1 Lec 1: Introduction 9 Level of measurement 9 Central ... · 1 Lec 1: Introduction 9 Level of measurement 9 Central ... ... 1.

1 Lec1:Introduction...............................................................................................................................................................91.1 Levelofmeasurement........................................................................................................................................91.2 Centraltendency.................................................................................................................................................91.3 Normaldistribution............................................................................................................................................91.4 ANOVA...............................................................................................................................................................9

1.4.1 F-testvsT-test.......................................................................................................................................................101.4.2 Flexibleandpowerfultool.....................................................................................................................................10

1.5 Wheredoesknowledgecomefrom?.................................................................................................................101.6 Scientificmethod..............................................................................................................................................10

1.6.1 Inanutshell...........................................................................................................................................................101.6.2 Hypotheses,TheoriesandLaws............................................................................................................................10

1.7 Theory..............................................................................................................................................................111.7.1 Induction/InductiveReasoning..............................................................................................................................111.7.2 Deduction/DeductiveReasoning...........................................................................................................................11

1.7.2.1 Usedin............................................................................................................................................................................111.7.3 TheScientificMethodassumes.............................................................................................................................111.7.4 TheMethodischaracterisedby............................................................................................................................11

1.8 TheMethodinanutshell..................................................................................................................................111.9 Whatmakesagoodtheory...............................................................................................................................121.10 AScientificTheoryMustBeTestable.................................................................................................................121.11 AScientificTheoryshouldbeRefutable............................................................................................................12

1.11.1 ThelogicofRefutation......................................................................................................................................121.11.1.1 Introtologic....................................................................................................................................................................121.11.1.2 Anexampleofavalidlogicalinference..........................................................................................................................131.11.1.3 Anothervalidlogicalinference.......................................................................................................................................131.11.1.4 Denyingtheantecedent..................................................................................................................................................131.11.1.5 Affirmingtheconsequent...............................................................................................................................................141.11.1.6 Refuteit..........................................................................................................................................................................14

1.12 Thusitiswithscience….....................................................................................................................................142 Lec2:Experimentaldesign,VariablesandOperationalisation.............................................................................................15

2.1 ObjectivesofPsychologicalResearch................................................................................................................152.2 HowtoConductResearch.................................................................................................................................15

2.2.1 MajorMethodologicalApproaches.......................................................................................................................152.2.2 CategorizingResearchApproaches.......................................................................................................................15

2.3 Quantitative.....................................................................................................................................................152.4 TheVariable–akeyconceptinQuantitativeResearch.....................................................................................15

2.4.1 Whichofthesearevariables.................................................................................................................................162.4.2 VariablesinQuantitativeResearch........................................................................................................................162.4.3 Fundamentalquestion...........................................................................................................................................162.4.4 Othervariablesinquantitativeresearch...............................................................................................................16

2.4.4.1 ExtraneousVariables......................................................................................................................................................162.4.4.1.1 Confoundingvariable.................................................................................................................................................17

2.4.4.2 MediatingVariable/InterveningVariable......................................................................................................................172.4.4.3 ModeratingVariable.......................................................................................................................................................17

2.5 Theresearchproblem/question........................................................................................................................172.6 Workthroughanexample................................................................................................................................172.7 Aninterestingphenomena................................................................................................................................17

2.7.1 Isthereatheory?...................................................................................................................................................172.7.2 Theory/prediction/question..............................................................................................................................182.7.3 Researchquestion.................................................................................................................................................182.7.4 Howdoyouanswerthisquestion?.......................................................................................................................182.7.5 Somewaystoanswerresearchquestions.............................................................................................................182.7.6 Possiblestudyideas...............................................................................................................................................18

2.7.6.1 Gotoanightclubandasksomequestions.....................................................................................................................192.7.6.2 AKeyCharacteristicofScientificResearch.....................................................................................................................192.7.6.3 Howaboutoperationalisingattractiveness?..................................................................................................................192.7.6.4 Whatvalidinferencescanwedrawfromthis?...............................................................................................................19

2.8 Correlation........................................................................................................................................................202.8.1 TheissueofCausation...........................................................................................................................................20

2.8.2 InferringCausality..................................................................................................................................................202.9 Anexperimentshouldbe…...............................................................................................................................202.10 Someimportantethicalissues..........................................................................................................................202.11 ExperimentalApproach.....................................................................................................................................21

2.11.1 Advantages........................................................................................................................................................212.11.2 Disadvantage.....................................................................................................................................................21

2.12 ExperimentalResearchSettings........................................................................................................................212.12.1 InternetExperiments........................................................................................................................................212.12.2 Fieldexperiments..............................................................................................................................................212.12.3 Laboratoryexperiments....................................................................................................................................21

2.12.3.1 DifferentwayswecouldmanipulateIVs........................................................................................................................212.12.3.1.1 Beergogglesexperiment1.......................................................................................................................................222.12.3.1.2 Beergogglesexperiment2.......................................................................................................................................22

2.12.3.2 DifferentwayswecouldmanipulateIVs........................................................................................................................222.12.3.2.1 Beergogglesexperiment3.......................................................................................................................................22

2.12.3.3 DifferentwayswecouldmanipulateIVs........................................................................................................................222.12.3.3.1 Beergogglesexperiment4.......................................................................................................................................22

2.12.4 Potentialmanipulations....................................................................................................................................223 Lec3:Sampling,ValidityandReliability..............................................................................................................................22

3.1 TheissueofCausation......................................................................................................................................233.2 Findsomeparticipants......................................................................................................................................23

3.2.1 Somekeyterms.....................................................................................................................................................233.2.2 Aimofsampling.....................................................................................................................................................233.2.3 Representativeness...............................................................................................................................................233.2.4 Samplingbias.........................................................................................................................................................243.2.5 Samplingprocedures.............................................................................................................................................24

3.2.5.1 Probabilitysampling.......................................................................................................................................................243.2.5.2 Sub-typesofprobabilitysampling..................................................................................................................................24

3.2.5.2.1 Simplerandomsample...............................................................................................................................................243.2.5.2.2 Systematicrandomsample........................................................................................................................................243.2.5.2.3 Stratifiedsampling.....................................................................................................................................................25

3.2.5.2.3.1 SimpleRandomSamplingVersusStratifiedSampling........................................................................................253.2.5.2.4 Multi-stageClustersampling.....................................................................................................................................253.2.5.2.5 Multi-Stage/Multi-PhaseSampling............................................................................................................................263.2.5.2.6 AdvantagesofProbabilitySampling...........................................................................................................................263.2.5.2.7 Problemwithprobabilitysampling............................................................................................................................26

3.2.5.3 Non-probabilitysampling...............................................................................................................................................263.2.5.3.1 ConvenienceSamples................................................................................................................................................263.2.5.3.2 SnowballSampling.....................................................................................................................................................263.2.5.3.3 QuotaSample.............................................................................................................................................................273.2.5.3.4 Purposive/judgmentsampling...................................................................................................................................27

3.2.5.4 WhichSamplingMethod?...............................................................................................................................................273.2.6 Howmanypeopleshouldyoutest?......................................................................................................................27

3.2.6.1 DeterminingSampleSize1.............................................................................................................................................273.2.6.2 DeterminingSampleSize2.............................................................................................................................................283.2.6.3 DeterminingSampleSize3.............................................................................................................................................28

3.3 Makesomemeasurements...............................................................................................................................283.3.1 OperationalisationofIVsandDVs.........................................................................................................................283.3.2 ReliabilityandValidity...........................................................................................................................................283.3.3 ReliabilityandValidity...........................................................................................................................................28

3.3.3.1 Therelationshipbetweenreliabilityandvalidity............................................................................................................293.3.3.2 Reliability........................................................................................................................................................................293.3.3.3 TypeofReliabilitytest.....................................................................................................................................................29

3.3.3.3.1 Test-retestreliability..................................................................................................................................................293.3.3.3.1.1 ProblemwithTest-retest....................................................................................................................................29

3.3.3.3.2 Split-halfreliability:isyourmeasureinternallyconsistent........................................................................................293.3.3.3.2.1 Cronbach'sAlpha................................................................................................................................................30

3.3.3.3.3 Inter-raterorinter-observerreliability......................................................................................................................303.3.3.3.3.1 Calculationofinter-raterreliability....................................................................................................................30

3.3.3.4 Validity............................................................................................................................................................................303.3.3.4.1 TypesofValidity.........................................................................................................................................................30

3.3.3.4.1.1 FaceValidity........................................................................................................................................................313.3.3.4.1.2 Contentvalidity..................................................................................................................................................31

3.3.3.4.1.3 Criterion-relatedvalidity.....................................................................................................................................313.3.3.4.1.3.1 Criterion-relatedvalidity:Concurrentvalidity............................................................................................313.3.3.4.1.3.2 Criterion-relatedvalidity:PredictiveValidity..............................................................................................31

3.3.3.4.1.4 ConstructValidity...............................................................................................................................................323.3.3.4.1.4.1 Convergentvalidity.....................................................................................................................................323.3.3.4.1.4.2 Divergentvalidity........................................................................................................................................32

4 Lec4:ExperimentalDesignsandControl.............................................................................................................................334.1 TheExperiment.................................................................................................................................................334.2 InternalvsExternalValidity..............................................................................................................................33

4.2.1 FourStepstoInternalValidity...............................................................................................................................334.3 SomeWeakExperimentalDesigns....................................................................................................................33

4.3.1 Whatdolearnfromthis?.......................................................................................................................................344.3.2 ManipulationoftheIV...........................................................................................................................................344.3.3 IVsandStudyDesign.............................................................................................................................................34

4.3.3.1 FactorialDesign–isn’tthatabitcomplicated?..............................................................................................................344.3.3.2 FactorialDesigns.............................................................................................................................................................344.3.3.3 FactorialDesignLayoutExample....................................................................................................................................354.3.3.4 FactorialDesignNotation...............................................................................................................................................354.3.3.5 WeaknessesofFactorialDesigns....................................................................................................................................36

4.4 StrongExperimentalDesigns.............................................................................................................................364.4.1 Variability...............................................................................................................................................................36

4.5 SeparateandCompress....................................................................................................................................364.5.1 Separate.................................................................................................................................................................36

4.5.1.1 Separation:achievedbyIVOperationalisation...............................................................................................................364.5.1.2 DeterminingLevelsofIV.................................................................................................................................................36

4.5.2 Compress...............................................................................................................................................................364.5.2.1 Compression:IsachievedbycontrollingExtraneousVariables......................................................................................37

4.5.3 ExtraneousVariables:BGvsRM............................................................................................................................374.5.3.1 ThetroublewithBGdesigns...........................................................................................................................................374.5.3.2 Selection.........................................................................................................................................................................37

4.5.3.2.1 potentialforbiastoconfoundresults........................................................................................................................374.5.3.2.2 RandomAssignment/Allocation...............................................................................................................................374.5.3.2.3 Matching....................................................................................................................................................................37

4.5.3.2.3.1 IndividualMatching............................................................................................................................................374.5.3.2.3.2 DistributionMatching.........................................................................................................................................384.5.3.2.3.3 DifficultieswithMatching...................................................................................................................................38

4.5.3.2.4 AlternativelybuildtheEVintothedesign..................................................................................................................384.5.3.3 Couldusethesameparticipants.....................................................................................................................................384.5.3.4 ProblemswithRMDesigns.............................................................................................................................................38

4.5.3.4.1 Problem:OrderEffects...............................................................................................................................................384.5.3.4.1.1 Possiblesolution:Counterbalancing..................................................................................................................39

4.5.3.4.1.1.1 DifferentFlavoursofCounterbalancing......................................................................................................394.5.3.4.1.2 CarryOverEffects...............................................................................................................................................394.5.3.4.1.3 Counterbalancingisnotalwayspossible............................................................................................................394.5.3.4.1.4 AspecialCase:TimeasanIV..............................................................................................................................39

