This module provides an in-depth understanding of panel data analysis techniques, including fixed and random effects models, and their implementation in Stata. Learners will explore various methods for analyzing longitudinal data and handling cross-sectional time series structures. The course covers practical applications and diagnostic tests to ensure accurate modeling of complex datasets.

This module covers essential techniques for analyzing time series data, including smoothing trends, detecting and addressing autocorrelation, and applying advanced models like ARIMA and VAR for forecasting. Learners will gain practical skills in identifying patterns, testing for stationarity, and implementing statistical methods to improve predictive accuracy.

This module covers the fundamental concepts and practical steps involved in exploratory factor analysis. Learners will gain insights into how to extract, interpret, and refine factors using statistical tools like Stata. The module also explores how to compute composite scores and assess reliability in factor analysis.

This module provides an in-depth exploration of structural equation modeling (SEM) and confirmatory factor analysis (CFA), covering model specification, identification, assessment, and modification. Learners will develop skills in interpreting and evaluating both measurement and structural components of SEM models. The content also includes practical applications using Stata and theoretical foundations for latent variable analysis.

This module covers advanced statistical methods for handling complex data scenarios, including count data modeling, variable transformation, and robust techniques for missing data. Learners will gain practical skills in applying these methods to improve regression analysis and data interpretation.

This module covers essential Stata programming techniques such as macros, loops, and stored objects, along with dynamic reporting tools like dyndoc and putdocx. Learners will gain the ability to create reusable commands and generate reproducible documentation. The focus is on improving efficiency and clarity in data management and reporting.