Effect Size and Correlations

Module 36: Correlation Pitfalls
Effect Size and Correlations
• Larger sample sizes require a smaller correlation coefficient
to reach statistical significance
– Therefore, a weak relationship can be perceived a statistically
significant because of a large sample
• It is necessary to make a judgment as to the practical
importance of a significant correlation if there is a large
sample size
• Categories for Correlation Coefficients
– Small = .25 or less
– Medium = .25 to .40
– Large = .40 or more
Restriction of Range
• Correlation coefficients can be biased if the
full range of possible scores are not included
in the sample
Heterogeneity and Homogeneity
• Heterogeneity means that a sample contains a diverse
range of score across possible subgroups
• Homogeneity indicates that participants are similar
across subgroups that are potentially in the sample
Common Variance
• Common variance is the proportion of variance
that is shared across two variables
• A correlation coefficient is not a measure of
common variance
– r2 is a measure of common variance
Correlation Does NOT Imply Causation
• Correlations do not imply causation
• A significant relationship between two
variables does not indicate that variation in X
causes variation in Y