Hierarchical linear modeling in organizational research - Longitudinal data outside the context of growth modeling

被引:20
|
作者
Schonfeld, Irvin Sam
Rindskopf, David
机构
[1] CUNY City Coll, Sch Educ, New York, NY 10031 USA
[2] CUNY City Coll, Grad Ctr, New York, NY 10031 USA
关键词
hierarchical linear models; multilevel models; analysis of longitudinal data; methodology; occupational stress;
D O I
10.1177/1094428107300229
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
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.
引用
收藏
页码:417 / 429
页数:13
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