Preface
Contents
About the Author
1
Introduction to R and RStudio
1.1
The R Language
1.2
Install R
1.3
Install RStudio
1.4
Use RStudio
1.4.1
Basic operations
1.4.2
Data types in R
1.4.3
Data structures in R
1.4.4
R Packages
1.4.5
RStudio Projects
1.4.6
Import and Export Data
1.5
Mplus
2
Review of Basic Statistics
2.1
Calculating Variance and Covariance
2.1.1
Covariance
2.1.2
Correlation
2.1.3
Using Matrix Algebra
2.2
Correlation and Causation
2.3
Data Issues
2.3.1
What form should the data be in?
2.3.2
What form should the data be in?
2.3.3
Missing Data
2.3.4
Multicollinearity
3
Path Analysis
3.1
Syntax - R
3.1.1
Use a sample covariance matrix an input
3.1.2
Use sample correlation matrix and SDs as input
3.1.3
Path analysis model - Wheaton et al. (1977) example
3.1.4
Use raw data as input
3.2
Syntax - Mplus
3.2.1
Regression of Y on X1 and X2 - sample covariance matrix
3.2.2
Regression of Y on X1 and X2 - sample correlation matrix and SDs as input
3.2.3
Path analysis model - Wheaton et al. (1977) example
3.2.4
Use raw data as input
4
Confirmatory Factor Analysis
4.1
Syntax - R - One-Factor CFA
4.1.1
Use a sample covariance matrix an input
4.1.2
Calculate coefficient omega
\({\omega _u}\)
4.2
Syntax - R - Two-Factor CFA
4.2.1
An exmaple
4.2.2
Model comparison example - two-factor CFA model across time
4.3
Syntax - Mplus - One-Factor CFA
4.3.1
Use a sample covariance matrix an input
4.3.2
Calculate coefficient omega
\({\omega _u}\)
4.4
Syntax - Mplus - TWo-Factor CFA
4.4.1
An example
4.4.2
Model comparison example - two-factor CFA model across time
4.5
Model Respecification
4.5.1
Lagrange Multiplier test for adding paths
4.5.2
Wald test for dropping paths
4.5.3
Higher-order factor models
5
Full Structural Equation Models
5.1
Syntax - R
5.1.1
One-step SEM
5.1.2
Two-step SEM process
5.2
Syntax - Mplus
5.2.1
One-step SEM
5.2.2
Two-step SEM process
6
Multigroup Confirmatory Factor Analysis
6.1
Syntax - R
6.1.1
Analysis with data from single groups
6.1.2
Analysis with data from both groups
6.1.3
CFA with Ordered Categorical Variables – An example from Wang & Su (2013)
6.2
Syntax - Mplus
6.2.1
Analysis with data from single groups
6.2.2
Analysis with data from both groups
6.2.3
CFA with Ordered Categorical Variables – An example from Wang & Su (2013)
7
Models with Mean Structures
7.1
Syntax - R - MIMIC model
7.2
Syntax - R - Structured Means Modeling
7.2.1
Configural invariance model
7.2.2
Metric invariance/factor loading invariance
7.2.3
Scalar invariance/intercept invariance model
7.2.4
Partial invariance model
7.3
Syntax - Mplus - MIMIC model
7.4
Syntax - Mplus - Structured Means Modeling
7.4.1
Configural invariance model
7.4.2
Metric invariance/factor loading invariance
7.4.3
Scalar invariance/intercept invariance model
7.4.4
Partial invariance model
8
Latent Growth Models
8.1
Syntax - R
8.1.1
Linear latent growth models without means
8.1.2
Linear latent growth models with means
8.1.3
Linear latent growth models with predictors
8.2
Syntax - Mplus
8.2.1
Linear latent growth models without means
8.2.2
Linear latent growth models with means
8.2.3
Linear latent growth models with predictors
8.2.4
From Wang & Su (2013)
9
Multilevel Models
9.1
Syntax - R
9.1.1
A complex multilevel model example
9.2
Syntax - Mplus
9.2.1
A complex multilevel model example
9.2.2
An example of multilevel modeling – model 2 in Wang & Bergin (2017)
References
Structural Equation Modeling Using R and Mplus
References