1 / 27

PSY 1950 Post-hoc and Planned Comparisons October 6, 2008

PSY 1950 Post-hoc and Planned Comparisons October 6, 2008. Preamble. Presentations Tutoring Problem 1e: If you decide to reject the null hypothesis, you know the probability that you are making the wrong decision Visual depiction of F-ratio. Subpopulations.

thalia
Télécharger la présentation

PSY 1950 Post-hoc and Planned Comparisons October 6, 2008

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PSY 1950 Post-hoc and Planned Comparisons October 6, 2008

  2. Preamble Presentations Tutoring Problem 1e: If you decide to reject the null hypothesis, you know the probability that you are making the wrong decision Visual depiction of F-ratio

  3. Subpopulations • Cournot (1843): “...it is clear that nothing limits... the number of features according to which one can distribute [natural events or social facts] into several groups or distinct categories.” • e.g., the chance of a male birth: • Legitimate vs. illegitimate • Birth order • Parent age • Parent profession • Parent health • Parent religion • “… usually these attempts through which the experimenter passed don’t leave any traces; the public will only know the result that has been found worth pointing out; and as a consequence, someone unfamiliar with the attempts which have led to this result completely lacks a clear rule for deciding whether the result can or can not be attributed to chance.”

  4. Large Surveys and Observational Studies • Abundant data • Limited a priori hypotheses • e.g., Genome Superstruct Project (GSP) • Genetic testing • Cognitive testing • Structural brain imaging • Functional brain imaging

  5. ANOVA • One-way ANOVA • k(k-1)/2 possible pairwise comparisons • e.g., with 5 levels, 10 possible comparisons • Factorial ANOVA • The issue above plus • Multiple possible main effects/interactions • e.g., with a 2 x 2 x 2, 7 possible effects

  6. Families • Set of hypotheses = Family • Type I error rate for a set of hypotheses = Familywise error rate • e.g., across pairwise comparisons in one-way ANOVA • If no mean differences exist, what is the chance of finding a significant one? • e.g., across main effects/interactions in factorial ANOVA • If no main effects or interactions exist for a particular ANOVA, what is the chance of finding a significant one • e.g., whole experiment with multiple ANOVAs • If no effects exist for the entire experiment, what is the chance of finding a significant one?

  7. Family Size • "If these inferences are unrelated in terms of their content or intended use (although they may be statistically dependent), then they should be treated separately and not jointly” • Hochberg and Tamhane (1987) • e.g., suicide rates for 50 states, with 1225 possible pairwise comparisons • From a federal perspective, how big is the family? • How about from a state perspective?

  8. Familywise  • If family consists of two independent comparisons with  = .05, AND if both corresponding null hypotheses are true: • The probability of NOT making a Type I error on both tests is: .95 x .95 = .9025 • The probability of making one or more type I errors is: 1 - .9025 = .0975 • If family consists of c independent comparisons with  = .05, AND if all corresponding null hypotheses are true: • The probability of NOT making a Type I error on all tests is: (1 - .05)c • The probability of making one or more Type I errors is: 1 - (1 - .05)c

  9. A Priori vs. Post-hoc Comparisons • A priori comparisons • Chosen before data collection • Limited, deliberate comparisons • Post hoc (a posteriori) comparisons • Conducted after data collection • Exhaustive, exploratory comparisons

  10. Significance of Overall F • Prerequisite for some tests (e.g., Fisher’s LSD) • Efficient test of overall null hypothesis • Need MSwithin for many tests

  11. A Priori Comparisons • Single stage tests • Multiple t-tests • Linear contrasts • Bonferroni t (Dunn’s test) • Dunn-Sidak test • Multistage tests • Bonferroni/Holm

  12. Multiple t-tests • Replace s2pooled with MSwithin • Use dfwithin

  13. Linear Contrasts • Compare more than one mean with another mean

  14. Bonferroni t (Dunn’s Test) • If c independent tests are performed corrected =  / c pcorrected = p x c • Imprecise math • e.g., for pcorrected = .05 with c = 21, pcorrected 1.05 • pcorrected = 1 - (1 - .05)c Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilit. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3-62. Perneger, T.V. (1998). What is wrong with Bonferroni adjustments. BMJ,136,1236-1238.

  15. Dunn-Sidak Test • Identical to Bonferroni, except uses correct math • Less conservative than Bonferroni • e.g., for pcorrected = .05 with c = 10: • pBonferroni= .50 • pSidak= .40

  16. Multistage Bonferroni (e.g., Holm) • Calculate t for all c contrasts of interest • Order results based on |t| |t1| > |t2| > |t3| • Apply different Bonferroni corrections for  or p based on position in above sequence, stopping when t is insignificant • For t1, c1= 3; if p1 > .05/3, then… • For t2, c2= 2; if p2 > .05/2, then… • For t1, c1= 3; use  = .05/1

  17. Post-hoc Comparisons • Fisher’s LSD • Tukey’s test • Newman-Keuls test • The Ryan procedure (REGWQ) • Scheffe’s test • Dunnett’s test

  18. Fisher’s LSD Test • LSD = Least significant difference • Two-stage process: • Conduct ANOVA • If F is nonsignificant, stop • If F is significant… • Make pairwise comparisons using • Ensures familywise  = .05 for complete null • Ensures familywise  = .05 for partial null when c = 3

  19. Studenized Range Statistic (q) • If Ml and Ms represent the largest and smallest means and r is the number of means in the set: • Order means from smallest to largest • Determine r, calculate q, lookup p

  20. Tukey’s HSD Test • Determines minimum difference between treatment means that is necessary for significance • HSD = honestly significant difference

  21. Scheffe • Not for post-hoc pairwise comparisons • Not for a priori comparisons • Howell: “I can’t imagine when I would ever use it, but I have to include it here because it is such a standard test”

  22. Newman-Keuls (S-N-K) Test • Readjusts r based upon means tests • Doesn’t control for familywise  = .05

  23. Comparing Different Procedures

  24. Which Test? • One contrast • Simple: t-test • Complex: linear contrast • Several contrasts • A priori: Multistage Bonferroni (e.g., Holm) • Post-hoc: Fisher’s LSD • Many contrasts • Ryan REGRQ or Tukey • Find critical values for different tests • with a control: Dunnett • planned: Bonferroni • not planned: Scheffé

  25. Imaging Data • 200,000 tests on 200,000 voxels • 1000 false positives when  = .05 • Bonferroni? • No, requires voxel independence

More Related