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Julian F. Lohmann; Steffen Zitzmann; Martin Hecht – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…
Descriptors: Time Management, Behavioral Science Research, Predictive Validity, Predictor Variables
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Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2025
This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can…
Descriptors: Monte Carlo Methods, Structural Equation Models, Goodness of Fit, Robustness (Statistics)
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Dandan Tang; Steven M. Boker; Xin Tong – Structural Equation Modeling: A Multidisciplinary Journal, 2025
The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool -…
Descriptors: Test Validity, Factor Analysis, Replication (Evaluation), Social Science Research
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Hilley, Chanler D.; O'Rourke, Holly P. – International Journal of Behavioral Development, 2022
Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent…
Descriptors: Behavioral Science Research, Research Methodology, Longitudinal Studies, Behavior Change
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Ke-Hai Yuan; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…
Descriptors: Error of Measurement, Measurement Techniques, Social Science Research, Behavioral Science Research
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Ke-Hai Yuan; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models…
Descriptors: Structural Equation Models, Path Analysis, Weighted Scores, Comparative Testing
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Xu Qin; Lijuan Wang – Grantee Submission, 2023
Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and…
Descriptors: Causal Models, Mediation Theory, Computer Software, Statistical Analysis