Understanding oneway repeated Measures Designs
Qn1. What primarily distinguishes a oneway repeated measures ANOVA from a one-way ANOVA?
- The presence of multiple factors - The presence of a between-subjects factor. - The presence of a within-subjects factors. - None of the above
Qn2. All else being equal, which of the following is a reason to use a within-subjects factor instead of a between-subjects factor?
- The data is more reliable - The data exhibits less variance - The factors are easier to analyze - The exposure to confounds is less - Less time from each subject is required
Qn3. In a repeated measures experiment, why should we encode an Order factor and test whether it is statistically significant? (Mark all that apply)
- To examine whether the presentation order of conditions exerts a statistically significant effect on the response. - To examine whether any counterbalancing strategies we may have used were effective - To examine whether confounds may have affected our results - To examine whether our factors cause changes in our response - To examine whether out experiment discovered any differences
Qn4. How many subjects would be needed to fully counterbalance a repeated measures factor with four levels?
Qn5. For an even number of conditions, a balanced Latin Square contains more sequences than a Latin Square.
- True - False
Qn6. For a within-subjects factor of five levels, a balanced Latin Square would distribute which of the following number of subjects evenly across all sequences?
5, 15, 20,25,35
Qn7. Which is the key property of a long-format data table?
- Each row contains only one data point per response for a given subject. - Each row contains all of the data points per response for a given subject. - Each row contains all of the dependent variables for a given subject. - Multiple columns together encode all levels of a single factor. - Multiple columns together encode all measures for a given subject
Qn8. Which is not a reason why Likert-type responses often do not satisfy the assumptions of ANOVA for parametric analyses.
- Despite having numbers on a scale, the response is not actually numeric. - Responses may violate normality - The response distribution cannot be calculated - The response is ordinal - The response is bound to within, say, a 5- or 7-point scale.
Qn9. When is the Greenhouse-Geisser Correction necessary?
- When a within-subjects factor of 2+ levels violates sphericity - When a within-subjects factor of 2+ levels exhibits sphericity - When a within-subjects factor of 3+ levels violates sphericity - When a within-subjects factor of 3+ levels exhibits sphericity - None of the above
Qn10. If an omnibus Friedman test is non-significant, post hoc pairwise comparisons should be carried out with Wilcoxon signed-rank tests