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Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy
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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
Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites
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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