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 1

Restriction of Range Correlation coefficients can be biased if the full range of possible scores are not included in the sample 2

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 3

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 – r 2 is a measure of common variance 4

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 5