4.5.3.4.1.4.1 Maturation(internalevents)......................................................................................................................404.5.3.4.1.4.2 History(Externalevents).............................................................................................................................404.5.3.4.1.4.3 StatisticalRegression..................................................................................................................................404.5.3.4.1.4.4 Mortality.....................................................................................................................................................40

4.5.4 OtherthreatstoanExperiment’sValidity.............................................................................................................404.5.4.1 ExperimenterEffects.......................................................................................................................................................404.5.4.2 ParticipantEffects...........................................................................................................................................................41

4.5.4.2.1 ControlofParticipantEffects.....................................................................................................................................414.5.4.3 SituationalEffects...........................................................................................................................................................42

4.5.5 ControlGroups......................................................................................................................................................424.6 SoWGorBGinanutshell?................................................................................................................................42

5 Lec5:ReviewofDescriptiveStatisticsandHypothesis........................................................................................................435.1 DescriptivevsInferentialStatistics....................................................................................................................435.2 Thethingaboutequationsis…..........................................................................................................................435.3 Imaginewehaveasetofdata…........................................................................................................................43

5.3.1 Characterisingadataset.......................................................................................................................................435.3.2 TheNormalDistribution........................................................................................................................................43

5.3.3 TheShapeofDistributions-Modality...................................................................................................................435.3.4 TheShapeofDistributions:Kurtosis......................................................................................................................435.3.5 TheShapeofDistributions:skew..........................................................................................................................435.3.6 Forthemoment,wearejustgoingtoassume“normality”..................................................................................445.3.7 Sigmaå&Mean....................................................................................................................................................455.3.8 Equation................................................................................................................................................................465.3.9 df............................................................................................................................................................................46

5.4 ThePurposeofMeansandSD’s........................................................................................................................465.5 ThePurposeofMeansandSD’s........................................................................................................................465.6 DescriptivevsInferentialStatistics....................................................................................................................475.7 Howwedoinferentialstatistics........................................................................................................................475.8 Outlier..............................................................................................................................................................475.9 NormalDistribution..........................................................................................................................................475.10 Z:theStandardisedNormalDistribution...........................................................................................................47

5.10.1 TheZ-score........................................................................................................................................................475.10.2 TheZ-test..........................................................................................................................................................485.10.3 Whatwearedoinghereisaveryspecialcaseofhypothesistesting...............................................................48

5.11 T-tests...............................................................................................................................................................495.11.1 Whatist?..........................................................................................................................................................495.11.2 Thetdistributionvs.normaldistribution.........................................................................................................505.11.3 TypesofT-tests.................................................................................................................................................50

5.11.3.1 Howaboutcomparingdifferentgroups?........................................................................................................................505.11.3.1.1 IndependentGroups................................................................................................................................................50

5.12 Summaryofformulas........................................................................................................................................515.13 Error&StatisticalSignificance...........................................................................................................................515.14 Makingandcheckingassumptions....................................................................................................................515.15 AfinalnoteonStatisticalSignificance...............................................................................................................51

6 Lec6:MultipleGroupsDesigns&AnalysisofVariance(ANOVA).........................................................................................536.1 Whymorethan2groups...................................................................................................................................53

6.1.1 Morethantwogroupsofinterest.........................................................................................................................536.1.2 Examiningmultipletreatments.............................................................................................................................536.1.3 De-confoundingastudy.........................................................................................................................................536.1.4 Refiningourunderstanding...................................................................................................................................536.1.5 Lookingfornatureofrelationships.......................................................................................................................546.1.6 IVandDVRelationships.........................................................................................................................................546.1.7 SettingyourIVupwithyourDV............................................................................................................................54

6.2 t-tests...............................................................................................................................................................546.3 Analysingmultiplegroupdesigns......................................................................................................................546.4 t-testtoANOVA................................................................................................................................................556.5 TheFratio.........................................................................................................................................................556.6 VariabilityandANOVA......................................................................................................................................56

6.6.1 BGVariability(BG).................................................................................................................................................566.6.2 WGVariability(WG)..............................................................................................................................................566.6.3 VariabilityandANOVA...........................................................................................................................................56

6.7 Hypothesistesting&Fratio..............................................................................................................................566.7.1 ANOVAanalysesvariance,butittellsusaboutmeans.........................................................................................576.7.2 ANOVATable.........................................................................................................................................................576.7.3 ComputingBG/WGvariability...............................................................................................................................576.7.4 Example.................................................................................................................................................................57

6.7.4.1 ComputingWGvariability...............................................................................................................................................576.7.4.2 ComputingBGvariability................................................................................................................................................576.7.4.3 Sumofsquares(SS).........................................................................................................................................................586.7.4.4 SStotal................................................................................................................................................................................586.7.4.5 Computingtotalvariability.............................................................................................................................................586.7.4.6 Linearmodel...................................................................................................................................................................58

6.7.5 WhattodowithSSs...............................................................................................................................................586.7.6 Df...........................................................................................................................................................................59

6.7.6.1 DeterminingDf................................................................................................................................................................596.7.6.2 dfarealsopartitioned....................................................................................................................................................60

6.7.7 CalculatingMS&Fratio........................................................................................................................................60

6.7.8 ANOVASummaryTable.........................................................................................................................................606.7.9 InterpretingF.........................................................................................................................................................606.7.10 ReportingANOVA..............................................................................................................................................616.7.11 Summaryofanalysis..........................................................................................................................................61

6.8 Takehomepoints.............................................................................................................................................616.9 QuickNotes......................................................................................................................................................616.10 Next2weeks....................................................................................................................................................61

7 Lec7:FdistributionAssumptionsofANOVA.......................................................................................................................627.1 StatisticalHypothesisforIGANOVA..................................................................................................................627.2 SamplingError&Fdistribution.........................................................................................................................627.3 FDistribution....................................................................................................................................................62

7.3.1 F-distributionshape...............................................................................................................................................627.3.2 ProbabilityandtheF-distribution..........................................................................................................................637.3.3 ErrorsinHypothesisTesting..................................................................................................................................637.3.4 Errorrates..............................................................................................................................................................63

7.4 IGANOVAAssumptions....................................................................................................................................637.4.1 TheIndependenceAssumption.............................................................................................................................647.4.2 TheNormalityAssumption....................................................................................................................................64

7.4.2.1 Outliers...........................................................................................................................................................................657.4.2.1.1 Checkingfor“Outliers”..............................................................................................................................................657.4.2.1.2 Outliers:UseaZscore................................................................................................................................................65

7.4.2.2 Whattodowithoutliers.................................................................................................................................................657.4.2.2.1 Transformdatatoremoveoutlier..............................................................................................................................65

7.4.3 TheHomogeneityofVarianceAssumption...........................................................................................................667.4.3.1 Levenestatistics..............................................................................................................................................................667.4.3.2 Dealingwithbreaches.....................................................................................................................................................66

7.4.3.2.1 LoweringtheαLevel..................................................................................................................................................667.4.3.2.2 Distribution-FreeTests...............................................................................................................................................66

7.4.3.2.2.1 MajorRank-Ordertestscorrespondingtomajorparametrictests....................................................................677.4.3.2.2.1.1 Kruskal-WallisOne-WayANOVA.................................................................................................................67

7.4.4 DataTransformations............................................................................................................................................677.4.4.1 Logarithmictransformation............................................................................................................................................67

7.4.5 Stepsindoingatransform.....................................................................................................................................687.4.6 TotransformdatawithSPSS.................................................................................................................................687.4.7 ComputerBootstrapTechniques...........................................................................................................................687.4.8 ComparisonofMethods........................................................................................................................................687.4.9 NormalityandHomogeneitySummary.................................................................................................................697.4.10 Reporting...........................................................................................................................................................69

8 Lec8:PlannedComparisonsandPostHocTests.PowerandEffect.....................................................................................708.1.1 ANOVASummaryTable.........................................................................................................................................708.1.2 Fratiodoesnotpaintthewholepicture...............................................................................................................708.1.3 ApproachestoComparisons..................................................................................................................................70

8.2 Plannedcomparisons........................................................................................................................................718.2.1 AssigningWeightsorCoefficients.........................................................................................................................71

8.2.1.1 Plannedcontrasts...........................................................................................................................................................718.2.1.2 ComplexPlannedComparisons......................................................................................................................................72

8.2.2 TestingtheSignificanceofContrasts.....................................................................................................................728.3 PlannedComparisons.......................................................................................................................................72

8.3.1 AssumptionsofPlannedComparisons..................................................................................................................738.3.2 SPSS&PlannedComparisons................................................................................................................................738.3.3 WriteUpPlannedcontrasts...................................................................................................................................748.3.4 TypeIErrorRates..................................................................................................................................................74

8.4 PostHocComparisons.......................................................................................................................................758.4.1 SPSS&PostHocTests............................................................................................................................................768.4.2 SPSS&PostHocTests(table1).............................................................................................................................768.4.3 SPSS&PostHocTests(table2).............................................................................................................................778.4.4 WriteUpofpost-hocs............................................................................................................................................77

8.5 Summary–whichcomparisontouse................................................................................................................778.6 EffectSize.........................................................................................................................................................77

8.6.1 Etasquared(h2).....................................................................................................................................................78

8.6.1.1 EffectSizeforANOVA.....................................................................................................................................................788.6.1.2 ANOVASummaryTable..................................................................................................................................................788.6.1.3 Etasquared(h2)forourIGANOVA.................................................................................................................................788.6.1.4 Criteriaforassessingh2..................................................................................................................................................78

8.6.2 Cohen’sd...............................................................................................................................................................788.6.2.1 Plannedcontrastsexample.............................................................................................................................................798.6.2.2 Cohen’sdWorkedExample............................................................................................................................................79

8.6.3 InterpretingEffectSize..........................................................................................................................................798.6.4 ReportingEffectSize..............................................................................................................................................798.6.5 Examples................................................................................................................................................................79

8.7 ConsideringErrorsinStatisticalDecisionMaking..............................................................................................808.7.1 MinimisingError....................................................................................................................................................808.7.2 Power.....................................................................................................................................................................808.7.3 Designissues..........................................................................................................................................................818.7.4 PowerandSampleSize..........................................................................................................................................818.7.5 Power,Effect,SampleSize....................................................................................................................................818.7.6 AStrategyforUsingPowerandEffectSize...........................................................................................................81

9 Lec9:RMDesigns...............................................................................................................................................................839.1 Example............................................................................................................................................................839.2 Gettingstarted..................................................................................................................................................839.3 BrainstormingDesign........................................................................................................................................839.4 IVmanipulationwithRMDesign.......................................................................................................................849.5 RMDesigns.......................................................................................................................................................84

9.5.1 IssueswithRMdesigns..........................................................................................................................................859.5.1.1 Remediestoordereffects...............................................................................................................................................85

9.5.1.1.1 Counterbalancing.......................................................................................................................................................859.6 RManalysis.......................................................................................................................................................85

9.6.1 PartitioningSSinRMANOVA................................................................................................................................869.6.2 PartitioningDFinRMANOVA................................................................................................................................869.6.3 ConceptualSSformulaeRMANOVA.....................................................................................................................869.6.4 SSparticipants/subjects.......................................................................................................................................869.6.5 Anapproachtocalculation....................................................................................................................................869.6.6 SSerrororresidual................................................................................................................................................879.6.7 ObtainingourFratio.............................................................................................................................................879.6.8 RMDesignMoreSensitive.....................................................................................................................................879.6.9 TestingSignificance...............................................................................................................................................879.6.10 Assumptions......................................................................................................................................................88

9.6.10.1 BreachesofSphericity.....................................................................................................................................................889.6.10.1.1 TraditionalModel.....................................................................................................................................................88

9.6.10.1.1.1 EpsilonAdjustments.........................................................................................................................................889.6.10.1.1.2 SPSSoutputprovidesEpsilonadjustFtests.....................................................................................................89

