library(dplyr)
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library(tidyverse)
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library(ggplot2)
library(rmarkdown)
setwd("C:/Users/srish/Downloads")
#WVS7 2022 India survey dataset
df<- read.csv("V5IND.csv")
names(df)
## [1] "version..Version.of.Data.File"
## [2] "doi..Digital.Object.Identifier"
## [3] "A_YEAR..Year.of.survey"
## [4] "B_COUNTRY..ISO.3166.1.numeric.country.code"
## [5] "B_COUNTRY_ALPHA..ISO.3166.1.alpha.3.country.code"
## [6] "C_COW_NUM..CoW.country.code.numeric"
## [7] "C_COW_ALPHA..CoW.country.code.alpha"
## [8] "D_INTERVIEW..Interview.ID"
## [9] "J_INTDATE..Date.of.interview"
## [10] "FW_START..Year.month.of.start.fieldwork"
## [11] "FW_END..Year.month.of.end.fieldwork"
## [12] "K_TIME_START..Start.time.of.the.interview..HH.MM."
## [13] "K_TIME_END..End.time.of.the.interview..HH.MM."
## [14] "K_DURATION..Total.length.of.interview..minutes."
## [15] "Q_MODE..Mode.of.data.collection"
## [16] "N_REGION_ISO..Region.ISO.3166.2"
## [17] "G_TOWNSIZE..Settlement.size_8.groups"
## [18] "G_TOWNSIZE2..Settlement.size_5.groups"
## [19] "H_SETTLEMENT..Settlement.type"
## [20] "H_URBRURAL..Urban.Rural"
## [21] "I_PSU..Primary.Sampling.Unit.ID"
## [22] "O1_LONGITUDE..Geographical.Coordinates...Longitude"
## [23] "O2_LATITUDE..Geographical.Coordinates...Latitude"
## [24] "S_INTLANGUAGE..Language.in.which.interview.was.conducted"
## [25] "LNGE_ISO..Language.in.which.interview.was.conducted...ISO.639.1.Alpha.2.digit."
## [26] "E_RESPINT..Respondent.interested.during.the.interview"
## [27] "F_INTPRIVACY..Interview.privacy"
## [28] "E1_LITERACY..Respondent.s.literacy"
## [29] "W_WEIGHT..Weight"
## [30] "S018..Equilibration.weight.1000"
## [31] "PWGHT..Population.size.weight"
## [32] "Q1..Important.in.life..Family"
## [33] "Q2..Important.in.life..Friends"
## [34] "Q3..Important.in.life..Leisure.time"
## [35] "Q4..Important.in.life..Politics"
## [36] "Q5..Important.in.life..Work"
## [37] "Q6..Important.in.life..Religion"
## [38] "Q7..Important.child.qualities..good.manners"
## [39] "Q8..Important.child.qualities..independence"
## [40] "Q9..Important.child.qualities..hard.work"
## [41] "Q10..Important.child.qualities..feeling.of.responsibility"
## [42] "Q11..Important.child.qualities..imagination"
## [43] "Q12..Important.child.qualities..tolerance.and.respect.for.other.people"
## [44] "Q13..Important.child.qualities..thrift.saving.money.and.things"
## [45] "Q14..Important.child.qualities..determination.perseverance"
## [46] "Q15..Important.child.qualities..religious.faith"
## [47] "Q16..Important.child.qualities..unselfishness"
## [48] "Q17..Important.child.qualities..obedience"
## [49] "Q18..Neighbors..Drug.addicts"
## [50] "Q19..Neighbors..People.of.a.different.race"
## [51] "Q20..Neighbors..People.who.have.AIDS"
## [52] "Q21..Neighbors..Immigrants.foreign.workers"
## [53] "Q22..Neighbors..Homosexuals"
## [54] "Q23..Neighbors..People.of.a.different.religion"
## [55] "Q24..Neighbors..Heavy.drinkers"
## [56] "Q25..Neighbors..Unmarried.couples.living.together"
## [57] "Q26..Neighbors..People.who.speak.a.different.language"
## [58] "Q27..One.of.main.goals.in.life.has.been.to.make.my.parents.proud"
## [59] "Q28..Pre.school.child.suffers.with.working.mother"
## [60] "Q29..Men.make.better.political.leaders.than.women.do"
## [61] "Q30..University.is.more.important.for.a.boy.than.for.a.girl"
## [62] "Q31..Men.make.better.business.executives.than.women.do"
## [63] "Q32..Being.a.housewife.just.as.fulfilling"
## [64] "Q33..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women"
## [65] "Q33_3..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women..3.point.scale."
## [66] "Q34..Jobs.scarce..Employers.should.give.priority.to..nation..people.than.immigrants"
## [67] "Q34_3..Jobs.scarce..Employers.should.give.priority.to..nation..people.than.immigrants..3.point.scale."
## [68] "Q35..Problem.if.women.have.more.income.than.husband"
## [69] "Q35_3..Problem.if.women.have.more.income.than.husband..3.point.scale."
## [70] "Q36..Homosexual.couples.are.as.good.parents.as.other.couples"
## [71] "Q37..Duty.towards.society.to.have.children"
## [72] "Q38..It.is.children.duty.to.take.care.of.ill.parent"
## [73] "Q39..People.who.don.t.work.turn.lazy"
## [74] "Q40..Work.is.a.duty.towards.society"
## [75] "Q41..Work.should..always.come.first.even.if.it.means.less.spare.time"
## [76] "Q42..Basic.kinds.of.attitudes.concerning.society"
## [77] "Q43..Future.changes..Less.importance.placed.on.work"
## [78] "Q44..Future.changes..More.emphasis.on.technology"
## [79] "Q45..Future.changes..Greater.respect.for.authority"
## [80] "Q46..Feeling.of.happiness"
## [81] "Q47..State.of.health..subjective."
## [82] "Q48..How.much.freedom.of.choice.and.control"
## [83] "Q49..Satisfaction.with.your.life"
## [84] "Q50..Satisfaction.with.financial.situation.of.household"
## [85] "Q51..Frequency.you.family..last.12.month...Gone.without.enough.food.to.eat"
## [86] "Q52..Frequency.you.family..last.12.month...Felt.unsafe.from.crime.in.your.own.home"
## [87] "Q53..Frequency.you.family..last.12.month...Gone.without.needed.medicine.or.treatment.that.you.needed"
## [88] "Q54..Frequency.you.family..last.12.month...Gone.without.a.cash.income"
## [89] "Q55..In.the.last.12.month..how.often.have.you.or.your.family..Gone.without.a.safe.shelter.over.your.head"
## [90] "Q56..Standard.of.living.comparing.with.your.parents"
## [91] "Q57..Most.people.can.be.trusted"
## [92] "Q58..Trust..Your.family"
## [93] "Q59..Trust..Your.neighborhood"
## [94] "Q60..Trust..People.you.know.personally"
## [95] "Q61..Trust..People.you.meet.for.the.first.time"
## [96] "Q62..Trust..People.of.another.religion"
## [97] "Q63..Trust..People.of.another.nationality"
## [98] "Q64..Confidence..Churches"
## [99] "Q65..Confidence..Armed.Forces"
## [100] "Q66..Confidence..The.Press"
## [101] "Q67..Confidence..Television"
## [102] "Q68..Confidence..Labor.Unions"
## [103] "Q69..Confidence..The.Police"
## [104] "Q70..Confidence..Justice.System.Courts"
## [105] "Q71..Confidence..The.Government"
## [106] "Q72..Confidence..The.Political.Parties"
## [107] "Q73..Confidence..Parliament"
## [108] "Q74..Confidence..The.Civil.Services"
## [109] "Q75..Confidence..Universities"
## [110] "Q76..Confidence..Elections"
## [111] "Q77..Confidence..Major.Companies"
## [112] "Q78..Confidence..Banks"
## [113] "Q79..Confidence..The.Environmental.Protection.Movement"
## [114] "Q80..Confidence..The.Women.s.Movement"
## [115] "Q81..Confidence..Charitable.or.humanitarian.organizations"
## [116] "Q82..Confidence..Major.regional.organization..combined.from.country.specific."
## [117] "Q82_ASEAN..Confidence..The.Association.of.South.East.Asian.Nations..ASEAN."
## [118] "Q83..Confidence..The.United.Nations..UN."
## [119] "Q84..Confidence..International.Monetary.Found..IMF."
## [120] "Q85..Confidence..International.Criminal.Court..ICC."
## [121] "Q86..Confidence..North.Atlantic.Treaty.Organization..NATO."
## [122] "Q87..Confidence..The.World.Bank..WB."
## [123] "Q88..Confidence..The.World.Health.Organization..WHO."
## [124] "Q89..Confidence..The.World.Trade.Organization..WTO."
## [125] "Q90..International.organizations..being.effective.vs.being.democratic"
## [126] "Q91..Countries.with.the.permanent.seats.on.the.UN.Security.Council"
## [127] "Q92..Where.are.the.headquarters.of.the.International.Monetary.Fund..IMF..located."
## [128] "Q93..Which.of.the.following.problems.does.the.organization.Amnesty.International.deal.with."
