PISA Analizi 5 - BIFIEsurvey paketi ile Hiyerarsik Lineer Modelleme
install.packages("BIFIEsurvey")Verinin Hazirlanmasi
# Turkiye'nin ogrenci verisini suzelim
pisa_2015_stu_TUR <- subset(pisa_2015_stu,CNT=='Turkey')
# Sinif Tekrarina ait golge degiskeni (0:Hayir, 1:Evet)
pisa_2015_stu_TUR$Repeat <- ifelse(pisa_2015_stu_TUR$REPEAT=="Repeated a <grade>",0,1)
# Okul oncesi egitime ait golge degisken degiskeni (0:Hayir, 1:Evet)
pisa_2015_stu_TUR$durecec <- NA
pisa_2015_stu_TUR[which(pisa_2015_stu_TUR$ST125Q01NA=="I did not attend <ISCED 0>"),]$durecec <- 0
pisa_2015_stu_TUR[which(pisa_2015_stu_TUR$ST125Q01NA=="1 year or younger" |
pisa_2015_stu_TUR$ST125Q01NA=="2 years" |
pisa_2015_stu_TUR$ST125Q01NA=="3 years" |
pisa_2015_stu_TUR$ST125Q01NA=="4 years" |
pisa_2015_stu_TUR$ST125Q01NA=="5 years" |
pisa_2015_stu_TUR$ST125Q01NA=="6 years or older"),]$durecec <- 1
# Cinsiyete ait golge degisken (0:Kiz, 1:Erkek)
pisa_2015_stu_TUR$cins = ifelse(pisa_2015_stu_TUR$ST004D01T =="Male",1,0)
# Okul turune ait golge degiskenler (Referans grup: Genel Lise, tip1: ilkogretim, tip2: mesleki ve teknik lise)
pisa_2015_stu_TUR$tip1 <- ifelse(pisa_2015_stu_TUR$PROGN=="Turkey: Basic Education",1,0)
pisa_2015_stu_TUR$tip2 <- ifelse(pisa_2015_stu_TUR$PROGN=="Turkey: Vocational and Technical Secondary Education",1,0)
# Sinif seviyesi
# 7,8, ve 11 de cok ogrenci olmadigindan, iki gruba ayiriyoruz.
# 9. sinif ve alti, 10.sinif ve ustu
pisa_2015_stu_TUR$sinif <- ifelse(pisa_2015_stu_TUR$ST001D01T=="Grade 7" |
pisa_2015_stu_TUR$ST001D01T=="Grade 8" |
pisa_2015_stu_TUR$ST001D01T=="Grade 9",0,1)
# Okul verisini suzelim
pisa_2015_sch_TUR <- subset(pisa_2015_sch,CNT=='Turkey')
# Okul turune ait golge degisken
pisa_2015_sch_TUR$tur <- ifelse(pisa_2015_sch_TUR$SCHLTYPE=="Private Independent",1,0)
# Okul ve ogrenci verisini birlestirelim
pisa_2015_TUR <- merge(pisa_2015_stu_TUR,
pisa_2015_sch_TUR,
by=c("CNT","CNTSCHID") ,all=TRUE)Matematik
Modelde year alan ogrenciye ait degiskenler:
- ESCS : sosyo-ekonomik kulturel statuyu olcen bir index (standard puan)
- cins : Cinsiyet (0:Kiz, 1:Erkek)
- Repeat : Ogrencinin sinif tekrari yapip yapmadigini gosteren bir degisken (0:Hayir, 1:Evet)
- durecec : Ogrencinin okul oncesi aldigi etim suresi (0:Hayir, 1:Evet)
- OUTHOURS : Ogrencinin okul disinda haftalik ders calismak icin gecirdigi zaman (saat cinsinden)
- BELONG : Ogrencinin okula aidiyet hissini gosteren bir index (standard puan)
- ANXTEST : Ogrencinin test kaygisini gosteren bir index (standard puan)
- MOTIVAT : Ogrencinin ogrenmeye dair motivasyonunu gosteren bir index (standard puan)
- COOPERATE : Ogrencinin isbirliginden ne kadar hoslandigini gosteren bir index (standard puan)
- EMOSUPS : Ogrencinin aileden aldigi duygusal destegi gosteren bir index (standard puan)
- sinif : Sinif seviyesini gosteren bir golge degisken (0: 9 ve alti, 1:10 ve ustu)
