Measures of Association

The measures of association refer to a wide variety of coefficients (including bivariate correlation and regression coefficients) that measure the strength and direction of the relationship between variables; these measures of strength, or association, can be described in several ways, depending on the analysis.

There are certain points that a researcher should know in order to better understand the measures of statistical association.

First, the researcher should know that measures of association are not the same as measures of statistical significance.  It is possible for a weak association to be statistically significant; it is also possible for a strong association to not be statistically significant.

For measures of association, a value of zero signifies that no relationship exists.  In a correlation analysis, if the coefficient (r) has a value of one, it signifies a perfect relationship on the variables of interest. In regression analyses, if the standardized beta weight (β) has a value of one, it also signifies a perfect relationship on the variables of interest.  The researcher should note that bivariate measures of association (e.g., Pearson correlations) are inappropriate for curvilinear relationships or discontinuous relationships.


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