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DetailedContents Preface
Acknowledgments
AbouttheAuthor
PARTI:STATISTICALPRINCIPLES
1 ResearchPrinciples
LearningObjectives
Overview ResearchPrinciples
RationaleforStatistics
ResearchQuestions
TreatmentandControlGroups
RationaleforRandomAssignment
HypothesisFormulation
ReadingStatisticalOutcomes
AcceptorRejectHypotheses
VariableTypesandLevelsofMeasure
Continuous Interval Ratio
Categorical Nominal Ordinal
GoodCommonSense
KeyConcepts
PracticeExercises
2.Sampling
LearningObjectives
Overview Sampling
RationaleforSampling
Time
Cost
Feasibility
Extrapolation
SamplingTerminology
Population
SampleFrame
Sample
RepresentativeSample
ProbabilitySampling
SimpleRandomSampling
StratifiedSampling
ProportionateandDisproportionateSampling
SystematicSampling
AreaSampling
NonprobabilitySampling
ConvenienceSampling
PurposiveSampling
QuotaSampling
SnowballSampling
SamplingBias
OptimalSampleSize
GoodCommonSense
KeyConcepts
PracticeExercises
3 WorkinginSPSS
LearningObjectives
Video Overview SPSS
TwoViews:VariableViewandDataView
VariableView Name Type Width Decimals Label Values Missing Columns Align Measure Role
DataView
ValueLabelsIcon Codebook
SavingDataFiles
GoodCommonSense
KeyConcepts
PracticeExercises
PARTII:STATISTICALPROCESSES 4 DescriptiveStatistics
LearningObjectives
Videos
Overview DescriptiveStatistics
DescriptiveStatistics
Number(n)
Mean(μ)
Median Mode
StandardDeviation(SD)
Variance
Minimum Maximum Range
SPSS LoadinganSPSSDataFile RunSPSS DataSet
TestRun
SPSS DescriptiveStatistics:ContinuousVariables(age) StatisticsTables
HistogramWithNormalCurve SkewedDistribution
SPSS DescriptiveStatistics:CategoricalVariables(gender) StatisticsTables BarChart
SPSS DescriptiveStatistics:ContinuousVariable(age)SelectbyCategoricalVariable (gender) FemaleorMaleOnly
SPSS (Re)SelectingAllVariables
GoodCommonSense
KeyConcepts
PracticeExercises
5 tTestandMann-WhitneyUTest
LearningObjectives
Videos Overview tTest Example ResearchQuestion Groups Procedure
Hypotheses
DataSet
PretestChecklist
PretestChecklistCriterion1 Normality
PretestChecklistCriterion2 nQuota
PretestChecklistCriterion3 HomogeneityofVariance
TestRun
Results
PretestChecklistCriterion2 nQuota
PretestChecklistCriterion3 HomogeneityofVariance
pValue
HypothesisResolution
αLevel
DocumentingResults
TypeIandTypeIIErrors
TypeIError
TypeIIError
Overview Mann-WhitneyUTest
TestRun
Results
GoodCommonSense
KeyConcepts
PracticeExercises
6 ANOVAandKruskal-WallisTest
LearningObjectives
Videos
LayeredLearning
Overview ANOVA
Example
ResearchQuestion
Groups
Procedure
Hypotheses
DataSet
PretestChecklist
PretestChecklistCriterion1 Normality
PretestChecklistCriterion2 nQuota
PretestChecklistCriterion3 HomogeneityofVariance
TestRun
Results
PretestChecklistCriterion2 nQuota
PretestChecklistCriterion3 HomogeneityofVariance
Comparison1
Text:TextWithIllustrations
Comparison2 Text:Video
Comparison3
HypothesisResolution
TextWithIllustrations:Video
DocumentingResults
Overview Kruskal-WallisTest
TestRun
Results
GoodCommonSense
KeyConcepts
PracticeExercises
7 PairedtTestandWilcoxonTest
LearningObjectives
Videos
Overview PairedtTest
Pretest/PosttestDesign
Step1:Pretest
Step2:Treatment
Step3:Posttest
Example
ResearchQuestion
Groups
Procedure
Step1:Pretest
Step2:Treatment
Step3:Posttest
Hypotheses
DataSet
PretestChecklist
PretestChecklistCriterion1 NormalityofDifference
TestRun
Results
HypothesisResolution
