Albert Satorra ECP2010 CATEGÒRIQUES data sets: GSS.SAV …satorra/dades/Classe3_ECP2010.pdf · 2010-01-28 · Albert Satorra ECP2010 SEX & Marital Status & DEGREE > taula =ftable(SEX12,MARITALF12,

RelacionsentrevariablesCATEGÒRIQUES

datasets:GSS.SAV

Agres>dataTitanic

**GSS.SAV:marital&life**

AlbertSatorraECP2010

Relacionsentrevariables:

EstatCivil&Nivelld’educacióDadesdel’enquestaGSS.SAV


SintaxisR


### lectura de les dades data= read.spss("http://www.econ.upf.edu/~satorra/dades/GSS.SAV", use.value.labels = TRUE, to.data.frame = FALSE,max.value.labels = Inf, trim.factor.names = FALSE,trim_values = TRUE, reencode = NA)

>attach(data)

> names(data) [1] "AGE" "SEX" "EDUC" "INCOME91" "WRKSTAT" "RICHWORK" "SATJOB" "LIFE" "IMPJOB" [10] "INCOME4" "HRS1" "DEGREE" "ANOMIA5" "DEGREE2" "MAEDUC" "PAEDUC" "MACOLLEG" "PACOLLEG" [19] "SATJOB2" "MARITAL" "AGEWED" "SPWRKSTA" "SPHRS1" "SIBS" "ZODIAC" "SPEDUC" "SPDEG" [28] "PARTYID" "VOTE92" "PRES92" "POSTLIFE" "HAPPY" "HAPMAR" "JOBINC" "CLASSICL" "OPERA" [37] "COUNTRY" "RINCOM91" "TVHOURS" "FINRELA" "WIFEDUC" "HUSBEDUC" "INCOMDOL" "RINCMDOL" "ID" [46] "HUSBHR" "WIFEHR" "HUSBFT" "WIFEFT”

GSS.SAV:Variabilitatestadís>ca

• Població:Majorsd’edataUSA• Unitatsd’anàlisi:individusd’unaenquesta

• Variables:• DEGREE

• MARITAL


Distribuciómarginal:DEGREE


Less than HS

High school

Junior college

Bachelor

Graduate

Distribuciómarginal:MARITAL


MARRIED

WIDOWED

DIVORCED SEPARATED

NEVER MARRIED

MaritalStatus&DEGREE


filtre1=!(MARITAL=="NA")MARITALF1=MARITAL[filtre1]DEGREEF1=DEGREE[filtre1]filtre2=!((DEGREEF1=="DK")|(DEGREEF1=="NA"))MARITALF12=MARITALF1[filtre2]DEGREEF12=DEGREEF1[filtre2]table(MARITALF12,DEGREEF12)DEGREEF12MARITALF12 Less than HS High school Junior college Bachelor Graduate NAP DK NA MARRIED 119 407 49 139 80 0 0 0 WIDOWED 68 74 6 11 5 0 0 0 DIVORCED 31 137 15 19 11 0 0 0 SEPARATED 14 21 3 2 0 0 0 0 NEVER MARRIED 47 141 17 62 17 0 0 0 NA 0 0 0 0 0 0 0 0 >

a`r(MARITALF12,"levels")=levels(MARITALF12)[1:5]a`r(DEGREEF12,"levels")=levels(DEGREEF12)[1:5]

MaritalStatus&DEGREE


a`r(MARITALF12,"levels")=levels(MARITALF12)[1:5]a`r(DEGREEF12,"levels")=levels(DEGREEF12)[1:5]

table(MARITALF12,DEGREEF12)

DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 119 407 49 139 80 WIDOWED 68 74 6 11 5 DIVORCED 31 137 15 19 11 SEPARATED 14 21 3 2 0 NEVER MARRIED 47 141 17 62 17

summary(table(MARITALF12,DEGREEF12))Numberofcasesintable:1495Numberoffactors:2Testforindependenceofallfactors: Chisq=112.75,df=16,p‐value=1.343e‐16 Chi‐squaredapproxima>onmaybeincorrect

