Please use this identifier to cite or link to this item:
|Title:||Longitudinal data analyses using linear mixed models in SPSS : concepts, procedures and illustrations|
|Authors:||Shek, Daniel T. L.|
Ma, Cecilia M. S.
|Subjects:||Linear mixed models|
Hierarchical linear models
Longitudinal data analysis
|Source:||TheScientificWorldJOURNAL, 2011, v. 11, p. 42-76.|
|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.|
|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/
|Appears in Collections:||APSS Journal/Magazine Articles|
All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.