rule to determine if a potential relationship between two variables is worth examining further.

/in /by

In week 3, we used epsilons and 10-percent-point rule to determine if a potential relationship between two variables is worth examining further.

This week, we’ll use tests of “measures of association” to figure out the exact strength of a relationship between two variables. In addition, we’ll learn how to interpret SPSS outputs for measures of association tests such as lambda, gamma, and Pearson’s r， along with other possible tests. Remember that these tests are specific to the level of measurement that your variables are. In other words, one test may not work in a different relationship test. Here are the guidelines:

  1. Both DV and IV are nominal variables: Lambda (when it is not a 2X2 table)

  2. If it is a 2X2 table: Phi

  3. Both DV and IV are ordinal variables: Gamma

  4. One variable ordinal AND the other variable dichotomous nominal (like Yes/No, male/female, etc.): Gamma

  5. One variable ordinal AND the other variable nominal (not dichotomous, has more than 2 categories): Cramer’s V.

  6. Both DV and IV are I/R variables: Pearson’s r

To interpret the output:

Keep in mind measures of association is a statistical procedure based on Proportional Reduction of Error (PRE). Thus the format of interpretation will be:

……knowing the IV will reduce error in predicting the DV by *%.

Please note: Don’t just say “IV” and “DV” in your explanation. You need to enter your variables names for IV and DV, and replace * for the exact test value from the output. If the value of Lambda is .34, then it will be interpreted as 34%.

Ok, now it is time for you to try! Be sure to test the strength of association of your final project for this week’s forum discussion. You can download the class handout attached at the bottom of the page, or Click here for details.