9.6.10.1.2 MultivariateModelApproach..................................................................................................................................909.6.10.1.2.1 RMANOVA:Power&EffectSize&Comparisons.............................................................................................909.6.10.1.2.2 FollowUpTestsorPlannedComparisons........................................................................................................919.6.10.1.2.3 RunningaRMANOVAinSPSS..........................................................................................................................919.6.10.1.2.4 TraditionalModelOutput.................................................................................................................................929.6.10.1.2.5 MultivariateApproachOutput.........................................................................................................................929.6.10.1.2.6 ContrastsandPostHocs...................................................................................................................................92

9.6.10.1.2.6.1 OutputforLinearContrasts......................................................................................................................929.6.10.1.2.6.2 OutputforPost-hocTests.........................................................................................................................93

9.6.10.1.2.7 PlannedComparisonsusingSPSSPairedSamplest-tests.................................................................................939.6.10.2 Examplewriteup............................................................................................................................................................93

9.6.11 Summary...........................................................................................................................................................949.6.12 Whichapproachtouse?....................................................................................................................................94

10 Lec10:CorrelationandRegression.....................................................................................................................................9510.1 Correlation........................................................................................................................................................95

10.1.1 CorrelationRevisited.........................................................................................................................................9510.2 examplefromPYB110…....................................................................................................................................95

10.2.1 Calculatingthecorrelationcoefficientr............................................................................................................9610.2.1.1 ZscoresandPearson’sr:themissinglink.......................................................................................................................9610.2.1.2 Sowhatdoesthatmean?...............................................................................................................................................96

10.2.1.3 Correlationonlytellshalfofthestory...........................................................................................................................9610.3 Regression........................................................................................................................................................96

10.3.1 Aninvisiblelineofbestfit…..............................................................................................................................9710.3.2 Whydowecallitthe“lineofbestfit”?............................................................................................................9710.3.3 Howcanwedrawthislineofbestfit?..............................................................................................................9710.3.4 Slope..................................................................................................................................................................9710.3.5 Allhailthemightyregressionequation............................................................................................................97

10.3.5.1 HowdoIcalculatetheslope?.........................................................................................................................................9710.3.5.2 WhatistheY-axisintercept?..........................................................................................................................................98

10.3.5.2.1 Exampleofapositiveintercept................................................................................................................................9810.3.6 HowdoIcalculatetheintercept?.....................................................................................................................98

10.3.6.1 So...Usingthisinformationwecan................................................................................................................................9810.3.6.2 Calculationtable.............................................................................................................................................................9810.3.6.3 Plottingtheregressionline.............................................................................................................................................9910.3.6.4 Ohthepowerwewield...................................................................................................................................................99

10.3.7 Howaccurateismyprediction?........................................................................................................................9910.3.8 Residuals...........................................................................................................................................................99

10.3.8.1 Let’slookatsomeresiduals............................................................................................................................................9910.3.8.2 Towardsameasureofaccuracy....................................................................................................................................10010.3.8.3 StandardErroroftheEstimateorSEE..........................................................................................................................10010.3.8.4 TheEstimatedPopulationStandardErroroftheEstimate...........................................................................................100

10.3.9 BackintoSSland.............................................................................................................................................10110.3.9.1 Alittlesidenoteaboutr2andSEE.................................................................................................................................10110.3.9.2 TheAccuracyofPrediction...........................................................................................................................................10210.3.9.3 Whyisn’titsignificant?.................................................................................................................................................10210.3.9.4 Pullingitalltogether.....................................................................................................................................................10210.3.9.5 OurexampleinSPSS.....................................................................................................................................................102

10.3.9.5.1 SPSSRegressionOutput.........................................................................................................................................10310.3.10 AssumptionsforCorrelationandRegression..................................................................................................103

10.3.10.1 Possiblerelationships...............................................................................................................................................10310.3.10.2 APotentialProblemforCorrelationandRegression...............................................................................................10410.3.10.3 CorrelationDOESNOTimplyCausation...................................................................................................................104

10.4 Review.............................................................................................................................................................10411 Lec11:QualityofQualitativeResearch.............................................................................................................................106

11.1 ParadigmsinSocialResearch...........................................................................................................................10611.2 Quantitativevs.QualitativeResearch..............................................................................................................10611.3 QualitativeResearch–When?..........................................................................................................................10611.4 CriteriaforEvaluatingQualityofQuantitativeResearch..................................................................................10611.5 ElementsofRigourinQualitativeResearch......................................................................................................107

11.5.1 MethodologicalRigour....................................................................................................................................10711.5.2 InterpretiveRigour..........................................................................................................................................10711.5.3 DemonstratingRigour:DesignandMethods..................................................................................................10811.5.4 DemonstratingRigour:CodingandAnalysis...................................................................................................10811.5.5 DemonstratingRigour:Reflexivity...................................................................................................................109

11.5.5.1 PerspectiveoftheResearcher......................................................................................................................................10911.5.5.2 Reflexivity......................................................................................................................................................................10911.5.5.3 ReportingInformationabouttheResearcher...............................................................................................................10911.5.5.4 PreconceptionandBias–What’stheDifference?........................................................................................................110

11.5.5.4.1 Beingreflective.......................................................................................................................................................11011.5.5.4.2 WhoseStoryisitAnyway?.....................................................................................................................................110

12 Lec12:DoingQualitativeResearch...................................................................................................................................11112.1 ParadigmsinSocialResearch...........................................................................................................................11112.2 ApproachingQualitativeResearch...................................................................................................................11112.3 TheoreticalFrameworks...................................................................................................................................11112.4 Methodologies.................................................................................................................................................111

12.4.1 PeopleasResearchSubjects...........................................................................................................................11112.4.2 PeopleasResearchInformants.......................................................................................................................11212.4.3 PeopleasResearchPartners...........................................................................................................................11212.4.4 Examples.........................................................................................................................................................112

12.5 Method............................................................................................................................................................11212.5.1 SelectingParticipants......................................................................................................................................112

12.5.2 CollectingData................................................................................................................................................11312.5.2.1 Interacting(Mostcommon)..........................................................................................................................................11312.5.2.2 Observing......................................................................................................................................................................11412.5.2.3 Gathering......................................................................................................................................................................114

12.6 AnalysingData.................................................................................................................................................11412.7 Summary.........................................................................................................................................................115

13 CourseOverview..............................................................................................................................................................11613.1 TheMethodinanutshell.................................................................................................................................11613.2 Induction/InductiveReasoning........................................................................................................................11613.3 Deduction/DeductiveReasoning......................................................................................................................11613.4 AScientificTheoryMustBeTestable................................................................................................................11613.5 AScientificTheoryshouldbeRefutable...........................................................................................................11613.6 ObjectivesofPsychologicalResearch...............................................................................................................11613.7 CategorizingResearchApproaches...................................................................................................................11613.8 InferringCausality............................................................................................................................................11713.9 Operationalisation...........................................................................................................................................11713.10 ExtraneousVariables101.............................................................................................................................11713.11 Sampling......................................................................................................................................................11713.12 Representativeness......................................................................................................................................11713.13 Reliability&Validity....................................................................................................................................118

13.13.1 TypesofReliability..........................................................................................................................................11813.13.2 TypesofValidity..............................................................................................................................................118

13.14 ManipulationoftheIV.................................................................................................................................11813.14.1 IVsandDesign.................................................................................................................................................118

13.14.1.1 ThetroublewithBGdesigns....................................................................................................................................11813.14.1.2 ProblemswithRMDesigns.......................................................................................................................................118

13.15 AspecialCase:TimeasanIV........................................................................................................................11913.16 OtherthreatstoanExperiment’sValidity....................................................................................................11913.17 SoWithinorBGinanutshell?......................................................................................................................11913.18 Howwedoinferentialstatistics...................................................................................................................11913.19 ANOVASummary.........................................................................................................................................119

13.19.1 PartitioningSSinBGANOVA...........................................................................................................................12013.19.2 TheFdistribution............................................................................................................................................12013.19.3 IGANOVAAssumptions..................................................................................................................................12013.19.4 AprioriandPostHocComparisons..................................................................................................................12013.19.5 PowerandEffect.............................................................................................................................................12013.19.6 RMorDependentGroupsANOVA.................................................................................................................120

13.19.6.1 PartitioningSSinRMANOVA...................................................................................................................................12113.19.6.2 Assumptions.............................................................................................................................................................121

13.19.7 KeysforstudyingANOVA................................................................................................................................12113.20 CorrelationandRegression..........................................................................................................................121

13.20.1 Correlationonlytellshalfofthestory............................................................................................................12113.20.2 CorrelationandRegression.............................................................................................................................12113.20.3 Breakingdowntheequation...........................................................................................................................12113.20.4 PortionsoftheSumofSquares.......................................................................................................................12113.20.5 SPSSRegressionOutput..................................................................................................................................122

13.21 QualitativeResearch....................................................................................................................................122

KeyforthisstudynotesSS:SumofSquareBG:Between-Group,Between-Subject,Independent-GroupWG:Within-Group,Within-Subject1 Lec1:Introduction1.1 Levelofmeasurement Nominal

• Variablewithvaluesthatarenamesorcategories(thatis,theyarenamesratherthannumbers)- Nominalcomesfromtheideathatitsvaluesarenames- Variableinnameonly.category,numberdon’tnecessarymeananything,justacategory,

e.g.religion,gender(1=male,2=female)- Doesn’tdenoteanythingabouttherelativemagnitude

Ordinal/Rank-ordervariables(inorderonly)• numericvariableinwhichthevaluesareranked,suchasclassstandingorplacefinishedina

race.• numericvariableinwhichvaluescorrespondtotherelativepositionofthingsmeasured• differenceinmagnitudeimplied,Nosetmagnitudebetweenthe2• notequalintervalsbetweenranks• grouphasorder,e.g.race,1st2nd3rd,stillacategory1st(10seconds)2nd(11secs)3rd(14

secs),magnitude• ranks:e.g.,placeinclass,orderinahorserace• e.g.GPAbetweenbeing2ndand3rdintheclasscouldbedifferentto8thand9thInterval• variableinwhichthenumbersstandforapproximatelyequalamountsofwhatisbeing

measured• numericvariableinwhichdifferencesbetweenvaluescorrespondtodifferencesinthe

underlyingthingbeingmeasured• hasmagnitude• differenceinmagnitudeimplied• equalintervalsareassumed• e.g.,timeelapsed,temperature,ages,GPA,weight,stresslevel• e.g.GPA2.5and2.8meansaboutasmuchasthedifferencebetweenaGPAof3and3.3Ratio

1.2 Centraltendency Mean:arithmetic“average”Median–mid-pointMode–mostcommonvalue

1.3 Normaldistribution Weknowwhatthepopulationaverageis,andwhatpeoplevaryaroundtheaverage

1.4 ANOVA ANalysisOfVariance• akaF-test• Variance

o Comparing2groupsofpeople=comparing2differentprobabilityofdistribution

o ANOVAistesting,istherealsovariancebetweenthegroupsintermsoftheirmeanasisscaledbythevariancewithinthegroup.Doesitexceedcertainamount?

