Models for intensive longitudinal data

The book Models for intensive longitudinal data is an edited volume consisting of eleven chapters by 23 authors. These chapters are separate contributions without links to the other chapters. To escape the impression that this is a fragmented book, the editors Theodore A. Walls and Joseph L. Schafer start with a twelve-page Introduction. This Introduction gives an extensive overview of the chapters and the topics of the book. Also the relevance and some recurring themes regarding analyzing intensive longitudinal data are discussed. The first chapters of the book deal with existing techniques for longitudinal analysis. The initial chapters focus on multilevel models—which in various places are called hierarchical linear models, random-effects models, or mixed-effects models—and on marginal modeling through generalized estimating equations. These models can be fitted to data entirely with existing statistical software, but special considerations arise when applied to intensive longitudinal data. Later chapters introduce a variety of less well known but useful methodological tools from item response theory, functional data analysis, time series, state-space modeling, analysis of dynamical systems through stochastic differential equations, engineering control systems, and point process models. Real data examples are drawn from psychology, studies of smoking and alcohol abuse, brain imaging, and traffic engineering. Potential future applications to numerous other kinds of data are also used.