[PDF] What 2020’s Election Poll Errors Tell Us About the Accuracy of Issue





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Election Polling Errors across Time and Space

Our dependent variable is the simple absolute vote-poll error: the absolute value of the difference between party or candidate share of the polls and the 



Election Polling Errors across Time and Space

assessment of prediction errors in pre-election polls. Our analysis draws on more Further the claim that polling is in crisis and that poll errors are.





Election polling errors across time and space

of polling errors—controlling for a number of institutional and party features—that enables us to test whether poll errors have increased or decreased over 



An assessment of the causes of the errors in the 2015 UK General

It is therefore important that we understand what went wrong with the general election opinion polls in 2015 so that the risks of similar failures in the 



Failure and success in political polling and election forecasting1

17 ago 2021 of recent pre-election polling errors has been an ... Before discussing the successes and failures of political polls and forecasts ...



Election Polling Errors across Time and Space

assessment of prediction errors in pre-election polls. Our analysis draws on more Further the claim that polling is in crisis and that poll errors are.





What 2020s Election Poll Errors Tell Us About the Accuracy of Issue

2 mar 2021 opinion polling finds that errors of the magnitude seen in some of the 2020 election polls would alter measures of opinion on issues by an ...





Pre-election polls in 2020 had the largest errors in 40 years

The task force foundthat polling during the two weeks before the election overstated supportfor then-Democratic nominee Joe Biden by 3 9 percentage points whichwas the largest polling error



What 2020’s Election Poll Errors Tell Us About the Accuracy of Issue

For each poll in our primary dataset (i e polls conducted during the ?nal three weeksof the campaign) we estimate total survey error by computing the di?erence between: (1)support for the Republican candidate in the poll; and (2) the ?nal vote share for thatcandidate on election day



How Public Polling Has Changed in the 21st Century

Apr 19 2023 · This study represents a new effort to measure the nature and degree of change in how national public polls are conducted Rather than leaning on anecdotal accounts the study tracked the methods used by 78 organizations that sponsor national polls and publicly release the results



FOR RELEASE MARCH 2 2021 What 2020’s Election Poll Errors

Mar 2 2021 · Pew Research Center conducted this study to understand how errors in correctly representing the level of support for Joe Biden and Donald Trump in preelection polling could affect the accuracy of questions in those same polls (or other polls) that measure public opinion on issues



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Searches related to polling errors filetype:pdf

Failure and success in political polling and election forecasting1 Andrew Gelman 17 Aug 2021 1 A crisis in election polling Polling got a black eye after the 2016 election when Hillary Clinton was leading in the national polls and in key swing states but then narrowly lost in the Electoral College

Do polling errors affect public opinion?

    A new Pew Research Center analysis of survey questions from nearly a year’s worth of its public opinion polling finds that errors of the magnitude seen in some of the 2020 election polls would alter measures of opinion on issues by an average of less than 1 percentage point.

What are the main sources of error in polls?

    Changes in voter turnout drive one major source of error in polls. To accurately survey the electorate, most pollsters have to make an educated guess about who is going to show up on Election Day. Some use voter lists; others use algorithms, and still others rate people on their likelihood to vote.

What does a 'fail' in pre-election polls mean for polling?

    Official post-mortems are actively underway, but pre-election polls clearly understated support for Republicans across the country. So, what does this mean for polling? Some have suggested that two consecutive "fails" in presidential elections means that no poll can be trusted. This conclusion is too hasty and too broad.

How close do polls come to predicting election results?

    Some analyses of how close polls came to predicting results look only at surveys conducted just days before the election; others look at the average of polls conducted up to several weeks before, when some voters still had time to change their minds before casting ballots.

