Part 1

1. Part 1 Analysis of Covariance: ANCOVA

2. Analysis of Covariance • ANCOVA • Like an analysis of variance in which one or more variables (called covariates) have been controlled for • Analogous to a partial correlation

3. ANCOVA • Why bother with ANCOVA? ANCOVA offers 2 benefits… • First, ANCOVA can reduce the error term! Recall that all of statistics in the “F Family” are based on MSb / MSw. If we can reduce the error term (MSw) by removing covariates we can increase our sensitivity. • Second, ANCOVA can eliminate confounding variables! Confounding variables systematically co-vary with the independent variable. (Emphasis on “systematically”.) If we can eliminate the confounds, our inference can be stronger (…a better shot at drawing a cause/effect relation). What were the three necessary criteria for inferring causal relations?

4. ANCOVA • Example 1: ViagraD.V. = Libido I.V. = Dosage of Viagra: 3 levels… (Placebo, Low Dosage, High Dosage) • An initial ANOVA indicated a non-significant difference in libido (sex drive) across the 3 levels of Viagra. • The researchers considered that a participant’s libido might depend, too, on the partner’s libido. (It takes two to tango!) So, the researchers used the data from the ANOVA but now entered Partner’s Libido as a covariate, and ran an ANCOVA…

5. ANCOVA Initially ANOVA Was Run…no covariates. There was a non-significant effect of the various Viagra dosages on libido.

6. ANCOVA Subsequently, an ANCOVA Was Run: Covariate = Partner’s Libido. The effect of Viagra-Dosage is significant now, after ‘partialing out’ the (significant) effect of the partner’s libido!!! (note the reduction in the Error Term, despite the same ‘total’ SS)

7. The Joy of Stats… ANCOVA can reduce an error term, which can render a non-significant effect …significant!! Stats

8. ANCOVA • Example 2: D.V. = Social adjustment in school-age boys. I.V. = Parental Transitions…4 levels (No transitions, loss of father, new step- father 2 or more new step-fathers) • An ANOVA indicated a significant difference in social adjustment across the 4 levels of parental transition. • To eliminate the possibility that the significant effect could be explained by confounds with Parental SES and Per Capita Income, those variables were made covariates in an ANCOVA. The ANCOVA, too, was significant. So, parental transitions alone are significantly correlated with the D.V..

9. Part 2 Introduction To Multivariate Statistics

10. Independent vs. Dependent Variables • Independent variables • Divide groups from each other • Often based on random assignment • Analogous to predictor variables in regression • Dependent variables • Represent the effect of the experimental procedure • Analogous to criterion variables in regression

11. Introduction To Multivariate Statistics • So far this semester, each of our analyses has addressed just a single dependent variable at a time. • Univariate Analysis – Any statistical analysis that focuses on a single dependent variable, regardless of the number of I.V.s, or ‘predictor variables’. • We can now consider a more complicated case…

12. Multivariate Analyses • Multivariate Analysis – Any statistical analysis that focuses two or more dependent variable SIMULTANEOUSLY, regardless of the number of I.V.s, or ‘predictor variables’. • There are many different multivariate tests! We’ll begin with a MANOVA…

13. Part 3 MANOVA And Music Therapy For Chimpanzees

14. MANOVA • More than one dependent variable • Multivariate ANalysis Of VAriance • MANOVA • Like an analysis of variance with two or more dependent variables

15. MANOVA • Why bother with MANOVAs? • To appreciate the motivation for MANOVAs, let’s re-visit a question that we asked when began factorial designs…. • Critical Thinking Question: Why bother with factorial ANOVAs, when we can run a bunch of one-way ANOVAs?

16. MANOVA • Similarly, MANOVA offers a major advantage over running ‘many little ANOVAs’ (i.e., one for each D.V.)… • MANOVA is sensitive to relationships among dependent variables!!!! • ANOVA is not, because it address only one D.V. at a time.

17. MANOVA • Example: Can experienced drivers (5+ years), new drivers (1 year), and drunk drivers (legal conviction) be distinguished from each other based on a single DV –the number of pedestrians they kill? • ANOVA can tell us whether groups are distinguishable from each other on the basis of a single DV. • In this example, the groups may be indistinguishable -given this single D.V…

18. MANOVA • However, these groups might become readily distinguishable from each other if you simultaneously analyze the combination of…. • # of pedestrians killed, AND • # of lamp posts hit, AND • # of cars crashed into.

