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 unequal numbers of repeated observations for each subject, variable timing of observations, and missing data, features which are often characteristic of data from field studies. The application of HLM for the analysis of repeated psychological measures is discussed and illustrated here with depression data for college students. Strengths and limitations of the approach are discussed.