Smith, Stacy. Gender Inequality in Cinematic Content

7/28/2019 Smith, Stacy. Gender Inequality in Cinematic Content

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GenderInequalityinCinematicContent?

ALookatFemalesOnScreen&Behind-the-CamerainTop-Grossing2008Films

StacyL.Smith,PhD.

&

MarcChoueiti

AnnenbergSchoolforCommunication&Journalism

UniversityofSouthernCalifornia

3502WattWay,Suite222

LosAngeles,CA90089

[email protected]

Summary

Thisstudyexaminedthegenderofallspeakingcharactersandbehind-the-scenes

employeesonthe100top-grossingfictionalfilmsin2008.Atotalof4,370speakingcharacterswereevaluatedand1,227above-the-linepersonnel.Inadditiontoprevalence,

weassessedthehypersexualizationofonscreencharactersacrossthe100movies.Belowarethestudysmainfindings.

32.8percentofspeakingcharacterswerefemale.Putdifferently,aratioofroughly2malestoeveryonefemalewasobservedacrossthe100top-grossingfilms.Thoughstillgrossly

imbalancedgiventhatfemalesrepresentoverhalfoftheU.S.population,thisisthehighestpercentageoffemalesinfilmwehavewitnessedacrossmultiplestudies.

Thepresenceofwomenworkingbehind-the-cameraisstillabysmal.Only,8%ofdirectors,

13.6%ofwriters,and19.1%ofproducersarefemale.Thiscalculatestoaratioof4.90

malestoeveryonefemale.Filmswithfemaledirectors,writers,andproducerswere

associatedwithahighernumberofgirlsandwomenonscreenthanwerefilmswithonly

malesinthesegate-keepingpositions.Toillustrate,thepercentageoffemalecharacters

jumps14.3%whenoneormorefemalescreenwriterswereinvolvedinpenningthescript.

Femalescontinuetobehypersexualizedinfilm,particularly13-to20-yearoldgirls.A

substantiallyhigherpercentageofyoungfemales,incomparisontoyoungmales,areshownwearingsexuallyrevealingattire(39.8%vs.6.7%),partiallynaked(30.1%vs.10.3%),with

asmallwaist(35.1%vs.13.6%),andphysicallyattractive(29.2%vs.11.1%).Nogender

differencesemergedforchestsizeoridealbodyshapeforteenagedspeakingcharacters.


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GenderInequalityinCinematicContent?

ALookatFemalesOnScreen&Behind-the-CamerainTop-Grossing2008Films

StacyL.Smith,PhD.

&

MarcChoueiti

UniversityofSouthernCalifornia

AnnenbergSchoolforCommunication&Journalism

3502WattWay,Suite222

LosAngeles,CA90089

[email protected]

Thepurposeofthisstudywastoassessgenderprevalenceandtheportrayalofmaleand

femalespeakingcharactersinpopularmotionpicturecontent.Tothisend,wecontentanalyzed100ofthetop-grossingtheatrically-releasedfictionalfilmsfrom2008.1Below,

wepresentourkeyfindingsfromthisinvestigationandcompare2008trendstothosewe

observedin2007.Forallcomparisons,onlydifferencesof5%ormorewillbereportedas

meaningful.

KeyFindings

#1FemalesareStillUnderrepresentedinPopularFilm

Atotalof4,370speakingcharacterswithanidentifiablegenderwerecodedacrossthe100

films,with32.8%female(n=1,435)and67.2%male(n=2,935).2

Onscreen,thistranslatesinto2.045malestoeveryonefemale.Thoughstillgrosslyimbalanced,thisisthelowest

ratioofmalestofemaleswehaveobservedinourresearchoftop-grossingfilmstodate.3

Table1

OccupationalTitlebyEmployeeSex

Males Females Total

Directors 92%(n=103) 8%(n=9) 112

Writers 86.4%(n=223) 13.6%(n=35) 258

Producers 80.9%(n=693) 19.1%(n=164) 857

Total 1,019 208 1,227

Thegendergapwidenssubstantiallywhenwelookatthebiologicalsexofthoseworking

behind-the-camera.4AsshowninTable1,only8%ofdirectors,13.6%ofwriters,and

19.1%ofproducerswerefemaleacrossthe100top-grossingtheatrically-releasedfilmsof

2008.Behind-the-scenes,theratioofmaletofemaleemploymentis4.90toone!Clearly,

femalesaregrosslyunderrepresentedbothonscreenandbehind-the-camerainpopular


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motionpicturecontent.Thesefindingsaresurprisinggiventhatfemalescompriseover