• Forsimple(one-way)ANOVA:whatisratioofthevariabilitybetween2groupmeansdividedbythevariabilityoftheWGvariation

• Simplyaratiobetween2differenttypesofvariability• Totestiftheyaredifferenttoeachother

1.4.1 F-testvsT-test • T-test:testthenullhypothesis.Inotherwords–istherea‘significanteffect’inmydata?• So,–infact,F=t2• buttheANOVAismoreflexible,differencemorethantwogroups,twodimensions• BoththeT-testandtheANOVAarespecificcasesoftheGeneralLinearModel(apowerful

analytictool)

1.4.2 Flexibleandpowerfultool

• Howyouuseitdependsonthetypeofquestionyouwanttoask,andultimatelythisisintimatelyrelatedtoyourresearchdesign

• Inordertobesuccessful,youhavetomakedecisionabouthowyouaregoingtodoyouranalysisaspartoftheprocessofdesigningthewholeresearchmethodology

“Thegeneralwhowinsabattlemakesmanycalculationsinhistemplebeforethebattleisfought.Thegeneralwholosesabattlemakesbutfewcalculations.”―SunTzu,TheArtofWar

1.5 Wheredoesknowledgecomefrom?

Thesearesometraditionalideas(priortodevelopmentscientific/hypotheticaldeductivemethod)aboutwhereknowledgemightcomefrom.Butcanwerelyuponthem?• Authority:someonewithauthoritythatyoucantrust.Whatisareliablesource?

o Law,Professor,Newspaper??,Hitler• Intuition:justcometoyou,internallygeneratedanditseemsgood

o “gutfeelings”-theabilitytoacquireknowledgewithoutinferenceortheuseofreason• Rationality:Youdeduceitfromtheapplicationoflogicalprinciple

o Purereason–thetruthcanbederivedfromfirstprincipalsusinglogico Whataboutdistortedlogic?Witchesburnthereforetheyaremadeoutofwood,Wood

floats,Ducksfloat,Thereforeifthewomanweighsthesameasaduckshemustbeawitch.

• Empiricism:yousawitandmeasureito Seeingisbelievingo Amesroomisadistortedroomthatisusedtocreateanopticalillusion.

1.6 Scientificmethod Sometimesalsoreferredtoasthehypothetico-deductivemethod.Itischaracterisedbythe

developmentandsystematictestingoftheories.TheMethodinvolvesaspectsof

• Authority:trustscholarlyjournalsasareliablesourceofinformation• Intuition• Rationality:logicalrationalthinkingintermsofgenerationofhypotheses,andthe

structureoftestinghypotheses• Empiricism:actualgatheringofevidence

Scientificmethoduseswhatisgoodabouttheaboveaspects,butmaintainadegreeofscepticismtoo.Itischaracterisedbythedevelopmentoftheorieswhichhaveexplanatoryandpredictivecapacityandwhichmustbetestableandrefutable

1.6.1 Inanutshell • Youhaveatheorythatattemptstoexplainaparticularphenomenonofinterest• Thattheoryisusedtogeneratehypotheses–iftheoryXistrue,itfollowslogicallythatYshould

occur• Youtestthehypotheses

1.6.2 Hypotheses,Theories

andLaws• Ahypothesisisastatementthatcanbetested.Sothestatement,"Awatchedpotneverboils,"isa

validscientifichypothesisbecausewecantestit(andfindthatinthiscaseitisNOTsupportedbytheevidence).

• Atheoryisageneralprincipleorbodyofprinciplesthathasbeendevelopedtoexplainawidevarietyofphenomena.Itmustbeconsistentwithknownobservationsanditmusthavepredictive

power.Asnewknowledgeisgained,theoriesarerefinedtobetterexplainthedata.o Mustallowyoutomakenewprediction/hypothesis,whichyoucantest.Thenmodify

yourtheorybasedonthetest• Alawisamathematicalrelationshipthatisconsistentlyfoundtobetrue.E.g.,oneofthemost

famouslawsinphysicsisEinstein'se=mc^2.o Thereisnolawinpsychology,ithastheoryandhypothesis.So,nottooworry

1.7 Theory InductionandDeduction1.7.1 Induction/Inductive

Reasoning• Reasoningfromthespecifictothegeneral• Takingsomespecificsexamplasofcategoryofthings,thanassumeallthesecharacteristics

holdtrueacrossalltheseexamplas• E.g.Rainbowlorakeets,penguinsandeaglesallhavefeathersandbeaks.Therefore,toinduce

fromthis,allbirdshavefeathersandbeaks.• Makinginductiononthingsthatsomebirdshaveincommon,areallthingsbirdshavein

common• AlltheswanswehaveseensofarareblackinAustralia,thereforeallswansareblack(not

true)• Inductionreasoning:youarehopingtogeneralizewhatyouhaveobserveThelogicofdiscovery:TheoryDevelopment• Inductioncanbeuseful.Kindoflogicweusewhendevelopingtheory.Youcanhaveatheory

basedoninductiveprinciplefromtheobservations.Itcangowrong,butitisanimportantpartofscientificprocessfordevelopingtheory.

1.7.2 Deduction/Deductive

Reasoning• Oppositedirectiontodeduction• Reasoningfromthegeneraltothespecific,usinglogicalchainsofreasoning–syllogisms• E.g.allbirdshavefeathersandbeaks,rainbowlorakeetsarebirds,thereforerainbow

lorakeetshavefeathersandbeaks.Thelogicofjustification-Theorytesting• totestthehypothesis

1.7.2.1 Usedin • Developinghypotheses• IfXistrue,thenYshouldoccur• Hypothesesshouldbelogicalconsequencesofthetheory

o Ifyouhaveatheorythat“theheartistheseatoflove”ahypothesisthatfollowslogicallyfromthismightbethatifyouremovesomeone’shearttheywillno-longerbeabletolove,butthisalsomeansthepersonisdead.Needtobecarefulhowwetesthypotheses

• TestingHypotheses

1.7.3 TheScientificMethodassumes

• Thattheuniverseisordered,thereisastructureintheuniverse,thereisanunderliningprinciple

• Thatorder/structurewhichexistsisdiscoverable

1.7.4 TheMethodischaracterisedby

• Control• Operationism• Replication(thingthatyoucanshowtobetrueandcontinuouslyshowingthemtobetrue.It

isonlybydoingthingsanumberoftimes,andbereplicabledemonstrateable,thenwecanbegintobelieveinthem)

1.8 TheMethodina

nutshell• Youhaveatheorythatattemptstoexplaina

particularphenomenonofinterest• Thattheoryisusedtogeneratehypotheses–

iftheoryXistrue,itfollowslogicallythatYshouldoccur

• Youtestthehypotheses,thentrytorefutethetheory

• Ifnecessaryyouupdateyourtheorytoaccommodatethenewempiricalfindings

Thediagramiscyclical.Hopefullybyhavingthis

cyclicalprocess,weconvergeonknowingmoreandmorethetruth

1.9 Whatmakesagoodtheory

• Theoryiswherepredictioncomefrom,andexplainsphenomena• FromtheperspectiveoftheScientificMethod• Arethesegoodtheories?

- Aristotle’sTheoryofGeocentrism(Theoryoftheworld/earthisthecentreofeverything(theuniverse),thesun/star/planetallrevolvearoundtheearth)o Goodtheorybutincorrecto Testable,thereforerefutable.Itgeneratesthetheoryyoushouldbeabletomake

observationsoftheorbitofplanetsaroundtheearth- SigmundFreud’sPsychoanalyticTheory?

o Notagoodtheoryo Itdoesn’tgeneratepredictions,thereforecan’tberefutable.Onthebasisonhaving

somewillythoughtsaboutsuperego.Itmightbeagoodculturaltheory,itisnotastrongtestablescientifictheory.

- CharlesDarwin’sTheoryofEvolution?o Goodtheoryandcorrect,havereceivedoverwhelmingsupporto Itgeneratespredictionlikeevidenceofcommonancestorsinthefossilrecords,

testableandrefutable.Whethersomethingisagoodtheoryornot,isn’tthesameasaskingwhetheryoulikeit,isn’tthesameasaskingwhetheriscorrectornot.

1.10 AScientificTheoryMustBeTestable

• Scienceproceedsbymakingobservationsofnature(byperformingexperiments).Ifatheorydoesnotgenerateanyobservationaltests(orpredictions),thereisnothingthatascientistcandowithit.Ithastogeneratehypotheses

Considerthistheory:"Ouruniverseissurroundedbyanother,largeruniverse,withwhichwecanhaveabsolutelynocontact."• Isnottestable,thereforeisNOTagoodtheory(couldstillbetrue….)

1.11 AScientificTheoryshouldbeRefutable

Considerthistheory:"Thereareotherinhabitedplanetsintheuniverse."• ThisTheoryistestable(wecangotoanotherplanetandsee),butitisnota“good”scientific

theory(notrefutable).Here'swhy.Itmaybeeithercorrectorwrong.Ifitiscorrect,thereareseveralwaysthatitscorrectnesscouldbedemonstratedincluding:

1. wevisitanotherplanetandfindMorbolivingthere.2. radiotelescopesonearthbegintoreceivesignalsfromsomewhereintheAndromeda

Galaxythatappeartobererunsofthe"ILoveMorbo"show.3. Morbolandsinyourbackyardandsays,“IwilldestroyyoupunyEarthlings!”

But,sofarthishasnothappened

1.11.1 ThelogicofRefutation

TheWASONCardSelectionTaskTheruleis:ifthecardhasanevennumberononeside,theothersidemustbered.Whichcard(s)mustyouturnovertotestifthisruleisTRUE?• 3/8• red/8• 3/red• 8/brown(nottheanswerpeopleintuitivelythink,hencewhyoneneedstobecarefulabout

theapplicationoflogic)

1.11.1.1 Introtologic Asyllogismisalogicalchainofargument,isgenerallystructuredlikethis:1. Astatementthatdeclaresarule2. Astatementthatdescribesanobservationthatrelatestothatrule3. Aconclusionthatfollowsfromthatobservationinthecontextofthatrule

e.g.

1. IfitisThursdayatbetween10-12thereisaResearchMethodsLecture2. Itisaround11:30onThursday3. ThereforethereisaResearchMethodsLecture

• Inwhatfollows,don’tgethunguponwhetheryouthinkthe“rules”aretrueornot.

• Whatisimportantistounderstandthatthestructureofsometypesofargumentarelogicallyvalid

• Thatmeans,iftherulewereTRUEthentheconclusionmustalsobeTRUE• OtherstructuresorNOTlogicallyvalid.• Thatmeans,eveniftheruleistruewedocannottrusttheproposedconclusiontobetrue

1.11.1.2 Anexampleofavalidlogicalinference

Hereisanexampleofavalidlogicalstructure(modusponens)IfPthenQPThereforeQ(validstructure)IfMANEDthenMALEMANEDThereforeMALE(validstructure)Animportantthingtonotehereis:IfMANEDthenMALE¹IFMALEthenMANEDMANEDthenMALE:Manedlionisthesubsetofmalelion(malechildliondoesnothavemaned)MALEthenMANED:MalelionisthesubsetofmanedlionAbovetwoisnotthesamething.Male=Manedisanotherpossibleworld

1.11.1.3 Anothervalidlogicalinference

Hereisanexampleofavalidlogicalstructure(modusponens)IfPthenQNotQThereforeNotPIfMANEDthenMALENotMALEThereforenotMANEDMANEDthenMALE:Manedlionisthesubsetofmalelion(malechildliondoesnothavemaned)Maleandfemaledon’tintersect,then,ifitisnotmale,thencan’tbemaned.Anothervalidargument

1.11.1.4 Denyingtheantecedent

Denyingtheantecedent,isaformalfallacyofinferringtheinversefromtheoriginalstatement.Itiscommittedbyreasoningintheform:IfP,thenQ.NotP.Therefore,notQ.IfEVENthenREDNotEVENThereforenotRED

IfMANEDthenMALENotMANEDTherefore,notMALEInotherwords–don’tturnovertheTHREE–itdoesn’thelpTheruleis:ifthecardhasanevennumberononeside,theothersidemustbered.Whichcard(s)mustyouturnovertotestifthisruleisTRUE?IfEVENthenRED,NotEVEN,ThereforenotRED

1.11.1.5 Affirmingtheconsequent

Affirmingtheconsequent,isaformalfallacyofinferringtheconversefromtheoriginalstatement.Thecorrespondingargumenthasthegeneralform:

• Backwards• Fallacyofthinking

IfP,thenQ.Q.Therefore,P.IfEVENthenREDREDThereforeEVENIfMANEDthenMALEMALETherefore,MANEDInotherwords–don’tturnovertheRED–itdoesn’thelpTheruleis:ifthecardhasanevennumberononeside,theothersidemustbered.Whichcard(s)mustyouturnovertotestifthisruleisTRUE?IfEVENthenRED,RED,ThereforeEVENTurningthe3overcan’tdisproveit,itcan’tevenproveit.Turningtheredover,whetherit’sevenoroddnumber,itdoesn’thelpastheruledoesn’ttellyouanythingaboutthat.Thelogicalstructureisincorrect.Sothecorrectansweristhatweshouldturnoverthe:TheEIGHT- SinceifittheothersideisBrownthiswouldrefutethe“rule”TheBROWN

