This course provides an introduction to two main families of advanced data analysis techniques commonly used in contemporary psychological science, namely multilevel (e.g., students’ data nested into classes/schools, within-individual analysis of repeated measures) and multivariate linear regression modeling with latent and observed variables.
The course includes two main parts:
1) Multilevel modeling: Basics of linear mixed-effects regression; Data pre-processing and data preparation; Model evaluation and selection criteria; Coefficient interpretation and visualization
2) Multivariate modeling: Selected topics in structural equation modeling (basics of path analysis and confirmatory factor analysis); Data pre-processing and data preparation; Model evaluation, fit indices, and selection criteria; Coefficient interpretation and visualization
The course is characterized by an applied approach that emphasizes practical examples in the field of developmental, educational, and applied psychology. The course also includes practical exercises using the statistical software R.
The course includes two main parts:
1) Multilevel modeling: Basics of linear mixed-effects regression; Data pre-processing and data preparation; Model evaluation and selection criteria; Coefficient interpretation and visualization
2) Multivariate modeling: Selected topics in structural equation modeling (basics of path analysis and confirmatory factor analysis); Data pre-processing and data preparation; Model evaluation, fit indices, and selection criteria; Coefficient interpretation and visualization
The course is characterized by an applied approach that emphasizes practical examples in the field of developmental, educational, and applied psychology. The course also includes practical exercises using the statistical software R.
- Teacher: Luca Menghini