15
Nonparametric
Statistical Tests:
Chi-Square
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LEARNING OBJECTIVES
Differentiate parametric tests from nonparametric tests.
Calculate and interpret a chi-square goodness-of-fit test.
Calculate and interpret a chi-square test of independence.
Know when to use a Spearman rank-order correlation coefficient and a Mann–Whitney U test.
CHAPTER OVERVIEW
So far, all the tests covered in this text have had two things in common. One is that no matter the test—whether for a z, t, F, or r—the outcome variable has always been measured at the interval or ratio level. The other is that for each test, the outcome variable was supposed to be normally distributed in the population.
But, sometimes a researcher takes on a study where the outcome variable is ordinal or nominal. Sometimes, the outcome variable isn’t normally distributed. Tests for these situations, what are called nonparametric tests, are the subject of this chapter.
15.1 Introduction to Nonparametric Tests
15.2 The Chi-Square Goodness-of-Fit Test
15.3 Calculating the Chi-Square Test of Independence
15.4 Interpreting the Chi-Square Test of Independence
15.5 Other Nonparametric Tests