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######################### Parents' and Teacher's Judgments on Math and Reading ####################
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######################### Created by: Shally Novita ####################
######################### on 01.12.2020, last edited: 12.05.2023 ####################
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###Load data
setwd('C:/Users/shall/OneDrive/Dokumente/Project/learning and individual differences')
library(foreign)
data_cohort <- read.spss(file='SC2_CohortProfile_D_10-0-0.sav', use.value.labels = F, to.data.frame = T)
data_teach1 <- read.spss(file='SC2_pTarget_D_10-0-0.sav', use.value.labels=FALSE, to.data.frame=TRUE)
data_parent <- read.spss(file='SC2_pParent_D_10-0-0.sav', use.value.labels = F, to.data.frame = T)
data_comp <- read.spss(file='SC2_xTargetCompetencies_D_10-0-0.sav', use.value.labels= FALSE, to.data.frame=TRUE)
data_teach2 <- read.spss(file='SC2_pEducator_D_10-0-0.sav', use.value.labels=FALSE, to.data.frame=TRUE)
data_class <- read.spss(file='SC2_pCourseClass_D_10-0-0.sav', use.value.labels = F, to.data.frame = T)
library(dplyr)
##
## Attache Paket: 'dplyr'
## Die folgenden Objekte sind maskiert von 'package:stats':
##
## filter, lag
## Die folgenden Objekte sind maskiert von 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
var_cohort <- select(data_cohort, ID_t, wave, ID_i, ID_cc, tx80107, tx80220,
tx80522:tx80502, tx80505_R, tx80505_D,
tx8610y,tx8610m)
cohort_wave <- filter(var_cohort, wave %in% c(3,4,5,6))
cohort_wide <- reshape(data=cohort_wave,
idvar="ID_t",
timevar= "wave",
direction="wide")
cohort <- cohort_wide[!sapply(cohort_wide, function(x) all(is.na(x)))]
###teacher data1
var_teach1 <- select(data_teach1, 1:7, eb01030:eb01050, e41370c, e41370d,
t41203b)
teacher1_wave <- filter(var_teach1, wave %in% c(3,4,5,6))
teacher1_wide <- reshape(data=teacher1_wave,
idvar="ID_t",
timevar= "wave",
direction="wide")
## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for wave=3: first taken
## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for wave=4: first taken
## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for wave=5: first taken
## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for wave=6: first taken
teacher1 <- teacher1_wide[!sapply(teacher1_wide, function(x) all(is.na(x)))]
###parent data
var_parent <- select(data_parent, 1:4, pb01030:pb01050, p73170y, p731702,
p412000,p413000_g1D, p414000_g1D, p400000_g1, p403000_g1,
p731802_g3, p731852_g3, p731904_g14, p731954_g14, p728000, p724102, p724101,
p32903a, p32903b, p32903c, p32903d, p66600a)
parent_wave <- filter(var_parent, wave %in% c(3,4,5,6))
parent_wide <- reshape(data=parent_wave,
idvar="ID_t",
timevar= "wave",
direction="wide")
parent <- parent_wide[!sapply(parent_wide, function(x) all(is.na(x)))]
###competence data
competence <- select(data_comp, 1:7,mag1v051_c:mag1_sc2u,
mag1v051_sc2g2_c:mag2_sc1u, dgci110s_sc2g2_c:rxg2_sc3,
reg50110_sc2g4_c, mag5d041_sc2g4_c:reg4_sc2u)
###teacher data2
var_teach2 <- select(data_teach2, 1:4, e400000, e76212m_O, e76212y_R, e76212y_D, e762110)
teacher2_wave <- filter(var_teach2, wave %in% c(3,4,5,6))
teacher2_wave <- teacher2_wave[ which(teacher2_wave$ex20100 == 1), ]
teacher2_wide <- reshape(data=teacher2_wave,
idvar="ID_e",
timevar= "wave",
direction="wide")
teacher2 <- teacher2_wide[!sapply(teacher2_wide, function(x) all(is.na(x)))]
###class data
var_class <- select(data_class, 1:4, e19001c_D, e451000_D, e79201c_D)
var_class <- var_class[ which(var_class$ex20100 == 1), ]
class_wide <- reshape(data=var_class,
idvar="ID_e",
timevar= "wave",
direction="wide")
class <- class_wide[!sapply(class_wide, function(x) all(is.na(x)))]
###merge data
data0 <- merge(cohort, teacher1, by = "ID_t", all=T)
data1 <- merge(data0, parent, by="ID_t", all=T)
data2 <- merge(data1, competence, by="ID_t", all=T)
data2 <- rename(data2, ID_e = ID_e1.3)
data3 <- merge(data2, teacher2, by="ID_e", all=T)
data4 <- merge(data3, class, by="ID_e", all=T)
###calculating age of children
library(zoo)
##
## Attache Paket: 'zoo'
## Die folgenden Objekte sind maskiert von 'package:base':
##
## as.Date, as.Date.numeric
data5 <- data4
data5$birthday_c <- as.yearmon(paste(data5$tx8050y.3, data5$tx8050m.3), "%Y %m") #non string values
data5$testday_c <- as.yearmon(paste(data5$tx8610y.4, data5$tx8610m.4), "%Y %m")#non string values
data5$age_c <- as.numeric(difftime(data5$testday_c, data5$birthday_c)/365.25)
###calculating age of parent
data5$age_p <- as.numeric(data5$tx8610y.4 - data5$p73170y.3) #in the dataset only found birth year
###defining H-Education
data5$edu <- ifelse(is.na(data5$p731852_g3.3), data5$p731802_g3.3,
ifelse(data5$p731802_g3.3>data5$p731852_g3.3, data5$p731802_g3.3,
data5$p731852_g3.3))
###defining migration based on first language of parents
data6 <- data5
data6 <- mutate(data6, language = ifelse(is.na(p414000_g1D.4), p413000_g1D.4,
ifelse(p413000_g1D.4==1 | p414000_g1D.4==1, 1, 0)))
###recode gender and special needs
recode(data6$tx80501.3, '1' ='0', '2' = '1')
## [1] "0" "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" "1" "1" "1" "1" "1" "0" "0"
## [19] "0" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "1" "1" "0" "1" "0" "1" "0"
## [37] "1" "0" "0" "0" "0" "1" "0" "1" "0" "0" "0" "1" "1" "0" "0" "0" "1" "0"
## [55] "0" "1" "0" "1" "0" "0" "1" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1"
## [73] "0" "0" NA "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "1"
## [91] "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1"
## [109] "0" "0" "1" "1" "0" "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "0"
## [127] "1" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0"
## [145] "1" "0" "1" "1" "1" "0" "1" "0" "1" "0" "0" "1" "0" "1" "1" "1" "0" "0"
## [163] "0" "0" "1" "0" "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1"
## [181] "1" "0" "1" "0" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1" "1" "0" "0" "1"
## [199] "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "0" "0" "0" "1"
## [217] "0" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "0"
## [235] "0" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "0" "1" "1"
## [253] "0" "1" "0" "0" "1" "0" "1" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1" "1"
## [271] "0" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1"
## [289] "1" "0" "0" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1" "0" "1" "1" "1" "1"
## [307] "0" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "1" "1" "1" "0" "1" "0"
## [325] "0" "0" "1" "0" "1" "1" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0" "1" "1"
## [343] "1" "1" "1" "0" "1" "0" "0" "0" "0" "0" "1" "1" "1" "0" "1" "0" "1" "0"
## [361] "0" "0" "0" "0" "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "1" "1"
## [379] "0" "1" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0" "1" "0" "0" "0" "1"
## [397] "1" "1" "0" "0" "0" "1" "1" "1" "0" "0" "1" "0" "1" "1" "0" "1" "1" "1"
## [415] "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "1"
## [433] "1" "0" "1" "0" "1" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "0"
## [451] "1" "0" "0" "1" "1" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0" "0"
## [469] "1" "0" "1" "0" "0" "1" "1" "0" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1"
## [487] "0" "0" "0" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "1" "0" "1" "0" "1"
## [505] "0" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1" "1" "1"
## [523] "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "1" "1"
## [541] "0" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "0" "0" "1" "0" "1"
## [559] "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1" "0"
## [577] "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "1" "0"
## [595] "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1" "1" "0" "1"
## [613] "0" "0" "0" "0" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1"
## [631] "1" "0" "0" "1" "0" "0" "1" "0" "1" "0" "0" "1" "1" "1" "0" "0" "0" "1"
## [649] "1" "1" "0" "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "0"
## [667] "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "1" "0" "1" "1" "0" "1" "0"
## [685] "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "1"
## [703] "1" "1" "0" "1" "1" "0" "1" "1" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1"
## [721] "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "0" "1" "1" "0" "1" "1" "0" "0"
## [739] "0" "0" "1" "0" "0" "1" "1" "0" "1" "0" "0" "1" "0" "1" "1" "0" "1" "0"
## [757] "0" "1" "0" "1" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" NA "0"
## [775] "0" "0" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0" "0" "0" "0" "0" "1" "0"
## [793] "0" "1" "0" "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0" "0"
## [811] "0" "1" "1" "0" "1" "1" "0" "0" "0" "1" "1" "1" "0" "1" NA "1" "0" "0"
## [829] "0" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0"
## [847] "1" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "1" "0" "1" "0" "0" "0"
## [865] "1" "1" "0" "1" "1" "1" "0" "0" "1" "0" "1" "0" "1" "0" "1" "1" "0" "0"
## [883] "1" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "1" "0" "1" "0"
## [901] "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "1" "1"
## [919] "1" "0" "0" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1" "1" "1"
## [937] "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "0"
## [955] "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "0" "1" "1" "1" "1" "0" "1" "1"
## [973] "0" "0" "0" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "1" "0"
## [991] "0" "1" "1" "0" "1" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "0" "0" "0"
## [1009] "0" "1" "0" "1" "0" "0" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0"
## [1027] "1" "1" "0" "0" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "0" "1"
## [1045] "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0" "1" "0" "0" "0" "0"
## [1063] "1" "0" "1" "0" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1" "1" "0" "1" "0"
## [1081] "1" "0" "0" "0" "1" "1" "1" "1" "0" "0" "1" "1" "0" "0" "1" "1" "0" "0"
## [1099] "0" "0" "0" "1" "1" "0" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "1" "1"
## [1117] "1" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "0" "1" "0" "0" "0"
## [1135] "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "0"
## [1153] "0" "0" "1" "1" "0" "1" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1" "0" "1"
## [1171] "1" "1" "1" "0" "0" "0" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "1"
## [1189] "1" "0" "0" "1" "0" "0" "0" "0" "0" "1" "0" "1" "0" "1" "1" "0" "1" "0"
## [1207] "1" "0" "0" "0" "0" "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1"
## [1225] "0" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "0" "1" "1" "1" "0" "0"
## [1243] "1" "0" "0" "1" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "1" "1"
## [1261] "1" "0" "1" "0" "0" "0" "0" "0" "1" "0" "1" "1" "1" "1" "0" "0" "1" "1"
## [1279] "0" "0" "0" "0" "0" "0" "0" "1" "0" "1" "0" "1" "0" "0" "1" "1" "0" "1"
## [1297] "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1"
## [1315] "1" "1" "0" "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "1" "0" "1" "0"
## [1333] "1" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "0"
## [1351] "1" "0" "1" "0" "1" "0" "0" "1" "0" "1" "1" "1" "1" "1" "1" "0" "0" "0"
## [1369] "0" "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1"
## [1387] "0" "1" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1"
## [1405] "0" "0" "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "0" "0" "1" "1" "0" "1"
## [1423] "1" "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "1" "0" "1" "0" "1"
## [1441] "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "1"
## [1459] "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "1"
## [1477] "1" "1" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "0"
## [1495] "1" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "1" "0" "1" "1" "0" "0"
## [1513] "0" "1" "0" "0" "0" "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1" "1" "1"
## [1531] "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1"
## [1549] "1" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "1" "1" "1" "1" "0" "1"
## [1567] "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "1" "0"
## [1585] "0" "1" "1" "0" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "1" "1"
## [1603] "0" "1" "0" "1" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "1"
## [1621] "1" "0" "0" "0" "1" "0" "1" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "0"
## [1639] "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "0" "0" "1"
## [1657] "0" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "1"
## [1675] "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1" "1"
## [1693] "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "0" "1" "0"
## [1711] "1" "1" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1"
## [1729] "1" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "0" "1" "0" "0" "1" "0"
## [1747] "0" "1" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1" "0" "1" "1" "1" "0"
## [1765] "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0"
## [1783] "0" "0" "1" "0" "1" "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0"
## [1801] "1" "0" "0" "0" "0" "1" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "0"
## [1819] "0" "1" "1" "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "0"
## [1837] "0" "1" "0" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1"
## [1855] "0" "0" "0" "0" "1" "0" "1" "0" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1"
## [1873] "0" "0" "0" "1" "1" "0" "0" "1" "0" "1" "0" "1" "0" "1" "1" "0" "1" "1"
## [1891] "1" "0" "0" "0" "0" "1" "0" "1" "1" "0" "1" "0" "0" "1" "0" "0" "1" "0"
## [1909] "0" "1" "0" "1" "0" "1" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "0" "0"
