Transcript Analysis of Variance

Analysis of Variance
Emily H. Wughalter, Ed.D.
Summer 2008
Tests of Difference
t-tests
ANOVAs
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The final value found when calculating an ANOVA is
the F value. This is the statistic that is tested when
using an F table.
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t2=F
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ANOVAs
Can be used to test the difference on a dependent
variable between two levels of a single
independent variable with two levels
Can be used to test the difference on a dependent
variable between more than two levels of single
independent variable
Can be used to test the difference on a dependent
variable between two or more levels of two (or
more) independent variables
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ANOVA Summary (Source) Table
Source Table
Sum of
Squares
Between
Groups
Within
Groups
Total
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df
Mean
Square
F
Sig
Independent Variables with more than 2 levels
When an independent variable has more than two
levels and a significant effect is found then a post
hoc analysis is necessary to determine where the
actual differences exist.
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Some Post Hoc Tests
Sheffé (most conservative)
Dunn’s Test
Newman Keuls’ Test
Least Significant Difference (most liberal)
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Interpretation for Group Exercise
The hypothesis that fitness level would affect run in
seconds is rejected (F(2, 17)=10.59; p<.01).
Fitness level had a substantial (eta=.93) impact on
run in seconds. Approximately 87% of the variance
in run in seconds can be explained by knowing a
participant’s fitness level. It was found that those
with high fitness were significantly faster (M=442;
S=49.43) than those that are sedentary (M=582.60;
S=35.08); also, those with high fitness were
significantly different from those with moderate
fitness (M=524.00; S=77.40). No differences were
found between sedentary and moderately active.
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Examining Source Tables
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