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Yuan Fang; Lijuan Wang – Grantee Submission, 2024
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…
Descriptors: Structural Equation Models, Research Problems, Longitudinal Studies, Simulation
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Mauricio Garnier-Villarreal; Terrence D. Jorgensen – Grantee Submission, 2024
Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are use to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Indexes
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Ruoxuan Li; Lijuan Wang – Grantee Submission, 2024
Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…
Descriptors: Social Science Research, Causal Models, Formative Evaluation, Measurement
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Dae Woong Ham; Luke Miratrix – Grantee Submission, 2024
The consequence of a change in school leadership (e.g., principal turnover) on student achievement has important implications for education policy. The impact of such an event can be estimated via the popular Difference in Difference (DiD) estimator, where those schools with a turnover event are compared to a selected set of schools that did not…
Descriptors: Trend Analysis, Faculty Mobility, Academic Achievement, Principals
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Ke-Hai Yuan; Ling Ling; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…
Descriptors: Structural Equation Models, Computation, Social Science Research, Error of Measurement
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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