## [129] "Q94..Active.Inactive.membership..Church.or.religious.organization"
## [130] "Q95..Active.Inactive.membership..Sport.or.recreational.organization..football.baseball.rugby.team"
## [131] "Q96..Active.Inactive.membership..Art..music.or.educational.organization"
## [132] "Q97..Active.Inactive.membership..labor.union"
## [133] "Q98..Active.Inactive.membership..political.party"
## [134] "Q99..Active.Inactive.membership..Environmental.organization"
## [135] "Q100..Active.Inactive.membership..Professional.association"
## [136] "Q101..Active.Inactive.membership..charitable.humanitarian.organization"
## [137] "Q102..Active.Inactive.membership..consumer.organization"
## [138] "Q103..Active.Inactive.membership..self.help.group..mutual.aid.group"
## [139] "Q104..Active.Inactive.membership..women.s.group"
## [140] "Q105..Active.Inactive.membership..other.organization"
## [141] "Q106..Incomes.should.be.made.more.equal.vs.There.should.be.greater.incentives.for.individual.effort"
## [142] "Q107..Private.vs.state.ownership.of.business"
## [143] "Q108..Government.s.vs.individual.s.responsibility"
## [144] "Q109..Competition.good.or.harmful"
## [145] "Q110..Success..hard.work.vs.luck"
## [146] "Q111..Protecting.environment.vs..Economic.growth"
## [147] "Q112..Perceptions.of.corruption.in.the.country"
## [148] "Q113..Involved.in.corruption..State.authorities"
## [149] "Q114..Involved.in.corruption..Business.executives"
## [150] "Q115..Involved.in.corruption..Local.authorities"
## [151] "Q116..Involved.in.corruption..Civil.service.providers"
## [152] "Q117..Involved.in.corruption..Journalists.and.media"
## [153] "Q118..Frequency.ordinary.people.pay.a.bribe..give.a.gift.or.do.a.favor.to.local.officials.service.providers.in.order.to.get.services"
## [154] "Q119..Degree.of.agreement..On.the.whole..women.are.less.corrupt.than.men"
## [155] "Q120..Risk.to.be.held.accountable.for.giving.or.receiving.a.bribe"
## [156] "Q121..Impact.of.immigrants.on.the.development.of.the.country"
## [157] "Q122..Immigration.in.your.country..Fills.useful.jobs.in.the.workforce"
## [158] "Q123..Immigration.in.your.country..Strengthens.cultural.diversity"
## [159] "Q124..Immigration.in.your.country..Increases.the.crime.rate"
## [160] "Q125..Immigration.in.your.country..Gives.asylum.to.political.refugees"
## [161] "Q126..Immigration.in.your.country..Increases.the.risks.of.terrorism"
## [162] "Q127..Immigration.in.your.country..Helps.poor.people.establish.new.lives"
## [163] "Q128..Immigration.in.your.country..Increases.unemployment"
## [164] "Q129..Immigration.in.your.country..Leads.to.social.conflict"
## [165] "Q130..Immigration.policy.preference"
## [166] "Q131..Secure.in.neighborhood"
## [167] "Q132..Frequency.in.your.neighborhood..Robberies"
## [168] "Q133..Frequency.in.your.neighborhood..Alcohol.consumed.in.the.streets"
## [169] "Q134..Frequency.in.your.neighborhood..Police.or.military.interfere.with.people.s.private.life"
## [170] "Q135..Frequency.in.your.neighborhood..Racist.behavior"
## [171] "Q136..Frequency.in.your.neighborhood..Drug.sale.in.streets"
## [172] "Q137..Frequency.in.your.neighborhood..Street.violence.and.fights"
## [173] "Q138..Frequency.in.your.neighborhood..Sexual.harassment"
## [174] "Q139..Things.done.for.reasons.of.security..Didn.t.carry.much.money"
## [175] "Q140..Things.done.for.reasons.of.security..Preferred.not.to.go.out.at.night"
## [176] "Q141..Things.done.for.reasons.of.security..Carried.a.knife..gun.or.other.weapon"
## [177] "Q142..Worries..Losing.my.job.or.not.finding.a.job"
## [178] "Q143..Worries..Not.being.able.to.give.one.s.children.a.good.education"
## [179] "Q144..Respondent.was.victim.of.a.crime.during.the.past.year"
## [180] "Q145..Respondent.s.family.was.victim.of.a.crime.during.last.year"
## [181] "Q146..Worries..A.war.involving.my.country"
## [182] "Q147..Worries..A.terrorist.attack"
## [183] "Q148..Worries..A.civil.war"
## [184] "Q149..Freedom.and.Equality...Which.more.important"
## [185] "Q150..Freedom.and.security...Which.more.important"
## [186] "Q151..Willingness.to.fight.for.country"
## [187] "Q152..Aims.of.country..first.choice"
## [188] "Q153..Aims.of.country..second.choice"
## [189] "Q154..Aims.of.respondent..first.choice"
## [190] "Q155..Aims.of.respondent..second.choice"
## [191] "Q156..Most.important..first.choice"
## [192] "Q157..Most.important..second.choice"
## [193] "Q158..Science.and.technology.are.making.our.lives.healthier..easier..and.more.comfortable"
## [194] "Q159..Because.of.science.and.technology..there.will.be.more.opportunities.for.the.next.generation"
## [195] "Q160..We.depend.too.much.on.science.and.not.enough.on.faith"
## [196] "Q161..One.of.the.bad.effects.of.science.is.that.it.breaks.down.people.s.ideas.of.right.and.wrong"
## [197] "Q162..It.is.not.important.for.me.to.know.about.science.in.my.daily.life"
## [198] "Q163..The.world.is.better.off..or.worse.off..because.of.science.and.technology"
## [199] "Q164..Importance.of.God"
## [200] "Q165..Believe.in..God"
## [201] "Q166..Believe.in..life.after.death"
## [202] "Q167..Believe.in..hell"
## [203] "Q168..Believe.in..heaven"
## [204] "Q169..Whenever.science.and.religion.conflict...religion.is.always.right"
## [205] "Q170..The.only.acceptable.religion..is.my.religion"
## [206] "Q171..How.often.do.you.attend.religious.services"
## [207] "Q172..How.often.do.you.pray"
## [208] "Q173..Religious.person"
## [209] "Q174..Meaning.of.religion..To.follow.religious.norms.and.ceremonies.vs.To.do.good.to.other.people"
## [210] "Q175..Meaning.of.religion..To.make.sense.of.life.after.death.vs.To.make.sense.of.life.in.this.world"
## [211] "Q176..Degree.of.agreement..Nowadays.one.often.has.trouble.deciding.which.moral.rules.are.the.right.ones.to.follow"
## [212] "Q177..Justifiable..Claiming.government.benefits.to.which.you.are.not.entitled"
## [213] "Q178..Justifiable..Avoiding.a.fare.on.public.transport"
## [214] "Q179..Justifiable..Stealing.property"
## [215] "Q180..Justifiable..Cheating.on.taxes"
## [216] "Q181..Justifiable..Someone.accepting.a.bribe.in.the.course.of.their.duties"
## [217] "Q182..Justifiable..Homosexuality"
## [218] "Q183..Justifiable..Prostitution"
## [219] "Q184..Justifiable..Abortion"
## [220] "Q185..Justifiable..Divorce"
## [221] "Q186..Justifiable..Sex.before.marriage"
## [222] "Q187..Justifiable..Suicide"
## [223] "Q188..Justifiable..Euthanasia"
## [224] "Q189..Justifiable..For.a.man.to.beat.his.wife"
## [225] "Q190..Justifiable..Parents.beating.children"
## [226] "Q191..Justifiable..Violence.against.other.people"
## [227] "Q192..Justifiable..Terrorism.as.a.political..ideological.or.religious.mean"
## [228] "Q193..Justifiable..Having.casual.sex"
## [229] "Q194..Justifiable..Political.violence"
## [230] "Q195..Justifiable..Death.penalty"
## [231] "Q196..Government.has.the.right..Keep.people.under.video.surveillance.in.public.areas"
## [232] "Q197..Government.has.the.right..Monitor.all.e.mails.and.any.other.information.exchanged.on.the.Internet"
## [233] "Q198..Government.has.the.right..Collect.information.about.anyone.living.in..COUNTRY..without.their.knowledge"
## [234] "Q199..Interest.in.politics"
## [235] "Q200..How.often.discusses.political.matters.with.friends"
## [236] "Q201..Information.source..Daily.newspaper"
## [237] "Q202..Information.source..TV.news"
## [238] "Q203..Information.source..Radio.news"
## [239] "Q204..Information.source..Mobile.phone"
## [240] "Q205..Information.source..Email"
## [241] "Q206..Information.source..Internet"
## [242] "Q207..Information.source..Social.media..Facebook..Twitter..etc.."
## [243] "Q208..Information.source..Talk.with.friends.or.colleagues"
## [244] "Q209..Political.action..Signing.a.petition"
## [245] "Q210..Political.action..Joining.in.boycotts"
## [246] "Q211..Political.action..Attending.lawful.peaceful.demonstrations"
## [247] "Q212..Political.action..Joining.unofficial.strikes"
## [248] "Q213..Social.activism..Donating.to.a.group.or.campaign"
## [249] "Q214..Social.activism..Contacting.a.government.official"
## [250] "Q215..Social.activism..Encouraging.others.to.take.action.about.political.issues"
## [251] "Q216..Social.activism..Encouraging.others.to.vote"
## [252] "Q217..Political.actions.online..Searching.information.about.politics.and.political.events"
## [253] "Q218..Political.actions.online..Signing.an.electronic.petition"
## [254] "Q219..Political.actions.online..Encouraging.other.people.to.take.any.form.of.political.action"
## [255] "Q220..Political.actions.online..Organizing.political.activities..events..protests"
## [256] "Q221..Vote.in.elections..local.level"
## [257] "Q222..Vote.in.elections..national.level"
## [258] "Q223..Which.party.would.you.vote.for.if.there.were.a.national.election.tomorrow"
## [259] "Q223_ABREV..Party.preference.Abbreviation"
## [260] "Q223_LOCAL..Party.preference.Local.name"
## [261] "Q224..How.often.in.country.s.elections..Votes.are.counted.fairly"
## [262] "Q225..How.often.in.country.s.elections..Opposition.candidates.are.prevented.from.running"
## [263] "Q226..How.often.in.country.s.elections..TV.news.favors.the.governing.party"
## [264] "Q227..How.often.in.country.s.elections..Voters.are.bribed"
## [265] "Q228..How.often.in.country.s.elections..Journalists.provide.fair.coverage.of.elections"
## [266] "Q229..How.often.in.country.s.elections..Election.officials.are.fair"
## [267] "Q230..How.often.in.country.s.elections..Rich.people.buy.elections"
## [268] "Q231..How.often.in.country.s.elections..Voters.are.threatened.with..violence.at.the.polls"
## [269] "Q232..How.often.in.country.s.elections..Voters.are.offered.a.genuine.choice.in.the.elections"
## [270] "Q233..How.often.in.country.s.elections..Women.have.equal.opportunities.to.run.the.office"
## [271] "Q234..Some.people.think.that.having.honest.elections.makes.a.lot.of.difference.in.their.lives...other.people.think.that.it.doesn.t.matter.much"
## [272] "Q234A..How.much.would.you.say.the.political.system.in.your.country.allows.people.like.you.to.have.a.say.in.what.the.government.does."
