
Contents
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Introduction Introduction
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A Closer Look at Nonparametric Methods A Closer Look at Nonparametric Methods
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Classical Nonparametric Methods Classical Nonparametric Methods
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Nonparametric Methods Based on a Single Sample: The Binomial Test Nonparametric Methods Based on a Single Sample: The Binomial Test
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Nonparametric Methods for Comparing Two Independent Samples: The Wilcoxon Mann Whitney Test Nonparametric Methods for Comparing Two Independent Samples: The Wilcoxon Mann Whitney Test
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Nonparametric Methods for Two Dependent Samples: The Wilcoxon Signed Rank Test, Sign Test, and McNemar’s Test Nonparametric Methods for Two Dependent Samples: The Wilcoxon Signed Rank Test, Sign Test, and McNemar’s Test
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Nonparametric Methods for Comparing More Than Two Samples Nonparametric Methods for Comparing More Than Two Samples
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Comparing More Than Two Independent Samples: The Kruskal-Wallis Test Comparing More Than Two Independent Samples: The Kruskal-Wallis Test
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Nonparametric Methods for Repeated Measures Data: Friedman’s Test and Cochran’s Q Nonparametric Methods for Repeated Measures Data: Friedman’s Test and Cochran’s Q
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Nonparametric Correlation Coefficients: Spearman’s ρ and Kendall’s τ Nonparametric Correlation Coefficients: Spearman’s ρ and Kendall’s τ
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Nonparametric Analysis of Nominal Data: Chi-Square and Fisher’s Exact Tests Nonparametric Analysis of Nominal Data: Chi-Square and Fisher’s Exact Tests
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Modern Resampling-Based Nonparametric Methods Modern Resampling-Based Nonparametric Methods
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General Permutation Tests General Permutation Tests
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Application of Permutation Tests to One Sample Application of Permutation Tests to One Sample
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Permutation Tests Applied to Two Samples Permutation Tests Applied to Two Samples
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Nonparametric Bootstrap Methods Described Nonparametric Bootstrap Methods Described
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Application and Comparison of Nonparametric Bootstrap Confidence Interval Methods Application and Comparison of Nonparametric Bootstrap Confidence Interval Methods
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Other Applications of Bootstrap Methods Other Applications of Bootstrap Methods
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Bootstrap Methods and Permutation Tests Bootstrap Methods and Permutation Tests
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Nonparametric Curve Estimation Methods Nonparametric Curve Estimation Methods
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Nonparametric Density Estimation: The One-Variable Case Nonparametric Density Estimation: The One-Variable Case
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Simple Nonparametric Regression: Scatterplot Smoothing Simple Nonparametric Regression: Scatterplot Smoothing
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Extensions to Nonparametric Multiple Regression Extensions to Nonparametric Multiple Regression
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Statistical Software for Conducting Nonparametric Techniques Statistical Software for Conducting Nonparametric Techniques
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Concluding Remarks Concluding Remarks
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Nonparametric or Parametric Methods, or Both? Nonparametric or Parametric Methods, or Both?
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Future Directions Future Directions
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Acknowledgement Acknowledgement
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Author Note Author Note
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References References
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6 Nonparametric Statistical Techniques
Get accessTrent D. Buskirk, Department of Community Health-Biostatistics Division, Saint Louis University, MO
Lisa M. Willoughby, Department of Psychology, Saint Louis University, MO
Terry T. Tomazic, Department of Sociology and Criminal Justice, Saint Louis University, MO
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Published:01 October 2013
Cite
Abstract
The use of parametric or nonparametric approaches when analyzing data is a major decision that investigators often confront. Although this decision may impact the validity of the conclusions that investigators might draw, the decision to use nonparametric approaches is often overlooked as a viable option for a variety of reasons. The intent of this chapter is to help guide researchers on the application of both classical and modern, computationally intensive, nonparametric techniques for investigating hypotheses of interest. Demonstrations are offered using nonparametric and parametric techniques to highlight the differences in how statistical estimates and inferential results may differ between these methodologies.
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