DisentanglingBiasandVariance inElection Polls

HoushmandShirani-Mehr

StanfordUniversity

DavidRothschild

MicrosoftResearch

SharadGoel

StanfordUniversity

AndrewGelman

ColumbiaUniversity

February3,2018

Abstract

Itiswe llkno wnamongresearch ersandpractitione rsthatelec tionpollssuerfrom avar ietyofsamplingandnon- samp lingerrors,oftencollect ivelyr eferredtoastotal surveyerror.Re portedmarginsoferrortypic allyonlycapturesam pli ngvariability , andinpar ticul ar,generallyignorenon-samplingerr orsindeÞningthetargetpop ula- tion(e.g.,e rrorsduetouncer taintyinwhowillv ote).Herew eempir icallyanalyze

4,221polls for608state-leve lpres ide ntial,senatorial,andgubernatorialelect ionsbe-

tween1998and2014,allofwhi chw erecon duc teddur ing theÞnalthreewee ksofthe campaigns.Comparingtotheac tualelectionoutcomes,weÞn dthata veragesurve y errorasmeasured byr ootmeansquareerror(RMSE) isappro ximately3. 5percent- agepoi nts,abouttwiceaslargeas thatimpli edbymostreportedm argin soferror. Wedecom posesurveyerrorintoele ction-levelbiasandvar ianceterms.W eÞndthat averageabsoluteele ction-levelbiasisabou t2percentagepoints,indicatingthatpolls foragive ne lectionoftensh areacommoncomponentoferror. Thissharederr ormay stemfromthef actthatpolli ngorganization softenface similardi↵cultiesinreaching varioussubgroupsoft hepopulation,andthattheyr ely onsimilarsc reeningruleswhen estimatingwhowillvote.WealsoÞ ndthatave rageelection-le velvarianceis higher thanimpli edbysimplerandomsampling, inpart becausepollingorganizati onsoften usecomplex samplingdesignsandadj ustmentprocedures.We concludebydiscussin g howtheser esultshelpexpl ainpollingfailuresinthe2016 U.S.presiden tialelection, andoerrecom mendationstoimprovepollingpractice.

1In troduction

Electionpollingisarguab lythemostvisiblemanife stati onofstatisticsineveryday life, andembodi esoneofthegreatsuccesss toriesoft heÞeld :randomsa mpling.Asisreco unted insoma nytext books,thehugeb utuncontrolledLiteraryD igestpollwa strouncedby GallupÕssmall,nimblerand omsamplebackin193 6.Electionpollsar eahigh-proÞlerealit y checkonsta tistic almethods. Ithasl ongbeenk nownthatthema rginsoferro rsprovidedby surveyorgan izations, andreport edinthenews,understatethetotalsurv eyerror.This isanimp ortanttopic in samplingbutisdiculttoaddre ssinge neralfortworeasons.Fi rst,we liketodecompose errorintobiasa ndvariance,bu tthiscanonl ybedon ewithanyprecisionifwehavea large numberofsurveysando utcomesÑnot merelyalargen umberofrespo ndentsinanindividual survey.Second,assess mentoferrorrequiresag roundtruthforcomparison,wh ichist ypically notavaila ble,asthereasonforconductingasamp lesurv eyintheÞrst placeistoestima te somepopul ationcharacteristicthatisnotal readyknown. Inthepr esent paperwedecomposesurv eyerrorinalarge setofs tate-levelpre-election polls.Thisdataset resolvesboth oftheproblemsjustnot ed.First,thecombinationof multipleelectionsandmanystat esgivesusalargesampl eofpolls.S econd,wecancomp are thepolls toactualele ctionre sults.

1.1Backg round

Electionpollstypicallys urveyarandomsam pleofeligibleorlikelyvoters ,andthen generatepopulation-lev elestimatesbytakingaweigh tedav erageofresponses,wherethe weightsaredesign edtocorre ctforknowndi↵erencesbetweensam pleandpopulation . 1 This generalanalysisframew orkyieldsbothapointesti mateoftheelectionoutcome,a ndalsoan estimateoftheerrorintha tpredi ctiondueto samplevariance whicha ccountsfort hesurvey 1 Onecommonte chniqueforse ttingsurveyweightsisraking,in whichw eightsaredeÞnedsothatth e weighteddistributionsof variousdemographicfeatures(e.g.,age,sex,andrac e) ofrespondentsinth esample agreewithth emarginaldistr ibutionsin thetargetpopulation [Voss,Gelman,andKing,1995]. 2 weights[Lohr,200 9].Inpractice,howev er,pollingorganiza tionso ftenusetheweightsonlyin computingestimates,ignoringth emwhencomputingstandarderr orsandinsteadreporting

