Application of Hierarchical Linear Models to Assessing Change

Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A two-stage model of change is proposed here. At the first, or within-subject stage, an individual's status on some trait is modeled as a function of an individual growth trajectory plus random error. At the second, or between-subjects stage, the parameters of the individual growth trajectories vary as a function of differences between subjects in background characteristics, instructional experiences, and possibly experimental treatments. This two-stage conceptualization, illustrated with data on Head Start children, allows investigators to model individual change, predict future development, assess the quality of measurement instruments for distinguishing among growth trajectories, and to study systematic variation in growth trajectories as a function of background characteristics and experimental treatments.