Modelde year alan okula ait degiskenler:
- tip1 ve tip2 : Okul turlerini gosterir iki golge degisken, referans grup Genel Lise
- tur : okul turu (0=Devlet okulu, 1=Ozel okul)
# Once degiskenlere bi goz atalim
hist(pisa_2015_stu_TUR$ESCS)
table(pisa_2015_stu_TUR$cins)##
## 0 1
## 2938 2957 table(pisa_2015_stu_TUR$Repeat)##
## 0 1
## 631 5221 table(pisa_2015_stu_TUR$sinif)##
## 0 1
## 1394 4501 table(pisa_2015_sch_TUR$tur)##
## 0 1
## 180 6 table(pisa_2015_stu_TUR$durecec)##
## 0 1
## 2620 2662 hist(pisa_2015_stu_TUR$OUTHOURS)
hist(pisa_2015_stu_TUR$BELONG)
hist(pisa_2015_stu_TUR$ANXTEST)
hist(pisa_2015_stu_TUR$MOTIVAT)
hist(pisa_2015_stu_TUR$COOPERATE)
hist(pisa_2015_stu_TUR$EMOSUPS)
require("BIFIEsurvey")## Loading required package: BIFIEsurvey## Warning: package 'BIFIEsurvey' was built under R version 3.2.5## |-----------------------------------------------------------------
## | BIFIEsurvey 1.11-0 (2016-10-18)
## | http://www.bifie.at
## |-----------------------------------------------------------------
bifieobj.tur <- BIFIE.data.jack(pisa_2015_TUR,
jktype = "RW_PISA" ,
wgtrep="W_FSTURWT",
pvpre = paste0("PV",1:10),
cdata=FALSE )## +++ Generate BIFIE.data object
## |*|
## |-|mod <- BIFIE.twolevelreg( BIFIEobj=bifieobj.tur,
dep="MATH",
formula.fixed = ~ 1 + ESCS + cins + Repeat + durecec +
+ OUTHOURS + BELONG + ANXTEST + MOTIVAT + COOPERATE
+ EMOSUPS + tip1 + tip2 + sinif + tur,
formula.random = ~ 1,
idcluster="CNTSCHID",
wgtlevel2 = "W_SCHGRNRABWT",
wgtlevel1 = "W_FSTUWT",
se=TRUE)##
## Imputation 1 | Group 1 |--------## Warning in stats::pt(-abs(dfr$t), df = dfr$df): NaNs produced summary(mod)## ------------------------------------------------------------
## BIFIEsurvey 1.11-0 (2016-10-18)
## Function 'BIFIE.twolevelreg'
##
## Call:
## BIFIE.twolevelreg(BIFIEobj = bifieobj.tur, dep = "MATH", formula.fixed = ~1 +
## ESCS + cins + Repeat + durecec + +OUTHOURS + BELONG + ANXTEST +
## MOTIVAT + COOPERATE + EMOSUPS + tip1 + tip2 + sinif + tur,
## formula.random = ~1, idcluster = "CNTSCHID", wgtlevel2 = "W_SCHGRNRABWT",
## wgtlevel1 = "W_FSTUWT", se = TRUE)
##
## Date of Analysis: 2016-12-21 09:40:23
## Time difference of 0.7140009 secs
## Computation time: 0.7140009
##
## Multiply imputed dataset
##
## Number of persons = 5895
## Number of imputed datasets = 1
## Number of Jackknife zones per dataset = 80
## Fay factor = 0.05
##
## Number of persons: 4819
## Number of clusters: 185
##
## Statistical Inference for Two-Level Linear Regression
##
## parameter est SE t df p VarRep
## 1 beta_(Intercept) 425.0492 6.5402 64.99 0 NaN 42.7739
## 2 beta_ESCS 13.1837 1.5393 8.56 0 NaN 2.3695
## 3 beta_cins 21.5842 2.6715 8.08 0 NaN 7.1371
## 4 beta_Repeat 21.7684 4.2008 5.18 0 NaN 17.6466
## 5 beta_durecec -3.5507 2.4228 -1.47 0 NaN 5.8697
## 6 beta_OUTHOURS -0.2694 0.0841 -3.20 0 NaN 0.0071
## 7 beta_BELONG 1.4132 0.6972 2.03 0 NaN 0.4860
## 8 