What does this research mean for the field?

Nonparametric statistical methods provide a more appropriate and robust analytical approach in tax research when classical statistical assumptions cannot be verified or when analyzing ordinal data. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to highlight the advantages of nonparametric methods in tax research when classical statistics assumptions are unmet.

June 15, 2026

The Use of Nonparametric Methods in Tax Research.

Key Points

The aim is to highlight the advantages of nonparametric methods in tax research when classical statistics assumptions are unmet.
Discusses nonparametric methods applicable to tax research.
Explains three specific nonparametric statistical procedures in detail.
Illustrates methods using data from Porcano (1987) on government tax incentives.
Validates and extends conclusions drawn by Porcano regarding tax incentives.
Shows utility of nonparametric methods over classical tests in certain tax research scenarios.

Abstract

Abstract Nonparametric statistical methods should be used in tax research whenever the assumptions of classical statistics cannot be verified or the data are based on a ranking (an ordinal scale), as with Likert scale measurements. This paper discusses the advantages of using the nonparametric counterparts in place of many popular classical tests. The appropriate uses of three nonparametric procedures-the rank correlation coefficient, Kendall's coefficient of concordance, and the Wilcoxon signed rank test-are explained in detail and are illustrated using data reported by Porcano (1987) concerning the effectiveness of government tax incentives to encourage investment in fixed assets. The results verify and extend Porcano's conclusions.

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