halftheU.S.population.5

#2FemalesWorkingBehind-the-ScenesSeemtoMatterforOnScreenFemaleCharacters

Filmswithoneormorefemalesworkinginkeydecision-makingpositionsbehind-the-

camerahaveahigherpercentageofonscreenfemalecharacters.AsshowninFigure1,the

percentageoffemalesonscreenissignificantlyhigherforfilmswithatleastonefemale

director(44.4%)thanthosewithonlymaledirectors(31.7%).6Asimilartrendwas

observedwiththegenderofwriters.Whencomparedtothosefilmswithonlymale

screenwriters,thepercentageoffemalesonscreenwassignificantlyhigherforfilmswith

oneormorefemalescreenwriters(43.7%vs.29.4%).Forproducers,asimilarbutless

pronouncedtrendwasobserved(filmswithonlymaleproducers=27.9%femaleson

screen;filmswithoneormorefemaleproducers=34.2%femalesonscreen).

Figure1

PercentageofFemaleCharactersbyGenderofBehind-the-ScenesEmployees

Thesefindingssuggestthatb-t-swomenmaybeadvocatingforonscreenfemalecharacters.Or,itmaybethecasethatstudiosaremorelikelytoattachabove-the-line

femalesasdirectors/writerswhendevelopingfemale-drivenstorylines.Itmustbenoted

thatthesefindingsareverysimilartotrendswehaveobservedacross100top-grossing

filmsin2007aswellasacross150AcademyAwardBestPicturenominatedfilmsbetween1977and2006.7

29.4%

31.7%

43.7%

44.4%

0% 10% 20% 30% 40% 50%

Writers

Directors

%ofFemaleSpeakingCharactersOnScreen

One(ormore)BTSFemaleNoBTSFemale


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#3FemalesareStillSexualized,ParticularlyTeenGirls

AsdepictedinFigure2,femalesweremorelikelythantheirmalecounterpartstobe

depictedinahypersexualizedlightintop-grossing2008films.8Femalesweremorelikely

tobeshownwearingsexyorrevealingattire(25.7%vs.5.1%),partiallynaked(23.7%vs.8.2%),possessingasmallwaist(20.1%vs.4%),andreferencedasphysicallyattractive

(15.1%vs.4.1%).Maleswereslightlymorelikelythanfemalestopossessalargechest

(17.5%vs.13.7%),thoughthisdifferencejustfellshortofthe5%criterion.Femaleswere

slightlymorelikelythanmalestobeshownwithanunrealisticoridealizedbodyshape(i.e.,

hourglassforfemales,invertedtriangleformales)butthedifferencewastrivial(1.5%).

Turningtoteenagedspeakingcharacters(13-to20-yearolds),weobservedsimilarand

morepronouncedpatternsofsexualizationbygender.AsportrayedinTable2,teenaged

femalesweremorelikelythanteenagedmalestobeinsexyattire,partiallyclad,possessa

smallwaist,andbereferencedasphysicallyattractive.9Nodifferencesemergedbygenderinchestsizeorunrealisticbodyideal,however.Thesefindingsaretroublinggiventhat

repeatedexposuretothinandsexyidealsmaycontributetonegativeeffectsinsomeviewersandreinforcepatternsoflookismintheentertainmentindustry.10SeeAppendixB

forfurtheranalysesofthehypersexualityindicatorsbyapparentage.

Table2

HypersexualityIndicatorsbyGenderofTeensinFilm

TeenagedMales TeenagedFemales

%insexyclothing 6.7%(n=11) 39.8%(n=41)

%partiallynaked 10.3%(n=17) 30.1%(n=31)

%attractive 11.1%(n=19) 29.2%(n=33)

%w/smallwaist 13.6%(n=20) 35.1%(n=33)

25.7% 23.7%

20.1%

15.1%

5.1%8.2%

4.0% 4.1%

0%

5%

10%

15%

20%

25%

30%

SexyClothing PartialNudity SmallWaist Attractive

Figure2

HypersexualityMeasuresbyCharacterGender

Females

Males


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#4LittleChangeBetween2007Filmsand2008Films

Wecomparedthe100top-grossingfilmsfrom2008tothe100top-grossingfilmsfrom

2007,acrossourmajorprevalenceandhypersexualitymeasures.Onlythosevariablesthat

werereliablycapturedandreportedinboth2007and2008areincludedinTable3. 11Usingour5%criterionfordemarcatingchange,onlyonevariablediffersbetween2007and