## [1927] "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0" "1" "0"
## [1945] "1" "1" "0" "1" "0" "1" "0" "1" "0" "1" "1" "1" "0" "1" "0" "1" "1" "1"
## [1963] "0" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "0" "1" "0" "1" "0"
## [1981] "1" "0" "1" "0" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "0" "1" "0"
## [1999] "0" "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "1"
## [2017] "1" "1" "0" "0" "0" "0" "1" "1" "0" "1" "0" "1" "0" "0" NA "1" "1" "0"
## [2035] "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" "1" "1" "1" "1"
## [2053] "0" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1" "0"
## [2071] "0" "1" "0" "1" "0" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1" "1"
## [2089] "0" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1"
## [2107] "0" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "1" "0"
## [2125] "1" "1" "1" "0" "1" "0" "0" "0" "0" "1" "1" "1" "1" "1" "1" "1" "1" "0"
## [2143] "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0"
## [2161] "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1"
## [2179] "0" "1" "0" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "1" "1" "0" "0" "0"
## [2197] "0" "0" "1" "1" "0" "0" "1" "1" "0" "1" "1" "1" "1" "0" "1" "0" "1" "1"
## [2215] "1" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0" "1" "1" "0" "1" "0" "1" "1"
## [2233] "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "0"
## [2251] "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "1" "0" "1"
## [2269] "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0" "0"
## [2287] "1" "0" "0" "1" "0" "0" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "1" "0"
## [2305] "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "1"
## [2323] "0" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1"
## [2341] "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" "1" "1" "1" "1" "0" "0" "0" "0"
## [2359] "1" "0" "1" "0" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0"
## [2377] "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "0" "0" "1" "0" "0" "1"
## [2395] "1" NA "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "1" "1"
## [2413] "0" "1" "1" "0" "1" "0" "0" "0" "0" "1" "0" "1" "1" "0" "0" "0" "1" "1"
## [2431] "1" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "0" "1" "1"
## [2449] "0" "1" "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1"
## [2467] "1" "1" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "0" "0" "1"
## [2485] "1" "1" "0" "1" "0" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "0" "0" "0"
## [2503] "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "1" "0" "1" "1" "1" "1" "0"
## [2521] "0" "0" "0" "0" "0" "1" "1" "0" "1" "0" "1" "1" "1" "0" "0" "1" "0" "0"
## [2539] "0" "0" "0" "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0"
## [2557] "1" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0"
## [2575] "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "0" "0" "1" "1" "0" "1" "1"
## [2593] "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0"
## [2611] "0" "1" "0" "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "1" "1" "0" "1"
## [2629] "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0"
## [2647] "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "1" "0" "0" "1"
## [2665] "0" "0" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1" "0" "1" "1" "1" "1" "0"
## [2683] "1" "0" "1" "1" "1" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1"
## [2701] "0" "1" "0" "0" "0" "1" "0" "0" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0"
## [2719] "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "0" "1" "1" "1" "1" "1" "1"
## [2737] "1" "0" "1" "0" "0" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1"
## [2755] "0" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1" "0" "0" "0" "0" "0"
## [2773] "1" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "0" "0" "0"
## [2791] "1" "0" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0" "1" "0" "0" "1" "1" "0"
## [2809] "0" "1" "0" "1" "1" "1" "1" "0" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0"
## [2827] "0" "1" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1"
## [2845] "1" "1" "0" "1" "0" "0" "1" "0" "0" "1" "1" "1" "0" "0" "1" "0" "1" "1"
## [2863] "1" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1"
## [2881] "0" "1" "0" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "1" "0" "1" "1" "1"
## [2899] "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "1" "0" "1" "1" "1" "0" "1" "0"
## [2917] "0" "1" "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" "0" "0" "0" "0" "1" "0"
## [2935] "1" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1" "1"
## [2953] "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "0" "1" "0" "0" "0" "1" "1" "0"
## [2971] "0" "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1"
## [2989] "1" "1" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1"
## [3007] "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "0" "1" "1"
## [3025] "0" "1" "1" "1" "0" "1" "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0"
## [3043] "1" "1" "0" "1" "0" "0" "1" "0" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0"
## [3061] "1" "1" "1" "1" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "0" "0" "0" "1"
## [3079] "0" "1" "0" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "0"
## [3097] "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1" "1"
## [3115] "1" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "0" "1" "0" "1" "1" "1"
## [3133] "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1"
## [3151] "0" "0" "1" "1" "1" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "0"
## [3169] "1" "1" "1" "1" "0" "1" "0" "1" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1"
## [3187] "0" "1" "0" "0" "0" "1" "0" "0" "1" "0" "1" "1" "1" "0" "0" "1" "1" "1"
## [3205] "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "0" "1" "1"
## [3223] "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "1" "1" "1" "0" "1" "0" "1" "1"
## [3241] "1" "0" "0" "0" "0" "1" "0" "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "1"
## [3259] "1" "1" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "1" "1" "0" "0" "1" "1"
## [3277] "0" "1" "1" "0" "1" "1" "1" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "1"
## [3295] "1" "1" "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1"
## [3313] "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1"
## [3331] "1" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "1" "1" "1" "0" "0" "0"
## [3349] "0" "0" "0" "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0"
## [3367] "1" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "0" "1" "1" "1" "1" "0"
## [3385] "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0" "1" "1" "0" "1" "0"
## [3403] "0" "0" "0" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "0" "0" "1" "1" "0"
## [3421] "0" "1" "0" "0" "0" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "1" "0"
## [3439] "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0"
## [3457] "0" "1" "0" "1" "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" "0" "1" "1"
## [3475] "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0"
## [3493] "0" "1" "1" "1" "0" "1" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "0"
## [3511] "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "1"
## [3529] "1" "0" "1" "1" "0" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1"
## [3547] "0" "1" "0" "1" "1" "0" "0" "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "1"
## [3565] "0" "1" "1" "0" "0" "1" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "1" "0"
## [3583] "1" "0" "1" "0" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "1" "0" "1" "1"
## [3601] "0" "0" "1" "0" "1" "0" "1" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0"
## [3619] "1" "0" "0" "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0"
## [3637] "1" "1" "0" "0" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0"
## [3655] "1" "0" "1" "0" "0" "1" "1" "0" "1" "1" "0" "0" "0" "1" "1" "1" "1" "0"
## [3673] "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "1" "0" "1"
## [3691] "0" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "0"
## [3709] "0" "1" "1" "0" "0" "1" "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "0" "1"
## [3727] "1" "0" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "1" "0"
## [3745] "0" "0" "1" "0" "1" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1"
## [3763] "0" "0" "1" "0" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "1" "1"
## [3781] "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "0" "0"
## [3799] "1" "0" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1"
## [3817] "0" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "0" "0"
## [3835] "1" "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "0" "0" "1"
## [3853] "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "0" "1" "0" "1" "0" "1" "0" "0"
## [3871] "0" "1" "0" "0" "1" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1" "1"
## [3889] "0" "0" "1" "0" "1" "1" "1" "0" "0" "0" "0" "1" "0" "1" "0" "0" "0" "0"
## [3907] "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "1"
## [3925] "0" "1" "0" "0" "0" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1"
## [3943] "1" "0" "0" "1" "0" "1" "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "1" "0"
## [3961] "0" "0" "1" "1" "1" "0" "0" "1" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0"
## [3979] "0" "1" "0" "1" "0" "0" "1" "1" "1" "1" "0" "1" "0" "0" "0" "0" "1" "1"
## [3997] "1" "1" "1" "0" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1"
## [4015] "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1"
## [4033] "0" "1" "1" "1" "1" "0" "0" "1" "1" "0" "0" "1" "1" "1" "1" "0" "1" "0"
## [4051] "0" "1" "1" "1" "1" "1" "0" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0" "1"
## [4069] "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "1" "0" "1" "1" "1" "1" "0" "1"
## [4087] "1" "0" "1" "1" "1" "0" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "0"
## [4105] "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0"
## [4123] "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1"
## [4141] "1" "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1"
## [4159] "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1"
## [4177] "0" "0" "0" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "0"
## [4195] "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "0" "0" "1" "1" "1" "0"
## [4213] "0" "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "1" "1"
## [4231] "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "0" "0" "0" "0" "1" "0" "1" "1"
## [4249] "0" "0" "0" "0" "0" "1" "0" "1" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0"
## [4267] "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "0" "1" "0" "1" "1" "1" "0" "0"
## [4285] "1" "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "0"
## [4303] "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "1" "1" "0" "0"
## [4321] "0" "0" "1" "1" "1" "1" "0" "1" "1" "0" "0" "1" "1" "0" "1" "1" "1" "1"
## [4339] "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "0"
## [4357] "1" "1" "0" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1" "0" "0" "1" "0"
## [4375] "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "1" "1" "0"
## [4393] "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "0" "1"
## [4411] "1" "0" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "0" "0" "0" "0" "0"
## [4429] "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "1" "1" "0" "0" "0"
## [4447] "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1" "1" "0" "0" "1" "1" "0" "1"
## [4465] "0" "1" "0" "0" "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1" "0" "0"
## [4483] "1" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "1" "0" "1" "0" "0" "1" "0"
## [4501] "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1" "0" "1" "1" "0" "1" "0" "1"
## [4519] "1" "0" "0" "1" "0" "1" "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "0"
## [4537] "0" "0" "1" "0" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "0" "1" "1"
## [4555] "0" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1"
## [4573] "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "1"
## [4591] "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "0" "1" "1"
## [4609] "0" "0" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "0"
## [4627] "0" "0" "0" "1" "0" "1" "0" "0" "0" "0" "0" "0" "0" "0" "0" "1" "0" "1"
## [4645] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1" "0"
## [4663] "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "0" "1" "1" "1" "1" "0" "0"
## [4681] "1" "1" "0" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "0" "0" "0" "1"
## [4699] "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "0" "0" "1" "0" "1" "0" "1" "0"
## [4717] "0" "0" "0" "1" "0" "1" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0" "1"
## [4735] "1" "1" "0" "1" "1" "0" "1" "0" "0" "1" "0" "1" "0" "0" "0" "1" "1" "0"
## [4753] "1" "0" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "1" "0"
## [4771] "0" "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0"
## [4789] "0" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "0" "0" "1" "0"
## [4807] "1" "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1" "1"
## [4825] "1" "0" "1" "1" "1" "0" "0" "1" "0" "0" "1" "1" "1" "1" "0" "1" "1" "1"
## [4843] "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "1" "0"
## [4861] "1" "0" "1" "0" "0" "0" "1" "0" "0" "1" "0" "1" "0" "0" "0" "0" "1" "1"
## [4879] "1" "0" "1" "0" "1" "1" "1" "0" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1"
## [4897] "0" "0" "0" "0" "0" "0" "1" "0" "1" "1" "0" "1" "1" "0" "1" "0" "0" "1"
## [4915] "1" "0" "1" "0" "1" "0" "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "0" NA
## [4933] "0" "1" "0" "1" "0" "0" "0" "0" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1"
