Analyzing change in short-term longitudinal research using cohort-sequential designs.

Abstract

This article illustrates a method for approximating longitudinal data analysis by combining information from different overlapping age groups to form a single developmental growth curve. Using this method, hypotheses about the form of growth, the extent of individual differences in growth, and factors that affect the rate and pattern of growth are investigated. The example used to illustrate this method examines the growth of externalizing behavior and of negativity in parent-child relationships during early adolescence using assessments from multiple methods and multiple informants. These 3 dimensions changed significantly during this period, with parental negativity increasing more rapidly after age 12. However, there were substantial individual differences in the pattern of change in these dimensions. Gender of child and type of family situation (nondivorced, divorced, and remarried) were investigated as possible factors affecting change.