beta_ANXTEST -6.0486 1.1038 -5.48 0 NaN 1.2183
## 9 beta_MOTIVAT 5.7447 1.0946 5.25 0 NaN 1.1981
## 10 beta_COOPERATE 3.7201 0.9044 4.11 0 NaN 0.8179
## 11 beta_EMOSUPS 0.5986 0.9175 0.65 0 NaN 0.8419
## 12 beta_tip1 -54.3428 12.3404 -4.40 0 NaN 152.2848
## 13 beta_tip2 -57.5352 6.1937 -9.29 0 NaN 38.3620
## 14 beta_sinif 31.6213 3.3659 9.39 0 NaN 11.3291
## 15 beta_tur -13.5937 11.8824 -1.14 0 NaN 141.1917
## 16 Var_(Intercept) 1448.7744 264.4403 5.48 0 NaN 69928.6494
## 17 ResidVar 3035.7952 28.8345 105.28 0 NaN 831.4268
## 18 ExplVar_Lev2_Fixed 1358.8330 248.4229 5.47 0 NaN 61713.9564
## 19 ExplVar_Lev2_Random 0.0000 0.0000 NaN NA NA 0.0000
## 20 ResidVar_Lev2 1448.7744 264.4403 5.48 0 NaN 69928.6494
## 21 ExplVar_Lev1_Fixed 727.1084 121.6848 5.98 0 NaN 14807.1859
## 22 ExplVar_Lev1_Random 0.0000 0.0000 NaN NA NA 0.0000
## 23 ResidVar_Lev1 3035.7952 28.8345 105.28 0 NaN 831.4268
## 24 Var_Total 6570.5110 422.2086 15.56 0 NaN 178260.1250
## 25 R2_Lev2 0.4840 0.0617 7.85 0 NaN 0.0038
## 26 R2_Lev1 0.1932 0.0227 8.53 0 NaN 0.0005
## 27 R2_Total 0.3175 0.0319 9.94 0 NaN 0.0010
## 28 ICC_Uncond 0.4273 0.0323 13.21 0 NaN 0.0010
## 29 ICC_UncondWB 0.4852 0.0341 14.23 0 NaN 0.0012
## 30 ICC_Cond 0.3231 0.0397 8.14 0 NaN 0.0016 par <- mod$stat[1:15,]
par <- par[order(abs(par[,2]),decreasing=T),]
as.matrix(paste0(par[,1]," ",round(par[,2],2),"(",round(par[,3],2),")"))## [,1]
## [1,] "beta_(Intercept) 425.05(6.54)"
## [2,] "beta_tip2 -57.54(6.19)"
## [3,] "beta_tip1 -54.34(12.34)"
## [4,] "beta_sinif 31.62(3.37)"
## [5,] "beta_Repeat 21.77(4.2)"
## [6,] "beta_cins 21.58(2.67)"
## [7,] "beta_tur -13.59(11.88)"
## [8,] "beta_ESCS 13.18(1.54)"
## [9,] "beta_ANXTEST -6.05(1.1)"
## [10,] "beta_MOTIVAT 5.74(1.09)"
## [11,] "beta_COOPERATE 3.72(0.9)"
## [12,] "beta_durecec -3.55(2.42)"
## [13,] "beta_BELONG 1.41(0.7)"
## [14,] "beta_EMOSUPS 0.6(0.92)"
## [15,] "beta_OUTHOURS -0.27(0.08)"OKUMA BECERILERI
Modelde year alan ogrenciye ait degiskenler:
- ESCS : sosyo-ekonomik kulturel statuyu olcen bir index (standard puan)
- cins : Cinsiyet (0:Kiz, 1:Erkek)
- Repeat : Ogrencinin sinif tekrari yapip yapmadigini gosteren bir degisken (0:Hayir, 1:Evet)
- durecec : Ogrencinin okul oncesi aldigi etim suresi (0:Hayir, 1:Evet)
- OUTHOURS : Ogrencinin okul disinda haftalik ders calismak icin gecirdigi zaman (saat cinsinden)
- BELONG : Ogrencinin okula aidiyet hissini gosteren bir index (standard puan)
- ANXTEST : Ogrencinin test kaygisini gosteren bir index (standard puan)
- MOTIVAT : Ogrencinin ogrenmeye dair motivasyonunu gosteren bir index (standard puan)
- COOPERATE : Ogrencinin isbirliginden ne kadar hoslandigini gosteren bir index (standard puan)
- EMOSUPS : Ogrencinin aileden aldigi duygusal destegi gosteren bir index (standard puan)
- sinif : Sinif seviyesini gosteren bir golge degisken (0: 9 ve alti, 1:10 ve ustu)
Modelde year alan okula ait degiskenler:
