This document discusses post hoc tests and effect sizes in analysis of variance (ANOVA). It explains that post hoc tests are used to determine specifically where differences lay between groups, while ANOVA only indicates if an overall difference exists. It provides guidance on running post hoc tests and computing effect sizes in SPSS, demonstrating output for post hoc tests and defining eta-squared as a measure of the percentage of variance accounted for by a factor.

1. Unit 6: Analysis of Variance (ANOVA) Lesson 2: Computing Post Hoc Tests and Effect Sizes EDER 6010: Statistics for Educational Research Dr. J. Kyle Roberts University of North Texas I know they’re different, but where?? Next Slide

2. Post Hoc Tests ANOVA only tells if there is a difference Post Hoc tests tell specifically where these differences lay Next Slide

3. Running Post Hocs in SPSS 2 Variables: Grouping Variable Score Variable Next Slide 5 3 8 7 7 1 5 7 7 2 3 8 8 5 9 7 8 6 7 9 White Hispanic Black Asian

4. Running an ANOVA in SPSS Analyze  Compare Means  One-Way ANOVA Next Slide

5. SPSS Output for Post Hoc Tests Next Slide

6. SPSS Output for Post Hoc Tests Next Slide

7. Effect Size Measures Next Slide Eta-squared – the percent variance accounted for by the factor (way) of the dependent variable How good is our way at describing the variation in the dependent variable?

8. Effect Size  2 = .513 Percent variance explained Next Slide Score variable SS = 101.80 Ethnicity SS = 52.20

9. Unit 6: Analysis of Variance (ANOVA) Lesson 2: Computing Post Hoc Tests and Effect Sizes EDER 6010: Statistics for Educational Research Dr. J. Kyle Roberts University of North Texas I know they’re different, but where??