## [4951] "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "1" "1" "1"
## [4969] "1" "0" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "1" "0" "1" "0"
## [4987] "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "1" "1" "1" "0" "1"
## [5005] "1" "0" "1" "1" "0" "0" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "0"
## [5023] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "1" "1" "0" "1"
## [5041] "0" "1" "0" "1" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "1" "0" "0" "0"
## [5059] "0" "1" "0" "1" "1" "0" "1" "1" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0"
## [5077] "0" "0" "1" "1" "1" "0" "0" "1" "0" "1" "1" "1" "1" "1" "0" "0" "1" "0"
## [5095] "1" "1" "0" "1" "1" "1" "1" "0" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0"
## [5113] "1" "1" "1" "0" "0" "1" "0" "1" "0" "0" "0" "1" "0" "1" "0" "0" "1" "0"
## [5131] "0" "0" "1" "1" "1" "1" "0" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1" "1"
## [5149] "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "1"
## [5167] "0" "0" "1" "0" "0" "0" "0" "0" "0" "1" "0" NA "1" "1" "1" "0" "0" "0"
## [5185] NA "1" "0" "0" "0" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "1"
## [5203] "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "1" "1" "1" "1" "1"
## [5221] "0" "1" "0" "0" "0" "0" "1" "1" "0" "1" "0" "0" "1" "1" "1" "1" "1" "1"
## [5239] "1" "0" "0" "0" "0" "1" "0" "1" "0" "0" "1" "1" "1" "1" "0" "1" "0" "1"
## [5257] "1" "1" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1" "0" "1" "1" "1" "1" "1"
## [5275] "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1"
## [5293] "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "1"
## [5311] "1" "0" "0" "0" "1" "1" "1" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1"
## [5329] "1" "1" "0" "1" "1" "1" "0" "0" "1" "1" "0" "0" "0" "1" "1" "1" "0" "0"
## [5347] "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "0"
## [5365] "0" "0" "1" "1" "0" "1" "0" "1" "1" "1" "0" "1" "1" "0" "0" "0" "0" "1"
## [5383] "0" "1" "1" "1" "0" "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0"
## [5401] "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "0"
## [5419] "1" "0" "1" "0" "1" "0" "0" "1" "0" "1" "0" "0" "1" "0" "1" "0" "1" "1"
## [5437] "1" "1" "1" "0" "1" "0" "0" "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "0"
## [5455] "1" "1" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "0" "1"
## [5473] "1" "0" "0" "0" "0" "0" NA "1" "1" "0" "1" "0" "0" "1" "0" "1" "0" "0"
## [5491] "0" "0" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1"
## [5509] "1" "1" "0" "1" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "0" "1"
## [5527] "0" "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "1" "1" "0" "0" "0" "0" "1"
## [5545] "1" "0" "0" "0" "0" "0" "0" "1" "0" "0" "1" "0" "0" "0" "1" "1" "0" "0"
## [5563] "0" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1"
## [5581] "1" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" NA "0" "1" "1" "1" "0" "1"
## [5599] "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1"
## [5617] "0" "0" "1" "0" "0" "1" "0" "0" "1" "1" "1" "0" "1" "1" "1" "0" "1" "0"
## [5635] "0" "0" "0" "0" "0" "0" "1" "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1"
## [5653] "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "0"
## [5671] "1" "0" "1" "1" "0" "0" "1" "1" "0" "1" "1" "1" "0" "1" "0" "0" "0" "1"
## [5689] "0" "1" "0" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "1" "0" "0" "0" "0"
## [5707] "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "0" "1" "0" "1" "0"
## [5725] "1" "0" "0" "0" "0" "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "0" "1"
## [5743] "0" "0" "1" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "1" "0"
## [5761] "1" "0" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0"
## [5779] "0" "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "1"
## [5797] "1" "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0" "0" "1"
## [5815] "1" "0" "1" "0" "0" "1" "0" "0" "0" "0" "1" "0" "1" "0" "0" "1" "1" "0"
## [5833] "0" "1" "0" "0" "0" "0" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1" "1" "1"
## [5851] "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "0" "0"
## [5869] "1" "1" "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1" "1" "1" "1" "1" "1"
## [5887] "1" "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0"
## [5905] "1" "1" "0" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "1"
## [5923] "0" "1" "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "0" "0" "0" "1" "1" "0"
## [5941] "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "0" "1" "1" "1"
## [5959] "0" "1" "1" "1" "1" "1" "1" "1" "0" "1" "0" "1" "0" "1" "0" "1" "0" "0"
## [5977] "0" "0" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0" "0" "1" "0" "0" "0" "1"
## [5995] "1" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "1" "1"
## [6013] "1" "0" "1" "0" "1" "0" NA NA NA NA NA NA NA NA NA NA NA NA
## [6031] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6049] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6067] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6085] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6103] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6121] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6139] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6157] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6175] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6193] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6211] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6229] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6247] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6265] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6283] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6301] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6319] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6337] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6355] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6373] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6391] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6409] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6427] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6445] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6463] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6481] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6499] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6517] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6535] NA NA NA NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA
## [6553] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6571] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6589] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6607] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6625] NA "1" NA NA NA NA NA "1" NA NA NA NA NA NA NA NA NA NA
## [6643] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6661] NA "1" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6679] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6697] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6715] NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6733] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0" NA NA
## [6751] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6769] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6787] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6805] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6823] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6841] NA NA NA NA NA NA NA NA NA NA NA "0" NA NA NA NA NA NA
## [6859] NA NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA NA NA
## [6877] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6895] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6913] NA NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6931] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6949] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [6967] NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0" NA NA "0"
## [6985] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7003] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7021] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7039] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7057] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0"
## [7075] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7093] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7111] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0" NA
## [7129] NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7147] NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7165] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "1"
## [7183] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7201] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0"
## [7219] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7237] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7255] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7273] NA NA "1" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7291] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7309] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7327] "1" NA NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA NA
## [7345] "1" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7363] NA NA NA NA NA "1" NA NA NA NA NA "1" NA NA NA NA NA NA
## [7381] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7399] NA NA NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA NA
## [7417] NA NA NA "0" NA NA "0" NA NA NA NA NA NA NA NA NA NA NA
## [7435] NA NA NA NA NA NA NA NA NA NA NA NA NA NA "1" NA NA NA
## [7453] NA NA NA NA NA NA NA NA NA NA NA "0" NA NA NA NA NA NA
## [7471] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7489] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7507] NA NA NA NA NA "1" NA NA NA NA NA "1" NA NA NA NA NA NA
## [7525] NA "1" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7543] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7561] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0"
## [7579] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7597] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7615] NA NA NA NA NA NA NA NA NA NA NA NA NA NA "1" "1" NA NA
## [7633] NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA
## [7651] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7669] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7687] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7705] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7723] NA NA NA NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA
## [7741] NA NA NA NA NA NA NA "0" "1" NA "1" NA NA NA NA NA NA NA
## [7759] NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7777] NA NA NA NA NA NA "1" NA NA NA NA NA NA NA NA NA NA "0"
## [7795] NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7813] NA NA NA NA "0" "1" NA NA NA NA "1" NA NA NA NA NA NA NA
## [7831] NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA NA NA "1"
## [7849] NA NA NA NA NA "0" NA NA NA NA NA NA NA "0" NA NA NA NA
## [7867] NA NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA "0" "0"
## [7885] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7903] NA NA NA "0" NA NA NA NA NA NA NA "1" NA NA NA "1" "0" NA
## [7921] NA NA NA "0" "1" "1" NA NA NA NA NA "1" NA NA NA NA "0" NA
## [7939] NA NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA
## [7957] NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA "1" NA NA
## [7975] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [7993] NA NA "0" NA NA NA NA "1" NA NA NA NA NA NA NA NA NA NA
## [8011] NA NA NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA NA
## [8029] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8047] NA NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA NA NA
## [8065] NA "1" "0" NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8083] NA NA NA NA NA NA NA NA NA NA NA NA NA NA "0" NA NA NA
## [8101] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8119] NA NA NA NA NA NA NA NA NA NA "0" NA NA NA NA NA NA NA
## [8137] NA NA NA NA NA "1" "0" NA NA NA NA "1" NA NA NA NA NA NA
## [8155] NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8173] "0" NA NA NA NA NA NA NA NA NA NA "0" NA NA NA NA NA NA
## [8191] NA "0" NA NA NA NA NA NA NA "0" NA NA NA NA NA NA NA NA
## [8209] NA NA NA NA NA NA "0" NA NA NA "1" NA NA NA NA NA NA "1"
## [8227] NA NA NA NA NA NA "1" NA NA "1" NA "0" NA "0" NA "1" NA NA
## [8245] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8263] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8281] NA NA NA "1" NA NA NA NA NA NA "0" NA NA NA NA NA NA NA
## [8299] NA NA NA NA NA NA NA NA NA "0" NA NA NA NA NA NA NA "0"
## [8317] NA NA NA NA NA NA "1" NA NA NA NA NA NA NA NA NA NA NA
## [8335] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8353] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8371] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [8389] NA NA NA NA "0" NA NA NA NA NA NA NA NA NA NA "0" NA NA
## [8407] NA NA NA NA NA NA NA NA NA NA "1" NA NA NA NA NA NA NA
## [8425] NA NA "1" NA NA NA NA NA NA NA "0" NA NA NA "0" "0" NA NA
## [8443] "1" NA NA "1" "1" NA NA NA NA NA NA NA NA "1" NA "0" "1" NA
## [8461] "0" NA NA "0" NA NA "0" NA NA NA "1" NA "1" NA NA NA NA NA
## [8479] NA NA "1" NA "1" NA "0" NA "1" NA "1" NA NA "0" "1" NA NA NA
## [8497] NA "1" NA NA "0" NA NA NA "1" "1" NA NA NA NA NA NA "0" NA
## [8515] "1" "1" "0" NA NA NA NA NA "1" NA NA NA "0" NA NA NA NA "0"
## [8533] NA NA "0" NA NA NA NA "1" NA NA NA NA NA NA "1" "1" NA NA
## [8551] NA NA "1" NA NA NA NA NA NA "0" NA NA NA NA "1" "1" NA "0"
## [8569] NA "0" "0" NA NA "0" "0" NA NA NA NA NA "0" "1" NA "1" NA NA
## [8587] NA NA NA NA NA "1" NA "0" NA NA NA "0" "1" "1" NA "1" NA "0"
## [8605] NA "0" "0" NA NA NA NA NA NA "1" "0" "0" "1" NA NA NA NA "0"
## [8623] NA NA NA NA NA "1" NA NA "0" NA "1" NA NA NA "0" NA NA NA
## [8641] NA NA "0" "1" NA "0" NA NA NA NA "1" NA NA "1" NA NA NA NA
## [8659] NA NA NA NA NA NA "0" NA NA "1" NA NA NA NA "1" NA NA "0"
## [8677] NA NA NA NA "0" NA NA "1" NA NA NA "1" NA NA NA NA NA "1"
## [8695] "1" NA NA NA NA "1" "0" "1" "1" NA NA NA NA NA "0" NA NA NA
## [8713] NA "0" NA "0" NA NA "0" NA NA NA "0" NA "0" "0" NA "1" NA NA
## [8731] NA "0" "0" "0" NA "1" NA NA NA NA NA NA NA NA "1" "0" NA NA
## [8749] NA NA "1" NA "1" "1" "0" NA NA "1" "0" NA "1" "0" "1" "1" NA NA
## [8767] "1" "0" NA NA NA "0" NA NA "1" "0" NA NA "0" NA NA NA NA "1"
## [8785] NA NA "1" NA NA NA "1" "1" NA "0" "1" NA "0" NA NA "1" "1" NA
## [8803] NA NA "1" "1" "1" "0" NA NA "1" "0" NA "1" NA NA NA NA NA "0"
## [8821] "1" NA NA "1" NA NA NA NA NA "1" "1" "1" NA NA "0" "0" "0" "0"
## [8839] "0" "0" NA "0" NA NA NA NA "1" NA NA "1" NA NA NA "1" NA NA
## [8857] "1" NA "0" NA "0" NA "0" NA "1" NA NA NA NA NA NA NA "1" NA
## [8875] "0" "1" NA NA "1" NA NA NA NA "0" NA NA NA "1" NA NA NA NA
## [8893] "0" NA NA NA NA NA "0" NA "0" "0" NA "0" NA NA NA NA "0" "1"
## [8911] "0" NA NA NA NA "0" NA NA "0" NA NA NA "0" NA "1" NA NA "0"
## [8929] "1" "0" NA NA NA NA "0" NA NA "1" NA NA NA NA NA NA NA "1"
## [8947] "1" NA "0" "1" NA "0" NA "0" "1" NA NA NA "1" "0" NA NA "0" "0"
## [8965] "1" "1" NA NA NA NA "1" NA NA "0" NA NA NA NA "1" "1" NA NA
## [8983] "0" NA NA "1" NA NA NA NA NA NA NA NA "1" NA NA NA "1" "1"
## [9001] NA "1" NA "1" "1" NA NA "0" "0" NA "0" "0" "1" "1" NA NA "1" NA
## [9019] NA NA NA "1" "0" "1" NA "0" NA "0" NA NA "1" NA NA "1" "0" NA
## [9037] NA NA NA NA "0" NA "0" "1" NA NA "1" "0" NA NA NA NA NA NA
## [9055] "1" "1" "1" "1" "0" NA NA NA NA NA "1" NA NA NA "1" NA NA "0"
## [9073] NA "0" NA NA NA "0" "0" "1" NA "1" "0" NA NA NA NA NA NA NA
## [9091] NA NA NA NA "0" "1" NA NA NA NA NA NA NA NA "0" NA NA "1"
## [9109] "0" NA NA "0" "0" "1" "1" NA NA NA "1" NA "0" NA NA NA NA "1"
## [9127] NA "1" NA "1" NA NA NA "0" "0" "0" "0" "1" NA "1" "0" "0" "1" NA
## [9145] NA "0" NA "0" NA NA NA "0" "0" NA "0" NA "1" NA "1" NA NA NA
## [9163] "0" NA "1" "1" NA "0" NA NA NA "1" NA NA NA "1" NA "1" NA NA
## [9181] NA "1" NA NA "0" "1" "0" "1" "1" NA "1" "0" NA "1" NA NA "0" "1"
## [9199] NA NA "0" NA NA NA NA "0" NA NA NA NA NA NA NA "0" "1" "1"
## [9217] NA NA "0" "0" NA "1" "1" NA "1" "1" "1" NA NA NA "0" "1" NA NA
## [9235] "0" "1" NA NA NA "1" "1" "0" "1" NA "1" "1" NA NA "0" NA NA "0"
## [9253] "0" NA "0" "1" "0" "0" "0" NA "0" "0" "0" NA "0" "1" "0" NA NA "0"
## [9271] NA "0" NA "1" "1" NA NA "1" "1" "0" "0" "0" "1" "1" NA "1" "0" "0"
## [9289] NA "0" NA "1" NA "0" NA NA "1" "1" "0" NA NA "1" NA NA "0" NA
## [9307] NA "0" "1" NA "0" "1" "0" "1" "0" "1" NA NA "0" NA NA "0" "1" "1"
## [9325] NA NA "0" "1" "0" "0" "1" NA "0" NA NA NA "1" "1" NA NA "1" NA
## [9343] NA "0" NA NA "1" "0" "0" "1" "1" NA "0" NA "0" "1" "1" NA NA "0"
## [9361] NA "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "0" NA "0" NA "1" "1" "0"
## [9379] "1" "1" "0" "1" "0" "0" NA "0" NA "1" "1" "1" "1" "1" "1" "1" "1" "1"
## [9397] "0" NA "1" "1" NA "1" "0" "0" NA NA "0" "0" "1" "0" "1" "1" "1" NA
## [9415] "1" "1" NA "1" "0" "0" "1" NA NA "1" "1" "0" "1" "0" "1" "1" "1" "1"
## [9433] "0" "1" "0" NA "1" NA "0" "1" "1" "1" "0" NA "1" "1" NA NA NA "0"
## [9451] "1" NA "0" "0" NA "1" "1" "0" "1" NA "0" NA "1" "1" "1" "0" "0" NA
## [9469] "0" "1" "0" "1" "0" NA NA "1" "0" NA NA "0" NA "1" "1" "0" "0" "1"
## [9487] NA "1" "1" "1" "0" "0" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0" "1"
## [9505] "0" "1" "1" "1" "0" "0" "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "1" "0"
## [9523] "1" "1" "0" "1" "0" "0" "1" "1" "1" "1" "0" "0" "0" "1" "1" "0" "0" "1"
## [9541] "0" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "1" "1" "1" "0"
## [9559] "1" "0" "1" "1" "0" "1" "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0"
## [9577] "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0" "1" "0"
## [9595] "1" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "1" "0" "1"
## [9613] "1" "0" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0"
## [9631] "0" "0" "1" "0" "0" "1" "0" "1" "0" "1" "0" "1" "0" "1" "0" "0" "0" "0"
## [9649] "0" "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "1" "1" "0" "1" "1" "1" "0"
## [9667] "0" "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1" "0" "1" "0"
## [9685] "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1" "0" "0" "1"
## [9703] "1" "1" "1" "1" "0" "0" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "1"
## [9721] "1" "1" "0" "0" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1"
## [9739] "0" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "1" "1" "0" "1" "0" "1" "1"
## [9757] "0" "1" "0" "1" "1" "1" "0" "0" "0" "1" "0" "0" "1" "1" "0" "1" "0" "0"
## [9775] "0" "0" "1" "1" NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [9793] NA NA NA NA NA NA NA NA "0" "1" "1" "1" "1" "0" "0" "0" "1" "1"
## [9811] "0" "1" "0" "0" "1" "0" "1" "0" "0" "1" "1" "0" "0" "1" "1" "1" "0" NA
## [9829] NA NA NA NA NA "1" NA "1" "0" NA "1" NA NA NA "0" "1" NA "1"
## [9847] "0" "1" "0" "1" "0" NA NA "1" NA "0" "0" "0" "0" "1" NA "0" "0" "1"
## [9865] "1" "1" "0"
###select cases
data7 <- data6[which(data5$wave_w4 ==1), ]
###Rename variables
data7 <- rename(data7, participation = tx80107.3)
data7 <- rename(data7, sex_p = p731702.4)
data7 <- rename(data7, sex_c = tx80501.3)
data7 <- rename(data7, sex_t = e762110.3)
data7 <- rename(data7, mig_t = e400000.3)
data7 <- rename(data7, judgMA1 = pb01050.3)
data7 <- rename(data7, judgMA2 = pb01050.4)
data7 <- rename(data7, judgMA1T = eb01050.3)
data7 <- rename(data7, judgMA2T = eb01050.4)
data7 <- rename(data7, judgRE1 = pb01060.3)
data7 <- rename(data7, judgRE2 = pb01060.4)
data7 <- rename(data7, judgRE1T = eb01031.3)
data7 <- rename(data7, judgRE2T = eb01031.4)
data7 <- rename(data7, reasoning = dgg2_sc3b)
data7 <- rename(data7, math1 = mag1_sc1)
data7 <- rename(data7, math2 = mag2_sc1)
data7 <- rename(data7, math4 = mag4_sc1)
data7 <- rename(data7, mig_class = e451000_D.3)
data7 <- rename(data7, ses_class = e79201c_D.3)
data7 <- rename(data7, sen_class = e19001c_D.3)
data7 <- rename(data7, read2 = rxg2_sc3)
data7 <- rename(data7, sen = tx80505_D.3)
data7 <- rename(data7, math_grade = p724102.5)
data7 <- rename(data7, reading_grade = p724101.5)
###Descriptives
library(Hmisc)
## Lade nötiges Paket: lattice
## Lade nötiges Paket: survival
## Lade nötiges Paket: Formula
## Lade nötiges Paket: ggplot2
##
## Attache Paket: 'Hmisc'
## Die folgenden Objekte sind maskiert von 'package:dplyr':
##
## src, summarize
## Die folgenden Objekte sind maskiert von 'package:base':
##
## format.pval, units
describe(data7$ID_t)
## data7$ID_t
## n missing distinct Info Mean Gmd .05 .10
## 6340 0 6340 1 2924478 163578 2002196 3004450
## .25 .50 .75 .90 .95
## 3005629 3007578 3017798 3018851 3019197
##
## lowest : 2000568 2000569 2000577 2000578 2000585
## highest: 3022061 3023006 3023187 3023439 3023458
##
## Value 2000000 2010000 3000000 3010000 3020000
## Frequency 543 1 532 3070 2194
## Proportion 0.086 0.000 0.084 0.484 0.346
##
## For the frequency table, variable is rounded to the nearest 10000
describe(data7$ID_e)
## data7$ID_e
## n missing distinct Info Mean Gmd .05 .10
## 5517 823 841 1 1011871 317.1 1011456 1011504
## .25 .50 .75 .90 .95
## 1011639 1011849 1012126 1012254 1012300
##
## lowest : 1011403 1011405 1011406 1011407 1011408
## highest: 1012338 1012339 1012340 1012341 1012342
describe(data7$ID_i.4)
## data7$ID_i.4
## n missing distinct Info Mean Gmd .05 .10
## 6340 0 361 1 1002740 126.5 1002566 1002584
## .25 .50 .75 .90 .95
## 1002646 1002743 1002835 1002887 1002911
##
## lowest : 1002555 1002556 1002557 1002558 1002559
## highest: 1002924 1002925 1002926 1002927 1002928
describe(data7$participation)
## data7$participation
## n missing distinct Info Mean Gmd
## 6340 0 2 0.233 2.83 0.3112
##
## Value 1 3
## Frequency 539 5801
## Proportion 0.085 0.915
library(plyr)
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attache Paket: 'plyr'
## Die folgenden Objekte sind maskiert von 'package:Hmisc':
##
## is.discrete, summarize
## Die folgenden Objekte sind maskiert von 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
classID <- select(data7, ID_t, ID_e)
describe(count(classID$ID_e), na.rm=T)
## count(classID$ID_e)
##
## 2 Variables 842 Observations
## --------------------------------------------------------------------------------
## x
## n missing distinct Info Mean Gmd .05 .10
## 841 1 841 1 1011861 319.1 1011447 1011491
## .25 .50 .75 .90 .95
## 1011624 1011839 1012116 1012254 1012299
##
## lowest : 1011403 1011405 1011406 1011407 1011408
## highest: 1012338 1012339 1012340 1012341 1012342
## --------------------------------------------------------------------------------
## freq
## n missing distinct Info Mean Gmd .05 .10
## 842 0 21 0.993 7.53 6.298 1 2
## .25 .50 .75 .90 .95
## 3 6 9 12 14
##
## lowest : 1 2 3 4 5, highest: 17 18 19 21 823
##
## Value 0 2 4 6 8 10 12 14 16 18 20
## Frequency 50 75 264 79 187 44 93 17 27 1 4
## Proportion 0.059 0.089 0.314 0.094 0.222 0.052 0.110 0.020 0.032 0.001 0.005
##
## Value 824
## Frequency 1
## Proportion 0.001
##
## For the frequency table, variable is rounded to the nearest 2
## --------------------------------------------------------------------------------
schoolID <- select(data7, ID_t, ID_e, ID_i.4)
describe(count(schoolID$ID_i.4), na.rm=T)
## count(schoolID$ID_i.4)
##
## 2 Variables 361 Observations
## --------------------------------------------------------------------------------
## x
## n missing distinct Info Mean Gmd .05 .10
## 361 0 361 1 1002740 124.7 1002573 1002591
## .25 .50 .75 .90 .95
## 1002647 1002740 1002833 1002888 1002910
##
## lowest : 1002555 1002556 1002557 1002558 1002559
## highest: 1002924 1002925 1002926 1002927 1002928
## --------------------------------------------------------------------------------
## freq
## n missing distinct Info Mean Gmd .05 .10
## 361 0 46 0.998 17.56 10.68 5 7
## .25 .50 .75 .90 .95
## 10 16 23 31 35
##
## lowest : 2 3 4 5 6, highest: 46 47 50 57 75
## --------------------------------------------------------------------------------
cschool <- select(data7, ID_e, ID_i.4)
count(cschool$ID_i.4)
## x freq
## 1 1002555 9
## 2 1002556 9
## 3 1002557 20
## 4 1002558 39
## 5 1002559 17
## 6 1002560 9
## 7 1002561 32
## 8 1002562 33
## 9 1002563 14
## 10 1002564 75
## 11 1002565 45
## 12 1002566 36
## 13 1002567 9
## 14 1002568 13
## 15 1002569 10
## 16 1002570 9
## 17 1002571 37
## 18 1002572 16
## 19 1002573 9
## 20 1002574 8
## 21 1002575 30
## 22 1002576 13
## 23 1002577 7
## 24 1002578 22
## 25 1002579 24
## 26 1002580 16
## 27 1002581 18
## 28 1002582 16
## 29 1002583 29
## 30 1002584 16
## 31 1002585 32
## 32 1002586 28
## 33 1002587 32
## 34 1002588 16
## 35 1002589 11
## 36 1002590 15
## 37 1002591 22
## 38 1002592 22
## 39 1002593 30
## 40 1002594 18
## 41 1002595 12
## 42 1002596 25
## 43 1002598 10
## 44 1002599 6
## 45 1002600 8
## 46 1002601 11
## 47 1002602 28
## 48 1002604 10
## 49 1002605 9
## 50 1002606 23
## 51 1002607 4
## 52 1002608 16
## 53 1002609 4
## 54 1002610 12
## 55 1002611 10
## 56 1002612 13
## 57 1002613 22
## 58 1002614 22
## 59 1002615 23
## 60 1002616 8
## 61 1002617 4
## 62 1002618 9
## 63 1002619 31
## 64 1002620 8
## 65 1002621 21
## 66 1002622 7
## 67 1002623 22
## 68 1002624 9
## 69 1002625 10
## 70 1002626 9
## 71 1002627 13
## 72 1002628 12
## 73 1002629 21
## 74 1002630 14
## 75 1002631 11
## 76 1002632 13
## 77 1002633 18
## 78 1002634 10
## 79 1002635 5
## 80 1002636 25
## 81 1002637 12
## 82 1002638 21
## 83 1002639 11
## 84 1002640 23
## 85 1002641 46
## 86 1002642 12
## 87 1002643 18
## 88 1002644 23
## 89 1002645 4
## 90 1002646 17
## 91 1002647 4
## 92 1002648 29
## 93 1002649 22
## 94 1002650 20
## 95 1002651 24
## 96 1002652 16
## 97 1002653 25
## 98 1002654 16
## 99 1002655 7
## 100 1002656 11
## 101 1002658 23
## 102 1002659 17
## 103 1002660 8
## 104 1002661 32
## 105 1002662 4
## 106 1002663 14
## 107 1002664 5
## 108 1002665 22
## 109 1002666 2
## 110 1002667 16
## 111 1002668 29
## 112 1002669 8
## 113 1002670 10
## 114 1002671 18
## 115 1002672 28
## 116 1002673 7
## 117 1002674 50
## 118 1002675 23
## 119 1002676 3
## 120 1002677 12
## 121 1002678 13
## 122 1002679 10
## 123 1002680 15
## 124 1002681 14
## 125 1002682 6
## 126 1002683 23
## 127 1002684 9
## 128 1002685 19
## 129 1002686 23
## 130 1002687 16
## 131 1002688 15
## 132 1002689 33
## 133 1002690 27
## 134 1002691 22
## 135 1002692 15
## 136 1002693 39
## 137 1002694 11
## 138 1002695 33
## 139 1002696 29
## 140 1002697 12
## 141 1002699 15
## 142 1002700 7
## 143 1002701 8
## 144 1002702 22
## 145 1002703 22
## 146 1002704 14
## 147 1002705 10
## 148 1002706 8
## 149 1002707 19
## 150 1002708 15
## 151 1002709 11
## 152 1002710 28
## 153 1002711 16
## 154 1002712 8
## 155 1002713 11
## 156 1002714 16
## 157 1002715 9
## 158 1002716 12
## 159 1002717 16
## 160 1002718 24
## 161 1002720 21
## 162 1002721 14
## 163 1002722 20
## 164 1002723 32
## 165 1002724 8
## 166 1002725 10
## 167 1002726 11
## 168 1002727 25
## 169 1002728 23
## 170 1002729 9
## 171 1002730 29
## 172 1002731 12
## 173 1002732 9
## 174 1002733 16
## 175 1002734 43
## 176 1002735 8
## 177 1002736 13
## 178 1002737 8
## 179 1002738 16
## 180 1002739 18
## 181 1002740 12
## 182 1002741 11
## 183 1002742 21
## 184 1002743 3
## 185 1002744 34
## 186 1002745 12
## 187 1002746 31
## 188 1002747 15
## 189 1002748 6
## 190 1002750 7
## 191 1002751 4
## 192 1002752 13
## 193 1002753 35
## 194 1002754 22
## 195 1002755 14
## 196 1002756 28
## 197 1002757 13
## 198 1002758 22
## 199 1002759 35
## 200 1002760 7
## 201 1002761 14
## 202 1002762 13
## 203 1002763 21
## 204 1002764 