DocumentingResults
Δ%Formula
Overview WilcoxonTest
TestRun
Results
GoodCommonSense
KeyConcepts
PracticeExercises
8.CorrelationandRegression PearsonandSpearman
LearningObjectives
Videos
Overview PearsonCorrelation
Example1 PearsonRegression
ResearchQuestion
Groups
Procedure
Hypotheses
DataSet
PretestChecklist
PretestChecklistCriterion1 Normality
TestRun
Correlation
Regression(ScatterplotWithRegressionLine)
Results
ScatterplotPoints
ScatterplotRegressionLine
PretestChecklistCriterion2 Linearity
PretestChecklistCriterion3 Homoscedasticity
Correlation
HypothesisResolution
DocumentingResults
NegativeCorrelation
NoCorrelation
Overview SpearmanCorrelation
Example2 SpearmanCorrelation
ResearchQuestion
Groups
Procedure
Hypotheses
DataSet
PretestChecklist
TestRun
Results
HypothesisResolution
DocumentingResults
AlternativeUseforSpearmanCorrelation
CorrelationVersusCausation
Overview OtherTypesofStatisticalRegression:MultipleRegressionandLogistic
Regression
MultipleRegression(R2)
LogisticRegression
GoodCommonSense
KeyConcepts
PracticeExercises
9 Chi-Square
LearningObjectives
Video
Overview Chi-Square
Example
ResearchQuestion Groups
Procedure
Hypotheses
DataSet
PretestChecklist
PretestChecklistCriterion1 n≥5perCell
TestRun
Results
PretestChecklistCriterion1 n≥5perCell
HypothesisResolution
DocumentingResults
GoodCommonSense
KeyConcepts
PracticeExercises
PARTIII:DATAHANDLING
10 SupplementalSPSSOperations
LearningObjectives
DataSets
Overview SupplementalSPSSOperations
GeneratingRandomNumbers
SortCases
DataSet
SelectCases
DataSet
Recoding
DataSet
ImportingData
ImportingExcelData
DataSet
ImportingASCIIData(GenericTextFile)
DataSet
SPSSSyntax
DataSet
DataSets
GoodCommonSense
KeyConcepts
PracticeExercises
Glossary Index
Preface Somewhere,somethingincredibleiswaitingtobeknown
CarlSagan
DownloadableDigitalLearningResources Download(andunzip)thedigitallearningresourcesforthisbookfromthewebsite study.sagepub.com/knappstats2e.Thiswebsitecontainstutorialvideos,prepareddatasets,andthesolutions toalloftheodd-numberedexercises.Theseresourceswillbediscussedinfurtherdetailtowardtheendofthe Preface
OverviewoftheBook Thisbookcoversthestatisticalfunctionsmostfrequentlyusedinscientificpublications Thisshouldnotbe consideredacompletecompendiumofusefulstatistics,however.Inothertechnologicalfieldsthatyouare likelyalreadyfamiliarwith(e.g.,wordprocessing,spreadsheetcalculations,presentationsoftware),youhave probablydiscoveredthatthe“90/10rule”applies:Youcanget90%ofyourworkdoneusingonly10%ofthe functionsavailable.Forexample,ifyouweretothoroughlyexploreeachsubmenuofyourwordprocessor,you wouldlikelydiscovermorethan100functionsandoptions;however,intermsofactualproductivity,90%of thetime,youareprobablyusingonlyabout10%ofthemtogetallofyourworkdone(eg,load,save,copy, delete,paste,font,tab,center,print,spell-check).Backtostatistics:Ifyoucanmasterthestatisticalprocesses containedinthistext,itisexpectedthatthiswillarmyouwithwhatyouneedtoeffectivelyanalyzethe majorityofyourowndataandconfidentlyinterpretthestatisticalpublicationsofothers
Thisbookisnotaboutabstractstatisticaltheoryorthederivationormemorizationofstatisticalformulas; rather,itisaboutappliedstatistics.Thisbookisdesignedtoprovideyouwithpracticalanswerstothe followingquestions:(a)WhatstatisticaltestshouldIuseforthiskindofdata?(b)HowdoIsetupthedata?(c) WhatparametersshouldIspecifywhenorderingthetest?and(d)HowdoIinterprettheresults?
