driven Design. Structural Equation Modeling: A Multidisciplinary Journal: Vol. 27, No. 2, pp. 330332.

Model Fit Estimation for Multilevel Structural Equation Models Struct. Equ. Model. (IF 3.638) Pub Date : 20190702
Lance M. Rappaport; Ananda B. Amstadter; Michael C. NealeStructural equation modeling (SEM) provides an extensive toolbox to analyze the multivariate interrelations of directly observed variables and latent constructs. Multilevel SEM integrates mixed effects to examine the covariances between observed and latent variables across many levels of analysis. However, while it is necessary to consider model fit, traditional indices are largely insufficient to

Flexible Treatment of TimeVarying Covariates with Time Unstructured Data Struct. Equ. Model. (IF 3.638) Pub Date : 20190716
Daniel McNeish; Tyler H. MattaTimevarying covariates (TVCs) are a common component of growth models. Though mixed effect models (MEMs) and latent curve models (LCMs) are often seen as interchangeable, LCMs are generally more flexible for accommodating TVCs. Specifically, the standard MEM constrains the effect of TVCs across timepoints whereas the typical LCM specification can estimate timespecific TVC effects, can include lagged

Comparison of Models for the Analysis of Intensive Longitudinal Data Struct. Equ. Model. (IF 3.638) Pub Date : 20190716
Tihomir Asparouhov; Bengt MuthénWe discuss the differences between several intensive longitudinal data models. The dynamic structural equation model (DSEM), the residual dynamic structural equation model (RDSEM) and the repeated measures longitudinal model are compared in several simulation studies. We show that the DIC can be used to select the correct modeling framework. We discuss the consequences of incomplete or incorrect modeling

Better Confidence Intervals for RMSEA in Growth Models given Nonnormal Data Struct. Equ. Model. (IF 3.638) Pub Date : 20190924
Keke LaiCurrently, the best confidence interval (CI) for RMSEA in covariance structure analysis given nonnormal data is proposed by BrosseauLiard, Savalei, and Li (BSL). A key assumption for the BSL CI often overlooked is that all the nonzero eigenvalues are equal in a matrix related to the model and data nonnormality. This assumption rarely holds in practice, especially for mean and covariance structure

Robustness of Individual Score Methods against Model Misspeciﬁcation in Autoregressive Panel Models Struct. Equ. Model. (IF 3.638) Pub Date : 20190916
Katinka Hardt; Martin Hecht; Manuel C. VoelkleDifferent methods to obtain individual scores from multiple item latent variable models exist, but their performance under realistic conditions is currently underresearched. We investigate the performance of the regression method, the Bartlett method, the Kalman filter, and the mean score under misspecification in autoregressive panel models. Results from three simulations show different patterns of

Examining the effect of missing data on RMSEA and CFI under normal theory fullinformation maximum likelihood Struct. Equ. Model. (IF 3.638) Pub Date : 20190905
Xijuan Zhang; Victoria SavaleiNormal theory fullinformation maximum likelihood (FIML) is a common estimation technique for incomplete data in structural equation modeling (SEM). However, it is not commonly known that approximate fit indices (AFIs) can be distorted, relative to their complete data counterparts, when FIML is used to handle missing data. In this article, we show that two most popular AFIs, the rootmeansquare error

Constrained Fourth Order Latent Differential Equation Reduces Parameter Estimation Bias for Damped Linear Oscillator Models Struct. Equ. Model. (IF 3.638) Pub Date : 20190905
Steven M. Boker; Robert G. Moulder; Gustav R. SjobeckSecondorder linear differential equations can be used as models for regulation since under a range of parameter values they can account for a return to equilibrium as well as potential oscillations in regulated variables. One method that can estimate parameters of these equations from intensive time series data is the method of Latent Differential Equations (LDE). However, the LDE method can exhibit

Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Nonnormal Data: A MonteCarlo Study Struct. Equ. Model. (IF 3.638) Pub Date : 20190814
Chuanji Gao; Dexin Shi; Alberto MaydeuOlivaresRecent research has provided formulae for estimating the maximum likelihood (ML) RMSEA when mean or mean and variance, corrections for nonnormality are applied to the likelihood ratio test statistic. We investigate by simulation which choice of corrections provides most accurate point RMSEA estimates, confidence intervals, and pvalues for a test of close fit under normality, and in the presence of

Predicting a Distal Outcome Variable From a Latent Growth Model: ML versus Bayesian Estimation Struct. Equ. Model. (IF 3.638) Pub Date : 20190712
Sanne C. Smid; Sarah Depaoli; Rens Van De SchootLatent growth models (LGMs) with a distal outcome allow researchers to assess longerterm patterns, and to detect the need to start a (preventive) treatment or intervention in an early stage. The aim of the current simulation study is to examine the performance of an LGM with a continuous distal outcome under maximum likelihood (ML) and Bayesian estimation with default and informative priors, under

Review: A Course in Item Response Theory and Modeling with Stata, and Using R for Item Response Theory Model Applications Struct. Equ. Model. (IF 3.638) Pub Date : 20200407
Sangdon LimA Course in Item Response Theory and Modeling with Stata , by Tenko Raykov & George A. Marcoulides, College Station, TX, Stata Press, 2018, 270 pp., $54.00 (paperback) ISBN: 9781597182669 Using R for Item Response Theory Model Applications , by Insu Paek & Ki Cole, New York, NY, Routledge, 2019, 272 pp., $49.95 (eBook) ISBN: 9781351008167

Not Positive Definite Correlation Matrices in Exploratory Item Factor Analysis: Causes, Consequences and a Proposed Solution Struct. Equ. Model. (IF 3.638) Pub Date : 20200313
Urbano LorenzoSeva; Pere J. FerrandoABSTRACT Leastsquares exploratory factor analysis based on tetrachoric/polychoric correlations is a robust, defensible and widely used approach for performing item analysis, especially in the first stages of scale development. A relatively common problem in this scenario, however, is that the interitem correlation matrix fails to be positive definite. This paper, which is largely intended for practitioners

Factor Score Regression in Connected Measurement Models Containing CrossLoadings Struct. Equ. Model. (IF 3.638) Pub Date : 20200313
Timothy Hayes; Satoshi UsamiFactor Score Regression (FSR) methods have received increased interest in the quantitative literature, with Croon’s biascorrecting method gaining particular traction. By fixing measurement parameters in place in an initial step, FSR methods aim to stymie the proliferation of bias in larger structural models that may contain misspecification. Although Croon’s approach was originally derived for factor

A Computationally More Efficient Bayesian Approach for Estimating ContinuousTime Models Struct. Equ. Model. (IF 3.638) Pub Date : 20200311
Martin Hecht; Steffen ZitzmannABSTRACT Continuoustime modeling is gaining in popularity as more and more intensive longitudinal data need to be analyzed. Current Bayesian software implementations of continuoustime models suffer from rather high, inadequate run times. Therefore, we apply a model reformulation approach to reduce run time. In a simulation study, we investigate the estimation quality and run time gain. We then illustrate

Estimation of Latent Variable Scores with Multiple Group Item Response Models: Implications for Integrative Data Analysis Struct. Equ. Model. (IF 3.638) Pub Date : 20200227
Pega Davoudzadeh; Kevin J. Grimm; Keith F. Widaman; Sarah L. Desmarais; Stephen Tueller; Danielle Rodgers; Richard A. Van DornIntegrative data analysis (IDA) involves obtaining multiple datasets, scaling the data to a common metric, and jointly analyzing the data. The first step in IDA is to scale the multisample itemlevel data to a common metric, which is often done with multiple group item response models (MGM). With invariance constraints tested and imposed, the estimated latent variable scores from the MGM serve as an

The Impact of Measurement Noninvariance on Latent Change Score Modeling: A Monte Carlo Simulation Study Struct. Equ. Model. (IF 3.638) Pub Date : 20200219
Eunsook Kim; Yan Wang; Siyu LiuMeasurement invariance (MI) overtime is required for meaningful interpretation of changes in the latent change score model (LCSM). In this simulation study, we investigate the impact of measurement noninvariance on the estimation of LCSM when proportional change model, dual change model, and their bivariate versions are used with mean composites or MIassumed measurement models. The results show that

