A composite index of quality of life for the Gauteng city-region: a principal component analysis approach
PUBLICATION YEAR: 2013
TITLE AUTHOR(S): T.Greyling
KEYWORDS: GAUTENG PROVINCE, HAPPINESS, QUALITY OF LIFE
DEPARTMENT: Developmental, Capable and Ethical State (DCES)
Print: HSRC Library: shelf number 10003
HANDLE: 20.500.11910/11312
URI: http://hdl.handle.net/20.500.11910/11312
TITLE AUTHOR(S): T.Greyling
KEYWORDS: GAUTENG PROVINCE, HAPPINESS, QUALITY OF LIFE
DEPARTMENT: Developmental, Capable and Ethical State (DCES)
Print: HSRC Library: shelf number 10003
HANDLE: 20.500.11910/11312
URI: http://hdl.handle.net/20.500.11910/11312
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Abstract
Central to improving people???s quality of life is the ability to measure this concept. This is, however, made difficult by the concept???s multi-dimensional nature. The primary research objective of this paper was to construct a composite index to measure and compare the quality of life of different demographic and socio-economic groups across the Gauteng City-Region (GCR) in South Africa. The second research objective was to determine the dimensions that explain the most variance in the data set of each of the different demographic and socio-economic groups. A method introduced by Nicolette et al. (2000) that employs Principal Component Analysis (PCA) to weight the index was used, and this paper represents the first attempt in South Africa to apply this method. PCA was also used to analyse variance between the demographic and socio-economic groups. The paper found the quality of life scores of urban, high income, male, Asian and White, and younger respondents to be higher than those of the other groups. Furthermore, the quality of life scores of Africans, low income, female, older, and non-urban dwellers were relatively low. The dimension 'housing and infrastructure' explained the most variance for the groups with lower quality of life scores, while the dimension 'social relationships' explained the most variance in the data set for the groups with higher quality of life scores. Furthermore, the dimension 'socio-economic status' explained a high proportion of variance in all the groups. These dimensions give an indication of the areas to be addressed to improve quality of life.-
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