Repeated measures ANOVA: Difference between revisions
wikademia>Jtneill →Assumptions: Sphericity 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 |
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==Assumptions== | ==Assumptions== | ||
Most of the assumptions for between-subjects ANOVA design apply, however the key variation is '''Sphericity''': | Most of the assumptions for between-subjects ANOVA design apply, however the key variation is '''Sphericity''': | ||
* Instead of the homogeneity of variance assumption ( | * 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. | ||
* | * 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. | ||
* Tested by [[w:Mauchly's sphericity test|Mauchly's sphericity test]]. | |||
==See also== | ==See also== | ||
Revision as of 21:28, 13 August 2008
| File:Wikademia.logo.png | 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:
- The same measure repeated over time
(e.g., self-confidence before, after, and following-up a psycho-social intervention), and/or - Across more than one condition
(e.g., experimental and control conditions), and/or - 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
- One-way repeated measures - repeated measures across one IV
- Two-way repeated measures - repeated measures across two IVs
- 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 (part of one-way (between-subjects) and factorial ANOVA designs), repeated-measures designs have the assumption of sphericity.
- 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.
- 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)