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Power Analysis for t-Test with Non-normal Data and Unequal Variances

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Quantitative Psychology (IMPS 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 196))

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Abstract

A Monte Carlo-based power analysis is proposed for t-test to deal with non-normality and heterogeneity in real data. The step-by-step procedure of the proposed method is introduced in the paper. For comparing the performance of the Monte Carlo-based power analysis to that of conventional pooled-variance t-test, a simulation study was conducted. The results indicate the Monte Carlo-based power analysis provided well-controlled empirical Type I error rate, whereas the conventional pooled-variance t-test failed to yield nominal-level Type I error rate. Both an R package and its corresponding online interface are provided to implement the proposed method.

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Notre Dame, 123A Hagger Hall, Notre Dame, IN, 46556, USA

    Han Du & Ke-Hai Yuan

  2. University of Notre Dame, 118 Haggar Hall, Notre Dame, IN, 46556, USA

    Zhiyong Zhang

Authors
  1. Han Du
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  2. Zhiyong Zhang
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  3. Ke-Hai Yuan
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Corresponding author

Correspondence to Han Du .

Editor information

Editors and Affiliations

  1. Research Institute for Child Development and Education, University of Amsterdam, Amsterdam, The Netherlands

    L. Andries van der Ark

  2. Department of Statistics, USBE, Umeå University, Umeå, Sweden

    Marie Wiberg

  3. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA

    Steven A. Culpepper  & Jeffrey A. Douglas  & 

  4. Department of Psychology, The Educational University of Hong Kong, Hong Kong, China

    Wen-Chung Wang

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Du, H., Zhang, Z., Yuan, KH. (2017). Power Analysis for t-Test with Non-normal Data and Unequal Variances. In: van der Ark, L.A., Wiberg, M., Culpepper, S.A., Douglas, J.A., Wang, WC. (eds) Quantitative Psychology. IMPS 2016. Springer Proceedings in Mathematics & Statistics, vol 196. Springer, Cham. https://doi.org/10.1007/978-3-319-56294-0_32

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