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 st...

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Bibliographic Details
Published in:Psychological bulletin 1987-01, Vol.101 (1), p.147-158
Main Authors: Bryk, Anthony S, Raudenbush, Stephen W
Format: Article
Language:English
Subjects:
Biological and medical sciences
Cognitive Development
Development
Fundamental and applied biological sciences. Psychology
Human
Mathematical Modeling
Psychology
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
Statistical Analysis
Statistical Estimation
Statistics
Statistics. Mathematics
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Summary: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.
ISSN:0033-2909
1939-1455
DOI:10.1037/0033-2909.101.1.147