## [273] "Q235..Political.system..Having.a.strong.leader.who.does.not.have.to.bother.with.parliament.and.elections"
## [274] "Q236..Political.system..Having.experts..not.government..make.decisions.according.to.what.they.think.is.best.for.the.country"
## [275] "Q237..Political.system..Having.the.army.rule"
## [276] "Q238..Political.system..Having.a.democratic.political.system"
## [277] "Q239..Political.system..Having.a.system.governed.by.religious.law.in.which.there.are.no.political.parties.or.elections"
## [278] "Q240..Left.right.political.scale"
## [279] "Q241..Democracy..Governments.tax.the.rich.and.subsidize.the.poor"
## [280] "Q242..Democracy..Religious.authorities.interpret.the.laws"
## [281] "Q243..Democracy..People.choose.their.leaders.in.free.elections"
## [282] "Q244..Democracy..People.receive.state.aid.for.unemployment"
## [283] "Q245..Democracy..The.army.takes.over.when.government.is.incompetent"
## [284] "Q246..Democracy..Civil.rights.protect.people.s.liberty.against.oppression"
## [285] "Q247..Democracy..The.state.makes.people.s.incomes.equal"
## [286] "Q248..Democracy..People.obey.their.rulers"
## [287] "Q249..Democracy..Women.have.the.same.rights.as.men"
## [288] "Q250..Importance.of.democracy"
## [289] "Q251..How.democratically.is.this.country.being.governed.today"
## [290] "Q252..Satisfaction.with.the.political.system.performance"
## [291] "Q253..Respect.for.individual.human.rights.nowadays"
## [292] "Q254..National.pride"
## [293] "Q255..Feel.close.to.your.village..town.or.city"
## [294] "Q256..Feel.close.to.your.district..region"
## [295] "Q257..Feel.close.to.your.country"
## [296] "Q258..Feel.close.to.your.continent"
## [297] "Q259..Feel.close.to.the.world"
## [298] "Q260..Sex"
## [299] "Q261..Year.of.birth"
## [300] "Q262..Age"
## [301] "X003R..Age.recoded..6.intervals."
## [302] "X003R2..Age.recoded..3.intervals."
## [303] "Q263..Respondent.immigrant"
## [304] "Q264..Mother.immigrant"
## [305] "Q265..Father.immigrant"
## [306] "Q266..Country.of.birth..Respondent"
## [307] "Q267..Country.of.birth..Mother.of.the.respondent"
## [308] "Q268..Country.of.birth..Father.of.the.respondent"
## [309] "Q269..Respondent.citizen"
## [310] "Q270..Number.of.people.in.household"
## [311] "Q271..Do.you.live.with.your.parents"
## [312] "Q272..Language.at.home"
## [313] "Q273..Marital.status"
## [314] "Q274..How.many.children.do.you.have"
## [315] "Q275..Highest.educational.level..Respondent..ISCED.2011."
## [316] "Q275A..Highest.educational.level..Respondent..country.specific."
## [317] "Q275R..Highest.educational.level..Respondent..recoded.into.3.groups."
## [318] "Q276..Highest.educational.level..Respondent.s.Spouse..ISCED.2011."
## [319] "Q276A..Highest.educational.level..Respondent.s.Spouse..country.specific."
## [320] "Q276R..Highest.educational.level..Respondent.s.Spouse..recoded.into.3.groups."
## [321] "Q277..Highest.educational.level..Respondent.s.Mother..ISCED.2011."
## [322] "Q277A..Highest.educational.level..Respondent.s.Mother..country.specific."
## [323] "Q277R..Highest.educational.level..Respondent.s.Mother..recoded.into.3.groups."
## [324] "Q278..Highest.educational.level..Respondent.s.Father..ISCED.2011."
## [325] "Q278A..Highest.educational.level..Respondent.s.Father..country.specific."
## [326] "Q278R..Highest.educational.level..Respondent.s.Father..recoded.into.3.groups."
## [327] "Q279..Employment.status"
## [328] "Q280..Employment.status...Respondent.s.Spouse"
## [329] "Q281..Respondent...Occupational.group"
## [330] "Q282..Respondent.s.Spouse...Occupational.group"
## [331] "Q283..Respondent.s.Father...Occupational.group..when.respondent.was.14.years.old."
## [332] "Q284..Sector.of.employment"
## [333] "Q285..Are.you.the.chief.wage.earner.in.your.house"
## [334] "Q286..Family.savings.during.past.year"
## [335] "Q287..Social.class..subjective."
## [336] "Q288..Scale.of.incomes"
## [337] "Q288R..Income.level..Recoded."
## [338] "Q289..Religious.denominations...major.groups"
## [339] "Q289CS9..Religious.denomination...detailed.list"
## [340] "Q290..Ethnic.group"
## [341] "Y001..Post.Materialist.index.12.item"
## [342] "Y002..Post.Materialist.index.4.item"
## [343] "Y003..Autonomy.Index"
## [344] "SACSECVAL..SACSECVAL...Welzel.Overall.Secular.Values"
## [345] "SACSECVALB..Overall.Secular.Values.Short.version"
## [346] "RESEMAVAL..RESEMAVAL...Welzel.emancipative.values"
## [347] "RESEMAVALB..Emancipative.values.Short.version"
## [348] "I_AUTHORITY..AUTHORITY...Welzel.defiance...1..Inverse.respect.for.authority"
## [349] "I_NATIONALISM..NATIONALISM...Welzel.defiance...2..Inverse.national.pride"
## [350] "I_DEVOUT..DEVOUT..Welzel.defiance...3..Inverse.devoutness"
## [351] "DEFIANCE..DEFIANCE...Welzel.defiance.sub.index"
## [352] "I_RELIGIMP..RELIGIMP...Welzel.disbelief..1..Inverse.importance.of.religion"
## [353] "I_RELIGBEL..RELIGBEL...Welzel.disbelief..2..Inverse.religious.person"
## [354] "I_RELIGPRAC..RELIGPRAC...Welzel.disbelief..3..Inverse.religious.practice"
## [355] "DISBELIEF..DISBELIEF...Welzel.disbelief.sub.index"
## [356] "I_NORM1..NORM1...Welzel.relativism..1..Inverse.norm.conform1"
## [357] "I_NORM2..NORM2...Welzel.relativism..2..Inverse.norm.conform2"
## [358] "I_NORM3..NORM3...Welzel.relativism..3..Inverse.norm.conform3"
## [359] "RELATIVISM..RELATIVISM...Welzel.relativism"
## [360] "I_TRUSTARMY..TRUSTARMY..Welzel.skepticism..1..Inverse.trust.in.army"
## [361] "I_TRUSTPOLICE..TRUSTPOLICE..Welzel.skepticism..2..Inverse.trust.in.police"
## [362] "I_TRUSTCOURTS..TRUSTCOURTS..Welzel.skepticism..3..Inverse.trust.in.courts"
## [363] "SCEPTICISM..SCEPTICISM...Welzel.skepticism.index"
## [364] "I_INDEP..INDEP..Welzel.autonomy.1..Independence.as.kid.quality"
## [365] "I_IMAGIN..IMAGIN..Welzel.autonomy.2..Imagination.as.kid.quality"
## [366] "I_NONOBED..Emancipative.Values.1..Obedience.not.kid.quality"
## [367] "AUTONOMY..AUTONOMY...Wezel.Autonomy.sub.index"
## [368] "I_WOMJOB..WOMJOB..Welzel.equality.1..Gender.equality..job"
## [369] "I_WOMPOL..WOMPOL..Welzel.equality.2..Gender.equality..politics"
## [370] "I_WOMEDU..WOMEDU..Welzel.equality.3..Gender.equality..education"
## [371] "EQUALITY..Emancipative.Values.2..Equality.sub.index"
## [372] "I_HOMOLIB..HOMOLIB..Welzel.choice.1..Homosexuality.acceptance"
## [373] "I_ABORTLIB..ABORTLIB..Welzel.choice.2..Abortion.acceptable"
## [374] "I_DIVORLIB..DIVORLIB..Welzel.choice.3..Divorce.acceptable"
## [375] "CHOICE..CHOICE...Welzel.choice.sub.index"
## [376] "I_VOICE1..VOICE1..Welzel.voice.1"
## [377] "I_VOICE2..VOICE2..Welzel.voice.2"
## [378] "I_VOI2_00..VOI2_00..Welzel.voice.3..auxiliary."
## [379] "VOICE..VOICE...Welzel.voice.sub.index"
## [380] "SECVALWGT..Weight.for.overall.secular.values"
## [381] "WEIGHT1A..Overall.Secular.Values.1..Weight1a"
## [382] "WEIGHT1B..Emancipative.Values.1..Weight1b"
## [383] "WEIGHT2A..Overall.Secular.Values.2..Weight2a"
## [384] "WEIGHT2B..Emancipative.Values.2..Weight2b"
## [385] "WEIGHT3A..Overall.Secular.Values.3..Weight"
## [386] "WEIGHT3B..Emancipative.Values.3..Weight3b"
## [387] "WEIGHT4A..Overall.Secular.Values.4..Weight.4a"
## [388] "WEIGHT4B..Emancipative.Values.4..Weight.4b"
## [389] "RESEMAVALBWGT..Weight.for.Emancipative.values"
## [390] "RESEMAVALWGT..Weight.for.Emancipative.values"
## [391] "SECVALBWGT..Weight.for.overall.secular.values.Short.Version"
## [392] "Y001_1..Materialist.postmaterialist.12.item.index..Component.1"
## [393] "Y001_2..Materialist.postmaterialist.12.item.index..Component.2"
## [394] "Y001_3..Materialist.postmaterialist.12.item.index..Component.3"
## [395] "Y001_4..Materialist.postmaterialist.12.item.index..Component.4"
## [396] "Y001_5..Materialist.postmaterialist.12.item.index..Component.5"
## [397] "H295..How.old.were.you.when.you.had.your.first.child"
## [398] "H296..How.many.children.would.you.like.to.have"
## [399] "H297..Free.choice.and.control.over.family.planning"
## [400] "H298..Honour.related.oppression..family.decides.on.young.women.s.life.choice"
## [401] "H299..Justifiable..Female.genital.circumcision"
## [402] "H300..Justifiable..To.use.contraceptives"
## [403] "H301..Justifiable..To.have.more.than.one.sexual.partner.at.the.same.time"
## [404] "H302..Justifiable..Getting.paid.for.porongraphic.photos.or.films"
## [405] "H311..How.often.in.neighbourhood..Sexual.assault.rape"
## [406] "H312..How.often.in.neighbourhood..Women.and.girls.trading.sex.for.money"
## [407] "H313..How.often.in.neighbourhood..Men.and.boys.hurting.women.and.girls"
## [408] "H314..How.often.in.neighbourhood..Men.and.boys.making.unwanted.sexual.comments.or.gestures.towards.girls.or.women"
## [409] "H315..A.husband.shouldn.t.have.to.do.household.chores"
## [410] "H316..A.man.who.discusses.important.decisions.with.his.wife.is.considered.weak"
## [411] "H317..A.man.should.pay.more.attention.to.his.mother.s.opinion.than.his.wife.s"
## [412] "H318..A.woman.who.shows.that.she.is.interested.in.sex.is.considered.vulgar"
## [413] "H319..Men.may.use.violence.to.keep.their.wives.in.line"
## [414] "H320..Parents.may.use.violence.or.threats.when.bringing.up.their.children"
## [415] "H321..A.woman.should.tolerate.violence.to.keep.the.family.together"
## [416] "H322..No.one.else.should.interfere.when.it.comes.to.violence.within.a.family"
## [417] "H326..If.a.man.has.a.girlfriend.or.wife..he.should.know.where.she.is.all.the.time"
## [418] "H331..It.is.important.for.girls.to.continue.their.schooling.even.if.they.become.pregnant.and.have.children"
## [419] "H332..A.girl.is.ready.for.marriage.once.she.starts.menstruating"
## [420] "H333..A.girl.should.honour.the.decisions.wishes.of.her.family.even.if.she.does.not.want.to.marry"
## [421] "H334..A.boy.should.honour.the.decisions.wishes.of.his.family.even.if.he.does.not.want.to.marry"
## [422] "H335..A.girl.should.wait.to.have.children.until.she.is.at.least.18.years.old..even.if.she.is.married"
## [423] "H336..It.is.safer.for.a.woman.to.give.birth.at.a.clinic.than.at.home"
## [424] "H337..Women.should.have.access.to.safe.abortion.services..to.terminate.an.unwanted.pregnancy."
## [425] "H338..It.is.a.woman.s.responsibility.to.avoid.getting.pregnant"
## [426] "H339..Only.when.a.woman.has.a.child.is.she.a.real.woman"
## [427] "H341..Contraceptives.should.be.available.for.everyone..whether.or.not.one.is.married"
## [428] "H342..Sexual.education.promotes.sexual.activity.among.young.people"
## [429] "H343..Sexual.education.helps.people.make.informed.decisions"
## [430] "H344..A.couple.who.cannot.conceive.should.have.access.to.infertility.services"
## [431] "H345..People.who.dress..act.or.identify.as.the.opposite.sex.should.be.treated.just.as.anyone.else"
## [432] "H349..A.homosexual.man.is.not.a..real.man."
## [433] "H350..A..real.man..should.have.as.many.sexual.partners.as.he.can"
## [434] "H351..A.man.should.use.violence..to.get.respect..if.necessary"
view(df)