39
## 205 1002765 19
## 206 1002766 21
## 207 1002767 23
## 208 1002768 57
## 209 1002769 24
## 210 1002770 21
## 211 1002771 8
## 212 1002772 10
## 213 1002773 25
## 214 1002774 28
## 215 1002775 14
## 216 1002776 16
## 217 1002777 23
## 218 1002778 9
## 219 1002779 9
## 220 1002780 17
## 221 1002781 22
## 222 1002782 20
## 223 1002783 15
## 224 1002784 9
## 225 1002785 17
## 226 1002786 6
## 227 1002787 29
## 228 1002788 4
## 229 1002789 21
## 230 1002790 13
## 231 1002791 13
## 232 1002792 9
## 233 1002793 26
## 234 1002794 18
## 235 1002795 16
## 236 1002796 7
## 237 1002797 14
## 238 1002798 5
## 239 1002799 7
## 240 1002800 12
## 241 1002801 19
## 242 1002802 38
## 243 1002803 28
## 244 1002804 3
## 245 1002805 9
## 246 1002807 14
## 247 1002808 12
## 248 1002809 24
## 249 1002810 20
## 250 1002811 24
## 251 1002812 21
## 252 1002813 6
## 253 1002814 8
## 254 1002815 17
## 255 1002816 10
## 256 1002817 22
## 257 1002818 12
## 258 1002819 16
## 259 1002820 24
## 260 1002821 28
## 261 1002822 12
## 262 1002823 26
## 263 1002824 19
## 264 1002825 14
## 265 1002826 10
## 266 1002827 28
## 267 1002828 12
## 268 1002829 23
## 269 1002831 14
## 270 1002832 24
## 271 1002833 6
## 272 1002834 20
## 273 1002835 31
## 274 1002836 31
## 275 1002837 23
## 276 1002838 14
## 277 1002839 21
## 278 1002840 15
## 279 1002841 10
## 280 1002842 32
## 281 1002843 7
## 282 1002844 18
## 283 1002845 6
## 284 1002846 5
## 285 1002847 42
## 286 1002848 33
## 287 1002849 23
## 288 1002850 11
## 289 1002851 14
## 290 1002852 10
## 291 1002853 5
## 292 1002854 39
## 293 1002855 34
## 294 1002856 10
## 295 1002857 14
## 296 1002858 19
## 297 1002859 23
## 298 1002860 28
## 299 1002861 8
## 300 1002862 10
## 301 1002863 17
## 302 1002865 29
## 303 1002866 7
## 304 1002867 15
## 305 1002868 21
## 306 1002869 20
## 307 1002870 20
## 308 1002871 15
## 309 1002872 32
## 310 1002873 21
## 311 1002874 10
## 312 1002875 4
## 313 1002876 15
## 314 1002877 39
## 315 1002878 12
## 316 1002879 12
## 317 1002880 28
## 318 1002881 8
## 319 1002882 21
## 320 1002883 13
## 321 1002884 26
## 322 1002885 21
## 323 1002886 5
## 324 1002887 35
## 325 1002888 17
## 326 1002889 9
## 327 1002890 25
## 328 1002892 9
## 329 1002893 14
## 330 1002894 27
## 331 1002895 35
## 332 1002896 10
## 333 1002898 13
## 334 1002900 6
## 335 1002901 22
## 336 1002902 26
## 337 1002904 9
## 338 1002905 16
## 339 1002906 12
## 340 1002907 12
## 341 1002908 16
## 342 1002909 14
## 343 1002910 20
## 344 1002911 15
## 345 1002912 16
## 346 1002913 4
## 347 1002914 11
## 348 1002915 3
## 349 1002916 21
## 350 1002917 9
## 351 1002918 15
## 352 1002919 21
## 353 1002920 22
## 354 1002921 10
## 355 1002922 30
## 356 1002923 9
## 357 1002924 47
## 358 1002925 33
## 359 1002926 8
## 360 1002927 18
## 361 1002928 26
describe(data7$sex_c)
## data7$sex_c
## n missing distinct Info Mean Gmd
## 6339 1 2 0.75 1.511 0.4998
##
## Value 1 2
## Frequency 3101 3238
## Proportion 0.489 0.511
describe(data7$sex_p)
## data7$sex_p
## n missing distinct Info Mean Gmd
## 4866 1474 2 0.269 1.9 0.1795
##
## Value 1 2
## Frequency 485 4381
## Proportion 0.1 0.9
describe(data7$sex_t)
## data7$sex_t
## n missing distinct Info Mean Gmd
## 5292 1048 2 0.145 1.949 0.09651
##
## Value 1 2
## Frequency 269 5023
## Proportion 0.051 0.949
describe(data7$age_c)
## data7$age_c
## n missing distinct Info Mean Gmd .05 .10
## 6277 63 66 0.997 7.729 0.4284 7.168 7.253
## .25 .50 .75 .90 .95
## 7.420 7.671 8.000 8.167 8.337
##
## lowest : 6.083504 6.086242 6.253251 6.420260 6.505133
## highest: 9.251198 9.336071 9.418207 9.500342 9.503080
sdage <- data7$age_c
sd(sdage, na.rm=T)
## [1] 0.384369
describe(data7$age_p)
## data7$age_p
## n missing distinct Info Mean Gmd .05 .10
## 4876 1464 44 0.997 39.07 6.149 30 32
## .25 .50 .75 .90 .95
## 35 39 43 46 48
##
## lowest : 18 22 23 24 25, highest: 60 61 62 63 66
age_psd = data7$age_p
sd(age_psd, na.rm="T")
## [1] 5.465084
describe(data7$judgRE2)
## data7$judgRE2
## n missing distinct Info Mean Gmd
## 4851 1489 5 0.862 3.421 0.9273
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 64 454 2342 1360 631
## Proportion 0.013 0.094 0.483 0.280 0.130
sdjRE <- data7$judgRE2
sd(sdjRE, na.rm=T)
## [1] 0.8776862
describe(data7$judgMA2)
## data7$judgMA2
## n missing distinct Info Mean Gmd
## 4858 1482 5 0.896 3.558 1.007
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 57 438 1999 1466 898
## Proportion 0.012 0.090 0.411 0.302 0.185
sdjMA <- data7$judgMA2
sd(sdjMA, na.rm=T)
## [1] 0.931264
describe(data7$judgRE2T)
## data7$judgRE2T
## n missing distinct Info Mean Gmd
## 5152 1188 5 0.934 3.178 1.25
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 381 1023 1777 1240 731
## Proportion 0.074 0.199 0.345 0.241 0.142
sdjRET <- data7$judgRET
sd(sdjRET, na.rm=T)
## [1] NA
describe(data7$judgMA2T)
## data7$judgMA2T
## n missing distinct Info Mean Gmd
## 5146 1194 5 0.923 3.345 1.176
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 279 744 1840 1489 794
## Proportion 0.054 0.145 0.358 0.289 0.154
sdjMAT <- data7$judgMA2T
sd(sdjMAT, na.rm=T)
## [1] 1.072018
describe(data7$judgRE1)
## data7$judgRE1
## n missing distinct Info Mean Gmd
## 5277 1063 5 0.812 3.346 0.839
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 47 470 2941 1249 570
## Proportion 0.009 0.089 0.557 0.237 0.108
sdjRE1 <- data7$judgRE1
sd(sdjRE1, na.rm=T)
## [1] 0.8209539
describe(data7$judgMA1)
## data7$judgMA1
## n missing distinct Info Mean Gmd
## 5325 1015 5 0.883 3.577 0.9551
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 34 392 2325 1614 960
## Proportion 0.006 0.074 0.437 0.303 0.180
sdjMA1 <- data7$judgMA1
sd(sdjMA1, na.rm=T)
## [1] 0.8889769
describe(data7$judgRE1T)
## data7$judgRE1T
## n missing distinct Info Mean Gmd
## 5391 949 5 0.921 3.197 1.19
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 327 1008 2077 1233 746
## Proportion 0.061 0.187 0.385 0.229 0.138
sdjRET1 <- data7$judgRE1T
sd(sdjRET1, na.rm=T)
## [1] 1.083131
describe(data7$judgMA1T)
## data7$judgMA1T
## n missing distinct Info Mean Gmd
## 5297 1043 5 0.906 3.32 1.095
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 215 756 2158 1453 715
## Proportion 0.041 0.143 0.407 0.274 0.135
sdjMAT1 <- data7$judgMA1T
sd(sdjMAT1, na.rm=T)
## [1] 1.008399
describe(data7$read2)
## data7$read2
## n missing distinct Info Mean Gmd .05 .10
## 5926 414 21 0.994 7.251 4.734 1 2
## .25 .50 .75 .90 .95
## 4 7 10 13 16
##
## lowest : 0 1 2 3 4, highest: 16 17 18 19 20
sdread2 <- data7$read2
sd(sdread2, na.rm=T)
## [1] 4.257599
describe(data7$read4)
##
## NULL
sdread4 <- data7$read2
sd(sdread4, na.rm=T)
## [1] 4.257599
describe(data7$math2)
## data7$math2
## n missing distinct Info Mean Gmd .05 .10
## 6167 173 1663 1 0.004614 1.316 -1.82236 -1.48350
## .25 .50 .75 .90 .95
## -0.79733 -0.02631 0.76733 1.54564 1.97148
##
## lowest : -4.54340 -3.53180 -3.52153 -3.50289 -3.30869
## highest: 4.31529 4.31567 4.33727 4.34073 4.35296
sdmath2 <- data7$math2
sd(sdmath2, na.rm=T)
## [1] 1.173924
describe(data7$math4)
## data7$math4
## n missing distinct Info Mean Gmd .05 .10
## 5491 849 2610 1 0.002186 1.264 -1.84210 -1.47005
## .25 .50 .75 .90 .95
## -0.68387 0.05689 0.77419 1.37389 1.83761
##
## lowest : -4.905058 -4.237821 -4.168551 -4.150749 -4.124969
## highest: 3.610323 3.664296 4.115057 4.253784 4.884146
sdmath4 <- data7$math4
sd(sdmath4, na.rm=T)
## [1] 1.129947
describe(data7$reasoning)
## data7$reasoning
## n missing distinct Info Mean Gmd .05 .10
## 6102 238 13 0.978 6.773 2.844 1 3
## .25 .50 .75 .90 .95
## 6 7 8 10 10
##
## lowest : 0 1 2 3 4, highest: 8 9 10 11 12
##
## Value 0 1 2 3 4 5 6 7 8 9 10
## Frequency 196 166 185 216 310 417 716 1099 1398 723 392
## Proportion 0.032 0.027 0.030 0.035 0.051 0.068 0.117 0.180 0.229 0.118 0.064
##
## Value 11 12
## Frequency 188 96
## Proportion 0.031 0.016
sdreasoning <- data7$reasoning
sd(sdreasoning, na.rm=T)
## [1] 2.615834
describe(data7$language)
## data7$language
## n missing distinct Info Sum Mean Gmd
## 4783 1557 2 0.457 897 0.1875 0.3048
language <- data7$language
table(language)
## language
## 0 1
## 3886 897
describe(data7$sen)
## data7$sen
## n missing distinct Info Mean Gmd
## 6262 78 2 0.083 1.028 0.05524
##
## Value 1 2
## Frequency 6084 178
## Proportion 0.972 0.028
describe(data7$edu)
## data7$edu
## n missing distinct Info Mean Gmd
## 5270 1070 7 0.938 14.92 2.578
##
## lowest : 9 10 12 13 15, highest: 12 13 15 16 18
##
## Value 9 10 12 13 15 16 18
## Frequency 67 117 334 1673 1057 577 1445
## Proportion 0.013 0.022 0.063 0.317 0.201 0.109 0.274
sdedu <- data7$edu
sd(sdedu, na.rm=T)
## [1] 2.329111
library(psych)
##
## Attache Paket: 'psych'
## Das folgende Objekt ist maskiert 'package:Hmisc':
##
## describe
## Die folgenden Objekte sind maskiert von 'package:ggplot2':
##
## %+%, alpha
alpha(data7[c("dgci2103_sc2g2_c", "dgci2105_sc2g2_c", "dgci2104_sc2g2_c",
"dgci2107_c", "dgci2108_c", "dgci2109_c", "dgci2106_sc2g2_c",
"dgci2204_sc2g2_c", "dgci2205_sc2g2_c", "dgci2203_sc2g2_c",
"dgci2206_sc2g2_c", "dgci2207_c")], check.keys=T)
##
## Reliability analysis
## Call: alpha(x = data7[c("dgci2103_sc2g2_c", "dgci2105_sc2g2_c", "dgci2104_sc2g2_c",
## "dgci2107_c", "dgci2108_c", "dgci2109_c", "dgci2106_sc2g2_c",
## "dgci2204_sc2g2_c", "dgci2205_sc2g2_c", "dgci2203_sc2g2_c",
## "dgci2206_sc2g2_c", "dgci2207_c")], check.keys = T)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.77 0.2 3 0.0046 0.57 0.22 0.17
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.74 0.75 0.76
## Duhachek 0.74 0.75 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## dgci2103_sc2g2_c 0.74 0.74 0.76 0.21 2.9 0.0048 0.020 0.16
## dgci2105_sc2g2_c 0.72 0.72 0.73 0.19 2.5 0.0052 0.015 0.16
## dgci2104_sc2g2_c 0.74 0.74 0.76 0.20 2.8 0.0049 0.020 0.16
## dgci2107_c 0.72 0.72 0.75 0.19 2.6 0.0051 0.018 0.16
## dgci2108_c 0.74 0.75 0.76 0.21 2.9 0.0047 0.018 0.19
## dgci2109_c 0.75 0.76 0.77 0.22 3.1 0.0046 0.017 0.23
## dgci2106_sc2g2_c 0.73 0.73 0.75 0.20 2.8 0.0049 0.017 0.16
## dgci2204_sc2g2_c 0.72 0.72 0.75 0.19 2.6 0.0052 0.018 0.16
## dgci2205_sc2g2_c 0.71 0.72 0.73 0.19 2.5 0.0053 0.014 0.16
## dgci2203_sc2g2_c 0.72 0.72 0.74 0.19 2.6 0.0052 0.019 0.13
## dgci2206_sc2g2_c 0.75 0.74 0.76 0.21 2.9 0.0047 0.019 0.23
## dgci2207_c 0.74 0.74 0.76 0.21 2.9 0.0047 0.019 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## dgci2103_sc2g2_c 6036 0.46 0.47 0.38 0.33 0.83 0.38
## dgci2105_sc2g2_c 6015 0.63 0.64 0.62 0.53 0.83 0.37
## dgci2104_sc2g2_c 6015 0.53 0.51 0.42 0.37 0.61 0.49
## dgci2107_c 6042 0.59 0.59 0.53 0.47 0.77 0.42
## dgci2108_c 5929 0.40 0.43 0.35 0.27 0.17 0.38
## dgci2109_c 5859 0.31 0.35 0.25 0.20 0.12 0.32
## dgci2106_sc2g2_c 6023 0.54 0.51 0.46 0.38 0.63 0.48
## dgci2204_sc2g2_c 6052 0.60 0.59 0.54 0.47 0.73 0.45
## dgci2205_sc2g2_c 6026 0.65 0.64 0.64 0.54 0.79 0.41
## dgci2203_sc2g2_c 6043 0.59 0.60 0.55 0.49 0.84 0.37
## dgci2206_sc2g2_c 6003 0.45 0.44 0.35 0.29 0.32 0.47
## dgci2207_c 5964 0.43 0.44 0.36 0.29 0.22 0.42
##
## Non missing response frequency for each item
## 0 1 miss
## dgci2103_sc2g2_c 0.17 0.83 0.05
## dgci2105_sc2g2_c 0.17 0.83 0.05
## dgci2104_sc2g2_c 0.39 0.61 0.05
## dgci2107_c 0.23 0.77 0.05
## dgci2108_c 0.83 0.17 0.06
## dgci2109_c 0.88 0.12 0.08
## dgci2106_sc2g2_c 0.37 0.63 0.05
## dgci2204_sc2g2_c 0.27 0.73 0.05
## dgci2205_sc2g2_c 0.21 0.79 0.05
## dgci2203_sc2g2_c 0.16 0.84 0.05
## dgci2206_sc2g2_c 0.68 0.32 0.05
## dgci2207_c 0.78 0.22 0.06
alpha(data7[c("rxg20001_c","rxg20002_c","rxg20003_c","rxg20004_c", "rxg20005_c",
"rxg20006_c","rxg20007_c","rxg20008_c","rxg20009_c", "rxg20010_c",
"rxg20011_c","rxg20012_c","rxg20013_c","rxg20014_c", "rxg20015_c",
"rxg20016_c","rxg20017_c","rxg20018_c","rxg20019_c", "rxg20020_c")])
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
##
## Reliability analysis
## Call: alpha(x = data7[c("rxg20001_c", "rxg20002_c", "rxg20003_c", "rxg20004_c",
## "rxg20005_c", "rxg20006_c", "rxg20007_c", "rxg20008_c", "rxg20009_c",
## "rxg20010_c", "rxg20011_c", "rxg20012_c", "rxg20013_c", "rxg20014_c",
## "rxg20015_c", "rxg20016_c", "rxg20017_c", "rxg20018_c", "rxg20019_c",
## "rxg20020_c")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.91 0.39 13 0.0013 0.77 0.26 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.93
## Duhachek 0.92 0.93 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## rxg20001_c 0.93 0.93 0.91 0.40 13 0.0013 0.018 0.41
## rxg20002_c 0.92 0.93 0.91 0.40 12 0.0013 0.018 0.41
## rxg20003_c 0.92 0.93 0.91 0.39 12 0.0013 0.018 0.40
## rxg20004_c 0.92 0.92 0.91 0.39 12 0.0014 0.018 0.41
## rxg20005_c 0.92 0.92 0.90 0.38 12 0.0014 0.015 0.39
## rxg20006_c 0.92 0.92 0.91 0.39 12 0.0014 0.018 0.40
## rxg20007_c 0.92 0.92 0.91 0.39 12 0.0014 0.019 0.40
## rxg20008_c 0.93 0.93 0.91 0.42 14 0.0012 0.013 0.42
## rxg20009_c 0.92 0.93 0.91 0.40 13 0.0013 0.019 0.41
## rxg20010_c 0.92 0.93 0.91 0.40 13 0.0013 0.018 0.41
## rxg20011_c 0.92 0.93 0.91 0.39 12 0.0013 0.019 0.41
## rxg20012_c 0.92 0.93 0.91 0.40 13 0.0013 0.019 0.41
## rxg20013_c 0.92 0.92 0.91 0.39 12 0.0014 0.019 0.39
## rxg20014_c 0.92 0.93 0.91 0.39 12 0.0013 0.019 0.41
## rxg20015_c 0.92 0.92 0.91 0.38 12 0.0014 0.019 0.39
## rxg20016_c 0.92 0.92 0.91 0.39 12 0.0014 0.019 0.38
## rxg20017_c 0.92 0.92 0.90 0.37 11 0.0014 0.016 0.38
## rxg20018_c 0.92 0.92 0.90 0.38 12 0.0014 0.018 0.38
## rxg20019_c 0.92 0.92 0.90 0.38 12 0.0014 0.018 0.38
## rxg20020_c 0.92 0.92 0.90 0.38 12 0.0014 0.018 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## rxg20001_c 5698 0.51 0.55 0.50 0.50 0.95 0.21
## rxg20002_c 5568 0.63 0.58 0.55 0.53 0.85 0.36
## rxg20003_c 5166 0.66 0.61 0.57 0.56 0.79 0.41
## rxg20004_c 5320 0.64 0.63 0.60 0.59 0.81 0.39
## rxg20005_c 5071 0.68 0.77 0.73 0.75 0.86 0.34
## rxg20006_c 4687 0.66 0.65 0.62 0.61 0.82 0.39
## rxg20007_c 4334 0.63 0.63 0.60 0.59 0.78 0.41
## rxg20008_c 3297 0.36 0.30 0.24 0.23 0.68 0.47
## rxg20009_c 2892 0.60 0.58 0.54 0.53 0.62 0.49
## rxg20010_c 2595 0.58 0.55 0.51 0.50 0.60 0.49
## rxg20011_c 2251 0.60 0.62 0.59 0.57 0.73 0.44
## rxg20012_c 1743 0.57 0.56 0.53 0.50 0.64 0.48
## rxg20013_c 1581 0.68 0.70 0.69 0.66 0.79 0.41
## rxg20014_c 1261 0.61 0.61 0.59 0.56 0.64 0.48
## rxg20015_c 1028 0.69 0.74 0.73 0.69 0.69 0.46
## rxg20016_c 887 0.65 0.70 0.68 0.65 0.62 0.49
## rxg20017_c 671 0.76 0.87 0.83 0.83 0.64 0.48
## rxg20018_c 584 0.67 0.76 0.74 0.71 0.57 0.50
## rxg20019_c 464 0.69 0.78 0.78 0.74 0.51 0.50
## rxg20020_c 410 0.70 0.80 0.78 0.76 0.40 0.49
##
## Non missing response frequency for each item
## 0 1 miss
## rxg20001_c 0.05 0.95 0.10
## rxg20002_c 0.15 0.85 0.12
## rxg20003_c 0.21 0.79 0.19
## rxg20004_c 0.19 0.81 0.16
## rxg20005_c 0.14 0.86 0.20
## rxg20006_c 0.18 0.82 0.26
## rxg20007_c 0.22 0.78 0.32
## rxg20008_c 0.32 0.68 0.48
## rxg20009_c 0.38 0.62 0.54
## rxg20010_c 0.40 0.60 0.59
## rxg20011_c 0.27 0.73 0.64
## rxg20012_c 0.36 0.64 0.73
## rxg20013_c 0.21 0.79 0.75
## rxg20014_c 0.36 0.64 0.80
## rxg20015_c 0.31 0.69 0.84
## rxg20016_c 0.38 0.62 0.86
## rxg20017_c 0.36 0.64 0.89
## rxg20018_c 0.43 0.57 0.91
## rxg20019_c 0.49 0.51 0.93
## rxg20020_c 0.60 0.40 0.94
alpha(data7[c("mag1v051_sc2g2_c", "mag2v071_c","mag2r031_c","mag2d061_c",
"mag1d131_sc2g2_c", "mag2r131_c","mag2v121_c","mag2q061_c",
"mag2r111_c","mag1d09s_sc2g2_c", "mag1z121_sc2g2_c", "mag2g12s_c",
"mag1d081_sc2g2_c","mag2g021_c","mag2r151_c","mag1v021_sc2g2_c",
"mag1z071_sc2g2_c","mag2d101_c","mag1g031_sc2g2_c","mag2v041_c",
"mag2q011_c","mag1r19s_sc2g2_c","mag2g091_c","mag2q051_c" )])
##
## Reliability analysis
## Call: alpha(x = data7[c("mag1v051_sc2g2_c", "mag2v071_c", "mag2r031_c",
## "mag2d061_c", "mag1d131_sc2g2_c", "mag2r131_c", "mag2v121_c",
## "mag2q061_c", "mag2r111_c", "mag1d09s_sc2g2_c", "mag1z121_sc2g2_c",
## "mag2g12s_c", "mag1d081_sc2g2_c", "mag2g021_c", "mag2r151_c",
## "mag1v021_sc2g2_c", "mag1z071_sc2g2_c", "mag2d101_c", "mag1g031_sc2g2_c",
## "mag2v041_c", "mag2q011_c", "mag1r19s_sc2g2_c", "mag2g091_c",
## "mag2q051_c")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.82 0.82 0.16 4.6 0.0035 0.93 0.26 0.16
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.8 0.81
## Duhachek 0.79 0.8 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## mag1v051_sc2g2_c 0.79 0.81 0.82 0.16 4.3 0.0037 0.0040
## mag2v071_c 0.79 0.81 0.82 0.16 4.4 0.0037 0.0040
## mag2r031_c 0.80 0.82 0.82 0.17 4.6 0.0036 0.0038
## mag2d061_c 0.79 0.81 0.81 0.16 4.3 0.0037 0.0031
## mag1d131_sc2g2_c 0.79 0.81 0.81 0.16 4.3 0.0037 0.0030
## mag2r131_c 0.79 0.82 0.82 0.16 4.4 0.0036 0.0041
## mag2v121_c 0.79 0.81 0.82 0.16 4.3 0.0037 0.0040
## mag2q061_c 0.80 0.82 0.82 0.17 4.6 0.0036 0.0036
## mag2r111_c 0.80 0.82 0.82 0.16 4.5 0.0036 0.0040
## mag1d09s_sc2g2_c 0.81 0.82 0.82 0.16 4.5 0.0034 0.0040
## mag1z121_sc2g2_c 0.79 0.81 0.82 0.16 4.4 0.0037 0.0041
## mag2g12s_c 0.80 0.81 0.82 0.16 4.3 0.0037 0.0040
## mag1d081_sc2g2_c 0.79 0.81 0.82 0.16 4.3 0.0037 0.0040
## mag2g021_c 0.79 0.82 0.82 0.16 4.4 0.0036 0.0040
## mag2r151_c 0.79 0.82 0.82 0.16 4.4 0.0036 0.0041
## mag1v021_sc2g2_c 0.79 0.81 0.82 0.16 4.4 0.0037 0.0041
## mag1z071_sc2g2_c 0.79 0.81 0.82 0.16 4.3 0.0037 0.0039
## mag2d101_c 0.79 0.82 0.82 0.16 4.4 0.0036 0.0041
## mag1g031_sc2g2_c 0.79 0.81 0.81 0.16 4.2 0.0037 0.0036
## mag2v041_c 0.79 0.81 0.81 0.16 4.3 0.0037 0.0037
## mag2q011_c 0.79 0.82 0.82 0.16 4.4 0.0036 0.0041
## mag1r19s_sc2g2_c 0.80 0.82 0.82 0.17 4.6 0.0035 0.0037
## mag2g091_c 0.79 0.81 0.81 0.16 4.3 0.0037 0.0038
## mag2q051_c 0.79 0.81 0.82 0.16 4.4 0.0037 0.0040
## med.r
## mag1v051_sc2g2_c 0.16
## mag2v071_c 0.16
## mag2r031_c 0.17
## mag2d061_c 0.16
## mag1d131_sc2g2_c 0.16
## mag2r131_c 0.16
## mag2v121_c 0.16
## mag2q061_c 0.17
## mag2r111_c 0.17
## mag1d09s_sc2g2_c 0.17
## mag1z121_sc2g2_c 0.16
## mag2g12s_c 0.16
## mag1d081_sc2g2_c 0.16
## mag2g021_c 0.16
## mag2r151_c 0.16
## mag1v021_sc2g2_c 0.15
## mag1z071_sc2g2_c 0.16
## mag2d101_c 0.16
## mag1g031_sc2g2_c 0.15
## mag2v041_c 0.15
## mag2q011_c 0.16
## mag1r19s_sc2g2_c 0.17
## mag2g091_c 0.16
## mag2q051_c 0.16
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mag1v051_sc2g2_c 6028 0.46 0.49 0.45 0.40 0.74 0.44
## mag2v071_c 5898 0.41 0.44 0.40 0.36 0.65 0.48
## mag2r031_c 6108 0.28 0.31 0.25 0.22 0.84 0.36
## mag2d061_c 5929 0.49 0.50 0.50 0.42 0.62 0.49
## mag1d131_sc2g2_c 5808 0.51 0.53 0.53 0.45 0.56 0.50
## mag2r131_c 5731 0.38 0.40 0.35 0.32 0.45 0.50
## mag2v121_c 6055 0.47 0.48 0.45 0.40 0.62 0.48
## mag2q061_c 5951 0.24 0.29 0.22 0.20 0.15 0.36
## mag2r111_c 5998 0.33 0.34 0.28 0.25 0.51 0.50
## mag1d09s_sc2g2_c 5912 0.44 0.37 0.31 0.28 2.75 1.02
## mag1z121_sc2g2_c 6087 0.41 0.45 0.41 0.37 0.21 0.41
## mag2g12s_c 5454 0.57 0.50 0.47 0.42 3.01 1.06
## mag1d081_sc2g2_c 6082 0.48 0.49 0.46 0.41 0.83 0.37
## mag2g021_c 6033 0.38 0.41 0.36 0.33 0.40 0.49
## mag2r151_c 6098 0.41 0.42 0.38 0.33 0.65 0.48
## mag1v021_sc2g2_c 5977 0.45 0.47 0.43 0.39 0.54 0.50
## mag1z071_sc2g2_c 6023 0.45 0.48 0.45 0.40 0.59 0.49
## mag2d101_c 6049 0.39 0.42 0.37 0.33 0.79 0.41
## mag1g031_sc2g2_c 6023 0.54 0.56 0.54 0.48 0.76 0.43
## mag2v041_c 6017 0.51 0.55 0.52 0.47 0.64 0.48
## mag2q011_c 5833 0.39 0.42 0.37 0.34 0.56 0.50
## mag1r19s_sc2g2_c 5376 0.32 0.30 0.23 0.21 4.65 0.56
## mag2g091_c 5755 0.48 0.51 0.49 0.44 0.62 0.49
## mag2q051_c 5834 0.43 0.47 0.43 0.38 0.82 0.38
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## mag1v051_sc2g2_c 0.26 0.74 0.00 0.00 0.00 0.00 0.05
## mag2v071_c 0.35 0.65 0.00 0.00 0.00 0.00 0.07
## mag2r031_c 0.16 0.84 0.00 0.00 0.00 0.00 0.04
## mag2d061_c 0.38 0.62 0.00 0.00 0.00 0.00 0.06
## mag1d131_sc2g2_c 0.44 0.56 0.00 0.00 0.00 0.00 0.08
## mag2r131_c 0.55 0.45 0.00 0.00 0.00 0.00 0.10
## mag2v121_c 0.38 0.62 0.00 0.00 0.00 0.00 0.04
## mag2q061_c 0.85 0.15 0.00 0.00 0.00 0.00 0.06
## mag2r111_c 0.49 0.51 0.00 0.00 0.00 0.00 0.05
## mag1d09s_sc2g2_c 0.01 0.13 0.23 0.36 0.27 0.00 0.07
## mag1z121_sc2g2_c 0.79 0.21 0.00 0.00 0.00 0.00 0.04
## mag2g12s_c 0.03 0.03 0.29 0.20 0.45 0.00 0.14
## mag1d081_sc2g2_c 0.17 0.83 0.00 0.00 0.00 0.00 0.04
## mag2g021_c 0.60 0.40 0.00 0.00 0.00 0.00 0.05
## mag2r151_c 0.35 0.65 0.00 0.00 0.00 0.00 0.04
## mag1v021_sc2g2_c 0.46 0.54 0.00 0.00 0.00 0.00 0.06
## mag1z071_sc2g2_c 0.41 0.59 0.00 0.00 0.00 0.00 0.05
## mag2d101_c 0.21 0.79 0.00 0.00 0.00 0.00 0.05
## mag1g031_sc2g2_c 0.24 0.76 0.00 0.00 0.00 0.00 0.05
## mag2v041_c 0.36 0.64 0.00 0.00 0.00 0.00 0.05
## mag2q011_c 0.44 0.56 0.00 0.00 0.00 0.00 0.08
## mag1r19s_sc2g2_c 0.00 0.00 0.00 0.02 0.30 0.68 0.15
## mag2g091_c 0.38 0.62 0.00 0.00 0.00 0.00 0.09
## mag2q051_c 0.18 0.82 0.00 0.00 0.00 0.00 0.08
###scale continous variables
data7$read2[data7$read2<0]<-NA
data7$read2Z <- scale(data7$read2)
data7$reasoningZ <- scale(data7$reasoning)
data7$eduZ <- scale(data7$edu)
summary(data7)
## ID_e ID_t ID_i.3 ID_cc.3.x
## Min. :1011403 Min. :2000568 Min. :1002555 Min. : -55
## 1st Qu.:1011639 1st Qu.:3005629 1st Qu.:1002646 1st Qu.:1002645103
## Median :1011849 Median :3007578 Median :1002743 Median :1002742103
## Mean :1011871 Mean :2924479 Mean :1002740 Mean :1001948894
## 3rd Qu.:1012126 3rd Qu.:3017798 3rd Qu.:1002835 3rd Qu.:1002835103
## Max. :1012342 Max. :3023458 Max. :1002928 Max. :1002928102
## NA's :823
## participation tx80220.3 tx80522.3 tx8610m.3 tx8610y.3
## Min. :1.00 Min. :1.000 Min. :0.0000 Min. :1.00 Min. :2013
## 1st Qu.:3.00 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:2.00 1st Qu.:2013
## Median :3.00 Median :1.000 Median :1.0000 Median :3.00 Median :2013
## Mean :2.83 Mean :1.024 Mean :0.9762 Mean :3.43 Mean :2013
## 3rd Qu.:3.00 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:5.00 3rd Qu.:2013
## Max. :3.00 Max. :2.000 Max. :1.0000 Max. :9.00 Max. :2013
## NA's :307 NA's :307
## tx8611m.3 tx8611y.3 tx80523.3 tx8620m.3 tx8620y.3
## Min. :2.000 Min. :2013 Min. :0.000 Min. : 5.000 Min. :2013
## 1st Qu.:3.000 1st Qu.:2013 1st Qu.:1.000 1st Qu.: 5.000 1st Qu.:2013
## Median :3.000 Median :2013 Median :1.000 Median : 6.000 Median :2013
## Mean :3.657 Mean :2013 Mean :0.842 Mean : 6.415 Mean :2013
## 3rd Qu.:5.000 3rd Qu.:2013 3rd Qu.:1.000 3rd Qu.: 8.000 3rd Qu.:2013
## Max. :6.000 Max. :2013 Max. :1.000 Max. :10.000 Max. :2013
## NA's :213 NA's :213 NA's :1002 NA's :1002
## tx80524.3 tx80525.3 sex_c tx8050m.3
## Min. :0.0000 Min. :0.0000 Min. :1.000 Min. : 1.000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.: 4.000
## Median :1.0000 Median :1.0000 Median :2.000 Median : 7.000
## Mean :0.8729 Mean :0.8831 Mean :1.511 Mean : 6.497
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.: 9.000
## Max. :1.0000 Max. :1.0000 Max. :2.000 Max. :12.000
## NA's :1 NA's :1
## tx8050y.3 sen ID_i.4 ID_cc.4.x
## Min. :2004 Min. :1.000 Min. :1002555 Min. : -55
## 1st Qu.:2005 1st Qu.:1.000 1st Qu.:1002646 1st Qu.:1002643101
## Median :2006 Median :1.000 Median :1002743 Median :1002741103
## Mean :2006 Mean :1.028 Mean :1002740 Mean : 993724586
## 3rd Qu.:2006 3rd Qu.:1.000 3rd Qu.:1002835 3rd Qu.:1002835102
## Max. :2007 Max. :2.000 Max. :1002928 Max. :1002928102
## NA's :78
## tx80107.4 tx80220.4 tx80522.4 tx8610m.4 tx8610y.4
## Min. :1.00 Min. :1 Min. :1 Min. : 1.00 Min. :2013
## 1st Qu.:3.00 1st Qu.:1 1st Qu.:1 1st Qu.:11.00 1st Qu.:2013
## Median :3.00 Median :1 Median :1 Median :11.00 Median :2013
## Mean :2.83 Mean :1 Mean :1 Mean :10.43 Mean :2013
## 3rd Qu.:3.00 3rd Qu.:1 3rd Qu.:1 3rd Qu.:11.00 3rd Qu.:2013
## Max. :3.00 Max. :1 Max. :1 Max. :12.00 Max. :2014
## NA's :62 NA's :62
## tx8611m.4 tx8611y.4 tx80523.4 tx8620m.4 tx8620y.4
## Min. : 1.00 Min. :2013 Min. :0.0000 Min. :2.000 Min. :2014
## 1st Qu.:11.00 1st Qu.:2013 1st Qu.:1.0000 1st Qu.:3.000 1st Qu.:2014
## Median :11.00 Median :2013 Median :1.0000 Median :3.000 Median :2014
## Mean :10.29 Mean :2013 Mean :0.7675 Mean :3.414 Mean :2014
## 3rd Qu.:12.00 3rd Qu.:2013 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:2014
## Max. :12.00 Max. :2014 Max. :1.0000 Max. :5.000 Max. :2014
## NA's :51 NA's :51 NA's :1474 NA's :1474
## tx80524.4 tx80525.4 tx80501.4 tx8050m.4
## Min. :0.000 Min. :0.0000 Min. :1.000 Min. : 1.000
## 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.: 4.000
## Median :1.000 Median :1.0000 Median :2.000 Median : 7.000
## Mean :0.847 Mean :0.7883 Mean :1.511 Mean : 6.497
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.: 9.000
## Max. :1.000 Max. :1.0000 Max. :2.000 Max. :12.000
## NA's :1
## tx8050y.4 tx80505_D.4 ID_i.5 ID_cc.5.x
## Min. :2004 Min. :1.000 Min. :1002555 Min. :-5.500e+01
## 1st Qu.:2005 1st Qu.:1.000 1st Qu.:1002646 1st Qu.: 1.003e+10
## Median :2006 Median :1.000 Median :1002743 Median : 1.003e+10
## Mean :2006 Mean :1.036 Mean :1002740 Mean : 8.798e+09
## 3rd Qu.:2006 3rd Qu.:1.000 3rd Qu.:1002835 3rd Qu.: 1.003e+10
## Max. :2007 Max. :2.000 Max. :1002928 Max. : 1.003e+10
## NA's :419
## tx80107.5 tx80220.5 tx80522.5 tx8610m.5 tx8610y.5
## Min. :1.00 Min. :1.000 Min. :0.0000 Min. : 1.00 Min. :2014
## 1st Qu.:3.00 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:11.00 1st Qu.:2014
## Median :3.00 Median :1.000 Median :1.0000 Median :11.00 Median :2014
## Mean :2.83 Mean :1.126 Mean :0.8986 Mean :11.14 Mean :2014
## 3rd Qu.:3.00 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:12.00 3rd Qu.:2014
## Max. :3.00 Max. :3.000 Max. :1.0000 Max. :12.00 Max. :2016
## NA's :823 NA's :823
## tx8611m.5 tx8611y.5 tx80523.5 tx8620m.5 tx8620y.5
## Min. : 1.0 Min. :2014 Min. :0.0000 Min. :3.000 Min. :2015
## 1st Qu.:11.0 1st Qu.:2014 1st Qu.:0.0000 1st Qu.:4.000 1st Qu.:2015
## Median :12.0 Median :2014 Median :1.0000 Median :5.000 Median :2015
## Mean :11.6 Mean :2014 Mean :0.6642 Mean :4.726 Mean :2015
## 3rd Qu.:12.0 3rd Qu.:2014 3rd Qu.:1.0000 3rd Qu.:5.000 3rd Qu.:2015
## Max. :12.0 Max. :2015 Max. :1.0000 Max. :6.000 Max. :2015
## NA's :863 NA's :863 NA's :2129 NA's :2129
## tx80524.5 tx80525.5 tx80501.5 tx8050m.5
## Min. :0.0000 Min. :0.0000 Min. :1.000 Min. : 1.000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.: 4.000
## Median :1.0000 Median :1.0000 Median :2.000 Median : 7.000
## Mean :0.7653 Mean :0.6756 Mean :1.513 Mean : 6.488
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.: 9.000
## Max. :1.0000 Max. :1.0000 Max. :2.000 Max. :12.000
## NA's :516 NA's :515
## tx8050y.5 tx80505_D.5 ID_i.6 ID_cc.6.x
## Min. :2004 Min. :1.000 Min. : -55 Min. :-5.500e+01
## 1st Qu.:2005 1st Qu.:1.000 1st Qu.:1002646 1st Qu.: 1.003e+10
## Median :2006 Median :1.000 Median :1002742 Median : 1.003e+10
## Mean :2006 Mean :1.046 Mean :1002581 Mean : 8.594e+09
## 3rd Qu.:2006 3rd Qu.:1.000 3rd Qu.:1002835 3rd Qu.: 1.003e+10
## Max. :2007 Max. :2.000 Max. :1002928 Max. : 1.003e+10
## NA's :515 NA's :1529
## tx80107.6 tx80220.6 tx80522.6 tx8610m.6 tx8610y.6
## Min. :1.00 Min. :1.00 Min. :0.0000 Min. : 1.00 Min. :2015
## 1st Qu.:3.00 1st Qu.:1.00 1st Qu.:1.0000 1st Qu.:11.00 1st Qu.:2015
## Median :3.00 Median :1.00 Median :1.0000 Median :12.00 Median :2015
## Mean :2.83 Mean :1.16 Mean :0.8892 Mean :11.19 Mean :2015
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.:1.0000 3rd Qu.:12.00 3rd Qu.:2015
## Max. :3.00 Max. :3.00 Max. :1.0000 Max. :12.00 Max. :2016
## NA's :3 NA's :728 NA's :728
## tx8611m.6 tx8611y.6 tx80523.6 tx8620m.6 tx8620y.6
## Min. : 1.00 Min. :2015 Min. :0.0000 Min. :2.000 Min. :2016
## 1st Qu.:11.00 1st Qu.:2015 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2016
## Median :12.00 Median :2015 Median :1.0000 Median :3.000 Median :2016
## Mean :11.51 Mean :2015 Mean :0.6241 Mean :3.486 Mean :2016
## 3rd Qu.:12.00 3rd Qu.:2015 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:2016
## Max. :12.00 Max. :2016 Max. :1.0000 Max. :6.000 Max. :2016
## NA's :902 NA's :902 NA's :2383 NA's :2383
## tx80524.6 tx80525.6 tx80501.6 tx8050m.6
## Min. :0.0000 Min. :0.0000 Min. :1.000 Min. : 1.000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.: 4.000
## Median :1.0000 Median :1.0000 Median :2.000 Median : 7.000
## Mean :0.8172 Mean :0.6276 Mean :1.513 Mean : 6.488
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.: 9.000
