Using the Correlate Function
The CORRELATE function in EZAnalyze allows you to see how two or more variables are related to each other. A correlation coefficient (glossary), simply put, is a number between -1 and +1 that describes the direction and degree of relationship between two variables. The direction of the relationship is indicated by the sign (positive or negative, + or -), while the degree of the relationship is indicated by the number itself, which is usually a decimal between 0 and 1. The higher the number, the stronger the relationship is between two variables - 0 would indicate that there is no relationship, while 1 would indicate that there is a perfect relationship. For example, if you obtained a correlation coefficient of .41 between the number of years of schooling and gross income, that would indicate that there was a positive relationship between the number years of schooling and gross income. Alternatively, if the correlation coefficient was -.41, that would indicate that there was a negative relationship. For more information on interpreting correlation coefficients, see the associated help file topic HERE for interpreting CORRELATE results reports.
To obtain correlation coefficients with your data, select "Advanced" from the EZAnalyze menu in Excel, and then choose "Correlation".
When the "Correlate" dialogue box appears, you will be presented with two lists of the variables in your data sheet.
A NOTE ON SELECTING VARIABLES. While EZAnalyze will let you choose any two numeric variables to correlate, variables that are categorical, such as gender, ethnicity, or learning disability status, are not appropriate to conduct correlation analyses with in EZAnalyze. Examples of common correlations of interest to educators are the number of days absent with GPA, standardized test scores and GPA, and the number of disciplinary referrals with standardized test scores.
When you click OK, a results report will be printed on a separate sheet for your review. (click on "results report" for information on how to interpret this analysis)