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

Daniel T L Shek1, Cecilia M S Ma

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Summary
This summary is machine-generated.

Linear mixed models (LMM) offer a robust approach for analyzing longitudinal data, addressing the independence assumption violations common in generalized linear models (GLM). This study details LMM application in SPSS using adolescent data.

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Area of Science:

  • Statistics
  • Psychology
  • Social Sciences

Background:

  • Longitudinal data analysis presents challenges, particularly regarding the independence of observations assumption in generalized linear models (GLM).
  • Linear mixed models (LMM), also known as hierarchical linear models, are increasingly utilized for analyzing changes in human behavior over time.

Purpose of the Study:

  • To outline the fundamental concepts of linear mixed models (LMM).
  • To describe the procedures for conducting LMM analyses within the SPSS statistical software.
  • To demonstrate the practical application of LMM in SPSS using real-world longitudinal data.

Main Methods:

  • Explanation of core linear mixed model (LMM) principles.
  • Step-by-step guide to implementing LMM analyses in SPSS, addressing documentation limitations.
  • Application of LMM to a six-wave longitudinal dataset from Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong.

Main Results:

  • The study successfully demonstrates the application of LMM in SPSS for analyzing longitudinal data.
  • Findings from the Project P.A.T.H.S. dataset illustrate the utility of LMM in understanding adolescent development over time.

Conclusions:

  • Linear mixed models (LMM) provide a suitable framework for analyzing longitudinal data, overcoming limitations of traditional methods.
  • The described SPSS procedures facilitate the use of LMM by researchers, despite existing documentation gaps.
  • The application highlights the value of LMM in psychological and social science research involving repeated measures.