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Repeated Measures

This resource explores the univariate analysis of repeated measures using the Hicks design of an experiment. It covers essential aspects such as randomization checks on pre- and post-raw data, treatment effects, and significant plots for examining pre-scores and post-scores. The analysis explains how deleting subjects from the model impacts error terms and F-tests. Additionally, it discusses the implications of interacting factors, and features statistical methods including ANOVA on paired score differences and the use of Dunnett’s test when a true control group is present.

gabriel-barry
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Repeated Measures

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  1. Repeated Measures Univariate Analysis

  2. Hicks Design of Experiment Example: Layout

  3. Raw Data Pre: Check on Randomization

  4. Raw Data Post: Check for Trt Effect

  5. Refined Plots for Pre Scores

  6. Can actually test Randomization with Pre scores

  7. Refined Plots for Post Scores

  8. Univariate Analysis Repeated MeasuresHave two error terms: S(G) and T*S(G)

  9. Suppose Subject was deleted from the analysis

  10. Model with Group and Test and Interaction

  11. So what happened? • When we drop terms from the model they go into… • If we drop two terms from the model they go into… • Since we now have only one error term, it is the combination of… • So for some F-tests it is… • For other F-tests it is… • The means of those Main Effects and Interaction have not changed. but what did change was…

  12. LS Means Plot Group

  13. Interaction Plot

  14. Tukey HSD on Interaction (be careful about the letters in JMP)

  15. What if we only look at paired differences?

  16. ANOVA on Paired Score Differences

  17. Tukey HSD

  18. Since a true Control Group exists, use Dunnett’s

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