This course is an introduction to theory and applications of event-history analysis, plus some elements of panel data analysis. Longitudinal data are commonly used to address many research questions in demography, social sciences, and epidemiology.
- Hans-Peter Blossfeld, Gotz Rohwer, Thorsten Schneider
(2019). Event History Analysis With Stata. 2nd Edition. Routledge.
- Stefani Scherer (2013). Analisi dei dati longitudinali. Un'introduzione pratica. Bologna: Il Mulino.
- Slides e altri materiali verranno forniti durante il corso.
Learning Objectives
This course covers univariate and multivariate (regression) methods for analysis of duration (event-history) and panel data, including their recent developments. Students also learn data management skills that are specific to conducting event-history analysis in Stata.
Finally, students will be able to apply the learned methods in the domain of social and demographic research.
Prerequisites
Statistical inference.
Teaching Methods
Face-to-face lessons and lab sessions.
Type of Assessment
Attending students:
- Delivering of regular assignments (10% of final mark);
- Student's discussion of the results of a longitudinal data analysis regarding a substantive socio-demographic theme to be agreed with the teachers (20% of final mark);
- Preparation and writing of the final project (70% of final mark).
Non-attending students: written exam that includes both exercises in Stata and questions on theory.
Course program
Introduction (Basic concepts and definitions, Event history data, censoring and truncation, discrete vs. continuous time); Event history data (Coding and data preparation, Life tables, Kaplan-Meier and related estimators, Stata applications, time-constant and time-varying variables); Introduction to parametric models (Exponential and piece-wise constant models); Modelling-related issues (Interactions and combinations of variables; model choice and goodness of fit); Parametric models (Weibull, Gompertz, Log-Logistic, Log-Normal); Cox model (Estimation, interpretation of parameters and model diagnostics, PH assumption); Competing risk models (Data preparation, estimation and interpretation); Advanced topics (Discrete time models, frailty models, unobserved heterogeneity); Introduction to panel data and related regression models (random and fixed effects).