## Max. :1.0000 Max. :1.0000 Max. :2.000 Max. :12.000
## NA's :283 NA's :282
## tx8050y.6 tx80505_D.6 tx20100.3 eb01030.3
## Min. :2004 Min. :1.000 Min. :0.0000 Min. :1.000
## 1st Qu.:2005 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:3.000
## Median :2006 Median :1.000 Median :1.0000 Median :3.000
## Mean :2006 Mean :1.039 Mean :0.9871 Mean :3.323
## 3rd Qu.:2006 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:4.000
## Max. :2007 Max. :2.000 Max. :1.0000 Max. :5.000
## NA's :282 NA's :1870 NA's :823 NA's :921
## judgRE1T eb01040.3 judgMA1T e41370c.3
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :3.00 Median :0.0000
## Mean :3.197 Mean :3.281 Mean :3.32 Mean :0.0999
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:0.0000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :1.0000
## NA's :949 NA's :1068 NA's :1043 NA's :823
## e41370d.3 tx20100.4 ID_e1.4 eb01030.4
## Min. :0.0000 Min. :0.0000 Min. :1011403 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1011675 1st Qu.:3.000
## Median :0.0000 Median :1.0000 Median :1011912 Median :3.000
## Mean :0.0395 Mean :0.9937 Mean :1011977 Mean :3.343
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1012223 3rd Qu.:4.000
## Max. :1.0000 Max. :1.0000 Max. :1012880 Max. :5.000
## NA's :823 NA's :1131 NA's :1131 NA's :1147
## judgRE2T eb01040.4 judgMA2T tx20100.5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:1.0000
## Median :3.000 Median :3.000 Median :3.000 Median :1.0000
## Mean :3.178 Mean :3.361 Mean :3.345 Mean :0.9958
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :1.0000
## NA's :1188 NA's :1173 NA's :1194 NA's :637
## ID_e1.5 eb01030.5 eb01031.5 eb01040.5
## Min. :1011406 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1011849 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.00
## Median :1012289 Median :3.000 Median :3.000 Median :3.00
## Mean :1014640 Mean :3.352 Mean :3.221 Mean :3.39
## 3rd Qu.:1018880 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :1019054 Max. :5.000 Max. :5.000 Max. :5.00
## NA's :1932 NA's :1952 NA's :1957 NA's :1971
## eb01050.5 tx20100.6 ID_e1.6 eb01030.6
## Min. :1.000 Min. :0.0000 Min. :1011406 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.0000 1st Qu.:1011905 1st Qu.:3.000
## Median :3.000 Median :1.0000 Median :1012696 Median :3.000
## Mean :3.368 Mean :0.9929 Mean :1015402 Mean :3.457
## 3rd Qu.:4.000 3rd Qu.:1.0000 3rd Qu.:1019026 3rd Qu.:4.000
## Max. :5.000 Max. :1.0000 Max. :1019800 Max. :5.000
## NA's :2026 NA's :704 NA's :2253 NA's :2286
## eb01031.6 eb01040.6 eb01050.6 e41370c.6
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :3.000 Median :0.0000
## Mean :3.316 Mean :3.528 Mean :3.442 Mean :0.1062
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:0.0000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :1.0000
## NA's :2295 NA's :2309 NA's :2326 NA's :2253
## e41370d.6 t41203b.6 pb01030.3 judgRE1
## Min. :0.0000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:3.000
## Median :0.0000 Median :1.000 Median :4.000 Median :3.000
## Mean :0.0228 Mean :1.487 Mean :3.667 Mean :3.346
## 3rd Qu.:0.0000 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :4.000 Max. :5.000 Max. :5.000
## NA's :2253 NA's :1441 NA's :1011 NA's :1063
## pb01040.3 judgMA1 p73170y.3 p731702.3 p400000_g1.3
## Min. :1.000 Min. :1.000 Min. :1947 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:1970 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :1974 Median :2.000 Median :1.000
## Mean :3.543 Mean :3.577 Mean :1974 Mean :1.901 Mean :1.322
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:1978 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :5.000 Max. :5.000 Max. :1995 Max. :2.000 Max. :3.000
## NA's :1029 NA's :1015 NA's :1412 NA's :1002 NA's :1410
## p403000_g1.3 p731802_g3.3 p731852_g3.3 p731904_g14.3
## Min. :1.000 Min. : 9.00 Min. : 9.00 Min. :11.74
## 1st Qu.:1.000 1st Qu.:13.00 1st Qu.:13.00 1st Qu.:38.55
## Median :1.000 Median :13.00 Median :13.00 Median :52.72
## Mean :1.336 Mean :14.33 Mean :14.19 Mean :52.11
## 3rd Qu.:1.000 3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.:70.09
## Max. :3.000 Max. :18.00 Max. :18.00 Max. :88.96
## NA's :2170 NA's :1070 NA's :1852 NA's :1626
## p731954_g14.3 p728000.3 p73170y.5 p731702.5 p413000_g1D.5
## Min. :11.56 Min. :1.000 Min. :1953 Min. :1.000 Min. :0.000
## 1st Qu.:30.47 1st Qu.:2.000 1st Qu.:1967 1st Qu.:2.000 1st Qu.:0.000
## Median :54.55 Median :2.000 Median :1972 Median :2.000 Median :0.000
## Mean :52.30 Mean :1.946 Mean :1972 Mean :1.904 Mean :0.173
## 3rd Qu.:73.38 3rd Qu.:2.000 3rd Qu.:1976 3rd Qu.:2.000 3rd Qu.:0.000
## Max. :88.96 Max. :2.000 Max. :1988 Max. :2.000 Max. :1.000
## NA's :2412 NA's :1006 NA's :6230 NA's :2129 NA's :6230
## p414000_g1D.5 p400000_g1.5 p403000_g1.5 p731802_g3.5
## Min. :0.000 Min. :1.000 Min. :1.000 Min. : 9.00
## 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:13.00
## Median :0.000 Median :1.000 Median :1.000 Median :15.00
## Mean :0.179 Mean :1.327 Mean :1.273 Mean :14.61
## 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:16.00
## Max. :1.000 Max. :3.000 Max. :3.000 Max. :18.00
## NA's :6273 NA's :6230 NA's :3780 NA's :2154
## p731852_g3.5 p731904_g14.5 p731954_g14.5 math_grade reading_grade
## Min. : 9.00 Min. :11.74 Min. :11.56 Min. :1.00 Min. :1.00
## 1st Qu.:13.00 1st Qu.:44.92 1st Qu.:32.50 1st Qu.:1.00 1st Qu.:2.00
## Median :15.00 Median :54.55 Median :56.00 Median :2.00 Median :2.00
## Mean :14.49 Mean :54.56 Mean :53.85 Mean :1.97 Mean :2.08
## 3rd Qu.:16.00 3rd Qu.:70.50 3rd Qu.:73.55 3rd Qu.:2.00 3rd Qu.:3.00
## Max. :18.00 Max. :88.96 Max. :88.96 Max. :5.00 Max. :6.00
## NA's :2769 NA's :2686 NA's :3845 NA's :2768 NA's :2773
## p66600a.5 p73170y.6 p731702.6 p400000_g1.6 p403000_g1.6
## Min. :1.000 Min. :1950 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1969 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :1974 Median :2.000 Median :1.000 Median :1.000
## Mean :2.979 Mean :1973 Mean :1.896 Mean :1.293 Mean :1.247
## 3rd Qu.:3.000 3rd Qu.:1978 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :7.000 Max. :1988 Max. :2.000 Max. :3.000 Max. :3.000
## NA's :2256 NA's :6265 NA's :2383 NA's :6265 NA's :6267
## p731802_g3.6 p731852_g3.6 p731904_g14.6 p731954_g14.6
## Min. : 9.00 Min. : 9.00 Min. :11.74 Min. :11.74
## 1st Qu.:13.00 1st Qu.:13.00 1st Qu.:30.78 1st Qu.:28.48
## Median :15.00 Median :15.00 Median :50.37 Median :53.15
## Mean :14.68 Mean :14.54 Mean :49.24 Mean :50.24
## 3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.:68.09 3rd Qu.:70.57
## Max. :18.00 Max. :18.00 Max. :88.70 Max. :86.72
## NA's :2407 NA's :3028 NA's :5856 NA's :6113
## p724102.6 p724101.6 p66600a.6 pb01030.4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000
## Median :2.000 Median :2.000 Median :3.000 Median :4.000
## Mean :2.061 Mean :2.117 Mean :3.201 Mean :3.689
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :2613 NA's :2617 NA's :6161 NA's :1479
## judgRE2 pb01040.4 judgMA2 p73170y.4 sex_p
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1953 Min. :1.0
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:1970 1st Qu.:2.0
## Median :3.000 Median :3.000 Median :3.000 Median :1974 Median :2.0
## Mean :3.421 Mean :3.563 Mean :3.558 Mean :1974 Mean :1.9
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:1978 3rd Qu.:2.0
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :1990 Max. :2.0
## NA's :1489 NA's :1490 NA's :1482 NA's :5989 NA's :1474
## p412000.4 p413000_g1D.4 p414000_g1D.4 p400000_g1.4
## Min. :1.000 Min. :0.0000 Min. :0.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000
## Median :2.000 Median :0.0000 Median :0.000 Median :1.000
## Mean :1.846 Mean :0.1518 Mean :0.159 Mean :1.473
## 3rd Qu.:2.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :4.000 Max. :1.0000 Max. :1.000 Max. :3.000
## NA's :5264 NA's :1557 NA's :3219 NA's :5989
## p403000_g1.4 p731802_g3.4 p731852_g3.4 p731904_g14.4
## Min. :1.000 Min. : 9.00 Min. : 9.00 Min. :11.56
## 1st Qu.:1.000 1st Qu.:13.00 1st Qu.:13.00 1st Qu.:28.48
## Median :1.000 Median :13.00 Median :13.00 Median :50.73
## Mean :1.491 Mean :14.43 Mean :14.29 Mean :50.02
## 3rd Qu.:1.000 3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.:68.55
## Max. :3.000 Max. :18.00 Max. :18.00 Max. :88.96
## NA's :6124 NA's :1520 NA's :2278 NA's :6015
## p731954_g14.4 p32903a.4 p32903b.4 p32903c.4
## Min. :13.34 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:29.16 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:4.000
## Median :51.56 Median :3.000 Median :4.000 Median :5.000
## Mean :51.66 Mean :2.917 Mean :3.478 Mean :4.603
## 3rd Qu.:72.30 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :88.70 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :6144 NA's :1481 NA's :1489 NA's :1482
## p32903d.4 p66600a.4 wave_w1 wave_w2
## Min. :1.000 Min. :1.000 Min. :0.00000 Min. :0.00000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :3.000 Median :3.000 Median :0.00000 Median :0.00000
## Mean :2.814 Mean :2.941 Mean :0.08423 Mean :0.08312
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :5.000 Max. :8.000 Max. :1.00000 Max. :1.00000
## NA's :1486 NA's :1692
## wave_w3 wave_w4 wave_w5 wave_w6
## Min. :0.0000 Min. :1 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1 Median :1.0000 Median :1.0000
## Mean :0.9762 Mean :1 Mean :0.8986 Mean :0.8888
## 3rd Qu.:1.0000 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1 Max. :1.0000 Max. :1.0000
##
## mag1v051_c mag1r141_c mag1g171_c mag1d131_c
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:0.000
## Median :1.0000 Median :0.0000 Median :1.000 Median :0.000
## Mean :0.5735 Mean :0.2543 Mean :0.824 Mean :0.482
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.000
## NA's :729 NA's :504 NA's :477 NA's :981
## mag1d132_c mag1z061_c mag1v01s_c mag1z20s_c
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.0000 Median :1.0000 Median :2.000 Median :2.000
## Mean :0.5868 Mean :0.5501 Mean :1.329 Mean :1.417
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :1.0000 Max. :1.0000 Max. :2.000 Max. :2.000
## NA's :1345 NA's :761 NA's :880 NA's :614
## mag1d09s_c mag1z121_c mag1g181_c mag1d081_c
## Min. :0.00 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :2.00 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :1.69 Mean :0.1146 Mean :0.5189 Mean :0.6271
## 3rd Qu.:3.00 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :3.00 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :687 NA's :696 NA's :703 NA's :497
## mag1r151_c mag1z111_c mag1v021_c mag1z071_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.5912 Mean :0.7145 Mean :0.3813 Mean :0.3497
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :526 NA's :929 NA's :1011 NA's :933
## mag1d041_c mag1g031_c mag1z161_c mag1v101_c
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.000 Median :1.0000
## Mean :0.7118 Mean :0.5016 Mean :0.462 Mean :0.7441
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## NA's :635 NA's :828 NA's :1017 NA's :806
## mag1r19s_c math1 mag1_sc2 mag1_sc1u
## Min. :0.0000 Min. :-5.2733 Min. :0.4784 Min. :-3.5595
## 1st Qu.:0.0000 1st Qu.:-0.7557 1st Qu.:0.4969 1st Qu.: 0.9581
## Median :1.0000 Median :-0.0133 Median :0.5273 Median : 1.7000
## Mean :0.5518 Mean : 0.0023 Mean :0.5636 Mean : 1.7159
## 3rd Qu.:1.0000 3rd Qu.: 0.7387 3rd Qu.:0.5817 3rd Qu.: 2.4526
## Max. :1.0000 Max. : 4.5997 Max. :2.0658 Max. : 6.3128
## NA's :1918 NA's :372 NA's :372 NA's :372
## mag1_sc2u mag1v051_sc2g2_c mag2v071_c mag2r031_c
## Min. :0.4784 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4969 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.5273 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.5636 Mean :0.7364 Mean :0.6534 Mean :0.8422
## 3rd Qu.:0.5816 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :2.0657 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :372 NA's :312 NA's :442 NA's :232
## mag2d061_c mag1d131_sc2g2_c mag2r131_c mag2v121_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :1.000
## Mean :0.6178 Mean :0.5565 Mean :0.4462 Mean :0.624
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## NA's :411 NA's :532 NA's :609 NA's :285
## mag2q061_c mag2r111_c mag1d09s_sc2g2_c mag1z121_sc2g2_c
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:2.000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :3.000 Median :0.0000
## Mean :0.1512 Mean :0.5103 Mean :2.752 Mean :0.2098
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :4.000 Max. :1.0000
## NA's :389 NA's :342 NA's :428 NA's :253
## mag2g12s_c mag1d081_sc2g2_c mag2g021_c mag2r151_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :3.000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :3.007 Mean :0.8339 Mean :0.3957 Mean :0.6466
## 3rd Qu.:4.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :4.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :886 NA's :258 NA's :307 NA's :242
## mag1v021_sc2g2_c mag1z071_sc2g2_c mag2d101_c mag1g031_sc2g2_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.5402 Mean :0.5892 Mean :0.7914 Mean :0.7574
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :363 NA's :317 NA's :291 NA's :317
## mag2v041_c mag2q011_c mag1r19s_sc2g2_c mag2g091_c
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:4.000 1st Qu.:0.0000
## Median :1.000 Median :1.0000 Median :5.000 Median :1.0000
## Mean :0.641 Mean :0.5555 Mean :4.647 Mean :0.6193
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:5.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000 Max. :5.000 Max. :1.0000