95%marginsof errorbased ontheform ulaforsimple randomsampling(SRS)Ñfor example

±3.5perce ntagepointsforanelectionsurve ywith800people.Appr opriatec orrect ionfor theÒdes igne↵ectÓcorr espondingtounequalweightswouldincr easemarg insoferror(see, forexam ple,Mercer[2016]).Theincrease inmargi noferrordependsont hepoll, assome surveyshaveself-weigh tingdesigns (i.e.,thesamplingisconstructedsothatnoweig htsare usedintheana lysis) whileothe rsweightonmanyfactors.Fors omeleadingpo lls,standard errorsshouldbein creasedbyafactorof30%to accountforthe weighting. 2 Thoughthisapproac htoquantifyi ngpollingerrorispopular andcon venient,itiswell knownbybothres earchersandprac titionersthatdiscre panciesbetweenpollresultsande lec- tionoutcome sareonlypartiallyattribu tabletos amplevarianc e[AnsolabehereandBelin,

1993].Asobservedin the extensiveliteratureontotalsurvey error[Biemer,2010,Groves

andLybe rg,2010],thereareatleastfouradditional types oferrorthat arenotre ßected intheu suallyre portedmarginsoferror: frame,nonresponse,measur ement,andspeciÞca- tion.Fra meerroroccursw henthereisa mismatchbetweenthes amplingframeandthe targetpopulation.F orexample,forphone-basedsurve ys,people withoutphoneswould neverbeinclu dedina nysample.Ofparticulari mport forelectionsur veys,thesampling frameincludesma nyadultswhoarenotlike lytovote,whi chpollstersrec ognizeand at- tempttocorrec tfor usinglikelyvotersscree ns,typically estimatedwith errorfromsurvey questions.Nonresponseerroroccu rswhenmissingvaluesaresystema ticallyrelated tothe response.Forexample,support ersofthet railingcandidatemaybeles slikelytorespond tosurv eys[Gelman,Goel,Rive rs,andRothschild,2016]. Withnonresponser atesexceeding 2 Forasamp li ngof96pollsfor2012senateelect ion s,o nly19reportedm arginsofe rr orhigherthanwhat onewould computeusingt heSRSformula,and14ofth eseexceptionswe reaccounted forbyYouGo v,a

pollingorganizationthatex plicitlynotesthatitinß atesvarianc etoadjustforthesurveyweights.Sim ilarly,

forasamp li ngof36state-levelpollsfort he 2012pr esidentialelection,only9reportedh igher-than-SRS marginsoferror.Compl ete surveyweightsare availablefor21ABC,CBS,andG allupsurveysconduct ed duringthe2012electionan ddeposite din toRoperCenterÕsiPOLL.To accountf ortheweightsinthese surveys,standarderrorsshoul donaveragebemultiplie dby1.3(withanint erquartilerangeof1. 2to 1.4 acrossthesurv eys),compar edtothestandarderrorsassumings implerandomsampling. 3

90%forelection surveys, thisisa growingconcern[PewResearc hCenter, 2016].Measure-

