1. Categorical Data Categorical IV – Categorical DV

2. Overview Defined: Frequencies or proportions amongst levels of variables. Variables: IV is categorical, DV is categorical Relationship: Relationship between two or more variables. Example: How do males/females vote guilty or not guilty? Assumptions: Typically you want greater than 5 per cell.

3. Final “main” type of analysis

4. Theory behind Chi-Square  How can you test the relationship between two (or more) categorical variables?  Compare the frequency you observe in cells to the frequency you would expect by chance. So compare count (sample) to expected count (population)

5. Theory behind Chi-Square  How can you test the relationship between two (or more) categorical variables?  Compare the frequency you observe in cells to the frequency you would expect by chance. So compare count (sample) to expected count (population)

6. Theory behind Chi-Square  How can you test the relationship between two (or more) categorical variables?

7. Pearson Chi-Square  Significance  Effect Size  Graph

8. Pearson Chi-Square  If you have more than a 2 x 2…  such as 2 x 3, 3 x 2, 3 x 3, etc.  … then the output is treated as an “Omnibus” test…  …and you conduct the follow-up tests by conducting multiple 2 x 2 tests.

9. Example of “Omnibus” and follow-up tests