Mplus Trees: Structural Equation Model Trees Using Mplus Struct. Equ. Model. (IF 3.638) Pub Date : 20200219
Sarfaraz Serang; Ross Jacobucci; Gabriela Stegmann; Andreas M. Brandmaier; Demi Culianos; Kevin J. GrimmStructural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes. Based on covariate information, SEM Trees can be used to create distinct subgroups containing individuals with similar parameter estimates. Currently, the structural equation modeling component of SEM Trees is implemented in the R packages OpenMx and lavaan

Network Mediation Analysis Using ModelBased Eigenvalue Decomposition Struct. Equ. Model. (IF 3.638) Pub Date : 20200219
Chang Che; Ick Hoon Jin; Zhiyong ZhangThis paper proposes a new twostage network mediation method based on the use of a latent network approach – modelbased eigenvalue decomposition – for analyzing social network data with nodal covariates. In the decomposition stage of the observed network, no assumption on the metric of the latent space structure is required. In the mediation stage, the most important eigenvectors of a network are

semopy: A Python Package for Structural Equation Modeling Struct. Equ. Model. (IF 3.638) Pub Date : 20200218
Anna A. Igolkina; Georgy MeshcheryakovStructural equation modeling (SEM) is a multivariate statistical technique for estimating complex relationships between observed and latent variables. Although numerous SEM packages currently exist, they each have limitations and more importantly they are not free or opensource. The only package that is both free and opensource is lavaan. However, because this package is written in R, it is often

Chisquare Difference Tests for Comparing Nested Models: An Evaluation with Nonnormal Data Struct. Equ. Model. (IF 3.638) Pub Date : 20200212
Goran Pavlov; Dexin Shi; Alberto MaydeuOlivaresThe relative fit of two nested models can be evaluated using a chisquare difference statistic. We evaluate the performance of five robust chisquare difference statistics in the context of confirmatory factor analysis with nonnormal continuous outcomes. The mean and variance corrected difference statistics performed adequately across all conditions investigated. In contrast, the mean corrected difference

Performance of Latent Growth Curve Models with Binary Variables Struct. Equ. Model. (IF 3.638) Pub Date : 20200212
Jason T. Newsom; Nicholas A. SmithA Monte Carlo simulation examined estimation difficulties and parameter and standard error bias for mean and variance estimates of binary latent growth curve models using mean and variance adjusted diagonally weighted least squares (WLSMV) and robust maximum likelihood (MLR). Small and medium effects of slope means and variances for longitudinal designs with three, five, and seven time points and sample

A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data Struct. Equ. Model. (IF 3.638) Pub Date : 20200212
Shaobo Jin; Johan Vegelius; Fan YangWallentinABSTRACT The literature on nonlinear structural equation modeling is plentiful. Despite this fact, few studies consider interactions between exogenous and endogenous latent variables. Further, it is well known that treating ordinal data as continuous produces bias, a problem which is enhanced when nonlinear relationships between latent variables are incorporated. A marginal maximum likelihoodbased

The Performance of ESEM and BSEM in Structural Equation Models with Ordinal Indicators Struct. Equ. Model. (IF 3.638) Pub Date : 20200212
Xinya Liang; Yanyun Yang; Chunhua CaoRecent developments allow for incorporating exploratory features into structural equation models (SEM). Two approaches, exploratory SEM (ESEM) and Bayesian SEM (BSEM), have been shown flexible of estimating complex SEM. This simulation study compared the performance of ESEM and BSEM for estimating structural regression models with ordinal indicators where crossloadings were present in selected factors

Generating nonnormal distributions via Gaussian mixture models Struct. Equ. Model. (IF 3.638) Pub Date : 20200205
Grant B. MorganThe purpose of this paper is to (1) present a method of generating nonnormal univariate and/or uncorrelated multivariate distributions using mixture models, and (2) compare the accuracy of generating nonnormal distributions using the mixturebased method against power transformation method and generalized lambda method. Monte Carlo methods were used to generate data with each of the three nonnormal

