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Nonparametric multiple comparisons.

Kimihiro Noguchi1, Riley S Abel2, Fernando Marmolejo-Ramos3

  • 1Department of Mathematics, Western Washington University, Bellingham, WA, 98225, USA. Kimihiro.Noguchi@wwu.edu.

Behavior Research Methods
|May 8, 2019
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Summary
This summary is machine-generated.

This study enhances a nonparametric multiple contrast test procedure (MCTP) for psychological research. The improved method accommodates various effect sizes, offering clearer interpretations and maintaining statistical control.

Keywords:
Effect sizeMultiple comparisonsNonparametric statistics

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Area of Science:

  • Psychology
  • Statistics
  • Biostatistics

Background:

  • Nonparametric multiple comparisons are crucial for statistical inference in psychological studies.
  • Existing rank-based multiple contrast test procedures (MCTP) have limitations in handling diverse effect sizes.

Purpose of the Study:

  • To review and improve a rank-based nonparametric multiple contrast test procedure (MCTP).
  • To enhance the MCTP by incorporating various effect sizes for broader applicability and easier interpretation in psychological research.

Main Methods:

  • Reviewed rank-based nonparametric multiple contrast test procedure (MCTP).
  • Proposed an improved procedure by transforming relative effects using the multivariate delta method and a log odds-type transformation.
  • Provided theoretical justifications for asymptotic strong control of the family-wise error rate (FWER).

Main Results:

  • The improved MCTP accommodates various effect sizes, yielding interpretations similar to Cohen's d.
  • The method demonstrates asymptotic strong control of the family-wise error rate (FWER).
  • The enhanced procedure was validated through a simulation study and a neuropsychological example.

Conclusions:

  • The enhanced rank-based MCTP offers a flexible and interpretable tool for nonparametric multiple comparisons in psychological research.
  • The proposed method ensures robust statistical control, making it suitable for complex data analysis.
  • The implementation in the 'nparcomp' R package ('mctp' function) facilitates its practical application.