19. MANOVA • Again, any of those D.V.s alone may have produced a non-significant ANOVA… • But a MANOVA is sensitive to the relations among those variables and may be able to achieve significance… • In short, a MANOVA can be more sensitive than ANOVA!!! That’s it’s first advantage.

20. MANOVA • A second advantage of MANOVA is it (like other multivariate tests) can evaluate “latent variables”… • Latent Variables – Are present implicitly, rather than explicitly. • A latent variable might only ‘potentially’ exist, and is contingent on an operational definition that synthesizes several explicitly defined D.V.s…

21. Agitated / Aggressive – From Article On Music Therapy for Chimps MANOVA Agitated / Aggressive Operationally defined by the following explicit D.V.s Aggression: Display-Charging: Display-Hunching: Threat: Pant Hoot You could run separate ANOVAs on each explicit DV, or run a MANOVA on “Agitated / Aggressive” (informally, an ‘uber’ variable)

22. Anxious/Fearful – From Article On Music Therapy for Chimps MANOVA Anxious/Fearful Operationally defined by the following explicit D.V.s Apprehension: Fear: Scratch: Yawning: Attachment: Locomotion: Vocalization You could run separate ANOVAs on each explicit DV, or run a MANOVA on “Anxious/Fearful” (informally, an ‘uber’ variable)

23. Excited – From Article On Music Therapy for Chimps MANOVA Excited Operationally defined by the following explicit D.V.s Food Barks: Pant Hoot to Scream: Tandem Walk: Non-Directed Display You could run separate ANOVAs on each explicit DV, or run a MANOVA on “Excited” (informally, an ‘uber’ variable)

24. Active/Explore – From Article On Music Therapy for Chimps MANOVA Active/Explore Operationally defined by the following explicit D.V.s Explore: Locomotion: Rough-And-Tumble Play You could run separate ANOVAs on each explicit DV, or run a MANOVA on “Active/Explore” (informally, an ‘uber’ variable)

25. Inactive / Relaxed – From Article On Music Therapy for Chimps MANOVA Inactive / Relaxed Operationally defined by the following explicit D.V.s Rest: Quiet Play: Groom: Foraging/Eating You could run separate ANOVAs on each explicit DV, or run a MANOVA on “Inactive / Relaxed” (informally, an ‘uber’ variable)

26. MANOVA MANOVA’s generate F statistics, just like ANOVAs. The p-values (‘sig’) values are also typically evaluated at the 0.05 level, just like ANOVAs. (no big wup!)

27. MANOVA The DF in this summary table is 2(that’s 3 minus 1). These are means, not p values! There were three levels of the I.V. Each D.V. (actually ‘uber’ variable) was measured Before (pre), During (test) and After (post) the chimps heard music.

28. Now They’ve added Time-of-Day as an IV MANOVA Music Affected Three Separate Dependent Variables, Each of which was a latent variable (‘uber variable’) Agitated/Aggressive Active/Explore Inactive/Relaxed The I.V.s in this Analysis Were Time of Day (AM/PM) And Music (pre, test, post) So this was a 2x3 within Subjects MANOVA! Each chimp was evaluated In each of the 2x3 Conditions The df here refers To the main effect of Time-of-day…2 levels…df=1

29. Now They’ve added Social Group as an IV MANOVA Music Affected Four Separate Dependent Variables, Each of which was a latent variable (‘uber variable’) Agitated/Aggressive Active/Explore – Solitary Active/Explore – Social Inactive/Relaxed The I.V.s in this Analysis Were Social Group (M, F, Mixed) And Music (pre, test, post) So this was a 3x3 Mixed MANOVA! Chimps were evaluated In each of the 3x3 Conditions The df here refers To the main effect of Social Group…3 levels…df=2

30. MANOVA • We will NOT calculate MANOVAs by hand! • Nor will we use SPSS to compute MANOVAs! • But if you were to do MANOVAs for your senior research here’s how you’d get started… • Analyze GLM  Multivariate(not univariate) The dependent variable box now allows you to slide in multiple DVs (rather than just 1 in the univariate case)