A SemiHierarchical Confirmatory Factor Model for Speeded Data Struct. Equ. Model. (IF 3.638) Pub Date : 20200129
Karl Schweizer; Andreas Gold; Dorothea KrampenThis paper presents the semihierarchical model for confirmatory factor analysis of speeded data. The semihierarchical model is achieved by integrating information from the second level of the hierarchical structure of speeded data into the customary confirmatory factor model. The second level of speeded data originates from subsets of participants responding on the basis of different sources. In

Indicators per Factor in Confirmatory Factor Analysis: More is not Always Better Struct. Equ. Model. (IF 3.638) Pub Date : 20200115
Jennifer KoranAlthough some research in confirmatory factor analysis has suggested that more indicators per factor are generally better, studies have also documented that sample size requirements increase as model size increases. The present study used Monte Carlo simulation to investigate the effect of indicators per factor on sample size requirements. Results demonstrated a nonlinear association between the number

Choosing Priors in Bayesian Measurement Invariance Modeling: A Monte Carlo Simulation Study Struct. Equ. Model. (IF 3.638) Pub Date : 20200115
Artur Pokropek; Peter Schmidt; Eldad DavidovMultigroup Bayesian structural equation modeling (MGBSEM) gained considerable attention among substantive researchers investigating crossgroup differences and methodologists exploring challenges in measurement invariance testing. MGBSEM allows for greater flexibility by applying elastic rather than strict equality constraints on item parameters across groups. This, however, requires a specification

Multilevel Modeling in the ‘Wide Format’ Approach with Discrete Data: A Solution for Small Cluster Sizes Struct. Equ. Model. (IF 3.638) Pub Date : 20200103
M.T. Barendse; Y. RosseelIn multilevel data, units at level 1 are nested in clusters at level 2, which in turn may be nested in even larger clusters at level 3, and so on. For continuous data, several authors have shown how to model multilevel data in a ‘wide’ or ‘multivariate’ format approach. We provide a general framework to analyze random intercept multilevel SEM in the ‘wide format’ (WF) and extend this approach for discrete

A Corrected GoodnessofFit Index (CGFI) for Model Evaluation in Structural Equation Modeling Struct. Equ. Model. (IF 3.638) Pub Date : 20191218
Kai Wang; Ying Xu; Chaolong Wang; Ming Tan; Pingyan ChenWe propose a Corrected GoodnessofFit Index (CGFI) for model evaluation in Structural Equation Modeling (SEM). The CGFI is essentially a corrected index that takes into account model complexity and downward bias caused by small sample size. Using simulations based on preset SEM models, we compared the properties of CGFI, GoodnessofFit (GFI), and Adjusted GoodnessofFit Index (AGFI) under different

Correct Point Estimator and Confidence Interval for RMSEA Given Categorical Data Struct. Equ. Model. (IF 3.638) Pub Date : 20191218
Keke LaiRMSEA estimation given nonnormal continuous data is usually based on the meanadjusted ( TM) or meanvarianceadjusted ( TMV) chisquare statistic, but a plain application of these statistics has poor performance. Savalei and colleagues gave a better way (the BSL method) to infer RMSEA using TM or TMV. However, the BSL method is applicable to continuous data only. For categorical data, currently RMSEA

Regularized Structural Equation Modeling to Detect Measurement Bias: Evaluation of Lasso, Adaptive Lasso, and Elastic Net Struct. Equ. Model. (IF 3.638) Pub Date : 20191212
Xinya Liang; Ross JacobucciCorrect detection of measurement bias could help researchers revise models or refine psychological scales. Measurement bias detection can be viewed as a variableselection problem, in which biased items are optimally selected from a set of items. This study investigated a number of regularization methods: ridge, lasso, elastic net (enet) and adaptive lasso (alasso), in comparison with maximum likelihood