SEX&MaritalStatus&DEGREEAlbertSatorraECP2010

> taula =ftable(SEX12,MARITALF12, DEGREEF12) > taula DEGREEF12 Less than HS High school Junior college Bachelor Graduate SEX12 MARITALF12 Male MARRIED 68 174 21 72 48 WIDOWED 9 14 2 3 3 DIVORCED 20 39 4 6 6 SEPARATED 4 4 1 0 0 NEVER MARRIED 24 73 9 26 10 Female MARRIED 51 233 28 67 32 WIDOWED 59 60 4 8 2 DIVORCED 11 98 11 13 5 SEPARATED 10 17 2 2 0 NEVER MARRIED 23 68 8 36 7 > round(prop.table(taula,1),3) DEGREEF12 Less than HS High school Junior college Bachelor Graduate SEX12 MARITALF12 Male MARRIED 0.178 0.454 0.055 0.188 0.125 WIDOWED 0.290 0.452 0.065 0.097 0.097 DIVORCED 0.267 0.520 0.053 0.080 0.080 SEPARATED 0.444 0.444 0.111 0.000 0.000 NEVER MARRIED 0.169 0.514 0.063 0.183 0.070 Female MARRIED 0.124 0.567 0.068 0.163 0.078 WIDOWED 0.444 0.451 0.030 0.060 0.015 DIVORCED 0.080 0.710 0.080 0.094 0.036 SEPARATED 0.323 0.548 0.065 0.065 0.000 NEVER MARRIED 0.162 0.479 0.056 0.254 0.049 apply(prop.table(taula,1),1,sum) [1] 1 1 1 1 1 1 1 1 1 1

Càlculsbàsicsambtaules


>taula=table(MARITALF12,DEGREEF12)margin.table(taula)[1]1495

MARGINALS:

margin.table(taula,1)MARITALF12MARRIEDWIDOWEDDIVORCEDSEPARATEDNEVERMARRIED79416421340284margin.table(taula,2)DEGREEF12LessthanHSHighschoolJuniorcollegeBachelorGraduate27978090233113

Anàlisibàsicadetaules


Proporcions:

>round(prop.table(taula),4) DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 0.0796 0.2722 0.0328 0.0930 0.0535 WIDOWED 0.0455 0.0495 0.0040 0.0074 0.0033 DIVORCED 0.0207 0.0916 0.0100 0.0127 0.0074 SEPARATED 0.0094 0.0140 0.0020 0.0013 0.0000 NEVER MARRIED 0.0314 0.0943 0.0114 0.0415 0.0114

>

Distribu.Condicionades(fila):Perfilsfila


> round(prop.table(taula,1),4) DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 0.1499 0.5126 0.0617 0.1751 0.1008 WIDOWED 0.4146 0.4512 0.0366 0.0671 0.0305 DIVORCED 0.1455 0.6432 0.0704 0.0892 0.0516 SEPARATED 0.3500 0.5250 0.0750 0.0500 0.0000 NEVER MARRIED 0.1655 0.4965 0.0599 0.2183 0.0599

apply(prop.table(taula,1),1, sum) MARRIED WIDOWED DIVORCED SEPARATED NEVER MARRIED 1 1 1 1 1

Distribu.Condicionades:Perfilscolumna


> round(prop.table(taula,2),4) DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 0.4265 0.5218 0.5444 0.5966 0.7080 WIDOWED 0.2437 0.0949 0.0667 0.0472 0.0442 DIVORCED 0.1111 0.1756 0.1667 0.0815 0.0973 SEPARATED 0.0502 0.0269 0.0333 0.0086 0.0000 NEVER MARRIED 0.1685 0.1808 0.1889 0.2661 0.1504

apply(prop.table(taula,2),2, sum) Less than HS High school Junior college Bachelor Graduate 1 1 1 1 1 >

Desviacionsdelaindependència


expected <- as.array(margin.table(taula,1)) %*% t(as.array(margin.table(taula,2))) / margin.table(taula) > expected DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 148.177926 414.26087 47.799331 123.747157 60.014716 WIDOWED 30.606020 85.56522 9.872910 25.559866 12.395987 DIVORCED 39.750502 111.13043 12.822742 33.196656 16.099666 SEPARATED 7.464883 20.86957 2.408027 6.234114 3.023411 NEVER MARRIED 53.000669 148.17391 17.096990 44.262207 21.466221 > taula DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 119 407 49 139 80 WIDOWED 68 74 6 11 5 DIVORCED 31 137 15 19 11 SEPARATED 14 21 3 2 0 NEVER MARRIED 47 141 17 62 17 > des=taula - expected DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED -29.17792642 -7.26086957 1.20066890 15.25284281 19.98528428 WIDOWED 37.39397993 -11.56521739 -3.87290970 -14.55986622 -7.39598662 DIVORCED -8.75050167 25.86956522 2.17725753 -14.19665552 -5.09966555 SEPARATED 6.53511706 0.13043478 0.59197324 -4.23411371 -3.02341137 NEVER MARRIED -6.00066890 -7.17391304 -0.09698997 17.73779264 -4.46622074 >

