Ebook
Nonparametric Statistics for NonStatisticians: A StepbyStep ApproachISBN: 9781118211250
264 pages
September 2011

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 NonStatisticians: A StepbyStep 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 contextbased examples that are outlined in a stepbystep 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 MannWhitney U test to compare two unrelated samples

Compare more than two related samples using the Friedman test

Employ the KruskalWallis 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 NonStatisticians is an ideal book for courses on nonparametric statistics at the upperundergraduate 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.
1.1 Objectives.
1.2 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.
1.7 Summary.
1.8 Practice Questions.
1.9 Solutions to Practice Questions.
2 Testing Data for Normality.
2.1 Objectives.
2.2 Introduction.
2.3 Describing Data and the Normal Distribution.
2.4 Computing and Testing Kurtosis and Skewness for Sample Normality.
2.5 The Kolmogorov–Smirnov OneSample Test.
2.6 Summary.
2.7 Practice Questions.
2.8 Solutions to Practice Questions.
3 Comparing Two Related Samples: The Wilcoxon Signed Ranks Test.
3.1 Objectives.
3.2 Introduction.
3.3 Computing the Wilcoxon Signed Ranks Test Statistic.
3.4 Examples from the Literature.
3.5 Summary.
3.6 Practice Questions.
3.7 Solutions to Practice Questions.
4 Comparing Two Unrelated Samples: The Mann–Whitney UTest.
4.1 Objectives.
4.2 Introduction.
4.3 Computing the Mann–Whitney UTest Statistic.
4.4 Examples from the Literature.
4.5 Summary.
4.6 Practice Questions.
4.7 Solutions to Practice Questions.
5 Comparing More Than Two Related Samples: The Friedman Test.
5.1 Objectives.
5.2 Introduction.
5.3 Computing the Friedman Test Statistic.
5.4 Examples from the Literature.
5.5 Summary.
5.6 Practice Questions.
5.7 Solutions to Practice Questions.
6 Comparing More than Two Unrelated Samples: The Kruskal–Wallis HTest.
6.1 Objectives.
6.2 Introduction.
6.3 Computing the Kruskal–Wallis HTest Statistic.
6.4 Examples from the Literature.
6.5 Summary.
6.6 Practice Questions.
6.7 Solutions to Practice Questions.
7 Comparing Variables of Ordinal or Dichotomous Scales: Spearman RankOrder, PointBiserial, and Biserial Correlations.
7.1 Objectives.
7.2 Introduction.
7.3 The Correlation Coefficient.
7.4 Computing the Spearman RankOrder Correlation Coefficient.
7.5 Computing the PointBiserial and Biserial Correlation Coefficients.
7.6 Examples from the Literature.
7.7 Summary.
7.8 Practice Questions.
7.9 Solutions to Practice Questions.
8 Tests for Nominal Scale Data: ChiSquare and Fisher Exact Test.
8.1 Objectives.
8.2 Introduction.
8.3 The ChiSquare GoodnessofFit Test.
8.4 The ChiSquare Test for Independence.
8.5 The Fisher Exact Test.
8.6 Examples from the Literature.
8.7 Summary.
8.8 Practice Questions.
8.9 Solutions to Practice Questions.
9 Test For Randomness: The Runs Test.
9.1 Objectives.
9.2 Introduction.
9.3 The Runs Test for Randomness.
9.4 Examples from the Literature.
9.5 Summary.
9.6 Practice Questions.
9.7 Solutions to Practice Questions.
Appendix A: SPSS at a Glance.
A.1 Introduction.
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 ChiSquare Distribution.
Table B.3: Critical Values for the Wilcoxon Signed Ranks Test Statistics, T.
Table B.4: Critical Values for the Mann–Whitney UTest Statistic.
Table B.5: Critical Values for the Friedman Test Statistic, F_{r} .
Table B.6: The Critical Values for the Kruskal–Wallis HTest Statistic.
Table B.7: Critical Values for the Spearman RankOrder Correlation Coefficient, r_{s}.
Table B.8: Critical Values for the Pearson ProductMoment Correlation Coefficient, r.
Table B.9: Factorials.
Table B.10: Critical Values for the Runs Test for Randomness.
Bibliography.
Index.
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 nonstatisticians

Utilizes SPSS® to demonstrate how to perform the book's numerous examples in a stepbystep 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