ABSTRACT
Perhaps the most important role for individual differences in the study of devel opment is to help generate and confirm hypotheses about processes of behavior change. Behavior change is the essence of development, yet the processes that effect change do not typically reveal themselves in tidy factorial experiments. Analysis of individual differences in behavior over time is one avenue that can lead toward a better understanding of the mechanisms of development. To explore how individual differences can inform the study of developmental
processes, a distinction must be made between analyses of developmental func tions and analyses of individual-difference stability (see also Emmerich, 1964; McCall, 1977; Sackett, Sameroff, Cairns, & Suomi, 1981; Wohlwill, 1973). A functional relation between behavior and age (or, simply, a developmental func tion) is a plot of the relation of age to a particular characteristic of the developing organism (e.g., height, intelligence, sociability). Individual differences in devel opmental functions can suggest ways in which the causal network operates. For example, Sackett et al. (1981) used idealized plots of behavior change to suggest ways in which different developmental processes may be realized in different individuals or groups. McCall, Appelbaum, and Hogarty (1973) analyzed indi vidual differences in developmental functions of IQ scores to find groups of children with similar patterns of change over age. Thus, individual differences in developmental functions can provide insights into differences in processes of change. Studies of individual-difference stability, on the other hand, focus on the rank
order of individuals on a particular behavior or characteristic. Stability, in this
sense, indicates that the relative rank order of individuals remains consistent over time and is generally believed to provide empirical evidence of an enduring property, perhaps a fundamental behavioral disposition or trait. Equally impor tant, characteristics that show individual-difference stability may be associated with different kinds of developmental processes than characteristics that are not stable. Far more effort in longitudinal research has been devoted to the study of
stability than to the study of individual differences in developmental functions. This imbalance is unfortunate, as the quest for stability is less directly relevant to developmental process than is analysis of developmental functions (see also Wohlwill, 1973). Perhaps the focus on stability results from the wide availability of methods for its assessment, particularly the Pearson correlation coefficient. In contrast, methods suitable to uncovering individual differences in patterns of behavior change over time are not so well known. The goal of this chapter is to facilitate progress toward understanding the
processes of behavior development by addressing statistical problems and solu tions in analyses of individual differences. Problems in the assessment of indi vidual-difference stability via correlations are addressed first. In particular, methods are discussed for testing the difference between two related and unrelat ed correlations and for testing the difference between two full matrices of cor relations (both related and unrelated). The major focus of the chapter, however, is on hierarchical cluster analysis, a method of grouping individuals into rela tively homogeneous sets. Cluster analysis is offered as one method of describing individual differences in developmental profiles, that is, individual differences in how behavior changes with time.