- SinceifittheothersideisEVENthiswouldrefutethe“rule”

1.11.1.6 Refuteit ThefundamentalpointisthatthewaytoTESTtheruleisbytryingtorefuteitratherthantryingtoproveitKarlPopper–allthewhiteswansintheworldcannotprovethetheory“allswansarewhite”–butasingleblackswancandisproveit

1.12 Thusitiswithscience…

Scienceprogressesbysystematicallyeliminatingfalsehoodsratherthandemonstratingtruths!Andonthatbombshell…

2 Lec2:Experimentaldesign,VariablesandOperationalisation2.1 Objectivesof

PsychologicalResearch

TodeveloptheoriesthatDescribe

– portrayingthephenomenonaccurately• e.g.,Piaget’stheoryofchilddevelopmentarosefromdetailedobservationsofhisown

children• describeaccuratelythephenomenathatareofinterest

Explain– identifyingthecause(s)ofthephenomenon– positexplanatorymechanism– causalrelationshipbetweenthings

• e.g.,socialconnectionanddepressionPredict

– identifyingriskfactorsofaphenomenoncanhelpyoutopredictwhenitmighthappen– generatenewprediction

• e.g.,whatfactorsbestpredictacademicsuccess

2.2 HowtoConductResearch

• Identifyphenomenaofinterest(thatinterestus),thatdescribing,explainingandmakingpredictionabout

• Readthescientificliterature,hasanyoneelsehadanythingsensibletosaysomethingabout▫ Isthereanestablishedtheorythatgeneratespredictionsaboutthephenomena,that

aretestable?▫ Ifnot,whatevidenceisneededtoallowatheorytobedeveloped.▫ Iftherearecompetingtheoreticalperspectives,askwhatevidenceisneededto

establishwhichtheoryiscorrect/thebest?• Formulatearesearchquestion• IdentifybestmethodtoaddresstheResearchQuestion

2.2.1 MajorMethodologicalApproaches

(DanainterpretedPatassayingthatwedon'tneedtoknowthetermPositivist,eticetcjustneedtoknowthetermsQuantitative&Qualitative,stillneedtoknowwhattheymean)

Quantitative• PositivistorEtic:Concernedwithuncoveringgeneralizablepatternandlawsbasedon

objectiveempiricaldata(tendstobedeductiveinnature)Qualitative• InterpretivistorEmic:Concernedwithsubjectiveinterpretation,personal/culturalmeaning,

contextspecific,notconcernedwithgeneralisabilitybutwithdeepunderstandinginlinewithinductiveapproaches.

*tiptoremember:

- Etic,tfortheory,Emic,mforme(interpreting)- Deductivesoundslikereductive,reducingfromgeneraltospecific- Inductiveistoincrease,specifictobroadertheory

2.2.2 CategorizingResearch

ApproachesQuantitativeversusQualitativeResearchQuantitativeStudies–collectnumericaldata,ordatathatcanbeconsideredinnumericaldata

– e.g.,ratingsofattractiveness,numberoftimesaratpressesabarinordertoberewarded,reactiontimes,peopleresponsestosurveys

QualitativeStudies–collectnon-numericaldatatoanswerresearchquestions,relatemoretopeople’sexperience,understandingandpersonalmeanings

– e.g.,pictures,clothingworn,interviewstatements,documentsMixedMethods

– quantitativedataprovidesanincompleteanalysisofwhatisbeinginvestigated,numerationofphenomena

– qualitativedataaddsadditionallevelofunderstanding,layerofmeanings

2.3 Quantitative Theygenerallyworklikethis• Youhaveahypothesis• Youcollectsomekindofnumericaldatatotestthathypothesis

2.4 TheVariable–akey • Variable

conceptinQuantitativeResearch

– somethingthatvaries– takesondifferentvaluesorcategories– e.g.,gender,anxietylevels,IQscores,on/off,heights,weights,theseareallthingsthat

vary.Wecannumerateorcategorisetheirlevelofvariability• CategoricalversusContinuousVariables

– CategoricalVariables• variesbytypeorkinde.g.,gender,religion,universitycourse,typeoftherapy• e.g.75%enrolledinpsychologyand25%inlaw,it’scategorical,onethingortheother.• NOMINALMEASUREMENT

– ContinuousVariables• variesbydegreeoramount• Continuesgradedspectrumofvaluesofaparticularvariable• e.g.,reactiontime,height,age,anxietylevel• INTERVAL/RATIOMEASUREMENT

2.4.1 Whichoftheseare

variablesInterval–IQ(ratioisunknown)Ratio–0(meaningful0,meansnonegativenumber)

Variable? Correct Type ScaleMale Gender categorical nominalWeight continuous ratio(sizeofintervalsisequaltoeachother,andit

hasmeaningful0(nonegativenumber),thatmeansifsomethingisweight20kg,itisexactlytwiceasheavyas10kg.Sothedifferentpointsofthescalehasameaningfulrelationshiptoeachother)

Reactiontime continuous ratio6foot2 Height continuous ratioblue Colour categorical nominalIQ continuous interval,thepointsbetweenpointsonthescaleare

assumedtobeequalandmeaningfullysobutthereisnoabsolute0fromwhichcanbecalibrated.Sowhilstthepointsonthescaleareassumetobeequalsizeinterval,theratiobetweenthemareunknown.

2.4.2 VariablesinQuantitativeResearch

• IndependentVariable(IV)– presumedtocausechangesinanothervariable– thevaryingofIVleadstochangesinDV– oftenmanipulatedbytheresearcher

o therapyvs.notherapyo alcoholdose(1unitversus2units)o locationoflearningwordlist(underwaterversusabovewater)

– needtoseeifthesechangesaffecttheoutcome• DependentVariable(DV)

– thepresumedeffectoroutcomeofthestudy– variablethatismeasuredbytheresearcherandinfluencedbytheIV– isthethingwemeasure,wehopehasbeeninfluencedbythemanipulationoftheIV– essentiallyisanythingthatyoumeasureintheexperiment

o behaviours,attitudes,feelingsmeasuredthroughtests,monitoring,questionnaires,numberofitemsrecalledonmemorytask,reactiontime,EEGdata

2.4.3 Fundamental

questionSothequestionthatisgenerallyaskedinaquantitativeresearchstudyis:• arechangesintheIVassociatedwithchangesintheDV?• OrdoeschangingtheIVcausechangesintheDV?

2.4.4 Othervariablesinquantitativeresearch

2.4.4.1 ExtraneousVariables

• variable/sthatcompeteswiththeIVinexplainingtheoutcomeorDV• allofthethings(youcan/can’timagine)thatmightimpactuponaperson’sabilitytoperform

atask• itisimportanttotrytocontrolforextraneousvariables,tonotallowittobesystematic

variabilityasafunctionofextraneousvariable

Isice-skatingfasterthanroller-skating?• Thethingweareinterestediniswhattypeofskatesarebeingusedinthisspeedtest.Sowhat

arethethingsthatmightimpingetheoutcome?• Whatkindofextraneousvariablesmightbeimportanttoconsiderhere?

o Theexperienceoftheskater(uncontrolledextraneousvariable)o Environmento Timeofthedayo Weightsdifferenceo Everythingthatcanvaryandhasanimpacttotheoutcomeofthestudy

2.4.4.1.1 Confounding

variable

Anextraneousvariablethatisallowedtoco-vary(tovarytogetherwithanothervariable)alongwiththelevelsoftheIVIsice-skatingfasterthanroller-skating?• Foundindividualswhoareconfidentandequallyexperienceintheuseofbothtypeofskate,

wehaveequippedthemwiththebestpossibleskate.Theyaretrainedtopeakleveloffitness• However,theconditionhasasystematicconfoundbecauseboththeskates(IV)andthe

coursedifferacrossthetests,inawaythatistotallycorrelated.HavingaconfoundisprettyseriousbecauseitmeansthatyoureallycannottellwhetheritistheIVortheconfoundthatisaffectingperformance.

- Uncontrolled3rdvariableisoperating.If2variablesareconfounded,theyareintertwinedsoyoucannotdeterminewhichofthevariableisoperatinginagivensituation

2.4.4.2 MediatingVariable

/InterveningVariable

• occursbetweentwoothervariablesinacausalchain- e.g.,anxietycausesdistraction(mediatingvariable)whichaffectsmemory- distractionhastheproximaleffectonmemoryperformance,notanxiety.- somethingthatintervenesbetweenonethingandanotherthing

2.4.4.3 Moderating

Variable• qualifyacausalrelationshipasdependentonanothervariable• qualifyacausalrelationshipbetweenIVandDV

- e.g.,theimpactofanxietyonmemoryisdifferentformenandwomen(sexisamoderatingvariable)

- genderismoderatingtheeffectofarelationshipbetweenanxietyandperformance

2.5 Theresearchproblem/question

Agoodtheorygenerateshypotheses–thesepredictionsgiverisetotheresearchproblem,orresearchquestion:• aninterrogativesentencethatstatestherelationshipbetweentwoormorevariablesorthe

keyresearchquestion• criteriaforgoodresearchproblems

- variablesshouldexpressaclearrelationship- statedinquestionform- capableofempiricaltesting

Soaresearchquestionshouldbe,specifiedinawaythatmakesclearwhatcausalrelationshipisbeingtested.• isnumberofhoursofCBTassociatedwithreducedanxietyscalescores?• arechangesintheIVassociatedwithchangesintheDV?

2.6 Workthroughanexample

• Identifyinganinterestingphenomenon• Relatingittotheory• Generatingahypothesis• Framingaresearchquestion• Identifyingvariables• Andconsideringdifferentmethodsforaddressingtheresearchquestion

2.7 Aninteresting

phenomenaShaneMacgowan¹JonnyDepp,but + =

2.7.1 Isthereatheory? The“InverseCinderella”theory

• Cinderellatheorysaysthingsturnsintopumpkinatmidnight• InverseCinderellatheory:everyonegetsmoreattractivewhentheclockstrikesmidnightWhat’swrongwiththistheory?• Wejustneedtolookatpeoplebeforeandaftermidnighttoknowthisisnottrue–easyto

disprove.The“BeerGoggles”Theory• Theingestionofalcoholhasanumberofeffectsonthehumanbrainincludingsimultaneously

increasinglevelsofsexualdesireanddecreasingaestheticjudgementwithrespecttothesuitabilityofpotentialsexualpartners

• Shane+alcohol=JohnnyDeppIsthisagoodtheory?Doesitgeneratepredictions?Isitrefutable?YES• Consistentwithexistingobservations• Generatespredictions(ifyougivesomeonealcohol,theirjudgementmightchange)

o testableo refutable

2.7.2 Theory/prediction/

question• Theory

– “Theingestionofalcoholhasanumberofeffectsonthehumanbrainincludingsimultaneouslyincreasinglevelsofsexualdesireanddecreasingaestheticjudgementwithrespecttothesuitabilityofpotentialsexualpartners”

• Prediction– “drinkingalcoholwillmakepeoplemoreattractedtopeoplewhomtheywould

normallyconsiderunattractive”• ResearchQuestion

– “doesalcoholconsumptionaffectattractivenessjudgments?”– dochangesinIVaffectsDV?

