Comparison between treatments in a repeated‐measurement design: anova and multivariate methods
01 Nov 1970-British Journal of Mathematical and Statistical Psychology (John Wiley & Sons, Ltd)-Vol. 23, Iss: 2, pp 147-163
TL;DR: In this article, several methods available for testing comparisons between treatments in a repeated-measurement design, within the framework of the general multinormal model, are investigated, and the weakest assumptions required for the validity of each F ratio are presented.
Abstract: This paper investigates several methods available for testing comparisons between treatments in a repeated-measurement design, within the framework of the general multinormal model. Various ratios are considered, and the weakest assumptions required for the validity of each F ratio are presented. When several F ratios are valid, power considerations are introduced for purposes of choice. Methods enabling the testing of validity assumptions are also presented, together with alternative multivariate statistics which can be used when no F ratio is valid. All procedures presented in the paper are illustrated by a numerical example.
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01 Sep 2005
TL;DR: A simple alternative method is proposed that provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions and how it can be implemented in SPSS.
Abstract: Within-subject ANOVAs are a powerful tool to analyze data because the variance associated to differences between the participants is removed from the analysis. Hence, small differences, when present for most of the participants, can be significant even when the participants are very different from one another. Yet, graphs showing standard error or confidence interval bars are misleading since these bars include the between-subject variability. Loftus and Masson (1994) noticed this fact and proposed an alternate method to compute the error bars. However, i) their approach requires that the ANOVA be performed first, which is paradoxical since a graph is an aid to decide whether to perform analyses or not; ii) their method provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions; iii) the method proposed is difficult to implement in commonly-used graphing software. Here we propose a simple alternative and sow how it can be implemented in SPSS.
1,750 citations
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