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Volume 11, Pages 42-76
Research Article

Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

1Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
2Public Policy Research Institute, The Hong Kong Polytechnic University, Hong Kong, China
3Kiang Wu Nursing College of Macau, Macau, China
4Division of Adolescent Medicine, Department of Pediatrics, University of Kentucky College of Medicine, Lexington, KY, USA
5Department of Sociology, East China Normal University, Shanghai, China

Received 27 September 2010; Revised 18 October 2010; Accepted 18 October 2010

Academic Editor: Joav Merrick

Copyright © 2011 Daniel T. L. Shek and Cecilia M. S. Ma.


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.