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Social Indicators Research

, Volume 120, Issue 2, pp 455–481 | Cite as

Development of a Multidimensional Living Conditions Index (LCI)

  • Vijaya Krishnan
Article

Abstract

The scope of this study ranges from the identification of key drivers of living conditions from a wide spectrum of context-based indicators to the development of a concrete composite measure of living conditions within the framework of a multivariate analysis. The Living Conditions Index (LCI) is a standardized aggregate score that summarizes five components and 18 indicators in a single number. Three different approaches, principal component analysis (PCA), range equalization (RE), and division by mean (DM) are used to assess the impact of different methods of weighting and standardization procedures on the composite. Between the RE and DM methods, the RE method is preferred because it accounts for wider variations and strong correlations to the PCA composite. In general, the PCA method appears promising, particularly for cross-community comparisons as it is based on a weighting scheme. Extreme variability between quintiles that comprise the LCI indicates that the score represents a summary of economic, housing, and cultural diversities. The paper advocates for a future plan of research in the light of identified gaps in data, and more emphasis on disparities in economic conditions. A major implication of the study is that the composite provides a new tool in child development research for characterizing community-based living conditions and detecting disparities in the distribution of child developmental outcomes.

Keywords

Living Conditions Index Macro-analysis Principal components analysis Range equalization Division by mean Canada 

Notes

Acknowledgments

The author thanks the Faculty of Extension, University of Alberta, Edmonton, for funding this project. The contents, however, are solely the responsibility of the author and do not represent the official views of the funding agency. The author would like to thank Dr. Susan Lynch, Director, Early Child Development Mapping Project (ECMap) Alberta and the ECMap team for their support in carrying out this project. Special thanks are also due to Shea Betts and Xian Wang for their help in extracting the Census data in the very initial stage of the project and Mr. Darcy Reynard for supplying the GIS data on the density variables.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Early Child Development Mapping Project (ECMap), Community-University Partnership (CUP), Faculty of ExtensionUniversity of AlbertaEdmontonCanada

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