User Guide

Chapter
21
One-Way ANOV
A
The One-Way ANOVA procedure produces a one-way analysis of variance for a
quantitati
ve dependent variable by a single factor (independent) variable. Analysis of
variance is used to test the hypothesis that several means are equal. This technique is
an extension of the two-sample t test.
In additio
n to determining that differences exist among the means, you may want
to know which means differ. There are two types of tests for comparing means: a
priori contrasts and post hoc tests. Contrasts are tests set up before running the
experimen
t, and post hoc tests are run after the experiment has been conducted. You
can also test for trends across categories.
Example. Doughnuts absorb fat in various amounts when they are cooked. An
experime
nt is set up involving three types of fat: peanut oil, corn oil, and lard.
Peanut oil and corn oil are unsaturated fats, and lard is a saturated fat. Along with
determining whether the amount of fat absorbed depends on the type of fat used, you
could se
t up an a priori contrast to determine whether the amount of fat absorption
differs for saturated and unsaturated fats.
Statistics. For each group: number of cases, mean, standard deviation, standard
error of
themean,minimum,maximum,and95%-confidence interval for the
mean. Levene’s test for homogeneity of variance, analysis-of-variance table and
robust tests of the equality of means for each dependent variable, user-specified a
priori
contrasts, and post hoc range tests and multiple comparisons: Bonferroni,
Sidak, Tukey’s honestly significant difference, Hochberg’s GT2, Gabriel, Dunnett,
Ryan-Einot-Gabriel-Welsch F test (R-E-G-W F), Ryan-Einot-Gabriel-Welsch
range
test (R-E-G-W Q), Tamhane’s T2, Dunnett’s T3, Games-Howell, Dunnett’s
C, Duncan’s multiple range test, Student-Newman-Keuls (S-N-K), Tukey’s b,
Waller-Duncan, Scheffé, and least-significant difference.
Data.
Factor variable values should be integers, and the dependent variable should be
quantitative (interval level of measurement).
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