Hierarchical Linear Modeling in Organizational Research
介绍了分层线性模型（HLM）在组织研究纵向数据分析中的应用，包括处理嵌套数据和删失问题，对从事职业压力等纵向研究的学者有参考价值。
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.