1. Ivan Katchanovski, Ph.D. POL 242Y-Y
Correlation July 7, 2008
Ivan Katchanovski, Ph.D.
POL 242Y-Y

2. Association between Variables at Interval-ratio and Ordinal Levels
Correlation: Association between variables at interval-ratio level
Can be used for ordinal variables with certain assumptions
Scattergrams: graphical plots of two variables
Provide visual information about the existence, strength and direction of the relationship
Independent variable (X): horizontal axis
Dependent variable (Y): vertical axis

3. Scattergram: Internet use and education level

4. Direction of association
Positive
Negative
No association

5. Correlation Coefficient (Pearson’s R)
ranges between –1 (negative association) 0 (no association) and 1 (positive association)
0: no association
0-0.25: weak association
: moderate association
: strong association
: very strong association

6. Statistical Significance of Correlation
Statistical Significance of Correlation Coefficient (R):
Statistically significant if in SPSS p(obtained)<p(critical)=.05 or .01 or .001
Statistically nonsignificant if SPSS p(obtained)>p(critical)=.05
Correlation does not always mean causation

7. Example
Research hypothesis: The level of economic development has a positive effect on civil liberties
Independent variable: the level of economic development
Interval-ratio
Dependent variable: civil liberties
Ordinal

8. Example Pearson Correlation Coefficient: .753 Statistical significance
Positive association in expected direction
Very strong association
Statistical significance
SPSS: p(obtained)=.000 <p(critical)=.001=.1%
Statistically significant at the .001 or .1% level
Research hypothesis: supported by correlation analysis
The level of economic development has a positive and statistically significant effect on civil liberties