2.7.3 Researchquestion IV:AlcoholIngestion-wecanoperationaliseintothefollowing

• couldbecategorical-YES/NO• couldbecontinuous–numberofdrinksDVAttractivenessJudgements• couldbecategorical• couldbecontinuous

2.7.4 Howdoyouanswerthisquestion?

• Designastudy• Findsomeparticipants• Makesomemeasurements• Analysethedata• Writeapaperexplainingwhatyouhavedone

2.7.5 Somewaystoanswer

researchquestions1. Naturalisticobservation:simplyobservethebehaviour,nomanipulation,e.g.animalsin

nature2. Correlationalstudy:makingmeasurementandaskingthereisarelationshipbetweendifferent

measurement3. Internetstudy:online4. Fieldexperiment:innaturalenvironmentbutwithmanipulation(differenttoNaturalistic)5. Laboratorybasedexperiment

2.7.6 Possiblestudyideas InchoosinghowbesttoaddressourresearchquestionweneedtoaskPossiblestudyideas Isitpossibletodothe

thingthatwewanttodo?(logistics)

IsitOKtodothethingwewanttodo?(ethics)

Willdoingwhatwewanttodotellusanythinguseful?(validity)

1. Gotoanightclubandwatchwhathappens(NaturalisticObservation) Yes Yes No

2. Gotoanightclubandasksomequestions Yes Yes No3. Getonfacebook,encourageyourfriendstoget

drunk,thenpostapictureofShaneMacGowan Yes Maybe No

andseehowmanylikesitgets(Internetexperiment)

4. TakeShaneMacGowantoanightclub,spikesomeone’sdrink,andseewhathappens(fieldexperiment)

No(wedon’tknowhim) No No

5. Gotoyourlabandperformanexperiment Yes Yes Yes

2.7.6.1 Gotoanightclubandasksomequestions

Name Drank Rating

Sandy 1bacardibreezer “yuk!”

Leslie 12schooners “phwoar!”

Gabby 2lemonruskis “meh”

Ashley 8bacardibreezers “bringiton!”

Pat 3whitewines “nochance!”

Tyler 6schooners “notbad!”

Drew 4mineralwaters “areyounuts?”

Morgan 3schooners “probablynot”

Wynn 5doublevodkas “maybe”

Sydney 7vodkaandredbull “definitely”

2.7.6.2 AKeyCharacteristicofScientificResearch

• Operationism– representingconstructsbyaspecificsetofdefinitionsoroperations– operationaldefinition

• definingaconceptbytheoperationsusedtorepresentormeasureit

Name Standarddrinks Rating

Sandy 1.5 “yuk!”

Leslie 18 “phwoar!”

Gabby 3 “meh”

Ashley 12 “bringiton!”

Pat 5.4 “nochance!”

Tyler 9 “notbad!”

Drew 0 “areyounuts?”

Morgan 4.5 “probablynot”

Wynn 10 “maybe”

Sydney 7.2 “definitely”

2.7.6.3 Howaboutoperationalisingattractiveness?

Outof10score/Attractivenessratings

Name Standarddrinks Rating

Sandy 1.5 1

Leslie 18 10

Gabby 3 4

Ashley 12 8

Pat 5.4 3

Tyler 9 6

Drew 0 1

Morgan 4.5 4

Wynn 10 7

Sydney 7.2 9

2.7.6.4 Whatvalidinferencescanwe

• Alcoholimpairsjudgement?o Wehaven’ttestedjudgementmoregenerally,wehaveonlytestedaspecific

Thisstudyhasn’toperationalisedthemeasuresverywell.Theyaren’tstatedoperationalizewithadegreeofrigorandspecificityandaccuracywithwhichwecouldreallygetmeaningfulthingsfrom

Howmightweoperationalisetheconceptof“alcoholintake”?standarddrinks,itisanoperationalizationofconceptoflevelofalcoholintake.Byasimpleformula,wecantransformoutmeasurementintosomethingoperationallyuseful

NowwehaveoperationalizeIVandDV,wewanttoseeifthereisarelationshipbetweenthem.OurdatashowthatalcoholintakeinCORRELATEDwithattractivenessratings

drawfromthis? judgement• Alcoholcausespeopletolowertheirthresholdfor“sufficientlyattractive”?

o Wecan’tsaythis.Whatwecansayis,o PeoplewhohaddrunkmorealcoholratedShaneMacGowanasbeingmore

attractive?

2.8 Correlation • PrecipitationinNewYorkcorrelateswithprecipitationinVermont• Isthisonethingcausing

theother?• Thesetwothingsare

highlycorrelated• Becausetheyare

geographicallyclosetoeachotherandsubjecttosameweathersystem

• Theweathersystemcausestherainfall:thegeographicproximallocationoftheregionsmoderatesthevariables/relationship

BUT,becarefulofcorrelationTheremightbeapossiblemediatingvariable,e.g.eatingcheesemaycausesbaddreamwhichcausedthebedsheetstangled,butunlikely.

IfonethingcausesanotherthingtheyMUSTbecorrelated<doesnotequal>IftwothingsarecorrelatedthereMUSTbeacausalrelationship(randomchance)

2.8.1 TheissueofCausation

Causation– aconditioninwhichoneevent(thecause)generatesanotherevent(theeffect)

Criteriaforidentifyingacausalrelation– cause(IV)mustberelatedtotheeffect(DV)(relationshipcondition)– changesinIVmustprecedechangesinDV(temporalordercondition,causemusthappen

beforeeffect)– nootherplausibleexplanationmustexistfortheeffect– weneedthesethingstobetruetoinferacause

therelationshipbetweenalcoholandShane,wehaven’testablishedcausality,becauseotherexplanationdoexist.Therearepeoplereallylikealcoholand/orShane.

2.8.2 InferringCausality Awelldesignedandappropriatelycontrolledandconductedexperimentcanallowinferencesaboutcausality

– Performanaction(manipulateIV)– Measuretheconsequences(changesinDV)– CONTROLforotherpossibleexplanations

2.9 Anexperimentshould

be…• Carefullydesigned• RigorouslyControlled(trytocontrolasmanyextraneousvariablesaspossible,and

avoidingconfound,ifwedon’t,wecan’tdrawcausalinfluences)• Replicable(othersshouldgetthesameresultsifcopiedthemethodandgetthesame

results)• Ethical

2.10 Someimportant

ethicalissues• Informedconsent(peopleshouldbe

asked,andconsenttotheparticipationofresearch)

• Righttoconfidentiality• Righttowithdraw• Donotcausephysicalormentalanguish,

harm/distress• Exampleofunethicalexperiment:Milgram(induceanxiety/stresstotheparticipant,

resultscan’tbetrusted)2.11 Experimental

Approach

2.11.1 Advantages • Causalinference–experimentalapproachisbestmethodforinferringcausation- causaldescriptionreferstoidentifyingtheconsequencesofmanipulatinganIV- causalexplanationreferstoexplainingthemechanismsthroughwhichthe

relationshipexists• Abilitytomanipulatevariables

- onlyscientificmethodologyinwhichvariablesaremanipulated• Control

- extraneousvariablesarecontrolledby:o holdingthemconstant,e.g.sameIQo usingrandomassignmento matching(methodthatareavailabletousthatfacilitatesexperimentalcontrol

withwhichwecanmakecausalinference,bybeingabletosaythisextraneousvariableisn’ttheexplanationaswehavetakenthiscontrolmeasuretocounteractpossibleeffectsofthisextraneousvariable)

2.11.2 Disadvantage • Doesnottesttheeffectsofnon-manipulatedvariables

– manypotentialIVscannotbedirectlymanipulated• e.g.,people’sages,gender

• ArtificialityorGeneralisability– referstopotentialproblemsingeneralisingfindingsfromlaboratorysettingsto

the“realworld”– peoplemaybehavedifferentlyinlabsettingvsnaturalenvironment

2.12 Experimental

ResearchSettings

2.12.1 InternetExperiments • advantages– accesstodiversepopulation– bringexperimenttoparticipant– largesampleandthusgreaterpower– costsavings

• disadvantages

– multiplesubmissions(fromsameperson)– lackofcontrol– self-selection– dropout

2.12.2 Fieldexperiments • anexperimentalresearchstudythatisconductedinareal-lifesetting

– advantage–maybeeasiertogeneralizefindings,cutouttheartificialityoflaboratorysetting,thereforegettingmorerealdataonhowpeoplebehaves

– disadvantage–lesscontrolofextraneousvariables,canbetimeconsuming• confederate

– useofdeception,apersonwhoisinleaguewiththeexperimenter,unbeknownsttotheparticipant

– e.g.peoplearemoregenerousandwillingtogivemoremoneyinthelabsetting.Peoplearelessgenerousinreallife,e.g.sellingbaseballcardsataconvention,reallifesetting–lessgenerous.

– Thisisbecauseinthelab,theyfeelthepressureofsocialjudgement.Theyaltertheirbehaviourtoconformtowhattheythinkisthenicewaytobehave

2.12.3 Laboratory

experimentsanexperimentalresearchstudythatisconductedinacontrolledlaboratorysetting

• advantage–morecontroloverextraneousvariables,e.g.sametimeoftheday,temperatureetc

• disadvantage–lessgeneralizationrelatedtoartificiality(lab)

2.12.3.1 Differentwayswecouldmanipulate

ExperimentalmanipulationExperimenterdetermineswhichleveloftheIVaparticipantistestedat;

3 Lec3:Sampling,ValidityandReliabilityHowdoyouansweraresearchquestion?

• Designastudy

IVs • eventmanipulation(e.g.presenceofalcoholvabsenceofalcohol),completecontrol• instructionalmanipulation(e.g.drinkalcoholquickly/slowly)

2.12.3.1.1 Beergoggles

experiment1• IV:DrinkType:alcohol,water(alcoholvsnon-alcohol)• DV:attractivenessofthepictureofShane

2.12.3.1.2 Beergoggles

experiment2

• IV:varythestandardofdrinks:e.g.nodrinks,onedrink,5drinks• DV:attractivenessofthepictureofShane

2.12.3.2 DifferentwayswecouldmanipulateIVs

Individualdifferencemanipulation• Althoughwecan’tallocatepeopletobemale/female,high/lowIQ• Quasiexperimentalmanipulationratherthantrueexperimentalmanipulation

§ Quasi-experimentsaresubjecttoconcernsregardinginternalvalidity,becausethetreatmentandcontrolgroupsmaynotbecomparableatbaseline.Withrandomassignment,studyparticipantshavethesamechanceofbeingassignedtotheinterventiongrouporthecomparisongroup.(Wikipedia)

• Wecouldtryandlookattheeffectsofindividualdifferencesacrossparticipants• Trytolookattheeffectsofvariablesrelatedtoindividualdifferences• AcharacteristicoftheparticipantdeterminestheleveloftheIVatwhichtheyaretested;

– Computeranxiousvs.non-computeranxious– Malevs.female– Levelofsocialsupportreceived(highvlow)

2.12.3.2.1 Beergoggles

experiment3

Isthereaneffectforalcoholvsnoalcoholbasedonindividual’ssexualpreference?Whethertheeffectsofalcoholontheattractivenessjudgementaregeneralthatyouwillsayeveryoneismoreattractivewhetheryouwouldconsiderthemasasexualpartnerornotvswhetheritismoderatedbywhethertheyarethekindofgenderpeoplewithwhothemwantstoengageinsexualactivity.Thesearesortofthingswecanstarttomakecasualinference.

2.12.3.3 DifferentwayswecouldmanipulateIVs

RepeatedMeasure(WithinGroup):eachparticipanttestedateachleveloftheIV;• SameparticipantiscontributingtomorethanoneIV• Moresensitivedesign(easiertodetecttheeffectofinterest)• Can’talwaysusethisdesign• Whenusedappropriately,itisareallygoodmethod

BetweenGroup:eachparticipanttestedatonlyoneleveloftheIV;

• Lesssensitivedesign• Oftenforcedtousethisdesign

MixedDesign:

• morethanoneIVwithatleastoneIVmanipulatedBG• andatleastoneWG.

