Models for prediction analysis in longitudinal research

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Abstract
This paper discusses models for Prediction Analysis (PA) in longitudinal research. It describes PA as a non‐standard log‐linear model (von Eye et al., 1993). Models for predictions in longitudinal data are introduced including Equi‐Finality models and Equi‐Causality models of development. Models are described for two and more occasions of measurement. The relationship between formulating prediction hypotheses and model specification is discussed. Data examples illustrate model application and selection of log‐linear models for parameter estimation. The discussion focuses on types of variable relationships and their translation into testable hypotheses.

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