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Hierarchical Linear Modeling in Organizational Research

Longitudinal Data Outside the Context of Growth Modeling

City College of the City University of New York The Graduate Center of the City University of New York, ISchonfeld{at}ccny.cuny.edu

David Rindskopf

The Graduate Center of the City University of New York

Organizational researchers, including those carrying out occupational stress research, often conduct longitudinal studies. Hierarchical linear modeling (HLM; also known as multilevel modeling and random regression) can efficiently organize analyses of longitudinal data by including within- and between-person levels of analysis. A great deal of longitudinal research has been conducted in the context of growth studies in which change in the dependent variable is examined in relation to the passage of time. HLM can treat longitudinal data, including data outside the context of the growth study, as nested data, reducing the problem of censoring. Within-person equation coefficients can represent the impact of Time t – 1 working conditions on Time t outcomes using all appropriate pairs of data points. Time itself need not be an independent variable of interest.

Key Words: hierarchical linear models • multilevel models • analysis of longitudinal data • methodology • occupational stress

Organizational Research Methods, Vol. 10, No. 3, 417-429 (2007)
DOI: 10.1177/1094428107300229


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