OUTLINE FOR CHAPTER
18
Statistical Tests on Ranks (Nonparametric
Tests)
- Introduction
- Qualitative variables and nominal data
- The simplest kind of quantitative data
- The five most popular nonparametric tests
- Obtaining Ranked Data
- Having people(i.e., "judges") rank things
- Arranging people, animals, or things in rank order
- Converting raw scores into ranks
- Reasons for Converting Scores on a Continuous Variable
into Ranks
- Assumptions underlying test procedures
- Sample size
- The data's level of measurement
- Five Popular Test Procedures Used With Ranks
- The median test
- The appropriate "setting"
- The null hypothesis in the median test
- Moving from the raw data to a calculated value
- The meaning of a rejected null hypothesis
- The Mann-Whitney U test
- The appropriate "setting"
- Moving from the raw data to a calculated value
- The meaning of a rejected null hypothesis
- The "decision rule" for comparing the calculated and critical
values
- The Kruskal-Wallis H test
- The appropriate "setting" and similarity to a one-way ANOVA
- Moving from the raw data to a calculated value
- The meaning of a rejected null hypothesis
- The "decision rule" for comparing the calculated and critical
values
- Post hoc investigations
- The Wilcoxon matched-pairs signed-ranks test
- The appropriate "setting"
- Moving from the raw data to a calculated value
- The meaning of a rejected null hypothesis
- Friedman's two-way analysis of variance of ranks
- The appropriate "setting" and similarity to a 1-way repeated-measures
ANOVA
- Moving from the raw data to a calculated value
- The meaning of a rejected null hypothesis
- Related Issues
- Large-sample versions of the tests on ranks
- The notion of a "large-sample approximation"
- The use of z and chi square in tests on ranks
- The needed sample sizes for large-sample approximations
- Ties
- How ties occur
- Three ways to deal with ties
- The relative power of nonparametric tests
- The meaning of "relative power"
- Conditions in which parametric tests have more power than
nonparametric tests and vice versa
- A Few Final Comments
- The quality of the research questions
- The assumptions of random samples and independence
- The term "distribution-free"
- Overlapping distributions
- Other nonparametric procedures
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