- tip1 ve tip2 : Okul turlerini gosterir iki golge degisken, referans grup Genel Lise
- tur : okul turu (0=Devlet okulu, 1=Ozel okul)
require("BIFIEsurvey")
mod <- BIFIE.twolevelreg( BIFIEobj=bifieobj.tur,
dep="READ",
formula.fixed = ~ 1 + ESCS + cins + Repeat + durecec +
+ OUTHOURS + BELONG + ANXTEST + MOTIVAT + COOPERATE
+ EMOSUPS + tip1 + tip2 + sinif + tur,
formula.random = ~ 1,
idcluster="CNTSCHID",
wgtlevel2 = "W_SCHGRNRABWT",
wgtlevel1 = "W_FSTUWT",
se=TRUE)##
## Imputation 1 | Group 1 |--------## Warning in stats::pt(-abs(dfr$t), df = dfr$df): NaNs produced summary(mod)## ------------------------------------------------------------
## BIFIEsurvey 1.11-0 (2016-10-18)
## Function 'BIFIE.twolevelreg'
##
## Call:
## BIFIE.twolevelreg(BIFIEobj = bifieobj.tur, dep = "READ", formula.fixed = ~1 +
## ESCS + cins + Repeat + durecec + +OUTHOURS + BELONG + ANXTEST +
## MOTIVAT + COOPERATE + EMOSUPS + tip1 + tip2 + sinif + tur,
## formula.random = ~1, idcluster = "CNTSCHID", wgtlevel2 = "W_SCHGRNRABWT",
## wgtlevel1 = "W_FSTUWT", se = TRUE)
##
## Date of Analysis: 2016-12-21 09:40:25
## Time difference of 0.706002 secs
## Computation time: 0.706002
##
## Multiply imputed dataset
##
## Number of persons = 5895
## Number of imputed datasets = 1
## Number of Jackknife zones per dataset = 80
## Fay factor = 0.05
##
## Number of persons: 4819
## Number of clusters: 185
##
## Statistical Inference for Two-Level Linear Regression
##
## parameter est SE t df p VarRep
## 1 beta_(Intercept) 455.3871 7.1316 63.85 0 NaN 50.8596
## 2 beta_ESCS 11.6011 1.7053 6.80 0 NaN 2.9082
## 3 beta_cins -12.7611 3.0370 -4.20 0 NaN 9.2234
## 4 beta_Repeat 24.9927 5.7170 4.37 0 NaN 32.6844
## 5 beta_durecec -3.5657 2.5263 -1.41 0 NaN 6.3823
## 6 beta_OUTHOURS -0.4561 0.0739 -6.17 0 NaN 0.0055
## 7 beta_BELONG 3.0167 0.7811 3.86 0 NaN 0.6102
## 8 beta_ANXTEST -5.5050 1.2275 -4.48 0 NaN 1.5067
## 9 beta_MOTIVAT 3.0406 1.2002 2.53 0 NaN 1.4406
## 10 beta_COOPERATE 7.0710 0.9980 7.09 0 NaN 0.9960
## 11 beta_EMOSUPS 4.3974 0.9796 4.49 0 NaN 0.9597
## 12 beta_tip1 -67.3428 11.7191 -5.75 0 NaN 137.3367
## 13 beta_tip2 -56.6184 6.2767 -9.02 0 NaN 39.3964
## 14 beta_sinif 28.9951 3.6650 7.91 0 NaN 13.4320
## 15 beta_tur -28.9369 10.2926 -2.81 0 NaN 105.9376
## 16 Var_(Intercept) 1151.7257 170.2046 6.77 0 NaN 28969.5987
## 17 ResidVar 2934.7623 27.4777 106.81 0 NaN 755.0264
## 18 ExplVar_Lev2_Fixed 1547.5864 242.9738 6.37 0 NaN 59036.2634
## 19 ExplVar_Lev2_Random 0.0000 0.0000 NaN NA NA 0.0000
## 20 ResidVar_Lev2 1151.7257 170.2046 6.77 0 NaN 28969.5987
## 21 ExplVar_Lev1_Fixed 774.5596 119.3435 6.49 0 NaN 14242.8816
## 22 ExplVar_Lev1_Random 0.0000 0.0000 NaN NA NA 0.0000
## 23 ResidVar_Lev1 2934.7623 27.4777 106.81 0 NaN 755.0264
## 24 Var_Total 6408.6340 305.4450 20.98 0 NaN 93296.6609
## 25 R2_Lev2 0.5733 0.0572 10.03 0 NaN 0.0033
## 26 R2_Lev1 0.2088 0.0219 9.54 0 NaN 0.0005
## 27 R2_Total 0.3623 0.0269 13.45 0 NaN 0.0007
## 28 ICC_Uncond 0.4212 0.0252 16.71 0 NaN 0.0006
## 29 ICC_UncondWB 0.4851 0.0332 14.60 0 NaN 0.0011
## 30 ICC_Cond 0.2818 0.0298 9.46 0 NaN 0.0009 par <- mod$stat[1:15,]
par <- par[order(abs(par[,2]),decreasing=T),]
