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Understanding Repeated Measures ANOVA in Pretest-Posttest Designs: Challenges and Alternatives

This overview discusses the complexities of using Repeated Measures ANOVA in analyzing pretest-posttest data. It highlights key concepts such as gain scores, potential issues with conservative F-values, interaction effects, and the importance of post-hoc tests. The text compares gain scores to Repeated Measures ANOVA, emphasizing that while the latter may provide a view of treatment effects, it often leads to confusion. Gain scores are presented as a simpler and potentially more effective alternative for analyzing treatment effects over time.

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Understanding Repeated Measures ANOVA in Pretest-Posttest Designs: Challenges and Alternatives

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  1. Using a Repeated Measures ANOVA to Analyze the Data from a Pretest-Posttest Design: A Potentially Confusing Task Schuyler Huck and Robert McLean

  2. Overview • Pretest-Posttest Design • Repeated Measures ANOVA • Gain Scores • Problems with Repeated Measures ANOVA • Conservative F value • Interaction Effect versus Gain Scores • Post-Hoc Tests • Advantages of Gain Scores over Repeated Measures

  3. Repeated Measures ANOVA- • Effect of treatment • Effect of time • Interaction between treatment & time • Gain Scores • Difference between pretest and posttest scores for each person • Posttest-pretest • Use a one-way ANOVA, main effect of treatment

  4. Problems with Repeated Measures ANOVA • F value is too small- pretest scores are included in effect of treatment, but no subject experienced treatment before the pretest scores. Treatment effects only influence posttest data. • Interaction effect is true main effect of treatment- the interaction examines the difference between groups depending upon pretest versus posttest scores. • Post Hoc Problems-Simple main effect tests run the risk of a “type IV error” and alpha values are controversial. Multiple comparison t-tests make sense only if gain score analyses was used.

  5. Gain Scores vs Repeated Measures ANOVA • F ratios of Repeated Measures ANOVA are not useful • F main effect treatment- not an accurate estimate of treatment effect. • F interaction is equivalent to gain scores. • F main effect of time- no true experimental value. • Gain scores • Equivalent information as ANOVA, but without the confusion and controversy. • Principle of parsimony- simpler is better!

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