Panel Data Analysis (Intensive)

Prerequisites

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module provides a brief introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations from a series of research units (eg. individuals, firms) as they move through time. This course focuses primarily on panel data with a large number of research units tracked for a relatively small number of time points.

The module begins by introducing key concepts, benefits and pitfalls of PDA. Students are then taught how to manipulate and describe panel data in Stata. The latter part of the module introduces random and fixed effects panel models for continuous and dichotomous outcomes. The course is taught through a mixture of lectures and practical sessions designed to give students hands-on experience of working with real-world data from the British Household Panel Survey.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• Students should be comfortable with multiple regression. Familiarity with Stata is helpful but not essential (notes and Stata code will be provided)
• You must have a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have access to the associated Moodle course page (http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/before-first-session)

Number of sessions: 2

• Introduction to PDA
• Manipulating and describing panel data
• Random effects, fixed effects and 'hybrid' panel models
• Panel models for dichotomous outcomes

Presentations, demonstrations and practicals

Stata on MCS

• Allison, P. (2009) Fixed Effects Regression Models. London: SAGE (chapters 1-3 are particularly relevant for this course).
• Taris, T. (2000) A Primer in Longitudinal Data Analysis. London: SAGE (this provides a useful introduction to key concepts in panel analysis).

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

• This is an intensive, one-day module.

Once in 2015/16