title: Single-factor repeated-measures designs: analysis and interpretation.
abstract: In this column we discussed the selection and interpretation of appropriate statistical tests for single-factor within-subjects/ repeated-measures designs and provided an example from the literature. The parametric tests that we discussed were the t test for paired or correlated samples and the single-factor repeated-measures ANOVA. We also mentioned four nonparametric tests to be used in single-factor within-subjects/repeated-measures designs, but they are relatively rare in the literature. The Compton et al. (2001) article did not provide effect size measures, but they could be computed from the means and standard deviations. Remember that a statistically significant t or ANOVA (even ifp < .001) does not mean that there was a large effect, especially if the sample was large. In the Compton example, the sample was quite small (N = 14), and the findings do reflect a large effect size.
authors.affiliation: Colorado State University, Fort Collins, USA.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Gliner
authors.forename: George A
authors.initials: GA
authors.lastname: Morgan
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Harmon
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric