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THE ELEMENTS 0F QUANTITATIVE ANALYSIS KYLE …

CHAPTER11 KYLEGORMANANDDANIELEZRAIOHNSONA sociolinguistwhohasgatheredsomuchdatatha tithasbecomedifficulttomakesenseoftheraw observationsmayturntographicalpresentati on,andtodescriptivestatistics,techniques fordistillingacollectionofdataintoafewke ynumericalvalues,allowingtheresearcherto focusonspecific,meaningfulpropertiesofth edataset(seelohnsoninpress).However,asoc iolinguistisrarelysatisfiedwithameresnap shotoflinguisticbehavior,anddesiresnotju sttodescribe,butalsotoevaluatehypotheses abouttheconnectionsbetweenlinguisticbeha vror,speakers, ( ,Lucas,Bayley,8rValli2001:43).

CHAPTER 11 KYLE GORMAN AND DANIEL EZRA IOHNSON A sociolinguistwho hasgatheredso much datathat it hasbecomedifficult to make senseof the raw observationsmay turn to graphical presentation,and

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Transcription of THE ELEMENTS 0F QUANTITATIVE ANALYSIS KYLE …

1 CHAPTER11 KYLEGORMANANDDANIELEZRAIOHNSONA sociolinguistwhohasgatheredsomuchdatatha tithasbecomedifficulttomakesenseoftheraw observationsmayturntographicalpresentati on,andtodescriptivestatistics,techniques fordistillingacollectionofdataintoafewke ynumericalvalues,allowingtheresearcherto focusonspecific,meaningfulpropertiesofth edataset(seelohnsoninpress).However,asoc iolinguistisrarelysatisfiedwithameresnap shotoflinguisticbehavior,anddesiresnotju sttodescribe,butalsotoevaluatehypotheses abouttheconnectionsbetweenlinguisticbeha vror,speakers, ( ,Lucas,Bayley,8rValli2001:43).

2 Asociolinguistwhosuspectsthatwomenandmen 111acertainspeechcommunitydifferintherat eatwhichtheyrealizethefinalconsonantofaw ordendingin<ing>withcoronal[n]ratherthanvelar[1]]wouldco llecttokensofthesewordsinthespeechofwome nandmen, ,intheformofadescrip tivestatisticoranappropriategraph,coulds uggestthatwomendifferfrommenintherateatw hichtheyusethesecompetingvariants,theset ech, , ,however, ,asingleinterviewmakesuponlyatinyfractio nofanyspeaker slifetimeoflanguage, ,wheretherearealwaysmorepossiblesubjects torunorstimulitopresent, ,itisalwayspossiblethatthesamplediffersq uantitativelyfromthepopuelation, ,butthewomeninasample,forinstance,maynot berepresenta ,usuallyanobserveddifference.

3 Inthesampledoesextendtothepopulationisca lledthealternativehypothesis,whereastheo pposingviewthatthereisnorealdiffer ,ifasociolinguistisinterestedintheassoci ationbetweengenderandspeechrate,thenthen ullhypothesisisthatspeechrateisconstanta crossgenders, ( ,aZ-score,t statistic,F statistic,orchi-squarestatistic),thencom putetheprobability,henceforththep-value, thatateststatisticaslargeorlargerwouldha veoccurredunderthenullhypothesis( ,nodiffer enceinthepopulation).Althoughthisthresho ldisarbitrary,aresultwherep< ]Sciences, ,p< lation.

4 Intheforegoingexample,thealternativehypo thesisonlyrequiresthattherebesomediffere ncebetweengroups, , ,asthelabel significant , ,generallywithhelpfromacomputer,tocalcul ateateststatisticandp-valuefromasetofdat a; ,thecontentsofthesampleareshapedbyconven iencefactors,suchasspeakers forinstance,aresearcherinterestedinstigm atizedspeechmayunfor-tunatelydiscovertha tlow-prestigespeakersaretheleastlikelyto agreetoaninterviewwithastranger ,theresearchermaydeployproportionalstrat ifiedsampling( ,Cedergren1973);ifthepopulationconsistso fmiddleaclaSsspeak-ers,whoaccountfor25pe rcentofthepopulation,andworkingclassspea kers,accountingfortheremaining75percent, theresearcherensuresthatthis1:3ratioofmi ddle toworking classspeakers{andtokens} (Bayley2002:118).

5 Whileitisinsomesenseimpossibletoincludee verypredictorthatmightberelevanttotheout comesofinterest,astatisticalmodelisoflit tleuseforinferringacausalconnectionbetwe enpredictorsandoutcomesifoneormoreimport antpredictorshavebeenomitted,Forinstance , ,andfindsthatbotharesignificant, , ,butwhentheyareQUANTITATIVEANALYSIS217co mbinedinthesameregressionmodel,onlyoneof thetwotag,phonologicalcontext)issignific anttheotherpredictor(cg,grammaticalcateg ory)issaidtohavebeensuppressedleg,Taglia monte&Templezoos).Suchasituationcouldari seifthetwopredictorsarecorrelated,forexa mple,ifcertaingrammati-calcategoriestend toco-occurwithcertainphonologicalcontext s( , ),but.

6 Dictorsstandinacausalrelationshipwiththe outcome( ,bothphonologicalcontextandgrammaticalca tegoryincreaserateofdeletion), , orthogonal, thatis, linear( ,stronglynonorthogonal) (2010)givesanexampleOfaspurioussocioling uisticfindingduetomulticollinearitybetwe enmeasuresofsocioeconomicstatus,anddemon stratesthemethodofresidualiaation, , ,bothinthefieldandthelaboratory,togather manydatapointsfromeachspeakerorsubject, ,itisnecessarytodistinguishbetweenagende reffectinthepopulationandthepresenceinth esampleofafewspeakerswhojusthappentobema leandfurthermoreare outliers fromtherestofthesample.

7 ,evenaftergender,age,andsocialstatusaret akenintoaccount(Guy1980,1991:5),speakeri dentityisastrongprefdictoroflinguisticbe havior, ,etc;everytokenfrom CelesteS. alsohasthesamevalueforthegenderpredictor ( female"),age(45),etc, , whetherpredictorsoroutcomesionacontinuou sorintegerscale,butconvertsthesevaluesto afew valued(oftenbinary) {( 2 totreatdatathatarenaturallymanpvaluedasa fewvvalueciscale}itusuallyincreasesthech anceofTypellerror,theerroroffailingtorej ectthenullhypothesisinthecasewhenthisnul ihypothesisisinfactfalse(Cohen1983).)

8 Ifaresearcherpositsasoundchangeinprogres sinaspeechcommunity,thena78 yeareoldspeakershouldbelessadvancedwithr especttothischangethana60-year oldspeaker,butifthesetwospeakersareplace dtogetherintothe 60yearsofageandolder bin, :binningusuallyrequirestheresearchertoar bitrarilychoosethenumberandlocationofthe cutpointts)betweenbins, foundereffect ofVARBRUL anditsdescendants, ,itisincorrecttoassumethatVARBRUL Sfeaturesetdelimitsthesetofpossiblesocio linguisticanalyses,andtheuseofcontinuous predictorsand/oroutcomesinsociolinguisti csdatesbackatleastasfarasLennig s(1978') ,andmorespecifically, ,whichanumberofstudieshavefoundtobecurvi lin ear,withinteriorsocialclassesusingthehig hestratesofanonstandardvariantofastablel inguisticvariable(Labov2001:3if.

9 Insuchcases,theappropriateresponsetothis problem,though,isnotadhocdichotomization ,butratherfortheresearchertoexplorethere lationshipsobservedinthedata( ,byplottingthepredictorandoutcome),andch oosingappropriate transformations , ,theexemplartheoryoflenition( ,Bybee2002)predictsarelationshipbetweent helogarithmofwordfrequencyandtherateofle nition, (2001:16 26) , (categoricalQUANTITATIVEANALYSIS219la, ).Ihefollowmgsectionconsidersmethodsorco ntinuousoutcomes,Withafocusonacousticmea surementsofvowelsTheconcludingsectiondis cussessomerecenttrendsinthefieldofstatis ticsofrel-evancetosociolinguists.

10 METHODSFORBINARYVARIABLESI nterpretingCross-TabulationsManyquantita tivesociolinguisticstudiescomparetwodist inctdiscretesen,tlcallyequivalentvariant sincomplementarydistribution.:3mmThe clii ,WilliamLabovelicitedtokensofthephrasefo urthfloor"fromemployeesinthreeManhattand epartmentstoresforthepurposeofstudyingth esocialstratificationofpost (Labov2006:chapter4)firstoch lishedin1966,doesnotincludeanyinferentia lstatistics,thecross:tabul:i)tio-oftheda ta( , )lendsitselftoaSimlestat' , spronouncepost-vocalicrin125tokens,anddo notin211tokens;rispresent aerctofthetime(:125/336).


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