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Structural Equation Modeling in R
Module 1: Concepts in Structural Equation Modeling
1.1 What is Structural Equation Modelling and when to use it
1.2 SEM History and difference between CB and PLS SEM
1.3 A simple explanation of SEM formation (including Reflective and Formative constructs
1.4 Model identification issues
1.5 Model estimators (ML,MLR,WLS,DWLS, PLS)
1.6 Graphical notations in SEM
1.7 Instructor's Studies Published using SEM
SEM Quiz 1
Module 2: Introduction to R Software
2.1 How to install R Console and R Studio
2.2 Variable specification and data operators
2.3 Data Types in R
2.4 Common functions in R
2.5 Basic Plots in R
2.6 Descriptive Statistics and Frequency Table in R
2.7 Correlation tests in R
2.8 Basic Statistical Tests in R (T-tests, ANOVA, OLS Regression)
SEM Quiz 2
Module 3: Prerequisites to Structural Equation Modeling
3.1 Step-by-step SEM flowchart
3.2 Data issues to consider for SEM
3.3 Data preparation for SEM
3.4 Loading data files in R environment
3.5 Loading required R packages for SEM
3.6 Descriptive statistics, multivariate normality and correlations of measurement items
3.7 Non-response bias check through Wave Test
SEM Quiz 3
Module 4: Factor Analysis, Reliability and Validity
4.1 Exploratory Factor Analysis in R
4.2 Factor reliability check using Cronbach Alpha
4.3 Confirmatory Factor Analysis in R
4.4 Divergent and Convergent Validity of Measurement Model
4.5 Extended Exploratory Factor Analysis
SEM Quiz 4
Module 5: Common Method Bias
5.1 Common method bias check using Herman’s Single factor test
5.2 Common method bias check using Common Latent Factor test
5.3 Common method bias check using Common Marker Variable test
SEM Quiz 5
Module 6: Structural Equation Modeling Estimation
6.1 SEM estimation in R using lavaan
6.2. Interpretation of the SEM coefficients
6.3 Mediation or indirect effect estimation in SEM
6.4 What and How to report SEM results in a manuscript
SEM Quiz 6
3.6 Descriptive statistics, multivariate normality and correlations of measurement items
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