Shared Variance 
Improved Prediction
 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.
 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.
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
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.
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.
 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

Effect size

  • 1.
  • 2.
     Significance: Isthere 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 couldbe 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 alsobe 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 ofshared 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 datafor 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