menterrorar iseswhenthesurv eyinstrumentitself a↵ectsthe response,forexampl edue toorder e↵ects[McFarl and,1981]orquestionwording[Smith ,1987].Finally,s peciÞ cation erroroccurswh enarespondentÕsin terpretati onofa questiondi↵ersfromwh atthesur veyor intendstoconvey(e.g., duetol anguagebarri ers).Inadditionto thesefourtyp esoferro r commontonearl yallsurv eys,electionpollssu↵erfrom anaddi tion alcomplication:shifting attitudes.Whereassurveystypica llyseektogaugewha trespondentswilldoon electionday, theycanonlydire ctlymeasure curren tbeliefs. Incont rasttoerrorsduet osamplev ariance,itisdi cultÑandperhapsimpossi bleÑto buildausefulandgen eral statistical theoryforth eremainingcompon entsoftotalsurvey error.Moreover,evenem piricallymeasuringt otalsurvey errorcanbedicult,asitinvol ves comparingtheresultsofrep eatedsurveysto agroundtruthobtain ed,for example,viaa census.Forthesereasons ,itisnot surprisingth atmanysurveyorganizatio nscont inuet o useestima tesoferrorbasedontheor eticalsam plingvariati on,simplya cknowl edgingthe limitationsoftheapproach.Indeed,Gallu p[2007 ]explicit lystatesthattheirmethodology assumesÒothersources oferror, suchas nonresponse,b ysomemem bersofthetargeted sampleareequal,Óan dfurtherno testhatÒothererro rsthatcan a↵ectsur veyvalidityin clude measurementerrorassociatedwith thequestionnai re,suchastranslation issuesandcover age error,whereapart orpartsofthetargetp op ulation...h ave azeroprobability ofb eingselected forth esurvey.Ó

1.2Ourstudy

Hereweemp irical lyandsystematicallystudyerrorine lectionpo lling,takingadvantage ofthefact thatm ultiplepolls aretypically conductedforeachelection,and thattheelection outcomecanb etaken tobethegroundtruth. Weinvestigate4,221 polls for608state-level presidential,senatorial,andgubernatori alelectionsbetween1998and2014,al lofwhichw ere conductedintheÞnalt hreeweeks oftheelectioncam paigns.Byfocusi ngontheÞnalweeks 4 ofthecampaigns, we seektominimize theimpactoferrorsdueto changinga ttitudesinthe electorate,andhencetoisol atethee ↵ectsofth erem ainingcomponen tsofsurveyerror. WeÞndt hattheaver agedi↵erencebetweenpo llresultsandel ectionoutcomesÑas mea- suredbyRMSEÑis 3.5percent agepoints,about twicethe errorimpliedbymostrep orted conÞdenceintervals. 3 Todeco mposethissurveyerrorinto election-le velbiasandvariance terms,wecarryouta Bayesi anmeta-analysis .WeÞndtha tav erageabsoluteelection-l evel biasisabout 2percenta gepoints,indicati ngt hatpollsforagivenelection oftenshareacom- moncompon entoferror.Thisresultislik elydr iveninpartbythefact thatmostpol ls,even whencond uctedbydi↵erentpollin gorganizations,relyonsimilarl ikelyvotermodels,and thussurprises inelectiondayturnoutcanhav ecomparable e↵ectsona llthep olls.Moreover , thesecorrelatedframe errorsextendtothevariousele ctionsÑpres idential,senatorial, and gubernatorialÑacrossthestate. 2Data

2.1Datades cription

Ourprima ryanalysisisbasedon4, 221pollscompletedduringt heÞnal threeweeksof

608state-level presidential,senatorial,andgub ernatorialelectionsbetween 1998and2014.

Pollsaretypi callycond uctedoverthecourseofsevera ldays,andfollowingconventio n,we throughoutass ociatetheÒdateÓofthepollwiththelast dateduring whichitwas inthe Þeld.Wedonotin cludeHo useele ctionsino uranalysissincepolli ngisonlyavai lablefora smallandnon-repr esentati vesubsetofsuchraces. Toco nstructthisdataset,westart edwiththe4, 154state-levelpollsforel ectio nsin