#selecting specific questions that form part of our index that fall under each category
dat7<- df%>%
select("Q184..Justifiable..Abortion", "Q186..Justifiable..Sex.before.marriage",
"Q33..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women","Q28..Pre.school.child.suffers.with.working.mother",
"Q31..Men.make.better.business.executives.than.women.do", "Q29..Men.make.better.political.leaders.than.women.do",
"Q262..Age", "Q288R..Income.level..Recoded.", "Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.",
"Q279..Employment.status", "Q260..Sex", "Q35..Problem.if.women.have.more.income.than.husband", "Q30..University.is.more.important.for.a.boy.than.for.a.girl",
"Q28..Pre.school.child.suffers.with.working.mother","Q32..Being.a.housewife.just.as.fulfilling", "Q240..Left.right.political.scale","A_YEAR..Year.of.survey"
)
#-1 value assigned to those who didn't want to reply, no analytical value, skews data, so replace value with 0
dat7 <- dat7 %>%
mutate(across(c(Q184..Justifiable..Abortion,
Q186..Justifiable..Sex.before.marriage,
Q33..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women,
Q28..Pre.school.child.suffers.with.working.mother,
Q31..Men.make.better.business.executives.than.women.do,
Q29..Men.make.better.political.leaders.than.women.do,
Q35..Problem.if.women.have.more.income.than.husband,
Q30..University.is.more.important.for.a.boy.than.for.a.girl,
Q28..Pre.school.child.suffers.with.working.mother,
Q32..Being.a.housewife.just.as.fulfilling),
~ ifelse(. == -1, 0, .)))
view(dat7)
#overall summary stats
summary(dat7)
## Q184..Justifiable..Abortion Q186..Justifiable..Sex.before.marriage
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 1.000 Median : 1.000
## Mean : 2.624 Mean : 2.173
## 3rd Qu.: 4.000 3rd Qu.: 2.000
## Max. :10.000 Max. :10.000
## Q33..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women
## Min. :0.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.479
## 3rd Qu.:4.000
## Max. :5.000
## Q28..Pre.school.child.suffers.with.working.mother
## Min. :0.000
## 1st Qu.:1.000
## Median :2.000
## Mean :1.984
## 3rd Qu.:3.000
## Max. :4.000
## Q31..Men.make.better.business.executives.than.women.do
## Min. :0.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.395
## 3rd Qu.:3.000
## Max. :4.000
## Q29..Men.make.better.political.leaders.than.women.do Q262..Age
## Min. :0.000 Min. :16.0
## 1st Qu.:1.000 1st Qu.:21.0
## Median :2.000 Median :33.0
## Mean :2.248 Mean :35.8
## 3rd Qu.:3.000 3rd Qu.:48.0
## Max. :4.000 Max. :90.0
## Q288R..Income.level..Recoded.
## Min. :-1.000
## 1st Qu.: 1.000
## Median : 2.000
## Mean : 1.931
## 3rd Qu.: 2.000
## Max. : 3.000
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.
## Min. :-1.000
## 1st Qu.: 1.000
## Median : 2.000
## Mean : 1.904
## 3rd Qu.: 2.000
## Max. : 3.000
## Q279..Employment.status Q260..Sex
## Min. :-1.000 Min. :1.000
## 1st Qu.: 3.000 1st Qu.:1.000
## Median : 5.000 Median :1.000
## Mean : 4.243 Mean :1.434
## 3rd Qu.: 6.000 3rd Qu.:2.000
## Max. : 8.000 Max. :2.000
## Q35..Problem.if.women.have.more.income.than.husband
## Min. :0.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.467
## 3rd Qu.:3.000
## Max. :5.000
## Q30..University.is.more.important.for.a.boy.than.for.a.girl
## Min. :0.000
## 1st Qu.:2.000
## Median :3.000
## Mean :2.798
## 3rd Qu.:4.000
## Max. :4.000
## Q32..Being.a.housewife.just.as.fulfilling Q240..Left.right.political.scale
## Min. :0.000 Min. :-1.000
## 1st Qu.:1.000 1st Qu.:-1.000
## Median :2.000 Median : 5.000
## Mean :2.211 Mean : 4.236
## 3rd Qu.:3.000 3rd Qu.: 7.000
## Max. :4.000 Max. :10.000
## A_YEAR..Year.of.survey
## Min. :2022
## 1st Qu.:2022
## Median :2022
## Mean :2022
## 3rd Qu.:2022
## Max. :2022
#creating column for overall score for each bucket
dat7 <- dat7 %>%
mutate(lib = Q184..Justifiable..Abortion + Q186..Justifiable..Sex.before.marriage,
sup = Q33..Jobs.scarce..Men.should.have.more.right.to.a.job.than.women + Q35..Problem.if.women.have.more.income.than.husband + Q30..University.is.more.important.for.a.boy.than.for.a.girl ,
trad =Q28..Pre.school.child.suffers.with.working.mother + Q31..Men.make.better.business.executives.than.women.do + Q29..Men.make.better.political.leaders.than.women.do + Q32..Being.a.housewife.just.as.fulfilling,
prog = lib + sup + trad)
view(dat7)
# summary stats of each
libstats <- dat7 %>%
summarise(
Mean = mean(lib, na.rm = TRUE),
Median = median(lib, na.rm = TRUE),
Standard_Deviation = sd(lib, na.rm = TRUE),
Min = min(lib, na.rm = TRUE),
Max = max(lib, na.rm = TRUE)
)
print(libstats)
## Mean Median Standard_Deviation Min Max
## 1 4.79669 3 4.027556 0 20
supstats <- dat7 %>%
summarise(
Mean = mean(sup, na.rm = TRUE),
Median = median(sup, na.rm = TRUE),
Standard_Deviation = sd(lib, na.rm = TRUE),
Min = min(sup, na.rm = TRUE),
Max = max(sup, na.rm = TRUE)
)
print(supstats)
## Mean Median Standard_Deviation Min Max
## 1 7.744681 7 4.027556 0 14
tradstats <- dat7 %>%
summarise(
Mean = mean(trad, na.rm = TRUE),
Median = median(trad, na.rm = TRUE),
Standard_Deviation = sd(trad, na.rm = TRUE),
Min = min(trad, na.rm = TRUE),
Max = max(trad, na.rm = TRUE)
)
print(tradstats)
## Mean Median Standard_Deviation Min Max
## 1 8.838652 9 3.006297 0 16
progstats <- dat7 %>%
summarise(
Mean = mean(prog, na.rm = TRUE),
Median = median(prog, na.rm = TRUE),
Standard_Deviation = sd(prog, na.rm = TRUE),
Min = min(prog, na.rm = TRUE),
Max = max(prog, na.rm = TRUE)
)
print(progstats)
## Mean Median Standard_Deviation Min Max
## 1 21.38002 21 6.993815 0 45
# barplots
ggplot(data = dat7, aes(x = lib)) +
geom_bar(fill = "lightblue") +
theme_minimal() +
labs(x = "scores", y = "respondents", title = "scores on liberation")
ggplot(data = dat7, aes(x = sup)) +
geom_bar(fill = "lightblue") +
theme_minimal() +
labs(x = "scores", y = "respondents", title = "scores on superiority of men")
ggplot(data = dat7, aes(x = trad)) +
geom_bar(fill = "lightblue") +
theme_minimal() +
labs(x = "scores", y = "respondents", title = "scores on traditional roles")
ggplot(data = dat7, aes(x = prog)) +
geom_bar(fill = "lightblue") +
theme_minimal() +
labs(x = "scores", y = "respondents", title = "scores on progressiveness")
#density plots
ggplot(data = dat7, aes(x = lib)) +
geom_density(fill = "blue", alpha = 1) +
labs(title = "liberation", x = "scores", y = "Density") +
theme_minimal()
ggplot(data = dat7, aes(x = sup)) +
geom_density(fill = "blue", alpha = 1) +
labs(title = "superiority", x = "scores", y = "Density") +
theme_minimal()
ggplot(data = dat7, aes(x = trad)) +
geom_density(fill = "blue", alpha = 1) +
labs(title = "traditionality", x = "scores", y = "Density") +
theme_minimal()
ggplot(data = dat7, aes(x = prog)) +
geom_density(fill = "blue", alpha = 1) +
labs(title = "progressiveness", x = "scores", y = "Density") +
theme_minimal()
##employment status
#liberation
t.test(dat7$Q279..Employment.status, dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q279..Employment.status and dat7$lib
## t = -4.9426, df = 2639, p-value = 8.193e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7726588 -0.3337242
## sample estimates:
## mean of x mean of y
## 4.243499 4.796690
ggplot(dat7, aes(x = factor(Q279..Employment.status), y = lib)) +
geom_boxplot() +
labs(title = "Distribution of Liberation Scores by Employment Status",
x = "Employment Status",
y = "Liberation Score") +
scale_x_discrete(labels = c("1" = "Unemployed", "2" = "Part-time", "3" = "Full-time",
"4" = "Self-employed", "5" = "Retired", "6" = "Student",
"7" = "Unable to work", "8" = "Other")) +
theme_minimal()
#superiority
t.test(dat7$Q279..Employment.status, dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q279..Employment.status and dat7$sup
## t = -38.904, df = 3145.8, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.677639 -3.324725
## sample estimates:
## mean of x mean of y
## 4.243499 7.744681
ggplot(dat7, aes(x = factor(Q279..Employment.status), y = sup)) +
geom_boxplot() +
labs(title = "Distribution of Male superiority Scores by Employment Status",
x = "Employment Status",
y = "Belief in Male Superiority Score") +
theme_minimal()
#traditional roles
t.test(dat7$Q279..Employment.status, dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q279..Employment.status and dat7$trad
## t = -50.495, df = 3119.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.773584 -4.416723
## sample estimates:
## mean of x mean of y
## 4.243499 8.838652
ggplot(dat7, aes(x = factor(Q279..Employment.status), y = trad)) +
geom_boxplot() +
labs(title = "Distribution of traditional Scores by Employment Status",
x = "Employment Status",
y = "Belief in traditional roles Score") +
theme_minimal()
# overall progressive
t.test(dat7$Q279..Employment.status, dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q279..Employment.status and dat7$prog
## t = -96.024, df = 2031.4, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -17.48651 -16.78654
## sample estimates:
## mean of x mean of y
## 4.243499 21.380024
ggplot(dat7, aes(x = factor(Q279..Employment.status), y = trad)) +