Desviacionsdelaindependència


> des=taula - expected > des DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED -29.17792642 -7.26086957 1.20066890 15.25284281 19.98528428 WIDOWED 37.39397993 -11.56521739 -3.87290970 -14.55986622 -7.39598662 DIVORCED -8.75050167 25.86956522 2.17725753 -14.19665552 -5.09966555 SEPARATED 6.53511706 0.13043478 0.59197324 -4.23411371 -3.02341137 NEVER MARRIED -6.00066890 -7.17391304 -0.09698997 17.73779264 -4.46622074

z=des/sqrt(expected) > z DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED -2.39697038 -0.35673989 0.17366502 1.37114435 2.57977277 WIDOWED 6.75924613 -1.25027392 -1.23257907 -2.87990457 -2.10065904 DIVORCED -1.38791109 2.45398906 0.60802207 -2.46398965 -1.27096405 SEPARATED 2.39189367 0.02855201 0.38147969 -1.69580206 -1.73879595 NEVER MARRIED -0.82425006 -0.58934585 -0.02345670 2.66614054 -0.96396749

Contrastji‐quadratd’independència


…. > z^2 DEGREEF12 MARITALF12 Less than HS High school Junior college Bachelor Graduate MARRIED 5.745467e+00 1.272634e-01 3.015954e-02 1.880037e+00 6.655228e+00 WIDOWED 4.568741e+01 1.563185e+00 1.519251e+00 8.293850e+00 4.412768e+00 DIVORCED 1.926297e+00 6.022062e+00 3.696908e-01 6.071245e+00 1.615350e+00 SEPARATED 5.721155e+00 8.152174e-04 1.455268e-01 2.875745e+00 3.023411e+00 NEVER MARRIED 6.793882e-01 3.473285e-01 5.502169e-04 7.108305e+00 9.292333e-01 > sum(z^2) [1] 112.7507

> summary(taula) Number of cases in table: 1495 Number of factors: 2 Test for independence of all factors: Chisq = 112.75, df = 16, p-value = 1.343e-16 Chi-squared approximation may be incorrect

CONClusió: Hi ha ASSOCIACIó Estadística

sable


> ftable(SEX,DEGREE,MARITAL) MARITAL MARRIED WIDOWED DIVORCED SEPARATED NEVER MARRIED NA SEX DEGREE Male Less than HS 68 9 20 4 24 0 High school 174 14 39 4 73 0 Junior college 21 2 4 1 9 0 Bachelor 72 3 6 0 26 1 Graduate 48 3 6 0 10 0 NAP 0 0 0 0 0 0 DK 0 0 0 0 0 0 NA 0 0 0 0 0 0 Female Less than HS 51 59 11 10 23 0 High school 233 60 98 17 68 0 Junior college 28 4 11 2 8 0 Bachelor 67 8 13 2 36 0 Graduate 32 2 5 0 7 0 NAP 0 0 0 0 0 0 DK 1 0 0 0 1 0 NA 0 1 0 0 1 0

>


> taula1=apply(UCBAdmissions, c(2,1), sum) > taula1 Admit Gender Admitted Rejected Male 1198 1493 Female 557 1278

> addmargins(taula1) Admit Gender Admitted Rejected Sum Male 1198 1493 2691 Female 557 1278 1835 Sum 1755 2771 4526

> round(prop.table(taula1,1),3) Admit Gender Admitted Rejected Male 0.445 0.555 Female 0.304 0.696

Taulesmul>variants:dadesd’admissióaUCBerkeley(I)

Taulesmul>variants:dadesd’admissióaUCBerkeley(II)


> ftable(UCBAdmissions,row.vars= c("Dept","Gender")) Admit Admitted Rejected Dept Gender A Male 512 313 Female 89 19 B Male 353 207 Female 17 8 C Male 120 205 Female 202 391 D Male 138 279 Female 131 244 E Male 53 138 Female 94 299 F Male 22 351 Female 24 317


> round(prop.table(ftable(UCBAdmissions,row.vars= c("Dept","Gender")),1),2) Admit Admitted Rejected Dept Gender A Male 0.62 0.38 Female 0.82 0.18 B Male 0.63 0.37 Female 0.68 0.32 C Male 0.37 0.63 Female 0.34 0.66 D Male 0.33 0.67 Female 0.35 0.65 E Male 0.28 0.72 Female 0.24 0.76 F Male 0.06 0.94 Female 0.07 0.93 >