2008films.Ahigherpercentage(+5.3%)offemaledirectorswerepresentinthe100top-

grossingfilmsof2008thaninthe100top-grossingfilmsof2007.Usingalargersampleof

250filmseachyear,otherresearchshowsasimilar(+3%)butlesspronouncedincreasein

femaledirectorsfrom2007to2008.12

Table3

ComparingFemalesinFilmfrom2007to2008

2007Films 2008Films Difference

%offemaleson

screen

29.9% 32.8% +2.9%

%offemale

directors2.7% 8% +5.3%

%offemale

writers11.2% 13.6% +2.4%

%offemaleproducers

20.5% 19.1% -1.4%

%offemalesin

sexyclothes27% 25.7% -1.3%

%offemales

partiallynaked21.8% 23.7% +1.9%

%offemalesthatareattractive

18.5% 15.1% -3.4%

Conclusion

Ourfindingsrevealthatmotionpicturecontentissendingtwoconsistentandtroublingmessagestoviewers.Thefirstisthatfemalesareoflesservaluethanaremales.Thisis

evidencedbytheironscreenpresenceandthelackofemploymentopportunitiesbehind-

the-camera.Thesecondisthatfemalesaremorelikelythanmalestobevaluedfortheirappearance.Roughlyafifthtoaquarterofallfemalespeakingcharactersaredepictedina

hypersexualizedlight.Thesenumbersjumpsubstantiallyhigherwhenonlyteenaged

femalesareconsidered.Thisresultisparticularlytroubling,giventhefrequencywithwhichyoungmalesandfemalesgotothemultiplex.


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Footnotes

1.ThelistoffilmswaspurchasedfromNielsenEDI.Onedocumentarymadethelist

(HannahMontana&MileyCyrus/BestofBothWorldsConcertTour)butwasremovedfrom

thesample.Assuch,the101stfilmonthelist(TheWomen)wasaddedtooursampletobringthetotalnumberofmoviesto100.SeeAppendixAforthelistoffilmsinthesample.

2.Themajorunitofanalysisforthisstudyisthespeakingcharacter.Charactersmayspeak

independentlyorinagroupcontext.Singlecharactersspeakovertlyanddiscerniblyon

screen.Groupcharacters,however,areidenticalinappearancebutspeaksequentially

makingtheirindependentidentityimpossibletoascertain.Assuch,groupsarevery

difficulttoquantifyreliablyintermsofsize.Only7unitsmetthedefinitionofagroupand

werecodedacrossthesample.Theyarenotincludedinanyoftheanalyses.

Foreachcharacter,aseriesofdemographicandhypersexualityindicatorsweremeasured.

Onlyasubsetofthosevariableswerereportedinthisresearchbrief.Intermsof

demography,apparentage(0-5,6-12,13-20,21-39,40-64,65+)andgender(male,female)weremeasured.Forhypersexuality,weusedvariablesderivedfromDowns,E.,&Smith,S.

L.(2009).Keepingabreastofhypersexuality:Avideogamecharactercontentanalysis.Sex

Roles.http://www.springerlink.com/content/1646t34676837317/fulltext.pdf.Forall

shape-andclothing-relatedvariables,onlythosecharacterswithbodiesthatapproximatehomosapiensmorethansomeotherspecieswereevaluated.

Sexuallyrevealingclothing(SRC)referstotightoralluringapparelthatmayarouseinterest

inothercharacters.SRCwascodedaspresentorabsent.Nuditymeasurestheamountof

exposedskindepictedbetweenthemidchestandhighupperthighregion.Nudityhasthreevalues:none(i.e.,noexposedskin),some(i.e.,exposedcleavage,midriff,and/or

upperthighregion),full(i.e.,exposureofgenitalarea,buttocks;forfemalesonly,alsoincludesnippleexposure).Foranalysis,thelattertwocategorieswerecollapsed.Itmust

benoted,however,thatmostinstancesofexposedskinfallintothepartialcategory(91%

orn=495).Statedanotherway,only9%(n=47)oftheinstancesofexposedskinwere

codedasfullnudity.

