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. The same measure repeated over time
   (e.g., self-confidence before, after, and following-up a psycho-social intervention), and/or
2. Across more than one condition
   (e.g., experimental and control conditions), and/or
3. Several related and comparable factors
   (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: Instead of the homogeneity of variance assumption (which is part of one-way (between-subjects) and factorial ANOVA designs, repeated-measures designs have the assumption of sphericity, tested by Mauchly's sphericity test.

See also

• Sphericity (Wikipedia)

External links

University of Canberra

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

Other

• Chapter 14 Within-Subjects ANOVA (HyperStat Online)