Taulesmul>variants::dadesd’admissióaUCBerkeley(III)

Taulesmul>variants:dadesTitanicAlbertSatorraECP2010

> ftable(Titanic) Survived No Yes Class Sex Age 1st Male Child 0 5 Adult 118 57 Female Child 0 1 Adult 4 140 2nd Male Child 0 11 Adult 154 14 Female Child 0 13 Adult 13 80 3rd Male Child 35 13 Adult 387 75 Female Child 17 14 Adult 89 76 Crew Male Child 0 0 Adult 670 192 Female Child 0 0 Adult 3 20 > >

Titanic:Survived&GenderAlbertSatorraECP2010

> ftable(Titanic, row.vars = 2, col.vars = 4) Survived No Yes Sex Male 1364 367 Female 126 344 > prop.table(ftable(Titanic, row.vars = 2, col.vars = 4)) Survived No Yes Sex Male 0.61971831 0.16674239 Female 0.05724671 0.15629259 > prop.table(ftable(Titanic, row.vars = 2, col.vars = 4),1) Survived No Yes Sex Male 0.7879838 0.2120162 Female 0.2680851 0.7319149 > prop.table(ftable(Titanic, row.vars = 2, col.vars = 4),2) Survived No Yes Sex Male 0.91543624 0.51617440 Female 0.08456376 0.48382560 > > >

Intercoursedata(Agres>,2002),:1990USAGeneralSocialSurvey


> mtable =read.table("http://www.econ.upf.edu/~satorra/dades/intercoursedata",header=T)

> mtable Race Gender Intercourse freq 1 White Male Yes 43 2 White Male No 134 3 White Female Yes 26 4 White Female No 149 5 Black Male Yes 29 6 Black Male No 23 7 Black Female Yes 22 8 Black Female No 36>

8.3:1990USAGeneralSocialSurvey:Numberofmonthlysexualintercourses.DATA:AccumulateddatabygenderareprovidedbyAgres>(2002,p.569–570).[Agres>,A.(2002),CategoricalDataAnalysis,2nded.,Wiley‐Interscience,Hobo‐ken,NJ.]DataareusedandreproducedwithpermissionofJohnWileyandSons,Inc.Page:284.> >

Intercoursedata(Agres>,2002),:1990USAGeneralSocialSurveyAlbertSatorraECP2010

> xtabs(freq ~ Race, mtable) Race Black White 110 352 > prop.table(xtabs(freq ~ Race, mtable)) Race Black White 0.2380952 0.7619048 > xtabs(freq ~ Intercourse, mtable) Intercourse No Yes 342 120 > prop.tabl((xtabs(freq ~ Intercourse, mtable)) Intercourse No Yes 0.7402597 0.2597403 > prop.table((xtabs(freq ~ Gender, mtable))) Gender Female Male 0.504329 0.495671

>

Intercoursedata(Agres>,2002),:1990USAGeneralSocialSurveyAlbertSatorraECP2010

> addmargins((xtabs(freq ~ Gender+Intercourse, mtable))) Intercourse Gender No Yes Sum Female 185 48 233 Male 157 72 229 Sum 342 120 462

> prop.table((xtabs(freq ~ Gender+ Intercourse, mtable)),1) Intercourse Gender No Yes Female 0.7939914 0.2060086 Male 0.6855895 0.3144105

> prop.table((xtabs(freq ~ Race+ Intercourse, mtable)),1) Intercourse Race No Yes Black 0.5363636 0.4636364 White 0.8039773 0.1960227 >

Intercoursedata(Agres>,2002),:Inter&Gender|RaceAlbertSatorraECP2010

ftable((xtabs(freq ~ Race+ Gender+Intercourse, mtable))) Intercourse No Yes Race Gender Black Female 36 22 Male 23 29 White Female 149 26 Male 134 43

> prop.table(multitaula,1) Intercourse No Yes Race Gender Black Female 0.6206897 0.3793103 Male 0.4423077 0.5576923 White Female 0.8514286 0.1485714 Male 0.7570621 0.2429379

AlbertSatorraECP2010### lectura de una taula trivariant: TaulamarginalAdmiss.&Gènere&Escola noms=expand.grid(admes = c("si","no"), genere= c("noi","noia"),escola = c("A","B","C")) freq=c(19,31,20,30, 7,43,40,210, 10,30, 30,70) taula=cbind(noms, freq =freq) ta=xtabs(freq ~ admes+genere+escola, data=taula)

