Indiana University Bloomington

Sociology S651
Topics In Quantitative Sociology: Longitudinal & Panel Data Analysis

Contact: (pmcmanus@indiana.edu) date
Capacity: 20
Sequence: Prerequisites: two semesters of linear and nonlinear regression
Pre-Requisites: See above: regression
Algebra Required: Matrix algebra is used for notation in course notes and texts.
Calculus Required: No calculus expected, never needed.
Contact Person for Authorization: Yes, grad secretary in sociology
Instructor: Patricia McManus
Days Per Week Offered: 1X 2-hr Lecture 1X 2hr/Lab
Website: http://oncourse.iu.edu
Recommended follow-up classes: Advanced Panel Data, Latent Growth Models, Survival/Event History Analysis
Syllabus: No Syllabus Avaliable
Keywords: regression models, panel data analysis, longitudinal data analysis, growth curves
Description: This special topics course covers applied statistical techniques for the analysis of repeat observations over time. The course draws on a range of models from various social science disciplines. The course begins with a review of the general linear regression model for continuous dependent variables and maintains a primary emphasis on models for continuous outcomes. Topics include (1) instrumental variables approaches, (2) error components and dynamic panel models in econometrics, (3) multilevel growth curve models, and as time permits (4) an introduction to event history models.
Books: a) Course notes;
b) Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data, 2nd Edition. Cambridge: MIT Press.
c) Rabe-Hesketh, Sophia and Anders Skrondal. 2008. Multilevel and Longitudinal Modeling Using Stata. 2nd Edition. College Station: Stata Press.
Similar text:
Gelman and Hill "Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press 2007).
Substantive Orientation: Social sciences, policy analysis, education, applied health sciences
Statistical Orientation: Observational data; Applied statistics
Applied/Theoretical: Applied with theory as context. No derivations, but students are expected to have a solid theoretical understanding of linear regression and its application to observational data
Software: Stata, GLLAMM
How Software is Used: Data analysis
Problem Sets: Yes
Data Analysis: Yes, in every problem set
Presentation: Project
Exams: No exam
Comments: This is an applied course: students are also required to submit two short critiques of empirical research articles in their field, along with a copy of the article.