geom_boxplot() +
labs(title = "Distribution of progressiveness by Employment Status",
x = "Employment Status",
y = "Belief in traditional roles Score") +
theme_minimal()
##Age
#liberation
t.test(dat7$Q262..Age, dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q262..Age and dat7$lib
## t = 75.843, df = 1896, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 30.20529 31.80890
## sample estimates:
## mean of x mean of y
## 35.80378 4.79669
dat7 <- dat7 %>%
mutate(age_bucket = cut(Q262..Age, breaks = c(0, 18, 25, 35, 45, 55, 65, 100),
labels = c("Under 18", "19-25", "26-35", "36-45", "46-55", "56-65", "Over 65")))
age_summary_lib <- dat7 %>%
group_by(age_bucket,lib) %>%
summarise(
Mean = mean(Q262..Age),
Median = median(Q262..Age),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(age_summary_lib)
## # A tibble: 129 × 4
## age_bucket lib Mean Median
## <fct> <dbl> <dbl> <dbl>
## 1 Under 18 0 16.4 16
## 2 Under 18 1 16.5 16
## 3 Under 18 2 16.6 16
## 4 Under 18 3 16.8 17
## 5 Under 18 4 16.6 17
## 6 Under 18 5 16.7 17
## 7 Under 18 6 17 17
## 8 Under 18 7 16.8 17
## 9 Under 18 8 17.1 17
## 10 Under 18 9 16.8 17
## # ℹ 119 more rows
ggplot(dat7, aes(x = age_bucket, y = lib, fill = age_bucket)) +
geom_boxplot() +
labs(title = "Liberation Scores by Age Group",
x = "Age Group",
y = "Liberation Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#sup
t.test(dat7$Q262..Age, dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q262..Age and dat7$sup
## t = 69.56, df = 1801.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 27.26796 28.85024
## sample estimates:
## mean of x mean of y
## 35.803783 7.744681
age_summary_sup <- dat7 %>%
group_by(age_bucket,sup) %>%
summarise(
Mean = mean(Q262..Age),
Median = median(Q262..Age),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(age_summary_sup)
## # A tibble: 94 × 4
## age_bucket sup Mean Median
## <fct> <dbl> <dbl> <dbl>
## 1 Under 18 0 16.5 16.5
## 2 Under 18 2 16.2 16
## 3 Under 18 3 16.6 16.5
## 4 Under 18 4 16.4 16
## 5 Under 18 5 16.6 16
## 6 Under 18 6 16.8 17
## 7 Under 18 7 16.5 16
## 8 Under 18 8 16.7 17
## 9 Under 18 9 16.6 17
## 10 Under 18 10 16.7 17
## # ℹ 84 more rows
ggplot(dat7, aes(x = age_bucket, y = sup, fill = age_bucket)) +
geom_boxplot() +
labs(title = "Belief in Male superiority Scores by Age Group",
x = "Age Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#tradtional
t.test(dat7$Q262..Age, dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q262..Age and dat7$trad
## t = 66.811, df = 1805.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 26.17355 27.75671
## sample estimates:
## mean of x mean of y
## 35.803783 8.838652
age_summary_trad <- dat7 %>%
group_by(age_bucket,trad) %>%
summarise(
Mean = mean(Q262..Age),
Median = median(Q262..Age),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(age_summary_trad)
## # A tibble: 108 × 4
## age_bucket trad Mean Median
## <fct> <dbl> <dbl> <dbl>
## 1 Under 18 0 16 16
## 2 Under 18 1 16.7 17
## 3 Under 18 2 16.6 17
## 4 Under 18 4 16.7 17
## 5 Under 18 5 16.7 17
## 6 Under 18 6 16.6 17
## 7 Under 18 7 16.6 16
## 8 Under 18 8 16.8 17
## 9 Under 18 9 16.8 17
## 10 Under 18 10 16.5 16
## # ℹ 98 more rows
ggplot(dat7, aes(x = age_bucket, y = trad, fill = age_bucket)) +
geom_boxplot() +
labs(title = "Belief in traditional norms Scores by Age Group",
x = "Age Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
# overall progressive
t.test(dat7$Q262..Age, dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q262..Age and dat7$prog
## t = 33.403, df = 2291.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.57697 15.27055
## sample estimates:
## mean of x mean of y
## 35.80378 21.38002
age_summary_prog <- dat7 %>%
group_by(age_bucket,prog) %>%
summarise(
Mean = mean(Q262..Age),
Median = median(Q262..Age),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(age_summary_sup)
## # A tibble: 94 × 4
## age_bucket sup Mean Median
## <fct> <dbl> <dbl> <dbl>
## 1 Under 18 0 16.5 16.5
## 2 Under 18 2 16.2 16
## 3 Under 18 3 16.6 16.5
## 4 Under 18 4 16.4 16
## 5 Under 18 5 16.6 16
## 6 Under 18 6 16.8 17
## 7 Under 18 7 16.5 16
## 8 Under 18 8 16.7 17
## 9 Under 18 9 16.6 17
## 10 Under 18 10 16.7 17
## # ℹ 84 more rows
ggplot(dat7, aes(x = age_bucket, y = prog, fill = age_bucket)) +
geom_boxplot() +
labs(title = "progressiveness Scores by Age Group",
x = "Age Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
##gender
#liberation
unique(dat7$Q260..Sex)
## [1] 2 1
t.test(dat7$Q260..Sex, dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q260..Sex and dat7$lib
## t = -34.088, df = 1742.2, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.556373 -3.169395
## sample estimates:
## mean of x mean of y
## 1.433806 4.796690
ggplot(dat7, aes(x = factor(Q260..Sex), y = lib)) +
geom_boxplot() +
labs(title = "Distribution of liberation scores across genders",
x = "Gender",
y = " Score") +
scale_x_discrete(labels = c("1" = "Male", "2" = "Female")) +
theme_minimal()
#superiority
t.test(dat7$Q260..Sex, dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q260..Sex and dat7$sup
## t = -86.649, df = 1786.1, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.453721 -6.168029
## sample estimates:
## mean of x mean of y
## 1.433806 7.744681
ggplot(dat7, aes(x = factor(Q260..Sex), y = sup)) +
geom_boxplot() +
labs(title = "Distribution of belief in male superiority scores across genders",
x = "Gender",
y = " Score") +
scale_x_discrete(labels = c("1" = "Male", "2" = "Female")) +
theme_minimal()
#traditional
t.test(dat7$Q260..Sex, dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q260..Sex and dat7$trad
## t = -99.967, df = 1782.9, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.550125 -7.259568
## sample estimates:
## mean of x mean of y
## 1.433806 8.838652
ggplot(dat7, aes(x = factor(Q260..Sex), y = trad)) +
geom_boxplot() +
labs(title = "Distribution of belief in traditional roles scores across genders",
x = "Gender",
y = " Score") +
scale_x_discrete(labels = c("1" = "Male", "2" = "Female")) +
theme_minimal()
##overall progressivess
t.test(dat7$Q260..Sex, dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q260..Sex and dat7$prog
## t = -117.02, df = 1708, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.28053 -19.61190
## sample estimates:
## mean of x mean of y
## 1.433806 21.380024
ggplot(dat7, aes(x = factor(Q260..Sex), y = prog)) +
geom_boxplot() +
labs(title = "Distribution progressiveness scores across genders",
x = "Gender",
y = " Score") +
scale_x_discrete(labels = c("1" = "Male", "2" = "Female")) +
theme_minimal()
##political leanings
#liberation
t.test(dat7$Q240..Left.right.political.scale, dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q240..Left.right.political.scale and dat7$lib
## t = -4.1138, df = 3378.2, p-value = 3.985e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8273202 -0.2932472
## sample estimates:
## mean of x mean of y
## 4.236407 4.796690
ggplot(dat7, aes(x = factor(Q240..Left.right.political.scale), y = lib)) +
geom_boxplot() +
labs(title = "Distribution of liberation scores across political affiliation",
x = "Political leanings",
y = " Score") +
theme_minimal()
#superiority
t.test(dat7$Q240..Left.right.political.scale, dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q240..Left.right.political.scale and dat7$sup
## t = -29.522, df = 3153.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.741277 -3.275271
## sample estimates:
## mean of x mean of y
## 4.236407 7.744681
ggplot(dat7, aes(x = factor(Q240..Left.right.political.scale), y = sup)) +
geom_boxplot() +
labs(title = "Distribution of belief in male superiority scores across political affiliation",
x = "Political leanings",
y = " Score") +
theme_minimal()
#traditional
t.test(dat7$Q240..Left.right.political.scale, dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q240..Left.right.political.scale and dat7$trad
## t = -38.48, df = 3178.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.836746 -4.367746
## sample estimates:
## mean of x mean of y
## 4.236407 8.838652
ggplot(dat7, aes(x = factor(Q240..Left.right.political.scale), y = trad)) +
geom_boxplot() +
labs(title = "Distribution of traditional beliefs scores across political affiliation",
x = "Political leanings",
y = " Score") +
theme_minimal()
##progressiveness
t.test(dat7$Q240..Left.right.political.scale, dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q240..Left.right.political.scale and dat7$prog
## t = -88.094, df = 2647.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -17.52521 -16.76202
## sample estimates:
## mean of x mean of y
## 4.236407 21.380024
ggplot(dat7, aes(x = factor(Q240..Left.right.political.scale), y = prog)) +
geom_boxplot() +
labs(title = "Distribution of progressivness scores across political affiliation",
x = "Political leanings",
y = " Score") +
theme_minimal()
unique(df$A_YEAR..Year.of.survey)
## [1] 2022
##level of education
#liberation
t.test(dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. and dat7$lib
## t = -29.044, df = 1807.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.087755 -2.697115
## sample estimates:
## mean of x mean of y
## 1.904255 4.796690
edu_lib <- dat7 %>%
group_by(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,lib) %>%
summarise(
Mean = mean(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
Median = median(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(edu_lib)
## # A tibble: 64 × 4
## Q275R..Highest.educational.level..Respondent..recoded.in…¹ lib Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 5 -1 -1
## 2 -1 6 -1 -1
## 3 -1 9 -1 -1
## 4 1 0 1 1
## 5 1 1 1 1
## 6 1 2 1 1
## 7 1 3 1 1
## 8 1 4 1 1
## 9 1 5 1 1
## 10 1 6 1 1
## # ℹ 54 more rows
## # ℹ abbreviated name:
## # ¹​Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.
ggplot(dat7, aes(x = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), y = lib, fill = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.))) +
