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Showing 1 to 15 of 16 results Save | Export
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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
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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
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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
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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
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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
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Maxwell, Scott E.; Cole, David A.; Mitchell, Melissa A. – Multivariate Behavioral Research, 2011
Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can…
Descriptors: Psychological Studies, Mediation Theory, Bias, Research Methodology
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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
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Imai, Kosuke; Jo, Booil; Stuart, Elizabeth A. – Multivariate Behavioral Research, 2011
In this commentary, we demonstrate how the potential outcomes framework can help understand the key identification assumptions underlying causal mediation analysis. We show that this framework can lead to the development of alternative research design and statistical analysis strategies applicable to the longitudinal data settings considered by…
Descriptors: Research Design, Statistical Analysis, Research Methodology, Longitudinal Studies
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Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
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Lee, Sik-Yum; Xia, Ye-Mao – Psychometrika, 2008
In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…
Descriptors: Structural Equation Models, Bayesian Statistics, Evaluation Methods, Evaluation Research
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Skaalvik, Einar M.; Skaalvik, Sidsel – Teaching and Teacher Education: An International Journal of Research and Studies, 2010
The purpose of this study was partly to test the factor structure of a recently developed Norwegian scale for measuring teacher self-efficacy and partly to explore relations between teachers' perception of the school context, teacher self-efficacy, collective teacher efficacy, teacher burnout, teacher job satisfaction, and teachers' beliefs that…
Descriptors: Teacher Effectiveness, Job Satisfaction, Self Efficacy, Teacher Burnout
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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
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Prevatt, Frances; Petscher, Yaacov; Proctor, Briley E.; Hurst, Abigail; Adams, Katharine – Educational and Psychological Measurement, 2006
Two competing structural models for the revised Learning and Study Strategies Inventory (LASSI) were examined. The test developers promote a model related to three uncorrelated components of strategic learning: skill, will, and self-regulation. Other investigators have shown empirical support for a three-factor correlated model characterized by…
Descriptors: College Students, Structural Equation Models, Learning Strategies, Factor Analysis
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Johnson, Bruce; Stevens, Joseph J.; Zvoch, Keith – Educational and Psychological Measurement, 2007
Scores from a revised version of the School Level Environment Questionnaire (SLEQ) were validated using a sample of teachers from a large school district. An exploratory factor analysis was used with a randomly selected half of the sample. Five school environment factors emerged. A confirmatory factor analysis was run with the remaining half of…
Descriptors: Measures (Individuals), Statistical Analysis, Educational Environment, Structural Equation Models
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Whittaker, Tiffany A.; Stapleton, Laura M. – Multivariate Behavioral Research, 2006
Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…
Descriptors: Multivariate Analysis, Selection, Structural Equation Models, Evaluation Methods
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