1. Chapter 18 Some Other (Important) Statistical Procedures You Should Know About Part IV Significantly Different: Using Inferential Statistics

2. Multivariate Analysis of Variance (MANOVA) Used when there is more than one dependent variable Allows you to determine the best combination of dependent variables. Example: Adolescent Coping Scale Five Subscales of interest Effects of Gender, Race and Grade for each of the five scales Used when there is more than one dependent variable Allows you to determine the best combination of dependent variables. Example: Adolescent Coping Scale Five Subscales of interest Effects of Gender, Race and Grade for each of the five scales

3. Repeated Measures Analysis of Variance Participants are tested on a variable more than once Example: Monthly spelling tests Examining student performance over time How is this different from a dependent samples t-test? Your scores on Quests 1-4 Participants are tested on a variable more than once Example: Monthly spelling tests Examining student performance over time How is this different from a dependent samples t-test? Your scores on Quests 1-4

4. Analysis of Covariance Allows you to equalize initial differences between groups Especially useful when you want to “control” for a variable that might confound your results Effects of study time and sleep on test results. What about prior abilities in the subject matter? Allows you to equalize initial differences between groups Especially useful when you want to “control” for a variable that might confound your results Effects of study time and sleep on test results. What about prior abilities in the subject matter?

5. Multiple Regression You learned this in Chapter 15… Used when you have more than one predictor variable The BIG rules of multiple predictors… IVs should be correlated with the DV BUT IVs should not be correlated with each other…you want “unique” contributions You learned this in Chapter 15… Used when you have more than one predictor variable The BIG rules of multiple predictors… IVs should be correlated with the DV BUT IVs should not be correlated with each other…you want “unique” contributions

6. Factor Analysis Based on how well items are related to one another Factors Each factor represents several variables More efficient means of representing data that relate to each other on some theoretical construct Based on how well items are related to one another Factors Each factor represents several variables More efficient means of representing data that relate to each other on some theoretical construct

7. Path Analysis Examines the direction of relationships between variables Causality Examines the direction and strength of relationships Typically uses correlation coefficients to show the strengths of the relationships between the variables. Examines the direction of relationships between variables Causality Examines the direction and strength of relationships Typically uses correlation coefficients to show the strengths of the relationships between the variables.