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Understanding Oneway Repeated Measures Designs :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1

What primarily distinguishes a oneway repeated measures ANOVA from a oneway ANOVA?

❌ The presence of multiple factors.
❌ The presence of a between-subjects factor.
The presence of a within-subjects factor.
❌ The presence of both between- and within-subjects factors.
❌ None of the above.

Explanation:
A oneway repeated measures ANOVA is used when the same subjects are measured across multiple levels of a factor (within-subjects), unlike a standard oneway ANOVA.


Question 2

Which 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.

Explanation:
Within-subjects designs reduce between-participant variability, leading to lower variance and greater statistical power.


Question 3

Why should we encode and test an Order factor? (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 used were effective.
To examine whether an order confound has affected our results.
❌ To examine whether our factors cause changes in our response.
❌ To examine whether our experiment discovered any differences.

Explanation:
Order effects (learning, fatigue) can bias results. Testing Order checks for order-related confounds and effectiveness of counterbalancing.


Question 4

How many subjects are needed to fully counterbalance a factor with four levels?

❌ 4
❌ 8
❌ 16
24
❌ 32

Explanation:
Full counterbalancing requires n! sequences.
For 4 levels:
4! = 24


Question 5

For an even number of conditions, a balanced Latin Square contains more sequences than a Latin Square.

❌ True
False

Explanation:
For an even number of conditions, a balanced Latin Square has the same number of sequences as a standard Latin Square.


Question 6

For a within-subjects factor of five levels, how many subjects are evenly distributed in a balanced Latin Square?

❌ 5
❌ 15
❌ 20
25
❌ 35

Explanation:
A balanced Latin Square uses n sequences, and subjects must be a multiple of n.
For 5 levels → 5 × 5 = 25 subjects.


Question 7

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.

Explanation:
In long format, each row represents one observation (one subject × one condition).


Question 8

Which is NOT a reason Likert data violates ANOVA assumptions?

❌ Despite having numbers, the response is not truly numeric.
❌ Responses may violate normality.
The response distribution cannot be calculated.
❌ The response is ordinal.
❌ The response is bounded to a fixed scale.

Explanation:
The distribution can be calculated. The real issues are ordinal scale, bounded values, and non-normality.


Question 9

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.

Explanation:
Sphericity is only defined for 3 or more levels.
If violated → apply Greenhouse–Geisser correction.


Question 10

If an omnibus Friedman test is non-significant, post hoc tests should be carried out.

❌ True
False

Explanation:
If the omnibus test is not significant, post hoc comparisons are not justified.


🧾 Summary Table

Question Correct Answer(s) Key Concept
Q1 Within-subjects factor RM-ANOVA
Q2 Less variance Statistical power
Q3 1, 2, 3 Order effects
Q4 24 Full counterbalancing
Q5 False Latin Square
Q6 25 Balanced Latin Square
Q7 One observation per row Long format
Q8 Distribution cannot be calculated Likert data
Q9 3+ levels violate sphericity GG correction
Q10 False Friedman test