Models for Intensive Longitudinal Data

Modern information technology now allows the easy collection and storage of massive amounts of data. This development presents tremendous opportunities and challenges for scientists and statisticians. Traditionally, longitudinal study designs have most often used relatively short series of measurements through time (e.g., ≤10 measurement occasions), and statistical methodology for longitudinal data has concentrated on this case. However, recent technological innovations have led to the increasing popularity of studies that generate what Walls and Schafer term “intensive longitudinal data” (ILD). Such data, stemming from many (typically frequent) measurement occasions through time on a sample of subjects, arise in various disciplines. However, it is their growing prevalence in psychology and related areas of social science that has provided the primary motivation for the publication of Models for Intensive Longitudinal Data. This edited volume contains a collection of chapters that describe and illustrate statistical models and methodology appropriate for ILD. The contributing authors are mostly social scientists and statisticians, with many of the chapters written collaboratively across these disciplines. The emphasis is on explaining various classes of models: their definition, conceptualization, interpretation, relationship to other model classes, and usefulness for addressing questions of scientific interest. Technical, theoretical, and other aspects of limited interest to practitioners are kept to a minimum and, in some cases, relegated to appendixes. Nearly all of the chapters include applications that are pursued further than is typical in statistical publications, with novel substantive results presented in many cases. Topics represented in the applications include smoking behavior, patterns of alcohol use, relationship of feelings of control and choice over daily activities, marital intimacy, daily patterns of personality characteristics, neural mechanisms of attentional control, and traffic intensity. After a nicely written introduction that motivates, identifies themes in the analysis of ILD, and summarizes the chapters, the editors and their collaborators contribute two chapters that introduce and summarize the main ideas of two traditional methodologies for longitudinal data: the linear mixed model (also called multilevel model or hierarchical linear model) and generalized estimating equations. This material sets the stage for subsequent chapters that discuss functional multilevel models, item response models and their relationship to mixed models, semiparametric varying-coefficient linear mixed models, multilevel autoregressive models, state-space models, dynamical systems models based on differential equations, and point process models. A final chapter highlights some emerging technologies for the collection of ILD and discusses the associated statistical challenges. All of the chapters are written at an accessible level. Collectively, they do a nice job of bridging the gap between the social sciences and the field of statistics, clarifying the relationship between methodology and terminology used in the two areas while introducing a broad array of models and methods for ILD analysis. Although this volume will be of greatest value to quantitatively oriented researchers in psychology and other areas of social science, I also recommend it to statisticians and anyone else interested in the collection and analysis of ILD.