Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications

Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications

Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications

Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications


This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable. It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques. Each chapter features: *a minimum discussion of mathematical detail; *an empirical example applying the technique; and *a discussion of the software related to that technique. Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.


This volume began as a nightmare.

Once upon a time, life for social and behavioral scientists was (relatively) simple. When a research design called for repeated measures data, the data were analyzed with repeated measures analysis of variance. the bmdp 2V module was frequently the package of choice for the calculations.

Life today is more complicated. There are many more choices. Does the researcher need to model behavior at the level of the individual as well as at the level of the group? Should the researcher use the familiar and well-understood least-squares criterion? Should the researcher turn to the maximum likelihood criterion for assessing the overall fit of a model? Is it possible and is it desirable to represent the repeated measures data within structural equation modeling?

So the nightmare began as (shall we be dishonest and say) one night of deliberations among these choices. the thought then arose that it would be useful to have the statistical experts writing in the same volume about the possibilities and some of the dimensions that are pertinent to making these choices. Hence the origin of the present volume.

The issue of the analysis of repeated measures data has commonly been examined within the context of the study of change, particularly with respect to longitudinal data (cf., Collins & Horn, 1991; Gottman, 1995). This volume contains three chapters whose primary focus is on the study of growth over several years time (Raudenbush, chapter 2; Curran & Hussong, chapter 3; Duncan, Duncan, Li, & Strycker, chapter 7). Studies of change typically imply the expectation that variation, movement in scores, is generally unidirectional-generally up or generally down. Not all repeated measures data are concerned with change, and change is only one aspect of the variability that occurs within individuals. To illustrate, consider an example from the study of social behavior.

Personality, social, and organizational psychologists are often interested in the effects of situations on behavior: to what extent are individuals' behaviors consistent across sets of situations and to what extent does the behavior of individuals change as a function of the situation. For example, the focus might be on how people's dominant and submissive behaviors change as a function of being in a subordinate, co-equal, or supervisory work role. There might also be interest in whether people's responses to these . . .

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