Waistsizecapturesthecircumferenceofthetorsoduetofitnessorfat.Eachcharacterwas

codedassmall(i.e.,amidsectionthatcurvesinwardduetoashortageoffat),medium(i.e.,

amidsectionthatcurvesslightlyinwardoroutwardnaturally,duetofitnessorfat);or

large(i.e.,amidsectionthatcurvesoutwardandspillsoverthewaistlineduetoexcessfat).Beforerunningstatisticalanalyses,thelattertwocategorieswerecollapsed.Chestsize

referstotheexpansivenessofcharactersbreastregionforfemalesandpectoral/shoulderregionformales.Chestsizehasthreelevels;small(i.e.,nodefinitionorshapelinessin

pectoral/shoulderregionformales;brasizeAforfemales),medium(i.e.,average

definitionorshapelinessinpectoral/shoulderregionformaleslinesmayaccentuatemuscledevelopmentbutnotexcessivelyso;brasizelargeBorCforfemales),orlarge

(i.e.,excessiveshapelinessorcurvesinpectoral/shoulderregionformales;brasizeDorgreaterforfemales).Unrealisticbodyidealreferstothosebodyshapesthattypicallyarenot

attainablebyregularexerciseand/ordiet.Forfemales,theunrealisticidealisthe


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hourglassfigure.Theinvertedtrianglerepresentstheexaggeratedstandardformales.

Unrealisticidealwascodedaspresentorabsent.

Thelastappearancemeasureisphysicalbeauty,whichassesseswhetheroneormore

charactersverbally(e.g.,referringtoacharacterasgorgeous,pretty,handsome,oranyequivalentsynonym)and/ornonverbally(e.g.,whistling,starring)communicatethe

desirousnessofanothercharacter.Therewerethreelevelstothisvariable:notattractive,

attractive(i.e.,oneverbalornonverbalreference),orveryattractive(i.e.,twoormore

verbalornonverbalreferences).Again,thelattertwolevelswerecollapsedpriorto

analysis.Unlikethebody-andclothing-relatedvariables,physicalbeautywasmeasured

forallspeakingcharacters.

Itmustbenotedthatallvariables,demographicandhypersexuality,hadtwoadditional

levels.Notapplicablewasselectedwhenthemeasuredidnotapplytothecharacterbeing

evaluated(i.e.,SRCisnotapplicableifacharacter,bydesignandthecultures/helivesin,

doesnotwearclothes).Canttellwasusedwhentherewasnotsufficientinformationto

renderajudgment(i.e.,waistsizeofindividualobstructedwhensittingdownbehindalargedesk).

Intermsofevaluatingfilms,atotalof60studentswererecruitedtoourresearchteam

acrosstheFallof2009(n=35)aswellastheSpring(n=28),Summer(n=2),andFall(n=7)of2010.Somestudentsparticipatedmultipleterms(assuch,thetotaldoesnotsumto60).

Eachsemester,thesameinstructor(2ndauthor)trainedstudentsonthecodebookwithminimalassistancefromthe1stauthor.Trainingconsistedofattendinglecturesmultiple

timesperweekandcompletingaseriesoflabassignments/reliabilitydiagnostics.During

theregularsemester,traininglastsroughly6weeks.Overthesummer,trainingoccursovera3weekperiod.

Aftertraining,thefilmsinthesamplewererandomlyassignedforindividualevaluation.

Threetofiveresearchassistantsindependentlyevaluatedeachfilminthesampleatour

ASC&Jlab.Multiplestudentscodedeachmoviebecauseofthecomplexityofunitizing

charactersinmotionpicturecontent.Disagreementsinunitizingandvariablecodingwere

resolvedthroughdiscussion,afterthesecondauthorcomputedreliabilityandhighlighted

sourcesofdeviationamonggroupmemberscodingeachfilm.Breakingthesampleoffilms

intoquarters,thenumberofagreeduponlines(i.e.,speakingcharacters)seenbyallbut

onecoderineachgroupareasfollows:(Q1range=97.06%-90.20%,Q2range=89.66%-

82.14%,Q3range=80.85%-71.64%,Q4range=71.56%-53.85%).Onlyfivefilmsinthesamplehadlessthan60%ofcharactersseenbyallbutonecoder.

UsingPotterandLevine-Donnersteins(1999)formulaformultiplecoders,themedian

reliabilitycoefficientsacrossthestudysvariablesareasfollows:form(100%,

range=100%),age(100%,range=59%-100%),sex(100%,range=100%),sexuallyrevealing

clothing(100%,range=80%-100%),nudity(100%,range=80%-100%),chestsize(100%,

range=63%-100%),waistsize(100%,range=58%-100%),bodyrealism(100%,

range=80%-100%),andphysicalbeauty(100%,range=100%).Boththeunitizingand


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variablecodingwereconsistentwithourpreviousstudiesandreflectreliableevaluations

giventhecomplexityofcodingcinematiccontent.

