Catanova method for determining of zero partial association structures in multidimensional contingency tables

Zero partial association models correspond to the conditional independence relation of a variable pair,given the rest of variables in the model. For determining the model that best fits the data from the contingency tables measured at a nominal level,this study has used the Catanova test (with response as one of its variables,and factor as another one of its variables) instead of the chi-square test,which is used as the test statistic in Wermuth s backward elimination method with zero partial associations. Numerical analyses were performed on two samples,and the associations between these statistics were evaluated. Interpretations were provided for the obtained results.