1. Extension to ANOVA From t to F

2. Review Comparisons of samples involving t-tests are restricted to the two-sample domain Comparisons of samples involving t-tests are restricted to the two-sample domain Independent samples tests differences between samples in which cases do not in some way influence or contribute to one another. Independent samples tests differences between samples in which cases do not in some way influence or contribute to one another. –Experimental vs. Control, Gender etc. Paired samples involve testing differences among correlated samples Paired samples involve testing differences among correlated samples –Pre-Post test, Matched samples

3. Extension Analysis of Variance, as it is called even though most analyses involve an analysis of variance, allows us to move beyond the two-sample setting. Analysis of Variance, as it is called even though most analyses involve an analysis of variance, allows us to move beyond the two-sample setting. One-way between groups design One-way between groups design –Between groups factor(s) with 2 or more levels each One-way within groups design One-way within groups design –Repeated measures factor(s) with 2 or more levels Interactions Interactions Mixed design Mixed design

4. Between Groups Design For simplicity we can provide a simple overview of a three group setup For simplicity we can provide a simple overview of a three group setup Experimental setup: random assignment to control, treatment 1, and treatment 2 groups Experimental setup: random assignment to control, treatment 1, and treatment 2 groups Our null hypothesis is conceptually: Our null hypothesis is conceptually: H 0 : μ c = μ treat1 = μ treat2 The alternative is ‘ not H 0 ’ The alternative is ‘ not H 0 ’ The GLM is The GLM is Y ij = μ GM + τ j + e ij In other words, a person ’ s score is a reflection of the grand mean, the effect of being in group j (the mean difference between their group mean and the grand mean) and ‘ error ’ variance, the variance within that group j In other words, a person ’ s score is a reflection of the grand mean, the effect of being in group j (the mean difference between their group mean and the grand mean) and ‘ error ’ variance, the variance within that group j

5. An Obvious Problem The issue with ANOVA is that, like our nil hypothesis, it tests an hypothesis that no one is interested in. The issue with ANOVA is that, like our nil hypothesis, it tests an hypothesis that no one is interested in. The ANOVA, if statistically significant, can only tell us that there is some statistical difference among the groups (at least between the largest and smallest means) The ANOVA, if statistically significant, can only tell us that there is some statistical difference among the groups (at least between the largest and smallest means) However we are typically concerned with specific group comparisons (rendering a one way ANOVA fairly useless) However we are typically concerned with specific group comparisons (rendering a one way ANOVA fairly useless)

6. Multiple Comparisons Comparisons can be planned in advance (a priori) or conducted in exploratory fashion (post hoc) Comparisons can be planned in advance (a priori) or conducted in exploratory fashion (post hoc) Some techniques are applicable to either setting, but some only one or the other. Some techniques are applicable to either setting, but some only one or the other. Any one you would use today 1 does not require a significant omnibus ANOVA to be found first Any one you would use today 1 does not require a significant omnibus ANOVA to be found first With planned comparisons, one has an idea of what to expect or has thought enough about their situation to narrow down the hypotheses to test With planned comparisons, one has an idea of what to expect or has thought enough about their situation to narrow down the hypotheses to test –Control vs Treatments Treatment 1 vs. Treatment 2 However even post hocs do not have to be conducted on every single comparison possible However even post hocs do not have to be conducted on every single comparison possible Planned comparisons are more statistically powerful, and fewer comparisons are more statistically powerful than many Planned comparisons are more statistically powerful, and fewer comparisons are more statistically powerful than many A key issue with post hocs is the control of family-wise type I error rate, i.e. control of making a type I error among any of the pairwise comparisons (along with control for each comparison itself). A key issue with post hocs is the control of family-wise type I error rate, i.e. control of making a type I error among any of the pairwise comparisons (along with control for each comparison itself).

7. Within Groups Designs Much of the same goes for repeated measures designs Much of the same goes for repeated measures designs While more statistically powerful (all else being equal), there is an additional assumption (sphericity) While more statistically powerful (all else being equal), there is an additional assumption (sphericity) But in general we are looking for trends or specific comparisons within this design also But in general we are looking for trends or specific comparisons within this design also

8. Interactions What we really like with ANOVA is the ability to test interactions What we really like with ANOVA is the ability to test interactions For example with a academic study- random assignment to control and treatment groups, home vs. school setting. For example with a academic study- random assignment to control and treatment groups, home vs. school setting. Now we can see whether there is a difference in the treatment difference depending on which setting the student is in Now we can see whether there is a difference in the treatment difference depending on which setting the student is in –E.g. no treatment effect at home, treatment > control at school Whenever there is more than one factor, we will be primarily interested in interactions, since with a significant interaction, any main effect seen would be dependent on the levels of another factor Whenever there is more than one factor, we will be primarily interested in interactions, since with a significant interaction, any main effect seen would be dependent on the levels of another factor

9. Mixed Design With mixed designs we have at least one between groups factor and one within groups factor With mixed designs we have at least one between groups factor and one within groups factor –E.g. control vs. treatment, pre vs. post test We can test interactions between the two types of factors and if more factors are involved, between multiple factors of the same type and their interactions We can test interactions between the two types of factors and if more factors are involved, between multiple factors of the same type and their interactions

10. Summary Regardless of the complexity of the design, people end up interpreting specific group comparisons or one-way situations, so if you can understand t-tests and the one-way ANOVA, you’re fine for interpreting papers using more complex designs Regardless of the complexity of the design, people end up interpreting specific group comparisons or one-way situations, so if you can understand t-tests and the one-way ANOVA, you’re fine for interpreting papers using more complex designs –However, as you can probably tell, designs can get complicated quickly and one can easily get lost in the details ANOVA was extremely common when the typical approach to research was experimental, where it appropriate to use ANOVA was extremely common when the typical approach to research was experimental, where it appropriate to use However, as people looked at more complex models, common practice became to force non-experimental design into ANOVA (e.g. by categorizing continuous variables) because that’s what they knew However, as people looked at more complex models, common practice became to force non-experimental design into ANOVA (e.g. by categorizing continuous variables) because that’s what they knew But some statisticians consider ANOVA to be inappropriate, or at least uninterpretable, for non-experimental research But some statisticians consider ANOVA to be inappropriate, or at least uninterpretable, for non-experimental research –Some even suggest that an experiment is the only place one can find a true ‘interaction’ Unless you are conducting an experiment, you’d probably do well to attempt alternative approaches. Unless you are conducting an experiment, you’d probably do well to attempt alternative approaches.