Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4814
Title: Longitudinal data analyses using linear mixed models in SPSS : concepts, procedures and illustrations
Authors: Shek, DTL 
Ma, CMS 
Keywords: Linear mixed models
Hierarchical linear models
Longitudinal data analysis
SPSS
Project P.A.T.H.S.
Issue Date: 2011
Publisher: TheScientificWorldJOURNAL
Source: TheScientificWorldJOURNAL, 2011, v. 11, p. 42-76 How to cite?
Journal: TheScientificWorldJOURNAL 
Abstract: 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.
URI: http://hdl.handle.net/10397/4814
ISSN: 1537-744X
DOI: 10.1100/tsw.2011.2
Rights: ©2011 with author.
This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
TheScientificWorldJOURNAL is available online at: http://www.tswj.com and the open URL of the article: http://www.tswj.com/2011/246739/abs/
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