Relaxing the Proportionality Assumption in Latent Basis Models for Nonlinear Growth Struct. Equ. Model. (IF 3.638) Pub Date : 20191211
Daniel McNeishChange over time is frequently nonlinear, which can present unique statistical challenges. Generally, different approaches for nonlinear growth engage in a tradeoff between interpretable parameters, expedient estimation, or how specific the model must be about the nature of the nonlinearity. Latent basis models are one method that can circumvent tradeoffs that other methods necessitate: it is quick

Latent Interaction Modeling with Planned Missing Data Designs Struct. Equ. Model. (IF 3.638) Pub Date : 20191211
Jayden Nord; James A. Bovaird; Matthew S. FritzPlanned missing data (PMD) designs allow researchers to collect additional data under time constraints and to reduce participant burden, both of which can occur in social, behavioral, and educational research settings. The imposed missing data patterns, however, can hamper the efficiency of statistical models implemented to test hypotheses that are of interest to substantive researchers, including

When Good Loadings Go Bad: Robustness in Factor Analysis Struct. Equ. Model. (IF 3.638) Pub Date : 20191122
Kenneth A. BollenStructural misspecifications in factor analysis include using the wrong number of factors and omitting cross loadings or correlated errors. The impact of these errors on factor loading estimates is understudied. Factor loadings underlie our assessments of the validity and reliability of indicators. Thus knowing how structural misspecifications affect a factor loading is a key issue. This paper develops

Evaluation of Six Effect Size Measures of Measurement NonInvariance for Continuous Outcomes Struct. Equ. Model. (IF 3.638) Pub Date : 20191118
Heather J. Gunn; Kevin J. Grimm; Michael C. EdwardsMeasurement invariance is assessed in the factor analytic framework by testing differences in model fit of a sequential series of models; however, the statistical significance of these differences is influenced by many factors, including sample size. Effect sizes are independent of sample size and can be used to determine the magnitude and practical importance of an effect. We developed four new effect

Robust Bayesian Approaches in Growth Curve Modeling: Using Student’s t Distributions versus a Semiparametric Method Struct. Equ. Model. (IF 3.638) Pub Date : 20191111
Xin Tong; Zhiyong ZhangDespite broad applications of growth curve models, few studies have dealt with a practical issue – nonnormality of data. Previous studies have used Student’s t distributions to remedy the nonnormal problems. In this study, robust distributional growth curve models are proposed from a semiparametric Bayesian perspective, in which intraindividual measurement errors follow unknown random distributions

A Commentary on Lv and Maeda (2019) Struct. Equ. Model. (IF 3.638) Pub Date : 20191111
Suzanne Jak; Mike W.L. CheungMetaanalytic structural equation modeling (MASEM) is a statistical technique to fit hypothesized models on the combined data of multiple independent studies. Lv and Maeda (2019) present a simulation study on the performance of three fixedeffects correlationbased MASEM methods with varying levels of data missing completely at random (MCAR). In this commentary, we discuss several coding errors and

Comparing Methods for Multilevel Moderated Mediation: A Decomposedfirst Strategy Struct. Equ. Model. (IF 3.638) Pub Date : 20191108
Soyoung Kim; Sehee HongThe purpose of this study is to propose a decomposedfirst strategy for multilevel moderated mediation and to compare the performance of three moderated mediation approaches in multilevel structural equation modeling. The following approaches were compared in simulations to test coefficients that were decomposed level by level: orthogonal partitioning with centering within cluster, random coefficient

Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression Struct. Equ. Model. (IF 3.638) Pub Date : 20191106
Manuel Arnold; Daniel L. Oberski; Andreas M. Brandmaier; Manuel C. VoelkleDynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method

Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data Struct. Equ. Model. (IF 3.638) Pub Date : 20191106
PuiWa Lei; Levi K. ShiverdeckerMissing data and ordinal indicators are common in applied research involving latent constructs. Unfortunately, ordinal indicators violate the linearity assumption for conventional CFA that is routinely used to provide structural validity evidence for measurement instruments. Although robust maximum likelihood estimator (MLR) can deal with both missing data and nonnormality, it is generally inappropriate

