The Method to Analyse the Association between Objects and Variables in the Form of / x 2 Contingency Tables with Continuous Variables Additional Data

If the row and column categories of a contingency table sequentially seen, as objects and variables, then they are object data with discrete variables. Often from objects obtained additional data in the form of continuous variables. Based on these discrete and continuous variables, the more accurate method is necessary to analyse the associations between these variables. Treating discrete data as continuous is wrong, so this article aims to analyse the association of data in the form of / x 2 contingency tables with additional data that is continuous. From the data in the form of / x 2 contingency table, it converted using the simplification of correspondence analysis (SoCA), so that continuous principal coordinates obtained. Furthermore, the association between continuous variables was analysed using the cosine value of the angle between the two vectors. Case studies use poverty data in Indonesia, which published by the Central Statistics Agency (BPS-Statistics Indonesia). Data in the form of contingency tables are people population lived in poverty based on province and area of residence (urban or rural). Additional variables are poverty depth index, severity index, Gini ratio, food poverty line and non-food poverty line. The results of the analysis obtained information that in urban areas tend to have high Gini ratio, food poverty lines and non-food poverty lines, for rural areas tend to have a high poverty depth and severity index.