2.12.3.3.1 Beergoggles

experiment4

MultifactorialBeerGogglesExperiment

2.12.4 Potentialmanipulations

• Alcoholvsnoalcohol• Differentdosesofalcohol• Malevsfemale• Malevsfemalestimuluspictures• Alloftheabove

• Findsomeparticipants• Makesomemeasurements• Analysethedata• Writeapaperexplainingwhatyouhavedone

3.1 TheissueofCausation Criteriaforidentifyingacausalrelationship

– cause(IV)mustberelatedtotheeffect(DV)(relationshipcondition)– changesinIVmustprecedechangesinDV(temporalordercondition)

3.2 Findsomeparticipants Thisisknownassampling

Ifwewouldliketobeabletosaythatourdataallowustomakegeneralisableinferencesitisveryimportanttogetthisright!

3.2.1 Somekeyterms Population– Agroupofpeopleaboutwhomonewouldliketodrawsomemeaningfulconclusions,e.g.

• Adolescents• Peoplewithschizophrenia• QUTPsychologyundergraduates

Sample– Asubsetofthatpopulationthatisactuallyincludedinyourresearchstudyi.e.participants

• 150Year10students• 30outpatients• Everyonewhoattendswk3lecture

Samplingframe– Alistofmembers/elementsofapopulationfromwhichonemightobtainasample

• Electoralrole• Telephonedirectory• Studentenrolmentlist

Census– Alistofallthepeoplecomprisingaparticularpopulation.

• E.g.allthememberoftheAFLclubs

3.2.2 Aimofsampling Tomakegeneralisableinferencesaboutthepopulationonthebasisofmeasurementsfromyoursample.Itiscrucialthatyouhavearepresentativesample-asamplethatislikethepopulation.Thissimplymeansthatyoushouldselectasamplewhosetypicalcharacteristicsareapproximatelythesameasthetypicalcharacteristicsofthepopulation.Ifyoucan’tguaranteethatthisisso,youcan’tguaranteethatyourinferencesgeneralise.

3.2.3 Representativeness • SampleStatistic– Anumericcharacteristicofasample-(measured)– Somethingthatwemeasureinthesample

• PopulationParameter– Anumericcharacteristicofthepopulation-(oftennotknown)– Ifwehavearepresentativesample,thenthissamplestatisticswillbecloselyrelated

tothepopulationparameterwhatthatvaluewillbefortheentirepopulation• Responserate

– Whatproportionofpeopleresponded?• Samplingerror

– Thedifferenceinvaluebetweenthesamplestatisticandthepopulationparameter(dependsonsamplesize)

Thesmallerthesample,thelargerthesamplingerror.Ifthesampleistoosmall,itisnotlikelytoreflectthecharacteristicofthepopulationingeneral

3.2.4 Samplingbias Population:PeopleenrolledonPYB210Sample:peoplewhoattendthislectureHowwasthissampleselected?

- Ifitwasrandomsampling,thenstudentswouldtossedthecoinwhentheygotoutofthebed,heads:gotouni,tails:backtobed

- Inthiscase,peopleselectedthemselvestobepartofthesample

- Thisisnotarepresentativesample- Self-selection:thereisalwaysadanger

onpeoplewhoselectandwhodon’t.Datacan’ttrusted.Mighttherebesystematicdifferencesbetweenpeoplewhodoversusdon’t.

- Theonesthatarenotinthelecturemayhaveafulltimejob,childcareresponsibilityetc- Don’ttrustaself-selectingsample-anexampleofsamplingbias

3.2.5 Samplingprocedures

3.2.5.1 Probabilitysampling

• E.g.Tossingthecoin• Awaytoensurethatyoursampleisrepresentativeofthepopulation(onthe

characteristicsdeemedimportantforthestudy)• Basicprinciple:

– Asamplewillberepresentativeofthepopulationifallmembersofthepopulationhaveanequalchanceofbeingselectedinthesample

– Allowstheresearchertocalculatetherelationshipbetweenthesamplestatisticandthepopulationparameter

– Everyonehasanequalchanceofbeingselected=>representativeofthepopulation,providingyouhavelargeenoughsamplesize

3.2.5.2 Sub-typesof

probabilitysamplingo Simplerandomsampleo Systematicrandomsampleo Stratifiedrandomsamplingo Multistageclustersampling

3.2.5.2.1 Simplerandom

sample

• Eachmemberhasanequalandindependentchanceofbeingselected• Definethepopulation,listallmembers,assignnumbers

– Useatableofrandomnumberstoselect,e.g.alloddnumbers– Usea“lottery”method,pullnamesoutofahat– Useacomputerprogramtorandomlyselect

• WorkswellprovidingsamplesizeisnottoosmallExample:FirstisahistogramshowingtheIQscoresofapopulationof1,000,000people.ThepopulationmeanisanIQof100andtheSDis10IQpoints.Let’stakesomesamples.- Smallersamples=>movesawayfromthepopulation

meanandSD- Simplerandomsamplingworksreallywellprovidedthatyoursamplesizedon’tgettoo

small.3.2.5.2.2 Systematicrandom

sampleEveryKthperson

• Systematicismorehistoricwhencomputerwasn’taccessibletouseforrandomization.

• Randomlyselectthefirstpersonthendividethesizeofthepopulationbythesizeofthedesiredsample,andusethistodeterminetheintervalatwhichsampleisselected.– e.g.,toselectasampleof1000peoplefromalistof10,000,randomlyselectthefirst

personandstartthelistwiththem-thenselectevery10thpersonfromthelist• Needtoensurethelistofelementsisnotarrangedinawaythatmeanssystematic

samplingcouldleadtoabiasedsample(e.g.,studentlistinGPAorder!).– e.g.,differentresultsifyoustartwiththe2ndpersonandsampleevery10thperson

beyondthatthanifyoustartwiththe8thpersonandsampleevery10thperson• Wheneverpeopledon’thavetheequalandindependentchanceofbeingpicked,you

areintroducingpossiblefactorsofthingsgoingwrong.Whichshouldweprefer?Simple,orsystematicrandomsampling?

- Simple,lesschanceofanythingsystematicgoingon.

3.2.5.2.3 Stratifiedsampling - Ifyouwanttomakesuretheprofileofthesamplematchestheprofileofthepopulationonsomeimportantcharacteristicse.g.ethnicmix,gender.

- Dividepopulationintosubpopulations(strata)andrandomlysamplesfromthestrataWhyusestratifiedsampling?

- Whenthereisheterogeneitywithinthepopulation,andyouwanttoendupwithasamplewhosecharacteristicsreflecttheproportionalheterogeneityofthepopulation

- Canreducesamplingerrorbyensuringratiosreflectactualpopulation(e.g.,ratioofmalestofemales)

- ToensurethatsmallsubpopulationsareincludedinthesampleNB:

- canhaveproportionalrepresentationordisproportionaterepresentation- butdisproportionatesamplewouldnotbeusedtogeneralisetoentirepopulation,only

thesubgroups

3.2.5.2.3.1 SimpleRandomSamplingVersusStratifiedSampling

Ourpopulationis“AnimalsofWestQueenslandSavannah”–acensusrevealsthattheentirepopulationconsistsof60lions,30tortoisesand10rabbits.SimpleRandomSampling

- Notagoodinferenceofpopulationlevel- Becausethestratificationofthepopulationhasn’t

beenreflectedinthesampleMoreexampleofSimplerandomsampling,sometimeswegetitright

StratifiedRandomSamplingRegardlesshowmanytimeswedothestratifiedrandomsampling,wearealwaysgoingtoendupwiththisfigure,reflectsproportionsinthepopulation.

3.2.5.2.4 Multi-stageClustersampling

Beginwithasampleofgroupingandthensampleofindividualse.g.Ruralsample

- Defineruraltownshipsasthosewithpopulation<X- Getlistingofallrelevanttownships- Takearandomsampleoftownships- Randomlysamplepeoplefromwithintherandomlysampledtownships

- Ifallthesamplearefromthesametown,theremightbesomethingsystematically

differentaboutthattown,e.g.highwithunemployment- Bettertoselectsamplefrombunchofdifferenttown,randomlyselectsthetown,then

randomlyselectsthepeople(multi-stageofprocessinggoingon)

Whenmightyouusethis?- Whenyouhavedifferentregion,differentcharacteristics- e.g.Hungergame

3.2.5.2.5 Multi-Stage/Multi-

PhaseSampling

• Typeofrandomsamplingwhereby• Largersampleobtainedfirst• inordertoidentifymembersofasub-sample• Sub-samplethenrandomlychosenfromforstudy• Good(butcostly)waytoidentifynotreadilyidentifiablesubgroups

E.g.usingAustralianMentalHealthandWellbeingSurveytoidentifypeoplewithpsychoticillness

• largescalesurvey• Needpeoplewithpsychoticillness,“Lowprevalence”(1%ofpopulation)disordersstudy• fromAUSMentalHealthandWellbeingSurveytoidentifylowprevalentdisordersstudy• Thisishow(psychoticillness)peoplearerandomlyselectedbasedontheirprevious

involvement(AustralianMentalHealthandWellbeingSurvey)

3.2.5.2.6 AdvantagesofProbabilitySampling

- Nosystematicbias- Helpsovercomesamplingbias- Ensuresrepresentativeness!

3.2.5.2.7 Problemwith

probabilitysampling

- accesstolistofpeople- costly- difficult- youcanrandomlyselectsomeonebutthereisnoguaranteetheywillagreeto

participateinyourstudy.- Isthereasystematicdifferencebetweenpeoplewhoagreetoparticipateandthosethat

don'tagreetoparticipate?- Self-selection:askingpeople’sagreementtoparticipateinresearchisaformofself-

selection,andthiscancausebias 3.2.5.3 Non-probability

samplingNoteverymemberofthepopulationhasanequalchanceofbeingpartofthesampleWhyusethen?

- Therearenolistsforsomepopulationsunderstudy,- Logisticalorcostrelatedproblem- e.g.

o Thehomelesso Certainoccupations(e.g.,farmers)o Hiddenpopulations(e.g.,peopleinvolvedin“clandestine”activities)o Convenience/resourcerestrictions

3.2.5.3.1 Convenience

Samples- Mostusedinpsychology- Peoplehappentobeavailable• Asampleofavailableparticipants,e.g.,

– studentsenrolledinaparticularcourse– Peoplepassingaparticularlocation

• Self-selecting,non-random• systematicdifferenceonwhoyoumightbeexposedto,e.g.standing

outsidecentrelinkvscasino• Advantages:

– Easy,inexpensive• Disadvantages:

– Nocontroloverrepresentativeness

3.2.5.3.2 SnowballSampling • Likeasnowballrunningdownthehillandgathermoreasitgoes• Usedmainlyforhardtostudysub-populations• Identifyonememberforthestudy,thenaskingfortheirfriendstoparticipate

– e.g.,Gaymen,Homelessyoungpeople,Illegalimmigrants• Involvescollectingdatawithmembersofthepopulationthatcanbelocatedandthen

asksthosememberstoprovideinformation/contactsforothermembersofthepopulation

Problems

- peopletendtoassociatewithpeoplesimilartothemselveso e.g.hipsterbeardedguys,arebeardedmenmoredesirablethanothermen?

Dependingonthestudy,mayormaynotbeaproblem.- Peoplehavethesamenetworkofpeoplewhomayjustbelikethem,beardedmen

havingmorebeardfriends.

3.2.5.3.3 QuotaSample - Non-probabilitysamplingequivalentofastratifiedrandomsampleo Uknowthereisstratawithinyourpopulation,andyouwanttoreflectrelative

proportionofthosedifferentstratapopulationinthesample- Butyoudon’t/aren’tabletosamplerandomlyfromeachstrataasyoudoinstratified

randomsamples- Soyouusenon-probabilitysampling

Problem

- can'tguaranteerepresentativeness

3.2.5.3.4 Purposive/judgmentsampling

- Clearpurposetothesamplingstrategy:selectkeyinformants,atypicalcases,deviantcasesoradiversityofcases.

