Advanced Data

Course Objectives of Advanced Data

• Estimation of basic models in R/ Python

• Approaches to dealing with missing data.

• Data reduction techniques

 

Day 1
Principal components analysis

• Rescaling principal components

• Choosing the number of components

• Component scores 

Day 2
Factor analysis

•  Factor extraction and common factor analysis

•  Factor rotation and factor scores

Item response theory models

• Latent trait models and item response function

• Logistic and normal IRT models and interpreting the IRT score scale

Day 3
Structural equation modeling and path models

• Path diagrams

• Structural equations& designing SEMs•

• Confirmatory factor analysis

• Latent class models

Day 4
Clustering and cluster analysis, and association rules

• Classification in social sciences

• Hierarchical clustering; k-means clustering;

• Model-based clustering

• Visualization of clustering results

Day 5
Missing data

• Missing data generation and mechanisms

• Multiple imputations

• Pattern mixture models

• Data missing not at random

• Missing data in longitudinal studies