## 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
```