Experimental Design

# Experimental Design

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## Experimental Design

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1. Experimental Design http://thefifthlevel.blogspot.com/2011/05/prologue-genesis.html

2. Experimental Design • Strongestdesignwithrespect to internalvalidity

3. If X then Y and If not X, then not Y or Iftheprogramisgiven, thentheoutcomeoccurs and Iftheprogramis not given, thentheoutcomedoes not occur

4. Dilemma • 2 identicalgroups • 2 identicalcontexts • Same time • …. similarity

5. Course of Action • Randomlyassignpeople from a pool to the 2 groups •  probabilisticallyequivalent • One groupgetsthetreatment and theotherdoes not

6. RandomSelection and Assignment • Randomselectionishowyoudrawthesample of people foryourstudyfrom a population. • Random assignmentishowyouassignthe sample thatyoudraw to different groupsortreatments in yourstudy.

7. ProbabilisticEquivalence • Meansthatweknowperfectlytheoddsthatwe will find a differencebetweentwogroups. • Whenwerandomlyassign to groups, wecancalculatethechancethatthetwogroups will differ just because of therandomassignment.

8. Externalvalidity • Experiments aredifficult to carry out •  artificialsituation  high internalvalidity • Limitedgeneralization to real contexts –> limitedexternalvalidity ?

9. Two-Group Experimental Design • Simplest form: two-groupposttest-onlyrandomizedexperiment • No pretestrequired • Test fordifferences: t-testor ANOVA

10. Advantages • Strongagainstsingle-groupthreats and multi-groupthreats (exceptselection-mortality) • Strong againstselectiontesting and selection-instrumentation

11. Classifying Experimental Designs • Twocomponents: signal and noise • signal-enhancing experimental design (factorialdesign) • Noise-reducing experimental design (covariancedesignsorblockingdesigns)

12. Factorial Designs • A factoris a major independent variable • Time and setting • A levelis a subdivision of a factor. • Time (1h/4h), setting (pull-out/in-class) • 2 x 2 factorialdesign

13. Factorial Design • X11 = 1h and in-class • X12 = 1h and pull-out • X21 = 4h and in-class • X22 = 4h and pull-out

14. The Null Outcome • The null caseis a situationwherebothtreatmentshave no effect.

15. The Main Effects A maineffectis an outcomethatis a consistentdifferencebetweenlevels of a factor.

16. Main Effects

17. Main Effects

18. Interaction Effects • An interactionfactorexistswhendifferences on onefactordepending on thelevel of theotherfactor.

19. How do youknowifthereis an interaction in a factorialdesign? • Statisticalanalysis • Whenitcanbetalkedaboutonefactorwithoutmentioningtheotherfactor • In graphs of groupmeans – thelinesare not parallel

20. Interaction Effects

21. Interaction Effects

22. Factorial Design Variations • 2 x 3 Example Factor 1: Treatment • psychotherapy • behaviormodification Factor 2: Setting • inpatient • daytreatment • outpatient

23. Main Effects

24. Main Effects

25. Interaction Effect

26. Interaction Effect

27. Factorial Design Variations • A Three-FactorExample(2 x 2 x 3) Factor 1: Dosage • 100 mg • 300 mg Factor 2: Treatment • Psychotherapy • Behaviormodification Factor 3: Setting • Inpatient • Day treatment • Outpatient

28. 2 x 2 x 3 Design

29. IncompleteFactorial Design • Common useis to allowfor a controlorplacebogroupthatreceives no treatment

30. Randomized Block Design • Stratifiedrandomsampling • To reducenoiseorvariance in thedata • Division intohomogeneoussubgroups • Treatment implemented to eachsubgroup • Variabilitywithineach block islessthanthevariability of theentiresampleoreach block ismorehomogenousthantheentiregroup

31. Randomized Block Design • Stundentsare a homogenousgroupwithexception of semester freshman sophomore junior senior

32. Howblockingreducesnoise?

33. Covariance Designs (ANCOVA) • Pretest-posttestrandomizeddesign • Pre-programmeasure = covariate • Covaryitwiththeoutcome variable • Covariatesarethe variables youadjustfor • Effectisgoing to beremoved

34. Howdoes a CovariatereduceNoise?

35. Howdoes a CovariatereduceNoise?

36. Howdoes a CovariatereduceNoise?

37. Howdoes a CovariatereduceNoise?

38. Howdoes a CovariatereduceNoise?

39. Hybrid Experimental Designs • Are newstrainsthatareformedbycombiningfeatures of moreestablisheddesigns.

40. The Solomon Four-Group Design • Isdesigned to deal with a certaintestingthreat • 2 groupsarepre-tested, 2 are not • 2 groupsget a treatment, 2 do not

41. The Solomon Four-Group Design • T = Treatment Group, C = Control Group

42. The Solomon Four-Group Design • T = Treatment Group, C = Control Group

43. SwitchingReplication Design • Theimplementation of thetreatmentisrepeatedorreplicated. • In therepetition, thetwogroupsswitchroles • Finally, all participantshavereceivedthetreatment • Reducessocialthreats

44. SwitchingReplication Design • Period 1 – group 1 getsthetreatment • Period 2 – group 2 getsthetreatment

45. SwitchingReplication Design • Longtermtreatmenteffect group 1 improveseventhough no furthertreatment was given