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Predicting a Distal Outcome Variable From a Latent Growth Model: ML versus Bayesian Estimation Struct. Equ. Model. (IF 4.426) 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

Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Nonnormal Data: A MonteCarlo Study Struct. Equ. Model. (IF 4.426) 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

Constrained Fourth Order Latent Differential Equation Reduces Parameter Estimation Bias for Damped Linear Oscillator Models Struct. Equ. Model. (IF 4.426) 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

Examining the effect of missing data on RMSEA and CFI under normal theory fullinformation maximum likelihood Struct. Equ. Model. (IF 4.426) 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

Robustness of Individual Score Methods against Model Misspeciﬁcation in Autoregressive Panel Models Struct. Equ. Model. (IF 4.426) 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

Better Confidence Intervals for RMSEA in Growth Models given Nonnormal Data Struct. Equ. Model. (IF 4.426) 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

Comparison of Models for the Analysis of Intensive Longitudinal Data Struct. Equ. Model. (IF 4.426) 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

Flexible Treatment of TimeVarying Covariates with Time Unstructured Data Struct. Equ. Model. (IF 4.426) 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

Model Fit Estimation for Multilevel Structural Equation Models Struct. Equ. Model. (IF 4.426) 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

Review of Data Visualisation: A Handbook for Data Driven Design Struct. Equ. Model. (IF 4.426) 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.

ROBUSTNESS CONDITIONS FOR MIIV2SLS WHEN THE LATENT VARIABLE OR MEASUREMENT MODEL IS STRUCTURALLY MISSPECIFIED. Struct. Equ. Model. (IF 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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 4.426) 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

The Latent VariableAutoregressive Latent Trajectory Model: A General Framework for Longitudinal Data Analysis. Struct. Equ. Model. (IF 4.426) Pub Date : 20180101
Silvia Bianconcini,Kenneth A BollenIn recent years, longitudinal data have become increasingly relevant in many applications, heightening interest in selecting the best longitudinal model to analyze them. Too often traditional practice rather than substantive theory guide the specific model selected. This opens the possibility that alternative models might better correspond to the data. In this paper, we present a general longitudinal

Application of Latent Growth Curve Analysis with Categorical Responses in Social Behavioral Research. Struct. Equ. Model. (IF 4.426) Pub Date : 20180101
Tae Kyoung Lee,Kandauda K A S Wickrama,Catherine W O'NealLatent growth modeling allows social behavioral researchers to investigate withinperson change and betweenperson differences in withinperson change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution

Comparison of frequentist and Bayesian regularization in structural equation modeling. Struct. Equ. Model. (IF 4.426) Pub Date : 20180101
Ross Jacobucci,Kevin J GrimmResearch in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments

Finding Pure SubModels for Improved Differentiation of BiFactor and SecondOrder Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20171213
Renjie Yang,Peter Spirtes,Richard Scheines,Steven P Reise,Maxwell MansoffSeveral studies have indicated that bifactor models fit a broad range of psychometric data better than alternative multidimensional models such as secondorder models, e.g Rodriguez, Reise and Haviland (2016), Gignac (2016), and Carnivez (2016). Murray and Johnson (2013) and Gignac (2016) argue that this phenomenon is partially due to unmodeled complexities (e.g. unmodeled crossfactor loadings)

Exploratory Mediation Analysis via Regularization. Struct. Equ. Model. (IF 4.426) Pub Date : 20171212
Sarfaraz Serang,Ross Jacobucci,Kim C Brimhall,Kevin J GrimmExploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a twostage approach called exploratory mediation analysis via regularization (XMed) to better address these

A Comparison of Methods for Uncovering Sample Heterogeneity: Structural Equation Model Trees and Finite Mixture Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20171212
Ross Jacobucci,Kevin J Grimm,John J McArdleAlthough finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. It is our aim to compare and contrast these methods

An Empirical Assessment of the Sensitivity of Mixture Models to Changes in Measurement. Struct. Equ. Model. (IF 4.426) Pub Date : 20171028
Veronica T Cole,Daniel J Bauer,Andrea M Hussong,Michael L GiordanoThe current study explored the extent to which variations in selfreport measures across studies can produce differences in the results obtained from mixture models. Data (N = 854) come from a laboratory analogue study of methods for creating commensurate scores of alcohol and substanceuserelated constructs when items differ systematically across participants for any given measure. Items were manipulated

