Checklist for Structural Equation Modelling Here I present a checklist for robust structural equation modeling (SEM) in research articles. 1. Data cleaning 2. Check normality of data 3. Check for response bias 4. Develop a measurement model 4.1. Exploratory factor analysis 4.2. Confirmatory factor analysis 4.3. Correlations among latent factors 4.4. Convergent and divergent validity checks 4.5. Reliability checks 4.6.
Continue reading Common Issues in Structural Equation Modelling (SEM) Hello everyone, This post presents some very common issues we face when doing Structural Equation Modelling (SEM). Most of the problems and their remedies are either taken from a book or academic social interactions, e.g. Research Gate. Courtesy to all sources are mentioned along with the respective post/suggestion. Problem 1: What are the
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Structural Equation Modeling (SEM) is a multivariate statistical analysis technique that is used to analyze structural relationships among variables. SEM is the combination of factor analysis and multiple regression analysis. Usually factors are created using multiple observed variables through factor analysis. Those factors are called latent variables. Thereafter, multiple regression analysis is performed on latent variables level, not in observed
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