as.matrix(paste0(par[,1]," ",round(par[,2],2),"(",round(par[,3],2),")"))## [,1]
## [1,] "beta_(Intercept) 455.39(7.13)"
## [2,] "beta_tip1 -67.34(11.72)"
## [3,] "beta_tip2 -56.62(6.28)"
## [4,] "beta_sinif 29(3.66)"
## [5,] "beta_tur -28.94(10.29)"
## [6,] "beta_Repeat 24.99(5.72)"
## [7,] "beta_cins -12.76(3.04)"
## [8,] "beta_ESCS 11.6(1.71)"
## [9,] "beta_COOPERATE 7.07(1)"
## [10,] "beta_ANXTEST -5.51(1.23)"
## [11,] "beta_EMOSUPS 4.4(0.98)"
## [12,] "beta_durecec -3.57(2.53)"
## [13,] "beta_MOTIVAT 3.04(1.2)"
## [14,] "beta_BELONG 3.02(0.78)"
## [15,] "beta_OUTHOURS -0.46(0.07)"FEN
2015 yilinda FEN alanina dair ekstra degiskenler de olculdugu icin modelimiz daha genis.
Modelde year alan ogrenciye ait degiskenler:
- ESCS : sosyo-ekonomik kulturel statuyu olcen bir index (standard puan)
- cins : Cinsiyet (0:Kiz, 1:Erkek)
- Repeat : Ogrencinin sinif tekrari yapip yapmadigini gosteren bir degisken (0:Hayir, 1:Evet)
- durecec : Ogrencinin okul oncesi aldigi etim suresi (0:Hayir, 1:Evet)
- OUTHOURS : Ogrencinin okul disinda haftalik ders calismak icin gecirdigi zaman (saat cinsinden)
- BELONG : Ogrencinin okula aidiyet hissini gosteren bir index (standard puan)
- ANXTEST : Ogrencinin test kaygisini gosteren bir index (standard puan)
- MOTIVAT : Ogrencinin ogrenmeye dair motivasyonunu gosteren bir index (standard puan)
- COOPERATE : Ogrencinin isbirliginden ne kadar hoslandigini gosteren bir index (standard puan)
- EMOSUPS : Ogrencinin aileden aldigi duygusal destegi gosteren bir index (standard puan)
- sinif : Sinif seviyesini gosteren bir golge degisken (0: 9 ve alti, 1:10 ve ustu)
- DISCLISCI : Fen derslerinde siniftaki disiplin ortaminin seviyesini gosteren bir indeks
- TEACHSUP : Fen derslerinde ogretmen desteginin seviyesini gosteren bir indeks
- IBTEACH : Fen derslerinde sorgulamaya dayali ve ogrenci merkezli ogrenme uygulamalarinin seviyesini gosteren bir indeks
- SCIEEFF : Fen derslerindeki oz yeterliligin seviyesini gosteren bir indeks
- EPIST : Ogrencilerin bilime dair epistomolojik bilgisinin seviyesini gosteren bir indeks
Modelde year alan okula ait degiskenler:
- tip1 ve tip2 : Okul turlerini gosterir iki golge degisken, referans grup Genel Lise
- tur : okul turu (0=Devlet okulu, 1=Ozel okul)
# Modeldeki ek degiskenler
hist(pisa_2015_stu_TUR$DISCLISCI)
hist(pisa_2015_stu_TUR$TEACHSUP)
hist(pisa_2015_stu_TUR$IBTEACH)
hist(pisa_2015_stu_TUR$SCIEEFF)
hist(pisa_2015_stu_TUR$EPIST)
require("BIFIEsurvey")
# IBTEACH degiskeninin yonunun tersine cevrilmesi lazim, ogrenci merkezli aktiviteler ne kadar artiyorsa bu indeksin degeri