> addmargins(ta) , , escola = A

genere admes noi noia Sum si 19 20 39 no 31 30 61 Sum 50 50 100

, , escola = B


, , escola = C


, , escola = Sum


> ftable(ta, col.vars=c("admes"), row.vars=c("escola","genere")) admes si no escola genere A noi 19 31 noia 20 30 B noi 7 43 noia 40 210 C noi 10 30 noia 30 70

> prop.table(ftable(ta, col.vars=c("admes"), row.vars=c("escola","genere")),1) admes si no escola genere A noi 0.38 0.62 noia 0.40 0.60 B noi 0.14 0.86 noia 0.16 0.84 C noi 0.25 0.75 noia 0.30 0.70

addmargins(prop.table(ftable(ta, col.vars=c("admes"), row.vars=c("escola","genere")),1),2)

> dimnames(ta) $admes [1] "si" "no"

$genere [1] "noi" "noia"

$escola [1] "A" "B" "C"

…femtaulesbivariantsImul>variants


>mtaula = table(MARITAL, LIFE,SEX) > dim(mtaula) [1] 6 6 2 > apply(mtaula, c(1,2), sum) LIFE MARITAL NAP Dull Routine Exciting DK NA MARRIED 278 21 241 251 2 2 WIDOWED 52 17 54 40 0 2 DIVORCED 64 10 74 65 0 0 SEPARATED 15 6 11 8 0 0 NEVER MARRIED 87 11 79 108 1 0 NA 0 0 0 1 0 0 > table(LIFE) LIFE NAP Dull Routine Exciting DK NA 496 65 459 473 3 4 >

FitxerGSS.SAV…femtaulesbivariantsImul>variantsAlbertSatorraECP2010Llegim dades data= read.spss("http://www.econ.upf.edu/~satorra/dades/GSS.SAV", use.value.labels = TRUE, to.data.frame = FALSE,max.value.labels = Inf, trim.factor.names = FALSE,trim_values = TRUE, reencode = NA)

table(LIFE) LIFE NAP Dull Routine Exciting DK NA 496 65 459 473 3 4

table(MARITAL) MARITAL MARRIED WIDOWED DIVORCED SEPARATED NEVER MARRIED 795 165 213 40 286 NA 1 > table(MARITAL, LIFE) LIFE MARITAL NAP Dull Routine Exciting DK NA MARRIED 278 21 241 251 2 2 WIDOWED 52 17 54 40 0 2 DIVORCED 64 10 74 65 0 0 SEPARATED 15 6 11 8 0 0 NEVER MARRIED 87 11 79 108 1 0 NA 0 0 0 1 0 0

…eliminemcategoriesAlbertSatorraECP2010

> LIFESHORT = factor(as.character(LIFE), exclude=c("NAP","DK","NA")) > table(LIFESHORT) LIFESHORT Dull Exciting Routine 65 473 459 >MARITALSHORT = factor(as.character(MARITAL),exclude ="NA") >taula1 = table(MARITALSHORT,LIFESHORT) LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 10 65 74 MARRIED 21 251 241 NEVER MARRIED 11 108 79 SEPARATED 6 8 11 WIDOWED 17 40 54

>

MarginalsdelestaulesAlbertSatorraECP2010

margin.table(taula1) [1] 996

margin.table(taula1,1) MARITALSHORT DIVORCED MARRIED NEVER MARRIED SEPARATED WIDOWED 149 513 198 25 111 margin.table(taula1,2) LIFESHORT Dull Exciting Routine 65 472 459 addmargins(taula1) LIFESHORT MARITALSHORT Dull Exciting Routine Sum DIVORCED 10 65 74 149 MARRIED 21 251 241 513 NEVER MARRIED 11 108 79 198 SEPARATED 6 8 11 25 WIDOWED 17 40 54 111 Sum 65 472 459 996

Marital&LIFE:conjuntesImarginalsfilaIcolumnaAlbertSatorraECP2010

> taula1=table(MARITALSHORT,LIFESHORT) > prop.table(taula1) LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 0.010040161 0.065261044 0.074297189 MARRIED 0.021084337 0.252008032 0.241967871 NEVER MARRIED 0.011044177 0.108433735 0.079317269 SEPARATED 0.006024096 0.008032129 0.011044177 WIDOWED 0.017068273 0.040160643 0.054216867 > prop.table(taula1,1) LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 0.06711409 0.43624161 0.49664430 MARRIED 0.04093567 0.48927875 0.46978558 NEVER MARRIED 0.05555556 0.54545455 0.39898990 SEPARATED 0.24000000 0.32000000 0.44000000 WIDOWED 0.15315315 0.36036036 0.48648649