Itmustbenotedthatweidentifiedashiftinhowcoderswereassessingtwovariables:

waistsizeandbodyrealism.Assuch,thesevariableswerenotreportedinour2007report.Agroupofcoders(n=5)weretrainedandre-evaluatedalloftheunitizedspeaking

charactersin2008onthesetwomeasuresduringtheSpringof2010.Priortocoding,the

researchassistantsweretrainedandfourseparatereliabilitydiagnosticswerecomputed.

Themediancoefficients,basedonthePotter&Levine-Donnerstein(1999)formula,were

asfollows:waistsize(80%,range=80%)andbodyrealism(100%,range=100%).

3.Smith,S.L.,Choueiti,M.,Granados,A.&Erickson,S.(2008).AsymmetricalAcademy

Awards?Alookatgenderimbalanceinbestpicturenominatedfilmsfrom1977to2006.

http://annenberg.usc.edu/Faculty/Communication/~/media/93914BE9EB5

F4C2795A3169E5ACDB84F.ashx.Smith,S.L.&Choueiti,M.(2010a).Genderoppressionin

cinematiccontent?Alookatfemaleson-screenandbehind-the-cameraintop-grossing2007

films.annenberg.usc.edu/News%20and%20Events/News/~/.../07Gender

Key.ashxSmith,S.L.,&Choueiti,M.(2010b).Genderdisparityonscreenandbehindthecamerainfamily

films;Theexecutivereport.ReportavailableattheGeenaDavisInstituteforGenderand

Mediawebsite:http://thegeenadavisinstitute.org/Smith,S.L.,&Cook,C.A.(2008).Gender

stereotypes:AnanalysisofpopularfilmsandTV.LosAngeles,CA:GeenaDavisInstituteforGenderandMedia.

4.UsinginternetsourcesincludingbutnotlimitedtoIMDb.ProandInBaseline,the

biologicalsexof1,227directors,writers,andproducerswascoded.Thebiologicalsexof

10individualscouldnotbelocatedonline.Thosewithtraditionallyfemaleormalenames(n=5)wereassignedassuch(Becky,Robert)basedonBabyNames.com.Theremaining

(n=5)wereclassifiedasunknownandexcludedfromanalysis.5.U.S.CensusBureau(2010).FactSheetAmericanFactFinder:Census2000demographic

profilehighlights.http://factfinder.census.gov/home/saff/main.html?_lang=en

6.Thechi-squareanalysesofbiologicalsexofcontentcreatorandgenderofonscreen

speakingcharactersareasfollows:director,X2(1,4,370)=26.60,p


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9.Forteencharactersonly,thechi-squareanalysesofgenderbyhypersexualityindicator

areasfollows:sexuallyrevealingclothing,X2(1,268)=44.53,p


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Acknowledgements

Ourworkwouldnotbepossiblewithoutthehelpoftheadministration,faculty,andstaffat

theAnnenbergSchoolforCommunication&Journalism.Inparticular,wearegratefulfor

thesupportwehavereceivedfromDeanEarnestWilsonIII,Dr.LarryGross,Dr.CarolaWeil,KayHeitzman,andGretchenParker.Also,wearethankfulfortheCommunication

directorsstaff(ChristineLloreda,CarolKretzer,SharonKawakami)andtheAnnenberg

studentadvisors(CynthiaMartinez,MaryannWu,SarahHoldren,AnnieMateen,JaBari

Brown,IanKeil).Intermsofprojectoversight,weareindebtedtoAshleyPrescottand

ChristineYusmeticulousnessandcarewithallaspectsofthiscontentanalysis.Last,butby

nomeansleast,weareextremelythankfulforouramazingresearchteamofundergraduate

researchassistants.Wecouldnothavedonethisprojectwithoutyou!

