Hybrid correspondence analysis and correlation to analyze the market position from data with two qualitative and p-2 quantitative variables

Abstract

Analysis of the market position will be easier, efficient, and informative when based on a perceptual map, that can display objects and characteristics. With perceptual map we can identify the information about the objects similarity, the characteristics relationships, and relationship between the object and the characteristics. Characteristics of the object are usually more than two variables, those variables that sometimes are qualitative and some are quantitative. Based on that this paper introduce a method of analysis called hybrid correspondence analysis and correlation (HCAC), this method is the incorporation of correspondence analysis (CA) with biplot of correlation, where the mapped of two qualitative variables is using CA, and for quantitative variables is using correlations. HCAC produces a perceptual map that displays the object, the qualitative characteristics, and quantitative characteristics in a single map, without changing the quantitative data into qualitative data, so that the information obtained is more complete. A reference for mapping quality is counted from the first and second cumulative percent of eigenvalues where resulting from CA.