Repeated measures ANOVA

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Resource type: this resource contains a tutorial or tutorial notes.

Overview

The repeated measures design is also known as a within-subject design. In this design, participants may present scores for:

1. A measure repeated over time
   (e.g., self-confidence before, after, and following-up a psycho-social intervention), and/or
2. A measure repeated cross more than one condition
   (e.g., experimental and control conditions), and/or
3. Several related, comparable measures
   (e.g., sub-scales of an IQ test).

Repeated-measures designs can also be understood as an extension of the paired-samples t-test (to include comparison between more than two repeated measures).

Repeated-measures designs may also be combined with between-subjects factors to create mixed-design ANOVA. Multiple repeated-measures designs can also be tested using MANOVAs.

Why use?

By collecting data from the same participants under repeated conditions:

• Individual differences can be reduced/eliminated as a source of between-groups differences (which helps to create a more powerful test).
• Inferential testing becomes more powerful (the sample size is not divided between conditions/groups)

Possible designs

1. One-way repeated measures - repeated measures across one IV
2. Two-way repeated measures - repeated measures across two IVs
3. Two-way mixed split-plot design (SPANOVA) - repeated measures on one IV, independent groups on another IV

Assumptions

Most of the assumptions for between-subjects ANOVA design apply, however the key variation is Sphericity:

1. Instead of the homogeneity of variance assumption (part of one-way (between-subjects) and factorial ANOVA designs), repeated-measures designs have the assumption of sphericity.
2. Means that the variance of the population difference scores for any two conditions should be the same as the variance of the population difference scores for any other two conditions.
3. Tested by Mauchly's sphericity test.
4. When the significance level of Mauchly’s test is < 0.05 then sphericity cannot be assumed.
5. Note: the sphericity assumption is only relevant to the univariate (one-way) RM ANOVA. This assumption is commonly violated and so it is generally not recommended to use the univariate analyses – the p-values tend to be inaccurate to the extent that this assumption is violated. The alternative, a multivariate test, does not require the assumption of sphericity and is therefore recommended. The multivariate test is conducted on difference scores and evaluates whether the population means for the sets of difference scores are simultaneously equal to zero.

In addition, for SPANOVA, consider homogeneity of intercorrelations:

1. The pattern of intercorrelations across the levels of the repeated measures factor should be consistent from level to level of the between subjects factor
2. Tested using Box's M statistic.

Exercises

1. One-way repeated measures ANOVA:
   • AQUES.sav - Francis 3.3.7, p. 66 (5th ed.)
2. Two-way repeated measures
   • Repmeas.sav - Francis 3.3.8.2, p. 76 (5th ed.)
3. Mixed ANOVA
   • AQUES.sav - Francis 3.3.8.3, p. 81 (5th ed.)

See also

• Mixed-model design (Wikibooks)
• Repeated measures design (Wikipedia)
• Sphericity (Wikipedia)

External links

University of Canberra

• Repeated measures ANOVA (ucspace)
  • Repeated measures ANOVA Notes (Handout)

Other

• Chapter 14 Within-Subjects ANOVA (HyperStat Online)