1998Ð2013thatwerecollected andmadeavai lablebyFiveTh irtyEight,allofwhichwere

3 Mostreporte dmarginsoferrorassumeesti matesareunbiased, andr eport95%conÞden ceintervalsof approximately±3.5pe rcentagepointsforasampleof800re spondents.Thisi nturni mpl iestheRMSE forsuch asampleisappro xi mately1.8percentage points, abouthalfofourempiricalestimateofRMSE. Asdi scussedinFootnote2,manypollingor ganization sdonotadjustforsurve ywe ightswhenc omputing uncertaintyestimates,whichinpart explainsthisgap. 5 completedduringtheÞnalthree weeksofthecamp aigns.Wea ugmentthese pollswith the67c orres pondingonesfor2014postedonPollste r.com,wheref orconsiste ncyw iththe FiveThirtyEightdata,weconsideronlythoseco mpletedinthelas tthree weeksofthecam- paigns.Intotal,weend upwi th1,646pollsfor241sen atoria lelection s,1,496 pollsfor179 state-levelpresidentialel ections,and1,079pollsfor188gubernatorial elections. Inadd itiontoourprimarydataset describeda bove,we alsoconsider7,040pollscomplet ed duringthelast100day sof314sta te-levelpresiden tial,senato rial,andgub ernator ialelections between2004and2012.All pollsforthi ssecondar ydatasetwereob tained fromPollster.co m andRealCl earPolitics.com.Whereasthiscomplementarysetofpollscoversonlythemo re recentelections,ithastheadv antageofcontainingpollsconductedearlier inthec ampaign cycle.

2.2Dataex ploration

Foreachpol linourpr imarydataset(i. e.,poll sconductedd uringtheÞnalthreeweek s ofthecampaign), weestimate totalsurve yerrorbycomputingthe di↵erenceb etween:(1) supportfortheRepubli cancandida teinthep oll;and(2)theÞnalvotesharefort hat candidateonelectionday.Asis standa rdintheliterature,wecon sidertwo-partypoll andvote share:wedividesupport fortheR epublicancandidatebytotalsupp ortfor the RepublicanandDemo craticcandidates,excluding undecidedsandsupportersof anythird- partycandidates. Figure1showsthe dis tributionofthes edi↵erences,wherepositiveval uesonthex-axis indicatetheRepublicancand idatereceiv edmoresupportinthepollthani ntheelection. Werepeat thisprocesssep aratelyforsenato rial,guberna torial,andpresidentialpolls.Fo r comparison,thedottedlines showthetheor eticaldistribut ionofpollingerror sassuming simplerandomsamplin g(SRS).SpeciÞcall y,foreachsenatepolliweÞr stsimulatea nSRS pollingresultbydrawinga samplefromabin omiald istributionwithpara meter sn i andv r[i] wheren i isthen umberofresp ondentsinpolliwhoexpr essapreferenceforoneoft het wo 6

SenatorialGubernatorialPresidential

10%5%0%5%10%10%5%0%5%10%10%5%0%5%10%

0 100
200
300

Number of polls

Difference between poll results and election outcomes Figure1:Thedistribution ofpollinger rors (Republicanshareoftwo-party supportinthe pollminusRepublicanshare ofthetwo-p artyvoteintheelection)for state-levelpresidential, senatorial,andguber natorialele ctionpollsbetween1998and2014.Positive valuesindicate theRepublic ancandidatereceivedmor esupportin thepollthanintheelection.Forcompar- ison,thedashe dlinesshow thetheoretical distributionof polling error sassumingeachpoll isgenerate dviasimplerandomsampling. major-partycandidates,andv r[i] istheÞ naltwo-pa rtyvoteshareof theRepublicancandidate intheco rresponding electionr[i].Thed ottedlin esintheleft-handpa nelofFigure1sho w thedistribution oferrorsacros sthissetof synthetics enatepolls.Theoretic alSRSe rror distributionsaregeneratedanalogouslyforg ubernatori alandpresidentialpolls. Theploth ighlightstwo points.First,forallthreepoliti caloces,p ollingerrorsare approximatelycenteredatzero.Thus,atle astacrossalltheelection sandye arsthatwecon- sider,pollsaren otsystematicallybiased towardei therpar ty.Indeed,itwouldbesurprising ifweha dfound systematicerr or,sincepollster sarehighlymotivatedto noticeandcorrect foranysuc haggregat ebias.Secon d,thepollsexhibitsubstantia llylargererror sthanone wouldexpectf romSRS.Forexample, itisnotunco mmonforsenat orialandg ubernatorial pollstomisstheel ectionou tcomebymor ethan5per centagepoints,aneven tthatwould rarelyoccur ifrespondentsweresimple randomdraw sfromtheelectorate. Wequan tifythesepollingerrorsin termsofther ootmeansquareerror(R MSE). 4 The 4 AssumingNtobeth enumb erofpolls,f oreachpolli{1,...,N},lety i denotethetwo-part ysupport fortheR epublican candidate,andletv r[i] denotetheÞnaltwo- partyvotesh areoftheRe publicancandidate 7 senatorialandgubernatorial polls,in particular,havesubstantiall ylargerRMSE(3.7%and