Intermsofperformingtheactualstatisticalcalculations,wewillbeusingIBM® SPSS® *Statistics,anefficient statisticalprocessingsoftwarepackage Thisfacilitatesspeedandaccuracywhenitcomestoproducingquality statisticalresultsintheformoftablesandgraphs,butSPSSisnotanautomaticprogram.Inthesameway thatyourwordprocessordoesnotwriteyourpapersforyou,SPSSdoesnotknowwhatyouwantdonewith yourdatauntilyoutellit Fortunately,thoseinstructionsareissuedthroughclearmenus Yourjobwillbeto learnwhatstatisticalproceduresuitswhichcircumstance,toconfigurethedataproperly,toorderthe appropriatetests,andtomindfullyinterprettheoutputreports
The10chaptersaregroupedintothreeparts:
PartI:StatisticalPrinciples Thissetofchaptersprovidesthebasisforworkinginstatistics.
Chapter1:ResearchPrinciplesfocusesonfoundationalstatisticalconcepts,delineatingwhatstatistics are,whattheydo,andwhattheydonotdo
Chapter2:Samplingidentifiestherationaleandmethodsforgatheringarelativelysmallbundleofdata tobettercomprehendalargerpopulationoraspecializedsubpopulation
Chapter3:WorkinginSPSSorientsyoutotheSPSS(alsoknownasPASW,orPredictiveAnalytics Software)environment,sothatyoucancompetentlyloadexistingdatasetsorconfigureittocontaina newdataset
PartII:StatisticalProcesses Thesechapterscontaintheactualstatisticalproceduresusedtoanalyzedata.
Chapter4:DescriptiveStatisticsprovidesguidanceoncomprehendingthevaluescontainedin continuousandcategoricalvariables
Chapter5:tTestandMann-WhitneyUTest:Thettestisusedintwo-groupdesigns(e.g.,treatment vs control)todetectifonegroupsignificantlyoutperformedtheother Intheeventthatthedataarenot fullysuitabletorunattest,theMann-WhitneyUtestprovidesanalternative
Chapter6:ANOVAandKruskal-WallisTest:AnalysisofVariance(ANOVA)issimilartothettest, butitiscapableofprocessingmorethantwogroups Intheeventthatthedataarenotfullysuitableto runanANOVA,theKruskal-Wallistestprovidesanalternative
Chapter7:PairedtTestandWilcoxonTest:Thepairedttestisgenerallyusedtogatherdataona variablebeforeandafteraninterventiontodetermineifperformanceontheposttestissignificantly betterthanthatonthepretest Intheeventthatthedataarenotfullysuitabletorunapairedttest,the Wilcoxontestprovidesanalternative.
Chapter8:CorrelationandRegression PearsonandSpearmanusesthePearsonstatistictoassessthe relationshipbetweentwocontinuousvariables Intheeventthatthedataarenotfullysuitabletoruna Pearsonanalysis,theSpearmantestprovidesanalternative.TheSpearmanstatisticcanalsobeusedto assesstherelationshipbetweentwoorderedlists.
Chapter9:Chi-Squareassessestherelationshipbetweencategoricalvariables
PartIII:DataHandling ThischapterdemonstratessupplementaltechniquesinSPSStoenhanceyourcapabilities,versatility,anddata processingefficiency
Chapter10:SupplementalSPSSOperationsexplainshowtogeneraterandomnumbers,sortandselect cases,recodevariables,importnon-SPSSdata,andpracticeappropriatedatastorageprotocols.
AfteryouhavecompletedChapters4through9,thefollowingtablewillhelpyounavigatethisbookto efficientlyselectthestatisticaltest(s)bestsuitedtoyour(data)situation Fornow,itisadvisedthatyouskip thistable,asitcontainsstatisticalterminologythatwillbecoveredthoroughlyinthechaptersthatfollow
ParametricVersusNonparametric(Pronouncedpair-uh-metric) Inthepriortable(“OverviewofStatisticalFunctions”),youmayhavenoticedthatChapters5through8each containtwostatisticaltests
Thefirst(parametric)statisticaltestisusedwhenthedataarenormallydistributed,meaningthatthevariable(s) beingprocessedcontainsomeverylowvaluesandsomeveryhighvalues,butmostofthedatalandsomewhere inthemiddle inmostinstances,dataarearrangedinthisfashion Incaseswhereoneormoreofthe variablesinvolvedarenotnormallydistributed,orotherpretestcriteriaarenotmet,thesecond (nonparametric)statistictestisthebetterchoice.
