The contribution statistical test of categories to dependencies between qualitative variables is needed, because the existence of these categories will influence decision making. Decisions taken can be misleading, if a category does not contribute. If N is the matrix from 2×J or I×2 contingency table which is constructed from two qualitative variables, then the principal coordinates can be calculated using the simplification of correspondence analysis (SoCA). Principal coordinates produced by the SoCA are the same as conventional correspondence analysis (CA), but the SoCA presents a simpler calculation, because it is calculated directly from the elements of N. Principal coordinates derived from 2×J matrix N can be used to test the contribution of categories to dependencies between qualitative variables more simply, and obtained confidence intervals for each category.

In the last five years, poverty had been a major problem in West Java Province (WJP). Hence, it becomes a strategic development issue for the next five years. To contribute in eradicating the poverty, we aimed this study at identifying poverty factors and measuring their effects. There are two issues in modelling poverty in WJP. First, there was spatial dependency in poverty among regions. Second, due to data limitation, some important factors were not included in the empirical model. Both might lead to bias in regression parameter estimates. Accordingly, we applied a fixed effect panel spatial error model on the poverty rate in WJP. We found that health and economic welfare effected poverty.

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.

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