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A. R. Georgeson – Structural Equation Modeling: A Multidisciplinary Journal, 2025
There is increasing interest in using factor scores in structural equation models and there have been numerous methodological papers on the topic. Nevertheless, sum scores, which are computed from adding up item responses, continue to be ubiquitous in practice. It is therefore important to compare simulation results involving factor scores to…
Descriptors: Structural Equation Models, Scores, Factor Analysis, Statistical Bias
Kim, Eun Sook; Kwok, Oi-man; Yoon, Myeongsun – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Testing factorial invariance has recently gained more attention in different social science disciplines. Nevertheless, when examining factorial invariance, it is generally assumed that the observations are independent of each other, which might not be always true. In this study, we examined the impact of testing factorial invariance in multilevel…
Descriptors: Monte Carlo Methods, Testing, Social Science Research, Factor Structure
Peugh, James; Fan, Xitao – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Growth mixture modeling (GMM) has become a more popular statistical method for modeling population heterogeneity in longitudinal data, but the performance characteristics of GMM enumeration indexes in correctly identifying heterogeneous growth trajectories are largely unknown. Few empirical studies have addressed this issue. This study considered…
Descriptors: Structural Equation Models, Statistical Analysis, Longitudinal Studies, Evaluation Research
Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling
Depaoli, Sarah – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…
Descriptors: Markov Processes, Factor Analysis, Statistical Bias, Evaluation Research
Grimm, Kevin J.; An, Yang; McArdle, John J.; Zonderman, Alan B.; Resnick, Susan M. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to…
Descriptors: Memory, Older Adults, Brain, Structural Equation Models
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
Williams, Jason; MacKinnon, David P. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Recent advances in testing mediation have found that certain resampling methods and tests based on the mathematical distribution of 2 normal random variables substantially outperform the traditional "z" test. However, these studies have primarily focused only on models with a single mediator and 2 component paths. To address this limitation, a…
Descriptors: Intervals, Testing, Predictor Variables, Effect Size
Noar, Seth M. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Across a variety of disciplines and areas of inquiry, reliable and valid measures are a cornerstone of quality research. This is the case because to have confidence in the findings of our studies, we must first have confidence in the quality of our measures. This article briefly reviews the literature on scale development and provides an empirical…
Descriptors: Measures (Individuals), Factor Analysis, Structural Equation Models, Test Validity