# o kadar kucuk.
pisa_2015_TUR$IBTEACH <- -pisa_2015_TUR$IBTEACH
bifieobj.tur <- BIFIE.data.jack(pisa_2015_TUR,
jktype = "RW_PISA" ,
wgtrep="W_FSTURWT",
pvpre = paste0("PV",1:10),
cdata=FALSE )## +++ Generate BIFIE.data object
## |*|
## |-|mod <- BIFIE.twolevelreg( BIFIEobj=bifieobj.tur,
dep="SCIE",
formula.fixed = ~ 1 + ESCS + cins + Repeat + durecec +
+ OUTHOURS + BELONG + ANXTEST + MOTIVAT + COOPERATE
+ EMOSUPS + tip1 + tip2 + sinif + tur
+ DISCLISCI + TEACHSUP + IBTEACH + SCIEEFF + EPIST,
formula.random = ~ 1,
idcluster="CNTSCHID",
wgtlevel2 = "W_SCHGRNRABWT",
wgtlevel1 = "W_FSTUWT",
se=TRUE)##
## Imputation 1 | Group 1 |--------## Warning in stats::pt(-abs(dfr$t), df = dfr$df): NaNs produced summary(mod)## ------------------------------------------------------------
## BIFIEsurvey 1.11-0 (2016-10-18)
## Function 'BIFIE.twolevelreg'
##
## Call:
## BIFIE.twolevelreg(BIFIEobj = bifieobj.tur, dep = "SCIE", formula.fixed = ~1 +
## ESCS + cins + Repeat + durecec + +OUTHOURS + BELONG + ANXTEST +
## MOTIVAT + COOPERATE + EMOSUPS + tip1 + tip2 + sinif + tur +
## DISCLISCI + TEACHSUP + IBTEACH + SCIEEFF + EPIST, formula.random = ~1,
## idcluster = "CNTSCHID", wgtlevel2 = "W_SCHGRNRABWT", wgtlevel1 = "W_FSTUWT",
## se = TRUE)
##
## Date of Analysis: 2016-12-21 09:40:26
## Time difference of 0.8590019 secs
## Computation time: 0.8590019
##
## Multiply imputed dataset
##
## Number of persons = 5895
## Number of imputed datasets = 1
## Number of Jackknife zones per dataset = 80
## Fay factor = 0.05
##
## Number of persons: 4363
## Number of clusters: 180
##
## Statistical Inference for Two-Level Linear Regression
##
## parameter est SE t df p VarRep
## 1 beta_(Intercept) 442.0167 6.5134 67.86 0 NaN 42.4249
## 2 beta_ESCS 10.2708 1.5694 6.54 0 NaN 2.4630
## 3 beta_cins 6.0941 2.5889 2.35 0 NaN 6.7024
## 4 beta_Repeat 22.5515 4.5375 4.97 0 NaN 20.5887
## 5 beta_durecec -2.5293 2.3423 -1.08 0 NaN 5.4862
## 6 beta_OUTHOURS -0.3297 0.0732 -4.50 0 NaN 0.0054
## 7 beta_BELONG 0.1341 0.7629 0.18 0 NaN 0.5819
## 8 beta_ANXTEST -7.1294 1.0228 -6.97 0 NaN 1.0461
## 9 beta_MOTIVAT 1.3004 1.0577 1.23 0 NaN 1.1186
## 10 beta_COOPERATE 3.2894 0.9072 3.63 0 NaN 0.8231
## 