2. Significance:
Is there evidence that this event would be
unlikely, if the null hypothesis were true?
An result can be significant but the size of the
difference might be very small
If sample size is very large
If variability is quite small
Effect size can also be measured and
compared.
3. In Correlation, we computed r2 to see the
amount of shared variability between two
variables.
A correlation of r = .7 meant that 49% of the
variability was “shared” or “explained” by the
relationship of the two variables.
This gave us a measure that increased in a
linear way (unlike r) to talk about the size of
the correlation.
4. Effect size could be measured with Cohen’s d
as follows:
mean difference
standard deviation
d
d = .2 or less is a small effect size
d between .2 and .8 is a medium effect size
d greater than .8 is a large effect size
5. r2 can also be computed after a t-test using the
equation:
df
r
2
t
2
2
t
Interpretation: The percent of variability in
the variable that is due to treatment group.
6. Same idea of shared variance as we saw in r2
2 between SS
total SS
Interpretation: The percent of variability in
the variable that is due to treatment group.
7. Enter data for our sample problem
Instead of Group ABC, use codes
1, 2 and 3.
Add value labels for praise levels
Add variable names
Consult Cronk book
Do your own write-up of the
results, including a measure of
Effect Size.
Group X
A 7
A 6
A 5
A 8
A 3
A 7
B 4
B 6
B 4
B 7
B 5
B 7
C 3
C 2
C 1
C 3
C 4
C 1
ΣX 83
Mean 4.6111