User Guide

Chapter
34
Nonparametr
ic Tests
The Nonparametric Tests procedure provides several tests that do not require
assumptions about the shape of the underlying distribution:
Chi-Square Test. Tabulates a variable into categories and computes a chi-square
statistic based on the differences between observed and expected frequencies.
Binomial Test. Compares the observed frequency in each category of a dichotomous
variable with expected frequencies from the binomial distribution.
Runs Test. Tests whether the order of occurrence of two values of a variable is random.
One-Sample Kolmogorov-Smirnov Test. Compares the observed cumulative distribution
function for a variable with a specified theoretical distribution, which may be normal,
uniform, or Poisson.
Two-Independent-Samples Tests. Compares two groups of cases on one variable. The
Mann-Whitney U test, the two-sample Kolmogorov-Smirnov test, Moses test of
extreme reactions, and the Wald-Wolfowitz runs test are available.
Tests for Several Indepen dent Samples. Compares two or more groups of cases on one
variable. The Kruskal-Wallis test, the Median test, and the Jonckheere-Terpstra
test are available.
Two-Related-Samples Tests. Compares the distributions of two variables. The
Wilcoxon signed-rank test, the sign test, and the McNemar test are available.
Tests for Several Related Samples. Compares the distributions of two or more
variables. Friedman’s test, Kendall’s W,andCochran’sQ are available.
Quartiles and the mean, standard deviation, minimum, maximum, and number of
nonmissing cases are available for all of the above tests.
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