Shek Daniel T L, Ma Cecilia M S
Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong, PRC.
ScientificWorldJournal. 2011 Jan 5;11:42-76. doi: 10.1100/tsw.2011.2.
Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.
虽然有不同的方法可用于纵向数据分析，但基于广义线性模型（GLM）的分析因违反观测独立性假设而受到批评。另外，线性混合模型（LMM）通常用于理解人类行为随时间的变化。本文概述了围绕LMM（或分层线性模型）的基本概念。虽然SPSS是研究人员常用的统计分析软件包，但关于SPSS中LMM程序的文档并不全面，对用户也不友好。针对这一局限性，本文描述了在SPSS中基于LMM进行分析的相关程序。为了演示LMM分析在SPSS中的应用，本文展示了基于香港“成长的天空”计划（Positive Adolescent Training through Holistic Social Programmes，简称P.A.T.H.S.）收集的六波数据的研究结果。
