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Testing for Randomized Block Single-Case Designs by Combined Permutation Tests with Multivariate Mixed Data

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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications (SimStat 2019)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

Permutation tests are flexible nonparametric tests that can be applied to a wide variety of problems. They can also be adopted for the analysis of single-case designs, i.e., experimental designs characterized by the observation of a single entity over time. In this paper we propose an extension of permutation tests to analyze randomized block single-case designs where multivariate mixed data are observed. It takes advantage of the nonparametric combination (NPC) procedure, using an adequately defined combining function and test statistics, and makes it possible to tackle both two-sided and directional alternative hypotheses.

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References

  1. Barbiero, A., Ferrari, P.A.: Genord: Simulation of discrete random variables with given correlation matrix and marginal distributions. R package version 1.4.0. (2015)

    Google Scholar 

  2. Birnbaum, A.: Combining independent tests of significance. J. Am. Stat. Assoc. 49, 559–574 (1954)

    MathSciNet  MATH  Google Scholar 

  3. Birnbaum, A., et al.: Characterizations of complete classes of tests of some multiparametric hypotheses, with applications to likelihood ratio tests. Ann. Math. Stat. 26, 21–36 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ferrari, P.A., Barbiero, A.: Simulating ordinal data. Multivar. Behav. Res. 47, 566–589 (2012)

    Article  Google Scholar 

  5. Good, P.: Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer Science & Business Media, New York (2000)

    Book  MATH  Google Scholar 

  6. Haardörfer, R., Gagné, P.: The use of randomization tests in single-subject research. Focus Autism Develop. Disab. 25(1), 47–54 (2010)

    Article  Google Scholar 

  7. Heyvaert, M., Onghena, P.: Randomization tests for single-case experiments: state of the art, state of the science, and state of the application. J. Contextual Behav. Sci. 3(1), 51–64 (2014)

    Article  Google Scholar 

  8. Kratochwill, T., Levin, J.: Enhancing the scientific credibility of single-case intervention research: randomization to the rescue. Psychol. Methods 15(2), 124–144 (2010)

    Article  Google Scholar 

  9. Kratochwill, T.R., Hitchcock, J.H., Horner, R.H., Levin, J.R., Odom, S.L., Rindskopf, D.M., Shadish,W.R.: Single-case intervention research design standards. Remedial Spec. Educ. 34(1), 26–38 (2013)

    Article  Google Scholar 

  10. Onghena, P.: Single-case designs. In: Encyclopedia of Statistics in Behavioral Science. American Cancer Society, Atlanta (2005)

    Google Scholar 

  11. Onghena, P., Edgington, E.S.: Customization of pain treatments: Singlecase design and analysis. Clin. J. Pain 21(1), 56–68 (2005)

    Article  Google Scholar 

  12. Park, H.-S., Marascuilo, L., Gaylord-Ross, R.: Visual inspection and statistical analysis in single-case designs. J. Exp. Educ. 58(4), 311–320 (1990)

    Article  Google Scholar 

  13. Pesarin, F., Salmaso, L.: Permutation Tests for Complex Data: Theory, Applications and Software. John Wiley & Sons, Chichester (2010)

    Google Scholar 

  14. Plavnick, J.B., Ferreri, S.J.: Single-case experimental designs in educational research: a methodology for causal analyses in teaching and learning. Educ. Psychol. Rev. 25(4), 549–569 (2013)

    Article  Google Scholar 

  15. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna (2018)

    Google Scholar 

  16. Sierra, V., Solanas, A., Vicenç, Q.: Randomization tests for systematic single-case designs are not always appropriate. J. Exp. Educ. 73(2), 140–160 (2005)

    Article  Google Scholar 

  17. Smith, J.D.: Single-case experimental designs: a systematic review of published research and current standards. Psychol. Methods 17(4), 510 (2012)

    Article  Google Scholar 

  18. Solmi, F., Onghena, P.: Combining p-values in replicated single-case experiments with multivariate outcome. Neuropsychol. Rehab. 24(3–4), 607–633 (2014)

    Article  Google Scholar 

  19. Statisticat, LLC: Laplacesdemon tutorial. R package version 16.1.1. Bayesian-Inference.com (2018)

    Google Scholar 

  20. Tanious, R., Onghena, P.: Randomized single-case experimental designs in healthcare research: What, why, and how? Healthcare 7, 143 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Civil, Environmental and Architectural Engineering, University of Padova, Padua, Italy

    Rosa Arboretti Giancristofaro

  2. Department of Management and Engineering, University of Padova, Vicenza, Italy

    Riccardo Ceccato, Luca Pegoraro & Luigi Salmaso

Authors
  1. Rosa Arboretti Giancristofaro
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  2. Riccardo Ceccato
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  3. Luca Pegoraro
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  4. Luigi Salmaso
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Corresponding author

Correspondence to Luigi Salmaso .

Editor information

Editors and Affiliations

  1. Department of Statistics, University of Klagenfurt, Klagenfurt, Austria

    Jürgen Pilz

  2. Department of Stochastic Simulation, St. Petersburg State University, St. Petersburg, Russia

    Viatcheslav B. Melas

  3. Department of Artificial Intelligence and Human Interfaces, Paris Lodron University Salzburg, Salzburg, Austria

    Arne Bathke

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Giancristofaro, R.A., Ceccato, R., Pegoraro, L., Salmaso, L. (2023). Testing for Randomized Block Single-Case Designs by Combined Permutation Tests with Multivariate Mixed Data. In: Pilz, J., Melas, V.B., Bathke, A. (eds) Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications. SimStat 2019. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-40055-1_7

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