Advances in the statistical analysis of longitudinal data has been so rapid, that it has been difficult for empirically oriented social scientists to remain informed of all new developments in this important area of social methodology. This article offers some guidance on the use of various types of panel data analysis techniques, paying particular attention to the analysis of longitudinal panel data. The aim of this article is to describe in a succinct manner the logic underpinning a number of panel analysis techniques; outlining the types of inferences that can be drawn from employing specific techniques, and providing the reader with references to the literature associated with particular forms of panel data analysis. Five types of panel data analysis are discussed: Event history analysis, Sequential analysis, Hierarchical linear (or multi-level) modeling (with application to longitudinal data analysis), Structural equation modeling with longitudinal data, and use of Log- linear and Markov chain models for longitudinal data with categorical variables.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.