Longitudinal and Panel Data Analysis using Stata Training Course

This course equips participants with practical skills to analyze longitudinal and panel data using Stata. It focuses on data structuring, fixed and random effects, dynamic models, mixed models, diagnostics, and interpretation of repeated-measures data.

Target Groups

• Economists and econometricians
• Social science and health researchers
• Monitoring and evaluation analysts
• Statisticians and data scientists
• Postgraduate students and academics
• Policy analysts
• Managers and supervisors seeking to strengthen organizational practice in this field
• Consultants and professionals supporting related organizations

Course Objectives

By the end of this course, participants will be able to:

• Structure and manage panel and longitudinal datasets in Stata
• Describe within and between variation
• Fit pooled, fixed-effects, and random-effects models
• Select among models using appropriate tests
• Handle heteroskedasticity and autocorrelation
• Estimate dynamic panel models
• Apply mixed-effects models to repeated measures
• Analyze growth curves and change over time
• Manage attrition and missing data
• Interpret and present panel regression results

Course Modules

Module 1: Introduction to Longitudinal and Panel Data

• Types of longitudinal data
• Advantages and challenges
• Stata data structure
• Wide and long formats
• Panel declarations

Module 2: Data Preparation and Description

• Reshaping data
• Balanced and unbalanced panels
• Within and between variation
• Descriptive tools
• Graphing panel data

Module 3: Pooled and Fixed-Effects Models

• Pooled OLS
• Fixed-effects estimation
• Least squares dummy variables
• Interpretation
• Time effects

Module 4: Random-Effects and Model Selection

• Random-effects models
• Hausman test
• Breusch-Pagan test
• Choosing among models
• Robust standard errors

Module 5: Diagnostics and Robust Inference

• Heteroskedasticity
• Serial correlation tests
• Cluster-robust errors
• Cross-sectional dependence
• Panel unit roots

Module 6: Dynamic Panel Models

• Lagged dependent variables
• Endogeneity concerns
• Instrumental variables
• GMM estimators
• Specification tests

Module 7: Mixed-Effects and Multilevel Models

• Random intercepts and slopes
• Intraclass correlation
• Model fitting commands
• Model comparison
• Interpretation

Module 8: Growth Curve and Repeated Measures Analysis

• Modeling change over time
• Growth trajectories
• Time-varying covariates
• Nonlinear time effects
• Visualization

Module 9: Missing Data, Attrition, and Reporting

• Attrition patterns
• Missing data handling
• Sensitivity checks
• Formatting regression tables
• Communicating results

Module 10: Practical Exercises and Capstone Project

• Preparing and describing a sample panel dataset
• Estimating fixed- and random-effects models
• Testing and selecting models
• Fitting a mixed-effects growth model
• Presenting panel analysis results