3.9%,respectiv ely)thanSRS(2.0%and2.1%,respectively).I ncontrast,t heRMSEfors tate-

levelpresident ialpollsis2.5%,notmuchlarg erthanonewouldex pectfromS RS(2.0%). Becausereportedmargi nsoferrorare typicallyderivedfromth eoreticalS RSerrorrates, thetradit ionalintervalsaretoonarrow.Namely ,SRS-based95%conÞdenceinte rvalscov er theactual outcomeforonly73% ofsenatorialpolls ,74%ofgube rnatorialp olls,and 88% ofpres identialpolls.Itisnotimmediatelyc learwhypresidentia lpoll sfareb etter,but one possibilityisthatturnoutinsuchele ctions iseasiertopr edictandsothesepolls su↵erless fromsucherro r;inaddition ,presidentialpolls haveh ighervisibilityandsotheo rganizations thatconducts uchsurveysmayinves tmoreresour cesintotheirsampling andadjust ment procedures. Wehave thusfarfocus edonpollsco nductedin thethreeweekspriortoel ection day,in anattemptto minimizethe e↵ectsoferror duetoc hangingattitudes intheelec torate .To examinetherobustness ofthisass umption,wenowturntooursecondar ypoll ingdataset and,inFigure2 ,plota veragepollerro rasafu nctionofthe numberofdaystotheelec tion. Duetothe relativ elys mallnumberofpollsconducte donanygiv enday,weincludeineach pointintheplota llthep ollscompl etedinaseven-daywin dowcenteredat thefocalda te (i.e.,pollscomple tedwithinthreeda ysbeforeorafterthatday).Ase xpected,pollsearl yin thecampai gnseasonindeedexhibitmo reerrorthanthoset akennearelectiond ay.Average error,however,ap pearstostabilizeintheÞn alweeks,withlitt ledi↵erenceinRMSEon e monthbeforetheelecti onversusoneweek beforetheelect ion.Thus,thepolli ngerrorsthat wese eduringt heÞnalweeksofthecam paignsar elikelynotdr ivenbychangi ngatti tudes, butrather resultfromnon-sam plingerror,partic ularlyframea ndnonresponseerror.As notedearlier,mea surementandspeciÞcat ionerroralsolikelyplayarole,thoug helectio n pollsarearguably lesssuscepti bletosuchformsoferror. Inprin ciple,Figure1isconsistentwitht wopossibilities .O nonehan d,electionpolls inthec orrespondi ngelectionr[i].Then RMSEis 1 N N i=1 (y i ↵v r[i] 2 8

Senatorial

Gubernatorial

Presidential

0102030405060708090

0% 2% 4% 6% 8% 0% 2% 4% 6% 8% 0% 2% 4% 6% 8%

Days to Election

Root mean square poll error over time

Figure2:Pollerror,as measuredbyRMSE,overthec ourseof elections.TheRMSEon eachdayxindicatestheaverageer ror forpollscomplete dinaseven-daywindowcentered atx.Thedashe dvertic allineatthethr ee-weekmarkshowsthatpol lerr orisr elatively stableduring theÞnalstretchesof thecamp aigns,suggestingthat thediscrepancieswese e betweenpollresults andelectionoutcomesarebyand largenotduetoshifting attitudesin theelector ate. maytypical lybeunbiasedbuthavelargeva riance;o ntheotherhand,polls inanelection maygenerall yhavenon-zerobias,butina ggregatethesebia sescanceltoyieldthed epicted distribution.Ourgoalistoquantifythestr uctureofp ollingerrors.Bu tbeforefo rmally addressingthis taskwe carryoutthe followingsimpleanalysistobuild intuition.F oreach electionr,we Þrstcom putetheav eragepollestimate, øy r 1 |Squotesdbs_dbs19.pdfusesText_25
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