Exam 3 Review Sheets
Your individual review sheet (with items missed on previous exams) is available for pickup at the front desk. The generic version is below.
The final exam is cumulative, but weighted toward the final third of the course.
Be prepared to interpret printouts from one-way, factorial, repeated measures, and ANCOVA designs. Be prepared to interpret printouts from both fixed effects and random effects models.
Be very clear about the descriptive statistics that should be reported for each analysis of variance design.
Be very clear about the inferential statistics and effect size indicators that should be reported for each analysis of variance design.
Know the assumptions of ANOVA, ANCOVA, and Repeated Measures designs. Know how they are tested, and what strategies should be taken if these assumptions are violated.
Know how to compute the degrees of freedom for the effect portion of the IVs (both main and interaction effects). Understand the differences in how degrees of freedom for error terms are computed across randomized group and repeated measure designs and across random effects and fixed effects designs (you will not be required to compute these).
Know the primary advantages and disadvantages of one-way randomized group models, factorial randomized group models, repeated measures models, mixed designs (repeated measures and randomized groups), ANCOVA, Random Effects designs, and Latin Squares designs.
Know how models that mix random effects and fixed effects behave. Understand the similarities and differences between such a mixed factorial model and a randomized effects factorial model.
Know the applications and limitations of screening designs. Recognize common types of screening designs.
Know when it is necessary to use a post hoc comparison (a/k/a multiple comparison procedure or MCP). Understand issues of power and Type I errors associated with MCPs.
Understand sums of squares, mean squares, and their relation to the F ratio.
Be able to explain the relation between error df and power to detect a significant difference. Understand the relation between error df and F, p2, and p.
Know when to use Type I, Type II, and Type III sums of squares.
The final exam is cumulative, but weighted toward the final third of the course.
Be prepared to interpret printouts from one-way, factorial, repeated measures, and ANCOVA designs. Be prepared to interpret printouts from both fixed effects and random effects models.
Be very clear about the descriptive statistics that should be reported for each analysis of variance design.
Be very clear about the inferential statistics and effect size indicators that should be reported for each analysis of variance design.
Know the assumptions of ANOVA, ANCOVA, and Repeated Measures designs. Know how they are tested, and what strategies should be taken if these assumptions are violated.
Know how to compute the degrees of freedom for the effect portion of the IVs (both main and interaction effects). Understand the differences in how degrees of freedom for error terms are computed across randomized group and repeated measure designs and across random effects and fixed effects designs (you will not be required to compute these).
Know the primary advantages and disadvantages of one-way randomized group models, factorial randomized group models, repeated measures models, mixed designs (repeated measures and randomized groups), ANCOVA, Random Effects designs, and Latin Squares designs.
Know how models that mix random effects and fixed effects behave. Understand the similarities and differences between such a mixed factorial model and a randomized effects factorial model.
Know the applications and limitations of screening designs. Recognize common types of screening designs.
Know when it is necessary to use a post hoc comparison (a/k/a multiple comparison procedure or MCP). Understand issues of power and Type I errors associated with MCPs.
Understand sums of squares, mean squares, and their relation to the F ratio.
Be able to explain the relation between error df and power to detect a significant difference. Understand the relation between error df and F, p2, and p.
Know when to use Type I, Type II, and Type III sums of squares.
posted by Mari @ 8:38 AM
0 Comments:
Post a Comment
<< Home