Examining Trait × Method Interactions Using Mixture Distribution MultitraitMultimethod Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20171007
Kaylee Litson,Christian Geiser,G Leonard Burns,Mateu Servera 
Comparing models of change to estimate the mediated effect in the pretestposttest control group design. Struct. Equ. Model. (IF 4.426) Pub Date : 20170829
Matthew J Valente,David P MacKinnonModels to assess mediation in the pretestposttest control group design are understudied in the behavioral sciences even though it is the design of choice for evaluating experimental manipulations. The paper provides analytical comparisons of the four most commonly used models used to estimate the mediated effect in this design: Analysis of Covariance (ANCOVA), difference score, residualized change

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Struct. Equ. Model. (IF 4.426) Pub Date : 20170628
Gitta H Lubke,Ian Campbell,Dan McArtor,Patrick Miller,Justin Luningham,Stéphanie M van den BergModel comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on the selected bestfitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred

Growth Curve Modeling to Studying Change: A Comparison of Approaches Using Longitudinal Dyadic Data With Distinguishable Dyads. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Elizabeth M Planalp,Han Du,Julie M BraungartRieker,Lijuan WangAlthough methodology articles have increasingly emphasized the need to analyze data from two members of a dyad simultaneously, the most popular method in substantive applications is to examine dyad members separately. This might be due to the underappreciation of the extra information simultaneous modeling strategies can provide. Therefore, the goal of this study was to compare multiple growth curve

Structural Models for Binary Repeated Measures: Linking Modern Longitudinal Structural Equation Models to Conventional Categorical Data Analysis for Matched Pairs. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Jason T NewsomThe current widespread availability of software packages with estimation features for testing structural equation models with binary indicators makes it possible to investigate many hypotheses about differences in proportions over time that are typically only tested with conventional categorical data analyses for matched pairs or repeated measures, such as McNemar's chisquared. The connection between

Power in Bayesian Mediation Analysis for Small Sample Research. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Milica Miočević,David P MacKinnon,Roy LevyIt was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and biascorrected bootstrap confidence intervals at N≤ 200. Bayesian methods

Manylevel multilevel structural equation modeling: An efficient evaluation strategy. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Joshua N Pritikin,Michael D Hunter,Timo von Oertzen,Timothy R Brick,Steven M BokerStructural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically

Likelihoodbased confidence intervals for a parameter with an upper or lower bound. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Joshua N Pritikin,Lance M Rappaport,Michael C NealeThe precision of estimates in many statistical models can be expressed by a confidence interval (CI). CIs based on standard errors (SE) are common in practice, but likelihoodbased CIs are worth consideration. In comparison to SEs, likelihoodbased CIs are typically more difficult to estimate, but are more robust to model (re)parameterization. In latent variable models, some parameters may take on

Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior DrugUsing Behaviors. Struct. Equ. Model. (IF 4.426) Pub Date : 20170101
Saebom Jeon,Jungwun Lee,James C Anthony,Hwan ChungThis paper proposes a new type of latent class analysis, joint latent class analysis (JLCA), which provides a set of principles for the systematic identification of the subsets of joint patterns for multiple discrete latent variables. Inferences about the parameters are obtained by a hybrid method of EM and NewtonRaphson algorithms. We apply JLCA in an investigation of adolescent violent behavior

Regularized Structural Equation Modeling. Struct. Equ. Model. (IF 4.426) Pub Date : 20160712
Ross Jacobucci,Kevin J Grimm,John J McArdleA new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression,

A Note on the Use of Mixture Models for Individual Prediction. Struct. Equ. Model. (IF 4.426) Pub Date : 20160628
Veronica T Cole,Daniel J BauerMixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroupspecific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or subpopulations, rather than individual cases. The current article presents a general framework for

Using Multilevel Regression Mixture Models to Identify Level1 Heterogeneity in Level2 Effects. Struct. Equ. Model. (IF 4.426) Pub Date : 20160609
M Lee Van Horn,Yuling Feng,Minjung Kim,Andrea Lamont,Daniel Feaster,Thomas JakiThis paper proposes a novel exploratory approach for assessing how the effects of level2 predictors differ across level1 units. Multilevel regression mixture models are used to identify latent classes at level1 that differ in the effect of one or more level2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of

Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention. Struct. Equ. Model. (IF 4.426) Pub Date : 20160510
Trang Quynh Nguyen,Yenny WebbVargas,Ina M Koning,Elizabeth A StuartWe investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying

Regime Switching Modeling of Substance Use: TimeVarying and SecondOrder Markov Models and Individual Probability Plots. Struct. Equ. Model. (IF 4.426) Pub Date : 20160301
Michael C Neale,Shaunna L Clark,Conor V Dolan,Michael D HunterA linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of firstorder Markov switching probabilities was specified to be timeinvariant, regardless of the pair of occasions being considered. The first extension, timevarying transitions, specifies different Markov transition matrices between each pair of occasions. The second extension is secondorder

Test Reliability at the Individual Level. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Yueqin Hu,John R Nesselroade,Monica K Erbacher,Steven M Boker,S Alexandra Burt,Pamela K Keel,Michael C Neale,Cheryl L Sisk,Kelly KlumpReliability has a long history as one of the key psychometric properties of a test. However, a given test might not measure people equally reliably. Test scores from some individuals may have considerably greater error than others. This study proposed two approaches using intraindividual variation to estimate test reliability for each person. A simulation study suggested that the parallel tests approach

Modeling SelfRegulation as a Process Using a Multiple TimeScale Multiphase Latent Basis Growth Model. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Jonathan Lee Helm,Nilam Ram,Pamela M Cole,SyMiin ChowMeasurement burst designs, wherein individuals are measured intensively during multiple periods (i.e., 'bursts'), have created new opportunities for studying change at multiple timescales. This paper develops a model that may be useful in situations where the functional form of shortterm change is unknown, may consist of multiple phases, and may change over the longterm. Specifically, we combine

Modeling predictors of latent classes in regression mixture models. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Kim Minjung,Vermunt Jeroen,Bakk Zsuzsa,Jaki Thomas,Van Horn M LeeThe purpose of the current study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1step and 3step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that the step1 of the threestep

tetrad: A Set of Stata Commands for Confirmatory Tetrad Analysis. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Shawn Bauldry,Kenneth A BollenThis article provides a brief overview of Confirmatory Tetrad Analysis (CTA) and presents a new set of Stata commands for conducting CTA with supporting examples. The Stata command, tetrad, allows researchers to use modelimplied vanishing tetrads to test the overall fit of structural equation models (SEMs) with continuous endogenous variables and the relative fit of two SEMs with continuous endogenous

Improving Factor Score Estimation Through the Use of Observed Background Characteristics. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Patrick J Curran,Veronica Cole,Daniel J Bauer,Andrea M Hussong,Nisha GottfredsonA challenge facing nearly all studies in the psychological sciences is how to best combine multiple items into a valid and reliable score to be used in subsequent modelling. The most ubiquitous method is to compute a mean of items, but more contemporary approaches use various forms of latent score estimation. Regardless of approach, outside of largescale testing applications, scoring models rarely

Inference Based on the BestFitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach. Struct. Equ. Model. (IF 4.426) Pub Date : 20160101
Gitta H Lubke,Ian CampbellInference and conclusions drawn from model fitting analyses are commonly based on a single "bestfitting" model. If model selection and inference are carried out using the same data model selection uncertainty is ignored. We illustrate the Type I error inflation that can result from using the same data for model selection and inference, and we then propose a simple bootstrap based approach to quantify

Eliminating Bias in ClassifyAnalyze Approaches for Latent Class Analysis. Struct. Equ. Model. (IF 4.426) Pub Date : 20150124
Bethany C Bray,Stephanie T Lanza,Xianming TanDespite recent methodological advances in latent class analysis (LCA) and a rapid increase in its application in behavioral research, complex research questions that include latent class variables often must be addressed by classifying individuals into latent classes and treating class membership as known in a subsequent analysis. Traditional approaches to classifying individuals based on posterior

Longitudinal Dynamic Analyses of Depression and Academic Achievement in the Hawaiian High Schools Health Survey using Contemporary Latent Variable Change Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20150120
Jack McArdle,Fumiaki Hamagami,Janice Y Chang,Earl S HishinumaThe scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this paper we present multivariate data from a longitudinal cohortsequential study of high school students in Hawai'i (following McArdle, 2009; McArdle, Johnson, Hishinuma, Miyamoto

