Abstract:

Purpose: This paper deals with the use of permutation tests in the analysis of data homogeneity. The proposed solution is based on a non-parametric method, which consists in a using of combined permutation test. The strategy corresponds to Pesarin's non-parametric combination method (NPC). This non-classical method of statistical inference allows for the formulation of complex alternative hypotheses. Design/methodology/approach: The study considered the analysis of data in contingency table. The paper presents theoretical considerations and refers to the Monte Carlo simulation. The idea of the proposed method is illustrated with an example. Findings: The article presents a complex permutation procedure for assessing the overall ASL value. The applied non-parametric statistical inference procedure uses a combining function. A simulation study was conducted to determine the size and power of the test. A Monte Carlo simulation was used to compare the empirical power of tests with different forms of combining functions. The most powerful test was the permutation test based on a two-stage ASL determination method using the Fisher combining function. Practical implications: The proposed test can be used in the analysis of multidimensional economic phenomena. Originality/value: The paper presents a proposal for testing directional hypotheses based on data presented in contingency tables. A two-step algorithm is used to estimate the statistic distribution and evaluate the ASL value. The method is based on a test procedure using permutations of the data set. The advantage of the proposed method is its applicability even with small sample sizes.