Week 3 Factorial between participants ANOVA II Exam questions wi

Explain what an omnibus test is, and list the effects in a 2-way ANOVA that can be categorized as omnibus tests Any test resulting from the preliminary partitioning of variance in ANOVA Three effects: main effect IV1, main effect IV2 and interaction effect Identify the circumstances in which follow-up tests are required in 2-way factorial ANOVA If there is a difference between the means When following up main effects in a 2-way ANOVA: 1) Identify the research question (and hypothesis) being tested 2) Identify the type of means that are compared 3) Identify the type of statistical test (e.g. F, t, z) that is used 4) Explain how you would determine the number of follow-up tests that are needed 5) Explain what a significant main effect comparison tells you 1) What direction is the effect in 2) Marginal means 3) T test (protected) or linear contrasts 4) Only if the main effect is significant 5) there is a difference in the marginal means (somewhere) When following up a significant two-way interaction in a 2-way ANOVA: 1) Identify the two (possible) steps in the analyses 2) Identify the types of means that are being compared. 1) simple effects and simple comparisons 2) cell means When testing simple effects in a 2-way ANOVA: 1) Identify the research question (and hypothesis) being tested 2) Identify the type of statistical test (e.g. F, t, z) that is used 3) Identify the error term that is used 4) Identify how degrees of freedom are calculated for SSeffect / treatment and SSerror 5) Explain how you would determine the maximum number of simple effects that could be tested for a factor 6) Explain what a significant simple effect tells you In a 2-way ANOVA, explain how the variance is re-partitioned when testing: 1) The simple effects of Factor A 2) The simple effects of Factor B Identify the circumstances in which simple effects would need to be followed up When testing simple comparisons... 1) Identify the research question (and hypothesis) being tested 2) Identify the type of statistical test (e.g. F, t, z) that is used 3) Explain what a significant simple comparison tells you Identify the problems (and solutions) associated with simple comparisons Explain why significance tests are not that helpful when we want to determine the importance of findings Define effect sizes and explain why they are useful Two estimates of effect size are eta-squared (η2) and omega-squared (ϖ2): 1) Identify what each estimate describes 2) Explain which estimate is more biased than the other (and why) 3) Identify the factors that influence the difference between the two (and how) Explain the difference between eta-squared (η2) and partial eta-squared (η2p) in terms of what each estimate describes Identify the two key limitations of partial eta-squared (η2p) Add or remove terms You can also click the terms or definitions to blur or reveal them