With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…

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…

This study conducted secondary analysis of data from the National Longitudinal Transition Study-2 (NLTS2) to examine the degree to which student, family, and school constructs predicted self-determination outcomes. Multi-group structural equation modeling was used to examine predictive relationships between 5 students, 4 family, and 7 school…