Understanding oneway repeated Measures Designs

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?

 - 4,8,16,24,32

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

-True
-False