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Experimental Design

Experimental Design. Experimental Design. Strongest design with respect to internal validity. If X then Y and If not X, then not Y or If the program is given , then the outcome occurs and If the program is not given , then the outcome does not occur. Dilemma.

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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

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