> round(prop.table(taula1,2),3) LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 0.154 0.138 0.161 MARRIED 0.323 0.532 0.525 NEVER MARRIED 0.169 0.229 0.172 SEPARATED 0.092 0.017 0.024 WIDOWED 0.262 0.085 0.118

Freq.esperadessotaindependènciaAlbertSatorraECP2010

> expected <- as.array(margin.table(taula1,1)) %*% t(as.array(margin.table(taula1,2))) / + margin.table(taula1) > expected LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 9.723896 70.61044 68.66566 MARRIED 33.478916 243.10843 236.41265 NEVER MARRIED 12.921687 93.83133 91.24699 SEPARATED 1.631526 11.84739 11.52108 WIDOWED 7.243976 52.60241 51.15361 > des=taula1 - expected > des LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 0.2761044 -5.6104418 5.3343373 MARRIED -12.4789157 7.8915663 4.5873494 NEVER MARRIED -1.9216867 14.1686747 -12.2469880 SEPARATED 4.3684739 -3.8473896 -0.5210843 WIDOWED 9.7560241 -12.6024096 2.8463855


> z=des/sqrt(expected) > round(z,3) LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 0.089 -0.668 0.644 MARRIED -2.157 0.506 0.298 NEVER MARRIED -0.535 1.463 -1.282 SEPARATED 3.420 -1.118 -0.154 WIDOWED 3.625 -1.738 0.398

> sum(z^2) [1] 39.22023

df = (5-1)*(3-1)=8 Significació: > 1-pchisq(39.22023,4) [1] 6.273583e-0, molt alta! , hi ha associació!


> summary(taula1) Number of cases in table: 996 Number of factors: 2 Test for independence of all factors: Chisq = 39.22, df = 8, p-value = 4.474e-06 Chi-squared approximation may be incorrect

….. sum(z^2) [1] 39.22023

df = (5-1)*(3-1)=8 Significació: > 1-pchisq(39.22023,4) [1] 6.273583e-0, molt alta! , hi ha associació!

Taulesmul>variants:AlbertSatorraECP2010

> taula2=table(MARITALSHORT,LIFESHORT,SEX) > taula2 , , SEX = Male LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 6 19 25 MARRIED 6 121 121 NEVER MARRIED 3 56 39 SEPARATED 1 3 3 WIDOWED 4 4 14

, , SEX = Female LIFESHORT MARITALSHORT Dull Exciting Routine DIVORCED 4 46 49 MARRIED 15 130 120 NEVER MARRIED 8 52 40 SEPARATED 5 5 8 WIDOWED 13 36 40

Taulesmul>variants:MARITAL&LIFE|SEXAlbertSatorraECP2010

> ftable(taula2, row.vars= c("SEX","MARITALSHORT"), col.vars="LIFESHORT") LIFESHORT Dull Exciting Routine SEX MARITALSHORT Male DIVORCED 6 19 25 MARRIED 6 121 121 NEVER MARRIED 3 56 39 SEPARATED 1 3 3 WIDOWED 4 4 14 Female DIVORCED 4 46 49 MARRIED 15 130 120 NEVER MARRIED 8 52 40 SEPARATED 5 5 8 WIDOWED 13 36 40

Taulesmul>variants:MARITAL&LIFE|SEXAlbertSatorraECP2010

> round(prop.table(ftable(taula2, row.vars= c("SEX","MARITALSHORT"), col.vars="LIFESHORT"),1),3) LIFESHORT Dull Exciting Routine SEX MARITALSHORT Male DIVORCED 0.120 0.380 0.500 MARRIED 0.024 0.488 0.488 NEVER MARRIED 0.031 0.571 0.398 SEPARATED 0.143 0.429 0.429 WIDOWED 0.182 0.182 0.636 Female DIVORCED 0.040 0.465 0.495 MARRIED 0.057 0.491 0.453 NEVER MARRIED 0.080 0.520 0.400 SEPARATED 0.278 0.278 0.444 WIDOWED 0.146 0.404 0.449 >

Documents

Albert Satorra ECP2010 CATEGÒRIQUES data sets: GSS.SAV …satorra/dades/Classe3_ECP2010.pdf · 2010-01-28 · Albert Satorra ECP2010 SEX & Marital Status & DEGREE > taula =ftable(SEX12,MARITALF12,