Theprocedurefordeterminingifavariablecontainsdatathatarenormallydistributediscoveredthoroughly inChapter4(“DescriptiveStatistics”)
LayeredLearning Thisbookisarrangedinaprogressivefashion,witheachconceptbuildingonthepreviousmaterial As discussed,Chapters5,6,7,and8containtwostatisticseach:Thefirst(parametric)statisticisexplainedand demonstratedthoroughly,followedbythesecond(nonparametric)versionofthestatistic,sothatafter comprehendingthefirststatistic,thesecondisonlyashortstepforward;itshouldnotfeellikeadouble workload.
Additionally,Chapter5providestheconceptualbasisforChapter6.Specifically,Chapter5(“tTestand Mann-WhitneyUTest”)showshowtoprocessatwo-groupdesign(eg,Treatment:Control)todetermine ifonegroupoutperformedtheother Chapter6buildsonthatconcept,butinsteadofcomparingjusttwo groupswitheachother(e.g.,Treatment:Control),theANOVAandtheKruskal-Wallistestscancompare threeormoregroupswitheachother(eg,Treatment1 :Treatment2 :Control)todeterminewhichgroup(s) outperformedwhich.Essentially,thisisjustonestepupfromwhatyouwillalreadyunderstandfromhaving masteredthettestandMann-WhitneyUtestinChapter5,sothelearningcurveisnotassteep
Thepointis,youwillnotbestartingfromsquareoneasyouenterChapter6;youwillseethatyouarealready morethanhalfwaytheretounderstandingthenewstatistics,basedonyourcomprehensionoftheprior chapter.Thisformoflayeredlearningisakintosimplyaddingonemorelayertoanalreadyexistingcake, hencethelayercakeicon
DownloadableLearningResources TheexercisesinChapter3(“WorkinginSPSS”)includethedatadefinitions(codebooks)andcorresponding concisedatasetsprintedinthetextformanualentry;thiswillenableyoutolearnhowtosetupSPSSfrom thegroundup Thisisanessentialskillforconductingoriginalresearch
Chapters4through10teacheachstatisticalprocessusinganappropriateexampleandacorrespondingdata set Thepracticeexercisesattheendofthesechaptersprovideyouwiththeopportunitytomastereach statisticalprocessbyanalyzingactualdatasets Forconvenienceandaccuracy,thesepreparedSPSSdatasets areavailablefordownload.
Thewebsiteforthisbookisstudy.sagepub.com/knappstats2e,whichcontainsthefullydevelopedsolutionsto alloftheodd-numberedexercisessothatyoucanself-checkthequalityofyourlearning,alongwiththe followingresources
Videos
The(mp4)videosprovideanoverviewofeachstatisticalprocess,alongwithdirectionsforprocessingthe pretestchecklistcriteria,orderingthestatisticaltest,andinterpretingtheresults.
DataSet Thedownloadablefilesalsocontainsprepareddatasetsforeachexampleandexercisetofacilitatepromptand accurateprocessing.
TheexamplesandexercisesinthistextwereprocessedusingVersion18ofthesoftwareandshouldbe compatiblewithmostotherversions
ResourcesforInstructors Password-protectedinstructorresourcesareavailableonthewebsiteforthisbookat studysagepubcom/knappstats2eandincludethefollowing:
Allstudentresources(listedabove)
Fullydevelopedsolutionstoallexercises
EditablePowerPointpresentationsforeachchapter
MarginIcons Thefollowingiconsprovidechapternavigation(inthisorder)inChapters4to9:
Video†
TutorialvideodemonstratingtheOverview,PretestChecklist,TestRun,andResults
Overview Summaryofwhatastatisticaltestdoesandwhenitshouldbeused
DataSet† Specifieswhichprepareddatasettoload
PretestChecklist Instructionstocheckthatthedatameetthecriterianecessarytorunastatisticaltest
TestRun Proceduresandparametersforrunningastatisticaltest
Results InterpretingtheoutputfromtheTestRun
HypothesisResolution Accepting/rejectinghypothesesbasedontheResults
DocumentingResults Write-upbasedontheHypothesisResolution
Thefollowingiconsareusedonanas-neededbasis:
ReferencePoint Thispointisreferencedelsewhereinthetext(thinkofthisasabookmark)
KeyPoint Importantfact
LayeredLearning Identifieschaptersandstatisticalteststhatareconceptuallyconnected
TechnicalTip Helpfuldataprocessingtechnique
Formula UsefulformulathatSPSSdoesnotperformbutcanbeeasilyprocessedonanycalculator
*SPSSisaregisteredtrademarkofInternationalBusinessMachinesCorporation.
†Gotostudy.sagepub.com/knappstats2eanddownloadthetutorialvideos,prepareddatasets,andsolutions toalloftheodd-numberedexercises.