DataGenerating Mechanisms Versus ConstructivelyDefined Latent Variables in MultitraitMultimethod Analysis: A Comment on CastroSchilo, Widaman, and Grimm (2013). Struct. Equ. Model. (IF 4.426) Pub Date : 20141125
Christian Geiser,Tobias Koch,Michael EidIn a recent article, CastroSchilo, Widaman, and Grimm (2013) compared different approaches for relating multitraitmultimethod (MTMM) data to external variables. CastroSchilo et al. reported that estimated associations with external variables were in part biased when either the Correlated TraitsCorrelated Uniqueness (CTCU) or Correlated TraitsCorrelated (Methods  1) [CTC(M  1)] models were

Effect Size, Statistical Power and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis. Struct. Equ. Model. (IF 4.426) Pub Date : 20141021
John J Dziak,Stephanie T Lanza,Xianming TanSelecting the number of different classes which will be assumed to exist in the population is an important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) provides a datadriven way to evaluate the relative adequacy of a (K 1)class model compared to a Kclass model. However, very little is known about how to predict the power or the required sample size for the BLRT

Sensitivity Analysis of Multiple Informant Models When Data are Not Missing at Random. Struct. Equ. Model. (IF 4.426) Pub Date : 20140916
Shelley A Blozis,Xiaojia Ge,Shu Xu,Misaki N Natsuaki,Daniel S Shaw,Jenae Neiderhiser,Laura Scaramella,Leslie Leve,David ReissMissing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups may be retained even if only one member of a group contributes data. Statistical inference is based on the assumption that data

Examining Measure Correlations with Incomplete Data Sets. Struct. Equ. Model. (IF 4.426) Pub Date : 20140902
Tenko Raykov,Brooke C Schneider,George A Marcoulides,Peter A LichtenbergA twostage procedure for estimation and testing of observed measure correlations in the presence of missing data is discussed. The approach uses maximum likelihood for estimation and the false discovery rate concept for correlation testing. The method can be utilized in initial exploration oriented empirical studies with missing data, where it is of interest to estimate manifest variable interrelationship

Modeling Change in the Presence of NonRandomly Missing Data: Evaluating A Shared Parameter Mixture Model. Struct. Equ. Model. (IF 4.426) Pub Date : 20140712
Nisha C Gottfredson,Daniel J Bauer,Scott A BaldwinIn longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficientdependent missingness, is statistically nonignorable and can bias parameter estimates obtained from conventional growth models

Dyadic CurveofFactors Model: An Introduction and Illustration of a Model for Longitudinal NonExchangeable Dyadic Data. Struct. Equ. Model. (IF 4.426) Pub Date : 20140603
Tiffany A Whittaker,S Natasha Beretvas,Toni FalboThe analysis of longitudinal data collected from nonexchangeable dyads presents a challenge for applied researchers for various reasons. This paper introduces the Dyadic CurveofFactors Model (DCOFM) which extends the CurveofFactors Model (COFM) proposed by McArdle (1988) for use with nonexchangeable dyadic data. The DCOFM overcomes problems with modeling composite scores across time and instead

Determining the Number of Latent Classes in Single and MultiPhase Growth Mixture Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20140415
SuYoung KimStagesequential (or multiphase) growth mixture models are useful for delineating potentially different growth processes across multiple phases over time and for determining whether latent subgroups exist within a population. These models are increasingly important as social behavioral scientists are interested in better understanding change processes across distinctively different phases, such as

Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis. Struct. Equ. Model. (IF 4.426) Pub Date : 20140204
JennYun Tein,Stefany Coxe,Heining ChamLittle research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to interclass distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct

On Fitting a Multivariate TwoPart Latent Growth Model. Struct. Equ. Model. (IF 4.426) Pub Date : 20140101
Shu Xu,Shelley A Blozis,Elizabeth A VandewaterA 2part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2part

BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models. Struct. Equ. Model. (IF 4.426) Pub Date : 20140101
Kenneth A Bollen,Jeffrey J Harden,Surajit Ray,Jane ZaviscaSelecting between competing Structural Equation Models (SEMs) is a common problem. Often selection is based on the chi square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with SEMs compared to other fit indices. This article examines several new and old Information Criteria (IC) that approximate

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders. Struct. Equ. Model. (IF 4.426) Pub Date : 20131205
Shaunna L Clark,Bengt Muthén,Jaakko Kaprio,Brian M D'Onofrio,Richard Viken,Richard J RoseThe factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and