Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach
As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences.
Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields.
Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to:
Test data for normality and randomness
Use the Wilcoxon signed rank test to compare two related samples
Apply the Mann-Whitney U test to compare two unrelated samples
Compare more than two related samples using the Friedman test
Employ the Kruskal-Wallis H test to compare more than two unrelated samples
Compare variables of ordinal or dichotomous scales
Test for nominal scale data
A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice.
Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
1 Nonparametric Statistics: An Introduction.
1.3 The Nonparametric Statistical Procedures Presented in this Book.
1.4 Ranking Data.
1.5 Ranking Data with Tied Values.
1.6 Counts of Observations.
1.8 Practice Questions.
1.9 Solutions to Practice Questions.
2 Testing Data for Normality.
2.3 Describing Data and the Normal Distribution.
2.4 Computing and Testing Kurtosis and Skewness for Sample Normality.
2.5 The Kolmogorov–Smirnov One-Sample Test.
2.7 Practice Questions.
2.8 Solutions to Practice Questions.
3 Comparing Two Related Samples: The Wilcoxon Signed Ranks Test.
3.3 Computing the Wilcoxon Signed Ranks Test Statistic.
3.4 Examples from the Literature.
3.6 Practice Questions.
3.7 Solutions to Practice Questions.
4 Comparing Two Unrelated Samples: The Mann–Whitney U-Test.
4.3 Computing the Mann–Whitney U-Test Statistic.
4.4 Examples from the Literature.
4.6 Practice Questions.
4.7 Solutions to Practice Questions.
5 Comparing More Than Two Related Samples: The Friedman Test.
5.3 Computing the Friedman Test Statistic.
5.4 Examples from the Literature.
5.6 Practice Questions.
5.7 Solutions to Practice Questions.
6 Comparing More than Two Unrelated Samples: The Kruskal–Wallis H-Test.
6.3 Computing the Kruskal–Wallis H-Test Statistic.
6.4 Examples from the Literature.
6.6 Practice Questions.
6.7 Solutions to Practice Questions.
7 Comparing Variables of Ordinal or Dichotomous Scales: Spearman Rank-Order, Point-Biserial, and Biserial Correlations.
7.3 The Correlation Coefficient.
7.4 Computing the Spearman Rank-Order Correlation Coefficient.
7.5 Computing the Point-Biserial and Biserial Correlation Coefficients.
7.6 Examples from the Literature.
7.8 Practice Questions.
7.9 Solutions to Practice Questions.
8 Tests for Nominal Scale Data: Chi-Square and Fisher Exact Test.
8.3 The Chi-Square Goodness-of-Fit Test.
8.4 The Chi-Square Test for Independence.
8.5 The Fisher Exact Test.
8.6 Examples from the Literature.
8.8 Practice Questions.
8.9 Solutions to Practice Questions.
9 Test For Randomness: The Runs Test.
9.3 The Runs Test for Randomness.
9.4 Examples from the Literature.
9.6 Practice Questions.
9.7 Solutions to Practice Questions.
Appendix A: SPSS at a Glance.
A.2 Opening SPSS.
A.3 Inputting Data.
A.4 Analyzing Data.
A.5 The SPSS Output.
Appendix B: Tables of Critical Values.
Table B.1: The Normal Distribution.
Table B.2: The Chi-Square Distribution.
Table B.3: Critical Values for the Wilcoxon Signed Ranks Test Statistics, T.
Table B.4: Critical Values for the Mann–Whitney U-Test Statistic.
Table B.5: Critical Values for the Friedman Test Statistic, Fr .
Table B.6: The Critical Values for the Kruskal–Wallis H-Test Statistic.
Table B.7: Critical Values for the Spearman Rank-Order Correlation Coefficient, rs.
Table B.8: Critical Values for the Pearson Product-Moment Correlation Coefficient, r.
Table B.9: Factorials.
Table B.10: Critical Values for the Runs Test for Randomness.
Dale I. Foreman is associate professor in the School of Education and Human Development in the College of Arts and Sciences at Shenandoah University, where his teaching is focused on research, measurement, and statistics.
A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS, and supplemental tables of critical values are provided.
The book's FTP site houses supplemental data sets and solutions for further practice
Extensively classroom tested and proven to be effective for non-statisticians
Utilizes SPSS® to demonstrate how to perform the book's numerous examples in a step-by-step fashion
- Recognizes the continuous growth of nonparametric statistical applications and aids future and existing scientists and practitioners in interpreting and applying nonparametric statistic
Conveys nonparametric statistical procedures in a clear, straightforward manner and describes actual examples of nonparametric applications from diverse fields