Longitudinal data analyses using linear mixed models in SPSS: Concepts, procedures and illustrations

Tan Lei Shek, Man Sze Ma

Research output: Journal article publicationReview articleAcademic researchpeer-review

286 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)42-76
Number of pages35
Publication statusPublished - 5 Jan 2011


  • Hierarchical linear models
  • Linear mixed models
  • Longitudinal data analysis
  • Project P.A.T.H.S.
  • SPSS

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)

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