DEMO8084
Advanced Longitudinal Analysis
Co-ordinator – Prof Peter Brandon
Units: 6
Between semesters 1 & 2 intensive
Option for MSR (Social Research Methods)
Elective MSR
Prerequisite: DEMO8083 Introduction to Longitudinal Methods; and DEMO8007 Statistics for Social Scientists, at the level of Distinction or higher.
Note: students not enrolled in the Master of Social Research or the Graduate Certificate in Social Research require permission of the Masters Coordinator, Dr Robert Ackland, to enrol in this course. Please contact him on adsri.study@anu.edu.au.
Syllabus
This course is designed to give students an understanding of longitudinal analysis. It is an applied course with working examples of longitudinal methods. Prior knowledge of linear regression modelling and hypothesis testing is assumed. After a review of the linear regression model and its interpretation, the course then introduces basic techniques for analysing panel data. The focus of the course is on fixed and random effects models of analysis. Estimation strategies for linear and non-linear models are examined. Instruction in relevant computer software is given.
This course gives students skills in conducting advanced longitudinal techniques for analysis panel data. Students will participate in lectures and computer labs. Assessment is designed to advance learning through the application of longitudinal techniques to real-life panel data. By the end of the course participants will be able to conduct analysis using fixed and random effect models and evaluate research that use these measures. Participants will also be able to demonstrate the presentation of longitudinal results for a wide range of audiences.
Course Topics
- Overview of the design of longitudinal data and review of linear regression analysis
- General specification of the panel model (model accounting for time and unobserved effects)
- Fixed effects
- Random effects
- Presenting results
Preliminary Reading
Singer, J. and Willett, J. (2003) Applied Longitudinal Data Analysis. New York: Oxford University Press.
Assessment
3 Case studies (30% each)
Presentation (10%)
Course Aims
This course is designed to give students skills in applying advanced longitudinal techniques to longitudinal data.
Student Skills Objectives
By the end of the course participants will be able to conduct analysis using fixed and random effect models and evaluate research that use these measures. Participants will also be able to demonstrate the presentation of longitudinal results for a wide range of audiences.
