Abstract
The purpose of this paper was to use the metric Multidimensional scaling (MDS) to explore the ten dire household debt determinants in the context of South Africa. Macroeconomic data used was collected from the South African reserve bank and Statistics South African websites for the first quarters of 1990 to 2013. SPSS 22 was used to execute the analysis. A Standardized Residuals Sum of Squares (STRESS 1) measure calculated as 0.00077confirmed the best fit of the MDS model and the Tucker’s Coefficient of Congruence implied that 99.9% of variance in the model is accounted for by the two dimensions. This was also a confirmation that the ten selected determinants can better be represented in a two dimensional perpetual map. The findings revealed two profiles of household debts. Gross domestic product and house prices are associated with high levels of household debts. The remainder of the determinants is found to have low effects. MDS demonstrated its effectiveness in classifying household debt determinants according to their contribution. Also revealed is that an MDS is a useful tool to use in quantifying the ubiquitous, but slimy, notion of similarity.
Highlights
Multidimensional scaling, just like factor and cluster analyses is an exploratory data analysis tool used to condense a large amount of data and presenting it in a simple spatial map
The data used was collected from the South African Reserve Bank and Statistics South Africa spanning the period first quarter of 1990 to first quarter of 2013
A Standardized Residuals Sum of Squares (STRESS) 1 measure confirmed that the model has a best fit indicating that representing the ten determinants of household debt in two dimensions is almost excellent
Summary
Multidimensional scaling, just like factor and cluster analyses is an exploratory data analysis tool used to condense a large amount of data and presenting it in a simple spatial map This map communicates important relationships in the most economical manner (Mugavin, 2008).The author further emphasized multidimensional scaling (MDS) as having several advantages such as modelling nonlinear relationships among variables and handling nominal or ordinal data. The underlying dimensions extracted from the spatial structure of the data are thought by Ding (2006) to reflect hidden structures, or important relationships within it. This procedure is achieved by rescaling a set of dissimilarities measurements into distances assigned to specific locations in a spatial configuration. A visual representation of dissimilarities (or similarities) among objects, cases, or more broadly observations, will be provided (Jaworska and Chupetlovska-Anastasova, 2009)
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