Time series analysis and its relationship with longitudinal analysis.

This paper discusses the relationship between longitudinal analysis of inter-individual (co-)variation and time series analysis of intra-individual (co-)variation. To set the stage for this discussion, first a tutorial overview of modern techniques of multivariate time series analysis is given which highlights the central role of rate-space modeling. Some increasingly general instances of the state-spuce model are presented, followed by a concise description of two recent applications involving nonlinear state-space modeling of oscillatory finger motion and nonlinear growth, respectively. We then consider the question under which conditions longitudinal factor analysis of inter-individual covariation will yield the same results as dynamic factor analysis of single-subject time series data. The conditions concerned can be derived from ergodicity theory and turn out to be very restrictive. This implies that the results obtained in analyses of inter-individual variation (like the construction and validation of measurement scales) cannot be generalized to the assessment and prediction of individual developmental processes (e.g., in single-subject counseling). A simple illustration with simulated data is given, to the best of our knowledge for the first time, in which a factor analysis of inter-individual covariation yields a satisfactorily fitting solution that has no relationship whatsoever to the factor structures characterizing the intra-individual covariation of each subject in the sample.