geom_boxplot() +
labs(title = "Liberation Scores by INCOME Group",
x = "education Group",
y = "Liberation Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#superiority
t.test(dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. and dat7$sup
## t = -78.823, df = 1907, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.985742 -5.695109
## sample estimates:
## mean of x mean of y
## 1.904255 7.744681
edu_sup <- dat7 %>%
group_by(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,sup) %>%
summarise(
Mean = mean(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
Median = median(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(edu_sup)
## # A tibble: 46 × 4
## Q275R..Highest.educational.level..Respondent..recoded.in…¹ sup Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 4 -1 -1
## 2 -1 5 -1 -1
## 3 -1 6 -1 -1
## 4 1 0 1 1
## 5 1 1 1 1
## 6 1 2 1 1
## 7 1 3 1 1
## 8 1 4 1 1
## 9 1 5 1 1
## 10 1 6 1 1
## # ℹ 36 more rows
## # ℹ abbreviated name:
## # ¹​Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.
ggplot(dat7, aes(x = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), y = sup, fill = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.))) +
geom_boxplot() +
labs(title = "superiority Scores by INCOME Group",
x = "education Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#traditional
t.test(dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. and dat7$trad
## t = -92.072, df = 1899.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.082105 -6.786689
## sample estimates:
## mean of x mean of y
## 1.904255 8.838652
edu_trad <- dat7 %>%
group_by(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,trad) %>%
summarise(
Mean = mean(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
Median = median(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(edu_trad)
## # A tibble: 50 × 4
## Q275R..Highest.educational.level..Respondent..recoded.in…¹ trad Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 5 -1 -1
## 2 -1 6 -1 -1
## 3 1 0 1 1
## 4 1 1 1 1
## 5 1 2 1 1
## 6 1 3 1 1
## 7 1 4 1 1
## 8 1 5 1 1
## 9 1 6 1 1
## 10 1 7 1 1
## # ℹ 40 more rows
## # ℹ abbreviated name:
## # ¹​Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.
ggplot(dat7, aes(x = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), y = trad, fill = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.))) +
geom_boxplot() +
labs(title = "Traditional roles Scores by INCOME Group",
x = "education Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#overall
t.test(dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. and dat7$prog
## t = -113.9, df = 1729.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -19.81115 -19.14039
## sample estimates:
## mean of x mean of y
## 1.904255 21.380024
edu_prog <- dat7 %>%
group_by(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,prog) %>%
summarise(
Mean = mean(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
Median = median(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(edu_prog)
## # A tibble: 118 × 4
## Q275R..Highest.educational.level..Respondent..recoded.in…¹ prog Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 15 -1 -1
## 2 -1 17 -1 -1
## 3 -1 19 -1 -1
## 4 1 2 1 1
## 5 1 3 1 1
## 6 1 4 1 1
## 7 1 5 1 1
## 8 1 6 1 1
## 9 1 7 1 1
## 10 1 8 1 1
## # ℹ 108 more rows
## # ℹ abbreviated name:
## # ¹​Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.
ggplot(dat7, aes(x = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), y = prog, fill = factor(Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.))) +
geom_boxplot() +
labs(title = "Progressiveness Scores by INCOME Group",
x = "education Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
##income group
#liberation
t.test(dat7$Q288R..Income.level..Recoded., dat7$lib, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q288R..Income.level..Recoded. and dat7$lib
## t = -28.713, df = 1821.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.060965 -2.669532
## sample estimates:
## mean of x mean of y
## 1.931442 4.796690
income_lib <- dat7 %>%
group_by(Q288R..Income.level..Recoded.,lib) %>%
summarise(
Mean = mean(Q288R..Income.level..Recoded.),
Median = median(Q288R..Income.level..Recoded.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(income_lib)
## # A tibble: 68 × 4
## Q288R..Income.level..Recoded. lib Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 0 -1 -1
## 2 -1 1 -1 -1
## 3 -1 2 -1 -1
## 4 -1 3 -1 -1
## 5 -1 4 -1 -1
## 6 -1 6 -1 -1
## 7 -1 18 -1 -1
## 8 -1 20 -1 -1
## 9 1 0 1 1
## 10 1 1 1 1
## # ℹ 58 more rows
ggplot(dat7, aes(x = factor(Q288R..Income.level..Recoded.), y = lib, fill = factor(Q288R..Income.level..Recoded.))) +
geom_boxplot() +
labs(title = "Liberation Scores by INCOME Group",
x = "INCOME Group",
y = "Liberation Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#superiority
t.test(dat7$Q288R..Income.level..Recoded., dat7$sup, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q288R..Income.level..Recoded. and dat7$sup
## t = -78.169, df = 1933.1, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.959088 -5.667390
## sample estimates:
## mean of x mean of y
## 1.931442 7.744681
income_sup <- dat7 %>%
group_by(Q288R..Income.level..Recoded.,sup) %>%
summarise(
Mean = mean(Q288R..Income.level..Recoded.),
Median = median(Q288R..Income.level..Recoded.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(income_sup)
## # A tibble: 55 × 4
## Q288R..Income.level..Recoded. sup Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 2 -1 -1
## 2 -1 3 -1 -1
## 3 -1 4 -1 -1
## 4 -1 5 -1 -1
## 5 -1 6 -1 -1
## 6 -1 7 -1 -1
## 7 -1 8 -1 -1
## 8 -1 9 -1 -1
## 9 -1 10 -1 -1
## 10 -1 12 -1 -1
## # ℹ 45 more rows
ggplot(dat7, aes(x = factor(Q288R..Income.level..Recoded.), y = sup, fill = factor(Q288R..Income.level..Recoded.))) +
geom_boxplot() +
labs(title = "superiority Scores by INCOME Group",
x = "INCOME Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#tradition
t.test(dat7$Q288R..Income.level..Recoded., dat7$trad, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q288R..Income.level..Recoded. and dat7$trad
## t = -91.387, df = 1924.9, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.055442 -6.758978
## sample estimates:
## mean of x mean of y
## 1.931442 8.838652
income_trad <- dat7 %>%
group_by(Q288R..Income.level..Recoded.,trad) %>%
summarise(
Mean = mean(Q288R..Income.level..Recoded.),
Median = median(Q288R..Income.level..Recoded.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(income_trad)
## # A tibble: 60 × 4
## Q288R..Income.level..Recoded. trad Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 0 -1 -1
## 2 -1 2 -1 -1
## 3 -1 4 -1 -1
## 4 -1 5 -1 -1
## 5 -1 6 -1 -1
## 6 -1 7 -1 -1
## 7 -1 8 -1 -1
## 8 -1 9 -1 -1
## 9 -1 10 -1 -1
## 10 -1 11 -1 -1
## # ℹ 50 more rows
ggplot(dat7, aes(x = factor(Q288R..Income.level..Recoded.), y = trad, fill = factor(Q288R..Income.level..Recoded.))) +
geom_boxplot() +
labs(title = "Trdaitional role Scores by INCOME Group",
x = "INCOME Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
#overall
t.test(dat7$Q288R..Income.level..Recoded., dat7$prog, var.equal = FALSE)
##
## Welch Two Sample t-test
##
## data: dat7$Q288R..Income.level..Recoded. and dat7$prog
## t = -113.66, df = 1734.4, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -19.78419 -19.11297
## sample estimates:
## mean of x mean of y
## 1.931442 21.380024
income_prog <- dat7 %>%
group_by(Q288R..Income.level..Recoded.,prog) %>%
summarise(
Mean = mean(Q288R..Income.level..Recoded.),
Median = median(Q288R..Income.level..Recoded.),
.groups = 'drop' # This option removes the grouping structure afterwards
)
# Display the table
print(income_prog)
## # A tibble: 133 × 4
## Q288R..Income.level..Recoded. prog Mean Median
## <int> <dbl> <dbl> <dbl>
## 1 -1 2 -1 -1
## 2 -1 6 -1 -1
## 3 -1 8 -1 -1
## 4 -1 9 -1 -1
## 5 -1 10 -1 -1
## 6 -1 12 -1 -1
## 7 -1 13 -1 -1
## 8 -1 15 -1 -1
## 9 -1 16 -1 -1
## 10 -1 17 -1 -1
## # ℹ 123 more rows
ggplot(dat7, aes(x = factor(Q288R..Income.level..Recoded.), y = prog, fill = factor(Q288R..Income.level..Recoded.))) +
geom_boxplot() +
labs(title = "Progressiveness Scores by INCOME Group",
x = "INCOME Group",
y = "Score") +
theme_minimal() +
scale_fill_brewer(palette = "Paired")
###regressions
##Age
#overall
age_prog<- lm(prog ~ Q262..Age, dat = dat7)
summary(age_prog)
##
## Call:
## lm(formula = prog ~ Q262..Age, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.7357 -5.0112 -0.5881 4.5343 23.5259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.58177 0.40874 55.247 < 2e-16 ***
## Q262..Age -0.03356 0.01039 -3.231 0.00126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.974 on 1690 degrees of freedom
## Multiple R-squared: 0.00614, Adjusted R-squared: 0.005552
## F-statistic: 10.44 on 1 and 1690 DF, p-value: 0.001256
ggplot(dat7, aes(x = Q262..Age , y = prog)) +
geom_point(aes(color = Q262..Age), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of progressiveness Scores on Age",
x = "Age",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#liberation
age_lib<- lm(lib ~ Q262..Age, dat = dat7)
summary(age_lib)
##
## Call:
## lm(formula = lib ~ Q262..Age, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.059 -2.834 -1.880 1.550 15.458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.271048 0.235769 22.357 <2e-16 ***
## Q262..Age -0.013249 0.005992 -2.211 0.0272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.023 on 1690 degrees of freedom
## Multiple R-squared: 0.002885, Adjusted R-squared: 0.002295
## F-statistic: 4.889 on 1 and 1690 DF, p-value: 0.02716
ggplot(dat7, aes(x = Q262..Age , y = lib)) +
geom_point(aes(color = Q262..Age), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of liberation Scores on Age",
x = "Age",
