Author Description

By making both causal and process analyses possible, panel data has enjoyed increasing popularity in empirical science. In this compilation, several statistical techniques are presented in the face of a growing need to analyze panel data. Measurement error, missing data, heterogeneous populations and particular requirements for causal interference make the analysis of change more difficult. Readers will find up-to-date approaches covering a wide range of topics. Among these are loglinear and probit models, state space models, and structural equation and multilevel growth curve models of panel data.