- Samplinginawaytryingtofindparticularcharacteristic,togetparticularinformation- Oftenusedto:

– Selectcasesthatmightbeespeciallyinformative– Selectcasesinadifficult-to-reachpopulation– Selectcasesforin-depthinvestigation

Examples:

– Studyingtheproblemsexperiencedbynewimmigrants– Interviewkeypeopleinvolvedinagenciesthathelpimmigrantssuchasethnic

welfaregroups,communityimmigrationlegalaidgroups– Interviewingpeoplewithextensiveexperiencewithimmigrantslikelytoprovide

richdata– Comparisonofleft-wingandright-wingstudents

– Maynotbepossibletosampleallleft-wingandright-wingstudents– Instead,youcouldsamplethemembershipofleft(e.g.,SocialistAlliance)and

right-winggroupsoncampus(e.g.,youngliberals)

3.2.5.4 WhichSamplingMethod?

- Asamajoraimofquantitativeresearchistheabilitytogeneraliseresultstheultimatemethodisaprobabilitysamplingone.Representative

- Howeverthisisoftennotworkableorfeasiblegivenresources,time,thespecifictargetpopulation.

- Samplingmethodusedshouldbefullyexplainedtoparticipants- andcaveatsaboutthelikelygeneralisabilityofresultsmadeaccordinglysothatthe

readercanreviewyourresultsinaninformedway.- Wewillalwayshavenon-optimalsamplingmethodaswecan’tjusthavethecensusof

thewholepopulationandselectthesub-populationfromit.Thereforearesearchpaperneedstostateclearlywhathasbeendoneandtheproblemassociatedwithit.

3.2.6 Howmanypeople

shouldyoutest?SampleSize-aswehavealreadyseenthesizeofyoursamplecaninfluencehowrepresentativeitisofthepopulationItisthereforeimportanttoensurethatyoursamplesizethatisappropriate

3.2.6.1 DeterminingSampleSize1

HowmanyparticipantsdoIneedformystudy?- Largelydeterminedbytheanalysisyouplantoconductwiththedataderived.Howare

yougoingtotreatthedata?- Generallythemorecomplextheanalysisthelargerthesampleyourequire- Increasesinsamplesizebringwiththemincreasesinaccuracy/precision/reduces

samplingerror.- Greaterheterogeneityofthepopulation,greatervariationinthepopulation,thelarger

thesampleshouldbetocaptureandreflecttheheterogeneityinthesamplesize.- Therearemanytextswhichwillprovideyouwithsamplesizerequirementsforany

givenstatisticaltestaswellascalculationtoolswhichwillprovideyouwithasamplesizegivenanumberofparameters.

*Heterogeneity–beingdiverseincontent.

3.2.6.2 DeterminingSampleSize2

Largersamplesizesareneededifpopulationis:- Heterogeneous- youwanttobreakdownthesampleintomultiplesubcategories

o e.g.,lookatmalesandfemalesseparately- whenyouexpectasmalleffectorweakrelationship- whenyouuselessefficientmethodsofsampling

o e.g.,clustersampling- forsomestatisticaltechniques- ifyouexpectalowresponserate

ifyouareusingnotrepresentativesamplingmethod,thenerronthesideofhavinglargersample

3.2.6.3 DeterminingSampleSize3

Fivesimplerulesfordeterminingsamplesize1. ifpopulationislessthan100,useentirepopulation2. largersamplesizesmakeiteasiertodetectaneffectorrelationshipinthepopulation3. comparetootherresearchstudiesinareabydoingaliteraturereview4. useapowerTableforaroughestimate5. useasamplesizecalculator(e.g.,G-Power)- whatsamplesizeisneededforaneffectofaparticularsize

3.3 Makesome

measurements

3.3.1 OperationalisationofIVsandDVs

OperationalisationofIVs– Howareyougoingtomanipulateit?Howisitmanipulated(ifyoucan’t)?

OperationalisationofDVs– Howareyougoingtomeasureit?– Whatmeasurementsareyoutaking?

Howmightwemeasureintoxication?Example,DV:usingalcoholconsumption- Theirlooks,ortheirabilitytowalkstraightlineisnotgoodenough- Breathalyzerandbloodtestmayalsonotbegoodmeasurementforalcoholic,astheir

bodyisusedtotoxication.

3.3.2 ReliabilityandValidity Reliability- Doesourmeasurementinstrumentbehavesensibly?- Doesitalwaysmeasurethesamethinginthesameway?

Validity- Arewemeasuringwhatwethinkwearemeasuring?

o Arewemeasuringintoxicationwhenwemeasurebloodalcohollevel?o Doesthebloodmeasuregivesthesameresulteverytimeweuseit?

Ifwelookatsomeone,ismyviewofhisintoxicationthesameasyours?Theseareallquestionsweneedtoaskforreliabilityandvalidity.

3.3.3 ReliabilityandValidity Appliesmostlytoindexes/scales– Inpsychology,wetrytonumeratethereliabilityandvalidityofthingslikequestionnaire,

surveyHowdoweassesswhetherourmeasures/operationalisationsaregood?

– Aretheyvalid?– Aretheyreliable?

Snagisthatyoucan’tassesstheseuntilAFTERyouhavedevelopedyourquestionnairesandusedthem

– Thisiswhyapilottestcanbesobeneficial

– Thisiswhymanypeoplechosetouseestablishedmeasuresratherthandeveloptheirownandtaketherisk

– E.g.useIQtestExamples- Notreliable/notvalid:birthdayandstarsigns- Reliable/notvalid,judgepeople’sintelligencebasedontheirlooks(wethinkpeople

wearglassesaresmarter)- Measuringwitharuler(reliable,valid)- Notreliable/valid:notpossiblescenario,ifyoucan’trelyonyourmeasurement,you

can’ttrustitsvalidity

3.3.3.1 Therelationshipbetweenreliabilityandvalidity

Canameasurebereliablebutnotvalid?– Yes!Youcouldhaveaconsistentmeasurethatdoesnotactuallymeasuretheconstruct

Canameasurebevalidbutnotreliable?– No!Ifyourmeasuredoesn’tconsistentlyanddependablymeasuretheconstructit

cannotpossiblybemeasuringwhatitsaysit’smeasuringPhysicalmeasurementsclearandeasytoseethattheyarereliablyandvalidi.e.wecansee.Psychologicalmeasurementsarealittlebitmoretricky.

3.3.3.2 Reliability • Theconsistencyorrepeatabilityofthemeasuremento SayIweightmyselfonsomescalesatonepointintimeandthenweighmyself5

minslateranditsaysI’m5kilosheavier.o Conclusion:dodgyscale,don’tuseit.o Scientificconclusion:thescalesareanunreliablemeasurementinstrument

3.3.3.3 TypeofReliability

test• Stabilityofthemeasure(Test-retest)• Internalconsistencyofthemeasure(Split-half,Cronbach’salpha)• Agreementorconsistencyacrossraters(Inter-raterreliability)

• Acrossdifferentpeoplemakingthejudgementormends

3.3.3.3.1 Test-retestreliability

Doesyourtestmeasurethesamethingeverytimeyouuseit?• Addressesthestabilityofyourmeasure

• Sameanswerseverytime• Youadministerthemeasureatonepointintime(Time1)• thengivethesamemeasuretothesameparticipantsatalaterpointintime(Time2)• Hopingtherewillbeacorrelationbetweenthetwotimes• Youcorrelatethescoresonthetwomeasures• Ifitishighcorrelation,thenithashightest-retestreliability• Ifitistoolow,thenitisnotworthusingitasthetest-retestliabilityislow

3.3.3.3.1.1 Problemwith

Test-retestImaginethatyouwanttotestwhethergivingpeoplevitaminsupplementscanimprovesapersonsIQ.Twomainproblems:1. Memoryeffect

– youmightrememberthequestionsandlookuptheonesyoudidn’tknow2. Practiceeffect

– Performanceimprovesbecauseofpracticeintesttaking

• Iftooshortthere’sagreaterriskofmemoryeffects• Iftoolongthere’sariskofothervariables(e.g.,additionallearning)influencingresults

3.3.3.3.2 Split-halfreliability:

isyourmeasureinternallyconsistent

Psychologytestisnotsimplyaonequestionsurvey,e.g.areyouanextrovert?Testwillhaveasetofitems.byendorsingasetofitems,itwillleadyouhighin,e.g.extrovertcategory.Sointhetest,therearesetofsub-itemsthatrelatestotheconstructofextrovert,introvert.Inordertodefinethepersonality.Eachofthesepersonalitytraitsisassessedbydifferentsetofitems.Split-halfreliability:Arethedifferentitemsconstanttowhattheyaremeasuring?

• Youadministerasinglemeasureatonetimetoagroupofparticipants• But,foryourpurposes(ofunderstandofpsychometricqualityofyourexperiment)you

splitthemeasureintotwohalves.OdditemisgoingtopoolA,evenitemsisgoingtopoolB.

• andyoucorrelatethescoresonthetwohalvesofthemeasure(highercorrelationmeansgreaterreliability)

• e.g.IQtest,2setofquestionsinonetest.Ifbothsethavehighcorrelation.Thissuggests2halvesaremeasuringthesameconstruct.

• Thisway,youdon’tneedtodotest-retestreliability,rather,youaskinternalconsistencyofthetest

• Strength:eliminatesmemory&practiceeffects• Limitation:Arethetwohalvesreallyequivalent?

• UseCronbach’sAlpha(measureofinternalconsistency.Itisconsideredtobeameasureofscalereliability.)

3.3.3.3.2.1 Cronbach'sAlpha • Assessesthe‘internalconsistency’ofyourmeasure

– i.e.,tellsyouhowwelltheitemsorquestionsinyourmeasureappeartoreflectthesameunderlyingconstruct

• Youwouldgetgoodinternalconsistencyifindividualsrespondinapproximatelythesamewaytoquestionsonyoursurvey

– Differentitemsofthesametestmeasuringthesameconstruct• Mathematicallyit’stheequivalentoftheaverageofallpossiblesplit-halfreliabilities• Coefficientalphacanrangefrom0to1.00

– Thecloserthealphaisto1.00,thebetterthereliabilityofthemeasure

3.3.3.3.3 Inter-raterorinter-observerreliability

• Dodifferentratersmeasurethesamething?• Relythejudgementoftheobservers.• Checkingthematchbetweentwoormoreratersorjudges

o E.g.peopleobservethebehaviourofyoungbabieso codingvideosforinfant“lookingtime”–needtochecktheagreementamongst

thecoderso Codingthelengthoftimeaninfantislookingatoneparticularobjectvsanother

• Thereisadegreeofsubjectivityofinterpretationinthesekindsofmeasures.o Wastheinfantdirectlylookingattheobjectorclosetotheobject?

• Wherethereisapossibilityforsubjectivity,whatpeopleareinterestedinistheinter-raterreliability

o Ifpeoplearetrainedproperly,differentpeopleshouldbehighlycorrelatedwitheachotherwithrespecttothesubjectjudgementtheymake

• Arethedifferentpeoplemakingthejudgementbehavingsimilarlyinthesetofjudgementtheyaremaking?

o Highcorrelation=Highdegreeofreliability

3.3.3.3.3.1 Calculationofinter-raterreliability

– nominalorordinalscale– thepercentageoftimesdifferentratersagree

– intervalorratioscale– correlationcoefficient

3.3.3.4 Validity • Arewemeasuringwhatwethinkweare?

– Isourmeasurecredible,isitbelievable?• Whyisvalidityanissue?

– Forreliability,wecancomewiththeseclearmeasuresofthedegreetowhichmeasurementisreliable

– Forvalidity,many(ifnotmost)variablesinsocialresearchcannotbedirectlyobserved.Youhavetoinferonthebasisofsomething

• e.g.,motivation,satisfaction,helplessness• needtouseinstrumentsuchasquestionnaire

• Thechallenge:– Wecanquantifyreliability– Wecan’tquantifyvalidity– tomakeajudgmentcallaboutwhetherwearemeasuringwhatwethinkwe’re

measuring

3.3.3.4.1 TypesofValidity • Facevalidity