11 beta_EMOSUPS 0.5831 1.0370 0.56 0 NaN 1.0754
## 12 beta_tip1 -76.7755 12.9516 -5.93 0 NaN 167.7434
## 13 beta_tip2 -54.0052 5.7443 -9.40 0 NaN 32.9965
## 14 beta_sinif 27.1989 3.5464 7.67 0 NaN 12.5772
## 15 beta_tur -15.9261 12.6409 -1.26 0 NaN 159.7922
## 16 beta_DISCLISCI 2.7200 1.3863 1.96 0 NaN 1.9218
## 17 beta_TEACHSUP 2.5261 1.1314 2.23 0 NaN 1.2801
## 18 beta_IBTEACH 7.0497 0.8375 8.42 0 NaN 0.7014
## 19 beta_SCIEEFF 4.2799 0.8520 5.02 0 NaN 0.7259
## 20 beta_EPIST 8.1028 0.8377 9.67 0 NaN 0.7018
## 21 Var_(Intercept) 1106.4242 178.3028 6.21 0 NaN 31791.9034
## 22 ResidVar 2709.4634 33.3701 81.19 0 NaN 1113.5647
## 23 ExplVar_Lev2_Fixed 1408.3079 231.9996 6.07 0 NaN 53823.8230
## 24 ExplVar_Lev2_Random 0.0000 0.0000 NaN NA NA 0.0000
## 25 ResidVar_Lev2 1106.4242 178.3028 6.21 0 NaN 31791.9034
## 26 ExplVar_Lev1_Fixed 723.4814 98.5277 7.34 0 NaN 9707.7029
## 27 ExplVar_Lev1_Random 0.0000 0.0000 NaN NA NA 0.0000
## 28 ResidVar_Lev1 2709.4634 33.3701 81.19 0 NaN 1113.5647
## 29 Var_Total 5947.6769 324.3198 18.34 0 NaN 105183.3073
## 30 R2_Lev2 0.5600 0.0569 9.83 0 NaN 0.0032
## 31 R2_Lev1 0.2107 0.0188 11.19 0 NaN 0.0004
## 32 R2_Total 0.3584 0.0282 12.69 0 NaN 0.0008
## 33 ICC_Uncond 0.4228 0.0284 14.90 0 NaN 0.0008
## 34 ICC_UncondWB 0.4986 0.0335 14.88 0 NaN 0.0011
## 35 ICC_Cond 0.2900 0.0332 8.72 0 NaN 0.0011 par <- mod$stat[1:20,]
par <- par[order(abs(par[,2]),decreasing=T),]
as.matrix(paste0(par[,1]," ",round(par[,2],2),"(",round(par[,3],2),")"))## [,1]
## [1,] "beta_(Intercept) 442.02(6.51)"
## [2,] "beta_tip1 -76.78(12.95)"
## [3,] "beta_tip2 -54.01(5.74)"
## [4,] "beta_sinif 27.2(3.55)"
## [5,] "beta_Repeat 22.55(4.54)"
## [6,] "beta_tur -15.93(12.64)"
## [7,] "beta_ESCS 10.27(1.57)"
## [8,] "beta_EPIST 8.1(0.84)"
## [9,] "beta_ANXTEST -7.13(1.02)"
## [10,] "beta_IBTEACH 7.05(0.84)"
## [11,] "beta_cins 6.09(2.59)"
## [12,] "beta_SCIEEFF 4.28(0.85)"
## [13,] "beta_COOPERATE 3.29(0.91)"
## [14,] "beta_DISCLISCI 2.72(1.39)"
## [15,] "beta_durecec -2.53(2.34)"
## [16,] "beta_TEACHSUP 2.53(1.13)"
## [17,] "beta_MOTIVAT 1.3(1.06)"
## [18,] "beta_EMOSUPS 0.58(1.04)"
## [19,] "beta_OUTHOURS -0.33(0.07)"
## [20,] "beta_BELONG 0.13(0.76)"