y = "liberation Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#superiority
age_sup<- lm(sup ~ Q262..Age, dat = dat7)
summary(age_sup)
##
## Call:
## lm(formula = sup ~ Q262..Age, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0645 -1.9999 -0.3216 2.0364 6.8075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.322867 0.172517 48.244 < 2e-16 ***
## Q262..Age -0.016149 0.004384 -3.683 0.000237 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.944 on 1690 degrees of freedom
## Multiple R-squared: 0.007964, Adjusted R-squared: 0.007377
## F-statistic: 13.57 on 1 and 1690 DF, p-value: 0.0002375
ggplot(dat7, aes(x = Q262..Age , y = sup)) +
geom_point(aes(color = Q262..Age), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of superiority Scores on Age",
x = "Age",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#traditional roles
age_trad<- lm(trad ~ Q262..Age, dat = dat7)
summary(age_prog)
##
## Call:
## lm(formula = prog ~ Q262..Age, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.7357 -5.0112 -0.5881 4.5343 23.5259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.58177 0.40874 55.247 < 2e-16 ***
## Q262..Age -0.03356 0.01039 -3.231 0.00126 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.974 on 1690 degrees of freedom
## Multiple R-squared: 0.00614, Adjusted R-squared: 0.005552
## F-statistic: 10.44 on 1 and 1690 DF, p-value: 0.001256
ggplot(dat7, aes(x = Q262..Age , y = trad)) +
geom_point(aes(color = Q262..Age), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of traditional roles Scores on Age",
x = "Age",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
##employment
#overall
employ_prog<- lm(prog ~ Q279..Employment.status, dat = dat7)
summary(employ_prog)
##
## Call:
## lm(formula = prog ~ Q279..Employment.status, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.2660 -4.8591 -0.5626 4.6581 24.1409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.63848 0.36401 62.192 < 2e-16 ***
## Q279..Employment.status -0.29656 0.07594 -3.905 9.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.965 on 1690 degrees of freedom
## Multiple R-squared: 0.008944, Adjusted R-squared: 0.008358
## F-statistic: 15.25 on 1 and 1690 DF, p-value: 9.778e-05
ggplot(dat7, aes(x = Q279..Employment.status , y = prog)) +
geom_point(aes(color = Q279..Employment.status), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of employment roles Scores on Age",
x = "employment category",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#liberation
employ_lib<- lm(lib ~ Q279..Employment.status, dat = dat7)
summary(employ_lib)
##
## Call:
## lm(formula = lib ~ Q279..Employment.status, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.241 -2.588 -1.762 1.687 15.687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.96568 0.20810 28.667 < 2e-16 ***
## Q279..Employment.status -0.27548 0.04341 -6.346 2.84e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.982 on 1690 degrees of freedom
## Multiple R-squared: 0.02327, Adjusted R-squared: 0.02269
## F-statistic: 40.27 on 1 and 1690 DF, p-value: 2.839e-10
ggplot(dat7, aes(x = Q279..Employment.status , y = lib)) +
geom_point(aes(color = Q279..Employment.status), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of employment roles Scores on Age",
x = "Age",
y = "liberation Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#superiority
employ_sup<- lm(sup ~ Q279..Employment.status, dat = dat7)
summary(employ_sup)
##
## Call:
## lm(formula = sup ~ Q279..Employment.status, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.827 -1.805 -0.674 2.217 6.326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.65226 0.15445 49.545 <2e-16 ***
## Q279..Employment.status 0.02178 0.03222 0.676 0.499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.955 on 1690 degrees of freedom
## Multiple R-squared: 0.0002703, Adjusted R-squared: -0.0003213
## F-statistic: 0.4569 on 1 and 1690 DF, p-value: 0.4992
ggplot(dat7, aes(x = Q279..Employment.status , y = sup)) +
geom_point(aes(color = Q279..Employment.status), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of employment roles Scores on Age",
x = "Age",
y = "superiority Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
#traditional
employ_trad<- lm(trad ~ Q279..Employment.status, dat = dat7)
summary(employ_trad)
##
## Call:
## lm(formula = trad ~ Q279..Employment.status, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.8062 -1.9348 0.1081 2.1081 7.3224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.02053 0.15709 57.421 <2e-16 ***
## Q279..Employment.status -0.04286 0.03277 -1.308 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.006 on 1690 degrees of freedom
## Multiple R-squared: 0.001011, Adjusted R-squared: 0.00042
## F-statistic: 1.71 on 1 and 1690 DF, p-value: 0.1911
ggplot(dat7, aes(x = Q279..Employment.status , y = trad)) +
geom_point(aes(color = Q279..Employment.status), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of employment roles Scores on Age",
x = "Age",
y = "traditional Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red") # Gradient color based on age
## `geom_smooth()` using formula = 'y ~ x'
##Sex
#OVERALL
gender_prog<- lm(prog ~ Q260..Sex, dat = dat7)
summary(gender_prog)
##
## Call:
## lm(formula = prog ~ Q260..Sex, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.0157 -4.8956 -0.8556 4.1444 23.1444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.1757 0.5197 38.823 <2e-16 ***
## Q260..Sex 0.8399 0.3426 2.452 0.0143 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.983 on 1690 degrees of freedom
## Multiple R-squared: 0.003545, Adjusted R-squared: 0.002955
## F-statistic: 6.012 on 1 and 1690 DF, p-value: 0.01431
#liberation
gender_lib<- lm(lib ~ Q260..Sex, dat = dat7)
summary(gender_lib)
##
## Call:
## lm(formula = lib ~ Q260..Sex, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.006 -3.006 -2.006 1.477 15.477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4894 0.2993 18.342 <2e-16 ***
## Q260..Sex -0.4831 0.1973 -2.449 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.022 on 1690 degrees of freedom
## Multiple R-squared: 0.003536, Adjusted R-squared: 0.002946
## F-statistic: 5.997 on 1 and 1690 DF, p-value: 0.01443
#superiortiy
gender_sup<- lm(sup ~ Q260..Sex, dat = dat7)
summary(gender_sup)
##
## Call:
## lm(formula = sup ~ Q260..Sex, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.1703 -2.1703 -0.4186 1.8297 6.5814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6669 0.2182 30.557 < 2e-16 ***
## Q260..Sex 0.7517 0.1438 5.227 1.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.932 on 1690 degrees of freedom
## Multiple R-squared: 0.01591, Adjusted R-squared: 0.01533
## F-statistic: 27.32 on 1 and 1690 DF, p-value: 1.938e-07
#traditional
gender_trad<- lm(trad ~ Q260..Sex, dat = dat7)
summary(gender_trad)
##
## Call:
## lm(formula = trad ~ Q260..Sex, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.1621 -2.1621 0.4092 1.8379 7.4092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.0195 0.2228 36.00 < 2e-16 ***
## Q260..Sex 0.5713 0.1469 3.89 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.994 on 1690 degrees of freedom
## Multiple R-squared: 0.008876, Adjusted R-squared: 0.008289
## F-statistic: 15.13 on 1 and 1690 DF, p-value: 0.000104
##Income
#OVERALL
income_proge<- lm(prog ~ Q288R..Income.level..Recoded., dat = dat7)
summary(income_proge)
##
## Call:
## lm(formula = prog ~ Q288R..Income.level..Recoded., data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.4845 -4.9600 -0.4845 4.5155 23.5155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.4356 0.4414 41.77 < 2e-16 ***
## Q288R..Income.level..Recoded. 1.5245 0.2114 7.21 8.41e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.891 on 1690 degrees of freedom
## Multiple R-squared: 0.02984, Adjusted R-squared: 0.02927
## F-statistic: 51.98 on 1 and 1690 DF, p-value: 8.406e-13
ggplot(dat7, aes(x = Q288R..Income.level..Recoded. , y = prog)) +
geom_point(aes(color = Q288R..Income.level..Recoded.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of income groups and progressiveness",
x = "income",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#liberation
income_libe<- lm(lib ~ Q288R..Income.level..Recoded., dat = dat7)
summary(income_libe)
##
## Call:
## lm(formula = lib ~ Q288R..Income.level..Recoded., data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.557 -2.845 -1.845 1.866 17.288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4229 0.2555 13.395 < 2e-16 ***
## Q288R..Income.level..Recoded. 0.7113 0.1224 5.811 7.41e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.989 on 1690 degrees of freedom
## Multiple R-squared: 0.01959, Adjusted R-squared: 0.01901
## F-statistic: 33.77 on 1 and 1690 DF, p-value: 7.413e-09
ggplot(dat7, aes(x = Q288R..Income.level..Recoded. , y = lib)) +
geom_point(aes(color = Q288R..Income.level..Recoded.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of employment roles Scores on Age",
x = "income",
y = "Liberation Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#superiortiy
income_supe<- lm(sup ~ Q288R..Income.level..Recoded., dat = dat7)
summary(income_supe)
##
## Call:
## lm(formula = sup ~ Q288R..Income.level..Recoded., data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0479 -2.0479 -0.4804 2.2359 7.0872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.19658 0.18877 38.123 < 2e-16 ***
## Q288R..Income.level..Recoded. 0.28378 0.09043 3.138 0.00173 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.947 on 1690 degrees of freedom
## Multiple R-squared: 0.005794, Adjusted R-squared: 0.005205
## F-statistic: 9.848 on 1 and 1690 DF, p-value: 0.001729
ggplot(dat7, aes(x = Q288R..Income.level..Recoded. , y = sup)) +
geom_point(aes(color = Q288R..Income.level..Recoded.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of income on superiority",
