What do you do when you realize that the data set from the study that you have just completed violates the sample size or other requirements needed to apply parametric statistics? Nonparametric Statistics for Health Care Research was developed for such scenarios-research undertaken with limited funds, often using a small sample size, with the primary objective of improving client care and obtaining better client outcomes. Covering the most commonly used nonparametric statistical techniques available in statistical packages and on open-resource statistical websites, this well-organized and accessible Second Edition helps readers, including those beyond the health sciences field, to understand when to use a particular nonparametric statistic, how to generate and interpret the resulting computer printouts, and how to present the results in table and text format.
Chapter 1: Overview of Nonparametric Statistics
Common Characteristics of Parametric Tests
Development of Nonparametric Tests
Characteristics of Nonparametric Statistics
Use of Nonparametric Tests in Health Care Research
Some Common Misperceptions About Nonparametric Tests
Types of Nonparametric Tests
Chapter 2: The Process of Statistical Hypothesis Testing
Choosing Between a Parametric and a Nonparametric Test
Chapter 3: Evaluating the Characteristics of Data
Characteristics of Levels of Measurement
Assessing the Normality of a Distribution
Dealing With Outliers
Data Transformation Considerations
Examining Homogeneity of Variance
Evaluating Sample Sizes
Reporting Testing Assumptions and Violations in a Research Report
Chapter 4: "Goodness-of-Fit" Tests
The Binomial Test
The Chi-Square Goodness-of-Fit Test
The Kolmogorov-Smirnov One-Sample Test
The Kolmogorov-Smirnov Two-Sample Test
Chapter 5: Tests for Two Related Samples: Pretest-Posttest Measures for a Single Sample
The McNemar Test
The Sign Test
The Wilcoxon Signed Ranks Test
Chapter 6: Repeated Measures for More Than Two Time Periods or Matched Conditions
Cochran's Q Test
The Friedman Test
Chapter 7: Tests for Two Independent Samples
Fisher's Exact test
The Chi-Square Test for Two Independent Samples
The Wilcoxon-Mann-Whitney U test
Chapter 8: Assessing Differences Among Several Independent Groups
The Chi-Square Test for k Independent Samples
The Mantel-Haenszel Chi-Square Test for Trends
The Median Test
The Kruskal-Wallis One-Way ANOVA by Ranks
The Two-Way ANOVA by Ranks
Chapter 9: Tests of Association Between Variables
The Phi Coefficient
Cramér's V Coefficient
The Kappa Coefficient
The Point Biserial Correlation
Chapter 10: Logistic Regression
The Logic of Logistic Regression
The Odds Ratio and Relative Risk
Simple Bivariate Logistic Regression
Multiple Logistic Regression
