Measuring Relationships Among Items

1. Determine the data type. Continuous and categorical data require different statistical measures of association.
2. Define dependent and independent variables. Examine the meaning of the items to determine direction of causality, if any.
3. Select the correct tool. Statistics that use continuous data provide more sensitive measures of associations between items.
4. Meet distribution requirements. Examine data distributions to be sure they meet the requirements of the intended analysis tools.
5. Depend on cross-tabulation. Use cross-tabs to measure associations between variables if the data conditions required by other measures of association aren’t met.
6. Check causality carefully. Remember that an association between variables doesn’t automatically imply that one is causing the other.
7. Use prediction cautiously. Be sure that equations for prediction from regression analysis are valid and stay within the range of the database.
8. Make inferences carefully. Choose an appropriate level of probability for assessing the statistical significance of associations between variables.
9. Consider practical importance. Make a clear distinction between statistical significance and the practical implications of relationships between variables.