x = "income",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#traditional
income_trade<- lm(trad ~ Q288R..Income.level..Recoded., dat = dat7)
summary(income_trade)
##
## Call:
## lm(formula = trad ~ Q288R..Income.level..Recoded., data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.8749 -1.8749 0.1251 2.1251 7.6545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.81608 0.19075 40.975 < 2e-16 ***
## Q288R..Income.level..Recoded. 0.52943 0.09137 5.794 8.17e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.978 on 1690 degrees of freedom
## Multiple R-squared: 0.01948, Adjusted R-squared: 0.0189
## F-statistic: 33.57 on 1 and 1690 DF, p-value: 8.171e-09
ggplot(dat7, aes(x = Q288R..Income.level..Recoded. , y = trad)) +
geom_point(aes(color = Q288R..Income.level..Recoded.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of income on traditional",
x = "income",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
##education
#OVERALL
educa_prog<- lm(prog ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat = dat7)
summary(educa_prog)
##
## Call:
## lm(formula = prog ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,
## data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.572 -4.572 -0.572 4.428 21.428
##
## Coefficients:
## Estimate
## (Intercept) 17.5623
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 2.0048
## Std. Error
## (Intercept) 0.4544
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.2221
## t value
## (Intercept) 38.649
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 9.027
## Pr(>|t|)
## (Intercept) <2e-16
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. <2e-16
##
## (Intercept) ***
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.833 on 1690 degrees of freedom
## Multiple R-squared: 0.04599, Adjusted R-squared: 0.04543
## F-statistic: 81.48 on 1 and 1690 DF, p-value: < 2.2e-16
ggplot(dat7, aes(x = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. , y = prog)) +
geom_point(aes(color = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "regression of income and progressive scores",
x = "income",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#liberation
educa_lib<- lm(lib ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat = dat7)
summary(educa_lib)
##
## Call:
## lm(formula = lib ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,
## data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.375 -2.847 -1.847 1.680 15.680
##
## Coefficients:
## Estimate
## (Intercept) 3.7926
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.5273
## Std. Error
## (Intercept) 0.2666
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.1303
## t value
## (Intercept) 14.225
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 4.046
## Pr(>|t|)
## (Intercept) < 2e-16
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 5.45e-05
##
## (Intercept) ***
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.009 on 1690 degrees of freedom
## Multiple R-squared: 0.009593, Adjusted R-squared: 0.009007
## F-statistic: 16.37 on 1 and 1690 DF, p-value: 5.446e-05
ggplot(dat7, aes(x = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. , y = lib)) +
geom_point(aes(color = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of education roles Scores on liberation",
x = "education",
y = "Liberation Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#superiortiy
educa_sup<- lm(sup ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat = dat7)
summary(educa_sup)
##
## Call:
## lm(formula = sup ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,
## data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8159 -1.8159 -0.0724 2.1841 6.9276
##
## Coefficients:
## Estimate
## (Intercept) 6.32902
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.74342
## Std. Error
## (Intercept) 0.19302
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.09435
## t value
## (Intercept) 32.79
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 7.88
## Pr(>|t|)
## (Intercept) < 2e-16
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 5.84e-15
##
## (Intercept) ***
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.903 on 1690 degrees of freedom
## Multiple R-squared: 0.03544, Adjusted R-squared: 0.03487
## F-statistic: 62.09 on 1 and 1690 DF, p-value: 5.837e-15
ggplot(dat7, aes(x = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. , y = sup)) +
geom_point(aes(color = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of income on superiority",
x = "education",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#traditional
educa_trade<- lm(trad ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups., dat = dat7)
summary(educa_trade)
##
## Call:
## lm(formula = trad ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,
## data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9089 -1.9089 0.0911 2.0911 7.8252
##
## Coefficients:
## Estimate
## (Intercept) 7.4406
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.7341
## Std. Error
## (Intercept) 0.1966
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.0961
## t value
## (Intercept) 37.844
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 7.639
## Pr(>|t|)
## (Intercept) < 2e-16
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 3.64e-14
##
## (Intercept) ***
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.957 on 1690 degrees of freedom
## Multiple R-squared: 0.03338, Adjusted R-squared: 0.03281
## F-statistic: 58.36 on 1 and 1690 DF, p-value: 3.635e-14
ggplot(dat7, aes(x = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. , y = trad)) +
geom_point(aes(color = Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of education on traditional",
x = "income",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
##political leaning
#OVERALL
pol_prog<- lm(prog ~ Q240..Left.right.political.scale, dat = dat7)
summary(pol_prog)
##
## Call:
## lm(formula = prog ~ Q240..Left.right.political.scale, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.7727 -4.8491 -0.7727 4.5197 25.1509
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.1415 0.2480 81.215 < 2e-16 ***
## Q240..Left.right.political.scale 0.2924 0.0431 6.782 1.63e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.903 on 1690 degrees of freedom
## Multiple R-squared: 0.0265, Adjusted R-squared: 0.02592
## F-statistic: 46 on 1 and 1690 DF, p-value: 1.628e-11
ggplot(dat7, aes(x = Q240..Left.right.political.scale , y = prog)) +
geom_point(aes(color = Q240..Left.right.political.scale), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of political leanings Scores on Age",
x = "leanings",
y = "progress Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#liberation
pol_lib<- lm(lib ~ Q240..Left.right.political.scale, dat = dat7)
summary(pol_lib)
##
## Call:
## lm(formula = lib ~ Q240..Left.right.political.scale, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.935 -2.947 -1.762 1.855 15.843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9599 0.1421 27.870 < 2e-16 ***
## Q240..Left.right.political.scale 0.1975 0.0247 7.999 2.31e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.955 on 1690 degrees of freedom
## Multiple R-squared: 0.03648, Adjusted R-squared: 0.03591
## F-statistic: 63.98 on 1 and 1690 DF, p-value: 2.313e-15
ggplot(dat7, aes(x = Q240..Left.right.political.scale , y = lib)) +
geom_point(aes(color = Q240..Left.right.political.scale), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of education roles Scores on liberation",
x = "leanings",
y = "Liberation Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#superiortiy
pol_sup<- lm(sup ~ Q240..Left.right.political.scale, dat = dat7)
summary(pol_sup)
##
## Call:
## lm(formula = sup ~ Q240..Left.right.political.scale, data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.755 -1.755 -0.735 2.245 6.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.753250 0.106186 73.02 <2e-16 ***
## Q240..Left.right.political.scale -0.002023 0.018456 -0.11 0.913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.955 on 1690 degrees of freedom
## Multiple R-squared: 7.108e-06, Adjusted R-squared: -0.0005846
## F-statistic: 0.01201 on 1 and 1690 DF, p-value: 0.9127
ggplot(dat7, aes(x = Q240..Left.right.political.scale , y = sup)) +
geom_point(aes(color = Q240..Left.right.political.scale), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of income on superiority",
x = "leanings",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
#traditional
pol_trad<- lm(trad ~ Q240..Left.right.political.scale, dat = dat7)
summary(educa_trade)
##
## Call:
## lm(formula = trad ~ Q275R..Highest.educational.level..Respondent..recoded.into.3.groups.,
## data = dat7)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9089 -1.9089 0.0911 2.0911 7.8252
##
## Coefficients:
## Estimate
## (Intercept) 7.4406
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.7341
## Std. Error
## (Intercept) 0.1966
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 0.0961
## t value
## (Intercept) 37.844
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 7.639
## Pr(>|t|)
## (Intercept) < 2e-16
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. 3.64e-14
##
## (Intercept) ***
## Q275R..Highest.educational.level..Respondent..recoded.into.3.groups. ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.957 on 1690 degrees of freedom
## Multiple R-squared: 0.03338, Adjusted R-squared: 0.03281
## F-statistic: 58.36 on 1 and 1690 DF, p-value: 3.635e-14
ggplot(dat7, aes(x = Q240..Left.right.political.scale , y = trad)) +
geom_point(aes(color = Q240..Left.right.political.scale), alpha = 0.6) + # Points with transparency
geom_smooth(method = "lm", color = "blue", se = TRUE) + # Regression line with confidence interval
labs(title = "Linear Regression of education on traditional",
x = "leanings",
y = "Score") +
theme_minimal() +
scale_color_gradient(low = "yellow", high = "red")
## `geom_smooth()` using formula = 'y ~ x'
