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Suyoung Kim; Sooyong Lee; Jiwon Kim; Tiffany A. Whittaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits.…
Descriptors: Social Science Research, Behavioral Sciences, Structural Equation Models, Statistical Analysis
Emma Somer; Carl Falk; Milica Miocevic – Structural Equation Modeling: A Multidisciplinary Journal, 2024
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 multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and…
Descriptors: Scores, Structural Equation Models, Comparative Analysis, Sample Size
Smith, Carrie E.; Cribbie, Robert A. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Descriptors: Structural Equation Models, Error of Measurement, Statistical Analysis, Comparative Analysis
Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with…
Descriptors: Structural Equation Models, Monte Carlo Methods, Comparative Analysis, Statistical Analysis
Wu, Jiun-Yu; Kwok, Oi-man – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures…
Descriptors: Structural Equation Models, Surveys, Data Analysis, Comparative Analysis
Lubke, Gitta; Tueller, Stephen – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent…
Descriptors: Classification, Models, Statistical Analysis, Comparative Analysis
DeMars, Christine E. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and…
Descriptors: Item Response Theory, Structural Equation Models, Computation, Computer Software
Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques
Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, Ellen L.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and…
Descriptors: Structural Equation Models, Differences, Statistical Analysis, Models
Maydeu-Olivares, Alberto; Cai, Li; Hernandez, Adolfo – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be…
Descriptors: Factor Analysis, Research Methodology, Statistics, Item Response Theory
Kim, Su-Young; Kim, Jee-Seon – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This article investigates three types of stage-sequential growth mixture models in the structural equation modeling framework for the analysis of multiple-phase longitudinal data. These models can be important tools for situations in which a single-phase growth mixture model produces distorted results and can allow researchers to better understand…
Descriptors: Structural Equation Models, Data Analysis, Research Methodology, Longitudinal Studies
Savalei, Victoria – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Incomplete nonnormal data are common occurrences in applied research. Although these 2 problems are often dealt with separately by methodologists, they often cooccur. Very little has been written about statistics appropriate for evaluating models with such data. This article extends several existing statistics for complete nonnormal data to…
Descriptors: Sample Size, Statistics, Data, Monte Carlo Methods
Jones-Farmer, L. Allison – Structural Equation Modeling: A Multidisciplinary Journal, 2010
When comparing latent variables among groups, it is important to first establish the equivalence or invariance of the measurement model across groups. Confirmatory factor analysis (CFA) is a commonly used methodological approach to examine measurement equivalence/invariance (ME/I). Within the CFA framework, the chi-square goodness-of-fit test and…
Descriptors: Factor Structure, Factor Analysis, Evaluation Research, Goodness of Fit
Cheung, Mike W. L.; Chan, Wai – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
Descriptors: Structural Equation Models, Simulation, Social Sciences, Correlation
Zhang, Wei – Structural Equation Modeling: A Multidisciplinary Journal, 2008
A major issue in the utilization of covariance structure analysis is model fit evaluation. Recent years have witnessed increasing interest in various test statistics and so-called fit indexes, most of which are actually based on or closely related to F[subscript 0], a measure of model fit in the population. This study aims to provide a systematic…
Descriptors: Monte Carlo Methods, Statistical Analysis, Comparative Analysis, Structural Equation Models
Gold, Michael S.; Bentler, Peter M.; Kim, Kevin H. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This article describes a Monte Carlo study of 2 methods for treating incomplete nonnormal data. Skewed, kurtotic data sets conforming to a single structured model, but varying in sample size, percentage of data missing, and missing-data mechanism, were produced. An asymptotically distribution-free available-case (ADFAC) method and structured-model…
Descriptors: Monte Carlo Methods, Computation, Sample Size, Comparative Analysis