Intheelectroniceditionofthebookyouhavepurchased,thereareseveraliconsthatreferencelinks(videos,journalarticles)toadditional content Thoughtheelectroniceditionlinksarenotlive,allcontentreferencedmaybeaccessedatstudysagepubcom/knappstats2e ThisURLisreferencedatseveralpointsthroughoutyourelectronicedition
Acknowledgments SAGEandtheauthoracknowledgeandthankthefollowingreviewers,whosefeedbackcontributedtothe developmentofthistext:
MikeDuggan–EmersonCollege
TinaFreiburger–UniversityofWisconsin–Milwaukee
LydiaEcksteinJackson–AlleghenyCollege
JavierLopez-Zetina–CaliforniaStateUniversity,LongBeach
LinaRacicot,EdD–AmericanInternationalCollege
LindaM Ritchie–CentenaryCollege
ChristopherSalvatore–MontclairStateUniversity
BarbaraTeater–CollegeofStatenIsland,CityUniversityofNewYork
WeextendspecialthankstoAnnBagchiforherskillfultechnicalproofreading,tobetterensuretheprecision ofthistext WealsogratefullyacknowledgethecontributionofDeanCameron,whosecartoonsenliventhis book.
AbouttheAuthor HerschelKnapp,PhD,MSSW, hasmorethan25yearsofexperienceasahealthscienceresearcher;hehasprovidedprojectmanagement forinnovativeinterventionsdesignedtoimprovethequalityofpatientcareviamultisitehealthscience implementations.Heteachesmaster’s-levelcoursesattheUniversityofSouthernCalifornia;hehasalso taughtattheUniversityofCalifornia,LosAngeles,andCaliforniaStateUniversity,LosAngeles.Dr. Knapphasservedastheleadstatisticianonalongitudinalcancerresearchprojectandmanagedthe programevaluationmetricsforamultisitenonprofitchildren’scenter.Hisclinicalworkincludes emergency/traumapsychotherapyinhospitalsettings.Dr.Knapphasdevelopedandimplemented innovativetelehealthsystems,usingvideoconferencingtechnologytofacilitateoptimalhealthcare servicedeliverytoremotepatientsandtocoordinatespecialtyconsultationsamonghealthcareproviders, includinginterventionstodiagnoseandtreatpeoplewithHIVandhepatitis,withspecialoutreachto thehomeless Heiscurrentlyleadinganursingresearchmentorshipprogramandprovidingresearch andanalyticservicestopromoteexcellencewithinahealthcaresystem Theauthorofnumerousarticles inpeer-reviewedhealthsciencejournals,heisalsotheauthorofIntermediateStatisticsUsingSPSS (2018),PracticalStatisticsforNursingUsingSPSS(2017),IntroductoryStatisticsUsingSPSS(1sted, 2013),TherapeuticCommunication:DevelopingProfessionalSkills(2nded,2014),andIntroductionto SocialWorkPractice:APracticalWorkbook(2010).
PartIStatisticalPrinciples Chapter1ResearchPrinciples Thescientificminddoesnotsomuchprovidetherightanswersasasktherightquestions
ClaudeLévi-Strauss
LearningObjectives Uponcompletingthischapter,youwillbeableto:
Discusstherationaleforusingstatistics
Identifyvariousformsofresearchquestions
Differentiatebetweentreatmentandcontrolgroups
Comprehendtherationaleforrandomassignment
Understandthebasisforhypothesisformulation
Understandthefundamentalsofreadingstatisticaloutcomes
Appropriatelyacceptorrejecthypothesesbasedonstatisticaloutcomes
Understandthefourlevelsofmeasure
Determinethevariabletype:categoricalorcontinuous
Overview ResearchPrinciples Thischapterintroducesstatisticalconceptsthatwillbeusedthroughoutthisbook Applyingstatisticsinvolves morethanjustprocessingtablesofnumbers;itinvolvesbeingcuriousandassemblingmindfulquestionsinan attempttobetterunderstandwhatisgoingoninasetting.Asyouwillsee,statisticsextendsfarbeyondsimple averagesandheadcounts Justasatoolboxcontainsavarietyoftoolstoaccomplishavarietyofdiversetasks (e.g.,ascrewdrivertoplaceorremovescrews,asawtocutmaterials),thereareavarietyofstatisticaltests, eachsuitedtoaddressadifferenttypeofresearchquestion.