ROBUSTNESS CONDITIONS FOR MIIV2SLS WHEN THE LATENT VARIABLE OR MEASUREMENT MODEL IS STRUCTURALLY MISSPECIFIED. Struct. Equ. Model. (IF 3.638) Pub Date : 20181224
Kenneth A Bollen,Kathleen M Gates,Zachary FisherMost researchers acknowledge that virtually all structural equation models (SEMs) are approximations due to violating distributional assumptions and structural misspecifications. There is a large literature on the unmet distributional assumptions, but much less on structural misspecifications. In this paper we examine the robustness to structural misspecification of the Model Implied Instrumental Variable

TimeVarying Effect Sizes for Quadratic Growth Models in Multilevel and Latent Growth Modeling. Struct. Equ. Model. (IF 3.638) Pub Date : 20181220
Alan FeingoldMultilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coefficient for the effect of group on the slope from a linear GMA can be transformed into a modelestimated effect size for the group difference at the end of a study. Because effect sizes vary

Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales. Struct. Equ. Model. (IF 3.638) Pub Date : 20181113
Kevin L McKee,Lance M Rappaport,Steven M Boker,Debbie S Moskowitz,Michael C NealeDamped Linear Oscillators estimated by 2ndorder Latent Differential Equation have assumed a constant equilibrium and one oscillatory component. Lowerfrequency oscillations may come from seasonal background processes, which nonrandomly contribute to deviation from equilibrium at each occasion and confound estimation of dynamics over shorter timescales. Boker (2015) proposed a model of individual

Empirical Sample Size Guidelines for Use of Latent Difference Score Mediation. Struct. Equ. Model. (IF 3.638) Pub Date : 20181105
Melissa Simone,Ginger LockhartMediation models are commonly used to identify the mechanisms through which one variable influences another. Among longitudinal mediation methods, latent difference score mediation stands out due to its unique ability to capture nonlinear change over time. However, there is limited information regarding sample size demands to achieve adequate power with this method, resulting in few applications of

Cloud computing for voxelwise SEM analysis of MRI data. Struct. Equ. Model. (IF 3.638) Pub Date : 20181002
Joshua N Pritikin,J Eric Schmitt,Michael C NealeAs data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the shortterm rental of ample processing power. Recent software innovations have reduced the offort needed to take advantage of cloud computing. To demonstrate, we replicate a voxelwise examination of the genetic contributions

Sensitivity Analysis of the NoOmitted Confounder Assumption in Latent Growth Curve Mediation Models. Struct. Equ. Model. (IF 3.638) Pub Date : 20180911
Davood Tofighi,YuYu Hsiao,Eric S Kruger,David P MacKinnon,M Lee Van Horn,Katie A WitkiewitzLatent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that confounds indirect effects. One way to address this

MplusAutomation: An R Package for Facilitating LargeScale Latent Variable Analyses in Mplus. Struct. Equ. Model. (IF 3.638) Pub Date : 20180808
Michael N Hallquist,Joshua F WileyMplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish three objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter

A Tutorial in Bayesian Potential Outcomes Mediation Analysis. Struct. Equ. Model. (IF 3.638) Pub Date : 20180619
Milica Miočević,Oscar Gonzalez,Matthew J Valente,David P MacKinnonStatistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist

Handling Missing Data in the Modeling of Intensive Longitudinal Data. Struct. Equ. Model. (IF 3.638) Pub Date : 20180101
Linying Ji,SyMiin Chow,Alice C Schermerhorn,Nicholas C Jacobson,E Mark CummingsMyriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate timeseries models under different missing data mechanisms. They include

Recovering PredictorCriterion Relations Using CovariateInformed Factor Score Estimates. Struct. Equ. Model. (IF 3.638) Pub Date : 20180101
Patrick J Curran,Veronica T Cole,Daniel J Bauer,W Andrew Rothenberg,Andrea M HussongAlthough it is currently bestpractice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariateinformed factor score estimation can be used to provide manifest scores that are used in secondstage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine