ANALYZING INDIVIDUAL STATUS AND CHANGE WITH HIERARCHICAL LINEAR-MODELS - ILLUSTRATION WITH DEPRESSION IN COLLEGE-STUDENTS

A recently developed class of multilevel or hierarchical linear models (HLM) provides an intuitive and efficient way to estimate individual growth or change curves. The approach also models the between-subjects variation of the individual change curves with treatment factors and individual attributes. Unlike other repeated measures analysis methods common in the behavioral sciences, HLM allows the fit of data with un equal numbers of repeated observations for each subject, variable timi ng of observations, and missing data, features which are often charact eristic of data from field studies. The application of HLM for the ana lysis of repeated psychological measures is discussed and illustrated here with depression data for college students. Strengths and limitati ons of the approach are discussed.