UndergraduateResearchTeam

JuliaAllyn

SaimeYagmurAnisKellyAnthony

RosieEun-GyuhlBae

SimoneBessant

AlyssaBustamante

DeepthiCauligi

NoopurChhabra

MichelleChung

LenaCronin

KelliDavis

CaitlinDraguesku

BerozeDubash

AndreaEvansTiffanyFang

StephanieGall

AlejandraGarcia

JosephGeraghty

MatthewGray

ChloeHall

JennyHam

AshleyImpellitteri

AmyaJacobs

SarahJoseph

JustineKaoKeyairaKelly

RebeccaKirkman

WhitneyKollar

BrittanyLaHue

AnnaLarsson

StephanieLavayen

MorganeLeMarchandJaniceLeung

StephanieLeung

JordanLevitz

KristyLucero

NicolasMcManus

CynthiaMomdjian

KimberlyMorris

AlexandraNarma

KatrinaOngYiu

JessicaPajo

LizPerez

SashaRawjiEboneeRice

KrystleRuiz

MarinaSaleeb

HibahSamad

RachelSchmidt

LindseySchulze

CyrusShahriari

JenniferStambaugh

JessicaStern

NicoleTam

FarahTamaddonAnnieVought

HainaWang

TiffanyWen

JasmineWhite

SandyWu


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AppendixA

ListofFilmsintheSample

TheDarkKnight

IronMan

IndianaJonesandtheKingdom

oftheCrystalSkullHancock

WALL-E

KungFuPanda

Twilight

Madagascar:Escape2Africa

QuantumOfSolace

Dr.SuessHortonHearsAWho!

SexandtheCity

GranTorino

MammaMia!

MarleyAndMe

TheChroniclesofNarnia:PrinceCaspian

SlumdogMillionaire

TheIncredibleHulk

Wanted

GetSmart

TheCuriousCaseofBenjamin

Button

FourChristmases

Bolt

TropicThunder

BedtimeStories

TheMummy:Tombofthe

DragonEmperor

JourneytotheCenterofthe

Earth

EagleEye

StepBrothers

YouDon'tMessWiththeZohan

YesMan

10,000B.C.

BeverlyHillsChihuahua

HighSchoolMusical3:Senior

Year

PineappleExpress

Valkyrie

21

WhatHappensinVegasJumper

Cloverfield

TheDaytheEarthStoodStill

27Dresses

HellyboyII:TheGoldenArmy

VantagePoint

TheSpiderwickChronicles

Fool'sGold

SevenPounds

RoleModels

HannahMontana/MileyCyrus:

BestofBothWorldsConcertTour*

TheHappenning

ForgettingSarahMarshall

BabyMama

BurnAfterReading

StepUp2theStreets

SawV

TheStrangers

TheForbiddenKingdom

TheTaleOfDespereaux

Australia

TheHouseBunny

Nim'sIsland

MadeofHonor

CollegeRoadTrip

TheSisterhoodoftheTraveling

Pants2

SpeedRacer

PromNight

Rambo

WelcomeHomeRoscoeJenkins

TylerPerry'sMeettheBrowns

NightsinRodanthe

MaxPayne

RighteousKill

BodyofLiesLakeviewTerrace

MeettheSpartans

Harold&KumarEscapefrom

GuantanamoBay

FirstSunday

TheSecretLifeofBees

TylerPerry'sTheFamily

ThatPreys

DeathRace

Changeling

StarWars:TheCloneWars

TheReaderSemi-Pro

Fireproof

Doubt

DrillbitTaylor

Definitely,Maybe

TheLoveGuru

Milk

Transporter3

Quarantine

Nick&Norah'sInfinite

Playlist

ZackandMiriMakeaPorno

TheEye

Leatherheads

Mirrors

SpaceChimps

TheBankJob

Untraceable

Defiance

TheWomen


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AppendixB

SupplementalDataAnalyses

Belowaresupplementaldataanalysesofthehypersexualityvariables.Table4focuseson

genderdifferencesinhypersexualityamong21-to39-yearoldsand40-to64-yearolds.Forfemalesonly,Table5illuminatestherelationshipbetweenthehypersexuality

indicatorsandthreeagegroups.

Table4

GenderDifferencesacrossHypersexualityIndicatorswithinAgeGroup

2139yearsold 4064yearsold

Males Females Males Females

%insexyclothing 6.9% 32.4% 3.1% 14.9%

%partiallynaked 10.0% 30.5% 4.7% 14.2%

%attractive 5.1% 17.9% 1.9% 7.7%%w/smallwaist 2.8% 23.3% 1.1% 5.3%*

%w/largechest 16.7% 10.4% 22.8% 26.8%

%w/unrealbody 3.8% 5.8% 1.0% 0.9%*

Note:Chi-squareanalyseswithinagegroupbygenderwereconductedoneachofthehypersexuality

indicators.Allanalyseswerestatistically(p

Documents

Smith, Stacy. Gender Inequality in Cinematic Content