## NA's :323 NA's :507 NA's :964 NA's :585
## mag2q051_c math2 mag2_sc2 mag2_sc2u
## Min. :0.0000 Min. :-4.54340 Min. :0.4422 Min. :0.4422
## 1st Qu.:1.0000 1st Qu.:-0.79733 1st Qu.:0.4575 1st Qu.:0.4574
## Median :1.0000 Median :-0.02631 Median :0.4838 Median :0.4845
## Mean :0.8198 Mean : 0.00461 Mean :0.5254 Mean :0.5259
## 3rd Qu.:1.0000 3rd Qu.: 0.76733 3rd Qu.:0.5453 3rd Qu.:0.5453
## Max. :1.0000 Max. : 4.35296 Max. :1.7278 Max. :1.7282
## NA's :506 NA's :173 NA's :173 NA's :173
## mag2_sc1u dgci110s_sc2g2_c dgci120s_sc2g2_c dgg2_sc3a
## Min. :-2.321 Min. : 0.00 Min. : 0.00 Min. : 1.00
## 1st Qu.: 1.425 1st Qu.:10.00 1st Qu.:12.00 1st Qu.:21.00
## Median : 2.197 Median :12.00 Median :14.00 Median :26.00
## Mean : 2.227 Mean :12.72 Mean :14.49 Mean :26.88
## 3rd Qu.: 2.989 3rd Qu.:15.00 3rd Qu.:17.00 3rd Qu.:32.00
## Max. : 6.574 Max. :21.00 Max. :21.00 Max. :42.00
## NA's :173 NA's :249 NA's :349 NA's :228
## dgci2103_sc2g2_c dgci2105_sc2g2_c dgci2104_sc2g2_c dgci2107_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8269 Mean :0.8328 Mean :0.6095 Mean :0.7734
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :304 NA's :325 NA's :325 NA's :298
## dgci2108_c dgci2109_c dgci2204_sc2g2_c dgci2205_sc2g2_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.1741 Mean :0.1191 Mean :0.7274 Mean :0.7902
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :411 NA's :481 NA's :288 NA's :314
## dgci2203_sc2g2_c dgci2106_sc2g2_c dgci2206_sc2g2_c dgci2207_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.8362 Mean :0.6316 Mean :0.3167 Mean :0.2243
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :297 NA's :317 NA's :337 NA's :376
## reasoning rsci0001_c rsci0002_c rsci0003_c
## Min. : 0.000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.: 6.000 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:1.0000
## Median : 7.000 Median :1.0000 Median :1.000 Median :1.0000
## Mean : 6.773 Mean :0.8731 Mean :0.982 Mean :0.9537
## 3rd Qu.: 8.000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :12.000 Max. :1.0000 Max. :1.000 Max. :1.0000
## NA's :238 NA's :374 NA's :383 NA's :465
## rsci0004_c rsci0005_c rsci0006_c rsci0007_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.9627 Mean :0.8848 Mean :0.9025 Mean :0.9255
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :436 NA's :498 NA's :568 NA's :566
## rsci0008_c rsci0009_c rsci0010_c rsci0011_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.9272 Mean :0.9447 Mean :0.8881 Mean :0.9432
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :610 NA's :643 NA's :710 NA's :797
## rsci0012_c rsci0013_c rsci0014_c rsci0015_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8806 Mean :0.8656 Mean :0.9758 Mean :0.9149
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :929 NA's :1041 NA's :1247 NA's :1394
## rsci0016_c rsci0017_c rsci0018_c rsci0019_c
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.000 Median :1.0000 Median :1.0000
## Mean :0.8557 Mean :0.939 Mean :0.9696 Mean :0.9319
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## NA's :1682 NA's :1863 NA's :2059 NA's :2275
## rsci0020_c rsci0021_c rsci0022_c rsci0023_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.951 Mean :0.9617 Mean :0.9556 Mean :0.9482
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :2504 NA's :2686 NA's :2959 NA's :3117
## rsci0024_c rsci0025_c rsci0026_c rsci0027_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.937 Mean :0.948 Mean :0.958 Mean :0.954
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :3295 NA's :3781 NA's :3990 NA's :4207
## rsci0028_c rsci0029_c rsci0030_c rsci0031_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.944 Mean :0.886 Mean :0.923 Mean :0.933
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :4420 NA's :4738 NA's :4890 NA's :5064
## rsci0032_c rsci0033_c rsci0034_c rsci0035_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.892 Mean :0.892 Mean :0.904 Mean :0.926
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :5253 NA's :5383 NA's :5524 NA's :5610
## rsci0036_c rsci0037_c rsci0038_c rsci0039_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.886 Mean :0.901 Mean :0.904 Mean :0.899
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :5715 NA's :5797 NA's :5850 NA's :5923
## rsci0040_c rsci0041_c rsci0042_c rsci0043_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.871 Mean :0.831 Mean :0.875 Mean :0.835
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :5984 NA's :6020 NA's :6059 NA's :6085
## rsci0044_c rsci0045_c rsci0046_c rsci0047_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.819 Mean :0.826 Mean :0.753 Mean :0.679
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :6119 NA's :6145 NA's :6158 NA's :6181
## rsci0048_c rsci0049_c rsci0050_c rsci0051_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.745 Mean :0.798 Mean :0.793 Mean :0.698
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :6183 NA's :6112 NA's :6190 NA's :6224
## rsci0052_c rsci0053_c rsci0054_c rsci0055_c rsci0056_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.75 1st Qu.:1.00
## Median :1.000 Median :1.000 Median :1.000 Median :1.00 Median :1.00
## Mean :0.717 Mean :0.722 Mean :0.783 Mean :0.75 Mean :0.78
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.00
## NA's :6248 NA's :6261 NA's :6271 NA's :6280 NA's :6281
## rsci0057_c rsci0058_c rsci0059_c rsci0060_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.691 Mean :0.642 Mean :0.681 Mean :0.787
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :6285 NA's :6287 NA's :6293 NA's :6293
## rsci0061_c rsci0062_c rsci0063_c rsci0064_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.717 Mean :0.581 Mean :0.625 Mean :0.658
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :6294 NA's :6297 NA's :6300 NA's :6302
## rsci0065_c rsci0066_c rsci0067_c rsci0068_c rsci0069_c
## Min. :0.000 Min. :0.000 Min. :0.0 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :0.5 Median :1.000 Median :1.000
## Mean :0.545 Mean :0.581 Mean :0.5 Mean :0.645 Mean :0.714
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.0 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.0 Max. :1.000 Max. :1.000
## NA's :6307 NA's :6309 NA's :6312 NA's :6309 NA's :6312
## rsci0070_c rsg2_sc3 rxg20001_c rxg20002_c
## Min. :0.000 Min. : 0.00 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:15.00 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.000 Median :21.00 Median :1.0000 Median :1.0000
## Mean :0.556 Mean :21.36 Mean :0.9516 Mean :0.8499
## 3rd Qu.:1.000 3rd Qu.:27.00 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.000 Max. :69.00 Max. :1.0000 Max. :1.0000
## NA's :6313 NA's :226 NA's :642 NA's :772
## rxg20003_c rxg20004_c rxg20005_c rxg20006_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.7878 Mean :0.8102 Mean :0.8627 Mean :0.8186
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1174 NA's :1020 NA's :1269 NA's :1653
## rxg20007_c rxg20008_c rxg20009_c rxg20010_c
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.0000 Median :1.0000 Median :1.000 Median :1.000
## Mean :0.7824 Mean :0.6824 Mean :0.618 Mean :0.597
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.000
## NA's :2006 NA's :3043 NA's :3448 NA's :3745
## rxg20011_c rxg20012_c rxg20013_c rxg20014_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.735 Mean :0.637 Mean :0.789 Mean :0.638
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :4089 NA's :4597 NA's :4759 NA's :5079
## rxg20015_c rxg20016_c rxg20017_c rxg20018_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :0.692 Mean :0.621 Mean :0.639 Mean :0.572
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## NA's :5312 NA's :5453 NA's :5669 NA's :5756
## rxg20019_c rxg20020_c read2 reg50110_sc2g4_c
## Min. :0.000 Min. :0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 4.000 1st Qu.:1.0000
## Median :1.000 Median :0.000 Median : 7.000 Median :1.0000
## Mean :0.506 Mean :0.405 Mean : 7.251 Mean :0.8963
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:10.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.000 Max. :20.000 Max. :1.0000
## NA's :5876 NA's :5930 NA's :414 NA's :911
## mag5d041_sc2g4_c mag4q101_c mag4r021_c mag5v271_sc2g4_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.5625 Mean :0.1294 Mean :0.3981 Mean :0.2612
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :952 NA's :1502 NA's :974 NA's :1389
## mag4q011_c mag4r071_c mag4d131_c mag5q231_sc2g4_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.1617 Mean :0.2926 Mean :0.9274 Mean :0.2743
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1132 NA's :1087 NA's :900 NA's :1757
## mag5q301_sc2g4_c mag4v121_c mag5d051_sc2g4_c mag4q060_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.000 Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.295 Mean :0.5171 Mean :0.7966 Mean :0.0921
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1007 NA's :1024 NA's :1036 NA's :1857
## mag4d031_c mag5q140_sc2g4_c mag4v111_c mag4r041_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.3896 Mean :0.5172 Mean :0.1284 Mean :0.9037
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1219 NA's :2067 NA's :2190 NA's :1426
## mag4r042_c mag4q051_c mag4q091_c mag4q092_c
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0000
## Median :1.0000 Median :1.000 Median :1.000 Median :1.0000
## Mean :0.6587 Mean :0.734 Mean :0.683 Mean :0.5041
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.000 Max. :1.0000
## NA's :1740 NA's :1588 NA's :1971 NA's :2462
## mag4d14s_c mag5v071_sc2g4_c mag5r191_sc2g4_c mag4d081_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:4.000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :5.000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :4.338 Mean :0.8063 Mean :0.3582 Mean :0.7817
## 3rd Qu.:5.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :5.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :2371 NA's :2407 NA's :2686 NA's :2780
## math4 mag4_sc2 mag4_sc1u mag4_sc2u
## Min. :-4.9051 Min. :0.4916 Min. :-0.285 Min. :0.4916
## 1st Qu.:-0.6839 1st Qu.:0.5107 1st Qu.: 3.936 1st Qu.:0.5107
## Median : 0.0569 Median :0.5435 Median : 4.677 Median :0.5435
## Mean : 0.0022 Mean :0.5817 Mean : 4.622 Mean :0.5817
## 3rd Qu.: 0.7742 3rd Qu.:0.6133 3rd Qu.: 5.394 3rd Qu.:0.6133
## Max. : 4.8841 Max. :1.8822 Max. : 9.504 Max. :1.8821
## NA's :849 NA's :849 NA's :849 NA's :849
## reg5012s_sc2g4_c reg50130_sc2g4_c reg50140_sc2g4_c reg50150_sc2g4_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :2.000 Median :1.0000 Median :1.0000 Median :1.0000
## Mean :1.595 Mean :0.7644 Mean :0.7003 Mean :0.5857
## 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :2.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1387 NA's :929 NA's :1115 NA's :1042
## reg5016s_sc2g4_c reg50170_sc2g4_c reg50210_sc2g4_c reg50220_sc2g4_c
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:0.000
## Median :4.000 Median :0.0000 Median :1.000 Median :0.000
## Mean :3.484 Mean :0.2522 Mean :0.856 Mean :0.481
## 3rd Qu.:5.000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :5.000 Max. :1.0000 Max. :1.000 Max. :1.000
## NA's :1624 NA's :1015 NA's :952 NA's :1228
## reg50230_sc2g4_c reg50240_sc2g4_c reg50250_sc2g4_c reg5026s_sc2g4_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.000 Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.814 Mean :0.6919 Mean :0.5978 Mean :0.2471
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1054 NA's :1099 NA's :1179 NA's :1791
## reg50310_sc2g4_c reg50320_sc2g4_c reg50330_sc2g4_c reg50340_sc2g4_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :1.000
## Mean :0.7893 Mean :0.8285 Mean :0.8354 Mean :0.679
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## NA's :1384 NA's :1408 NA's :1414 NA's :1629
## reg50350_sc2g4_c reg50360_sc2g4_c reg50370_sc2g4_c reg50410_sc2g4_c
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.5399 Mean :0.7794 Mean :0.6323 Mean :0.4963
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :1584 NA's :1749 NA's :1907 NA's :2433
## reg5042s_sc2g4_c reg50430_sc2g4_c reg50440_sc2g4_c reg5045s_sc2g4_c
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :3.000 Median :0.0000 Median :0.0000 Median :2.000
## Mean :2.207 Mean :0.2319 Mean :0.3546 Mean :1.303
## 3rd Qu.:3.000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:2.000
## Max. :3.000 Max. :1.0000 Max. :1.0000 Max. :2.000
## NA's :2652 NA's :2894 NA's :3049 NA's :3109
## reg50460_sc2g4_c reg50510_sc2g4_c reg5052s_sc2g4_c reg50530_sc2g4_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:2.000 1st Qu.:0.000
## Median :0.000 Median :1.000 Median :3.000 Median :0.000
## Mean :0.409 Mean :0.736 Mean :2.361 Mean :0.355
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :3.000 Max. :1.000
## NA's :3322 NA's :3557 NA's :3875 NA's :3990
## reg50540_sc2g4_c reg5055s_sc2g4_c reg50560_sc2g4_c reg50570_sc2g4_c
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :2.000 Median :0.000 Median :1.000
## Mean :0.596 Mean :1.656 Mean :0.416 Mean :0.513
## 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :3.000 Max. :1.000 Max. :1.000
## NA's :4003 NA's :4294 NA's :4268 NA's :4248
## reg4_sc1u reg4_sc2u ex20100.3.x ID_cc.3.y
## Min. :-5.6656 Min. :0.3780 Min. :1 Min. :1.003e+09
## 1st Qu.:-1.5096 1st Qu.:0.4317 1st Qu.:1 1st Qu.:1.003e+09
## Median :-0.5944 Median :0.5027 Median :1 Median :1.003e+09
## Mean :-0.5964 Mean :0.5598 Mean :1 Mean :1.003e+09
## 3rd Qu.: 0.2583 3rd Qu.:0.6256 3rd Qu.:1 3rd Qu.:1.003e+09
## Max. : 3.8394 Max. :2.0914 Max. :1 Max. :1.003e+09
## NA's :851 NA's :851 NA's :879 NA's :879
## mig_t e76212y_D.3 sex_t ex20100.4.x
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1
## Median :3.000 Median :3.000 Median :2.000 Median :1
## Mean :2.929 Mean :3.342 Mean :1.949 Mean :1
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:1
## Max. :3.000 Max. :5.000 Max. :2.000 Max. :1
## NA's :1075 NA's :1167 NA's :1048 NA's :2014
## ID_cc.4.y e400000.4 e76212y_D.4 e762110.4
## Min. :1.003e+09 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.003e+09 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.003e+09 Median :3.000 Median :3.000 Median :2.000
## Mean :1.003e+09 Mean :2.914 Mean :3.305 Mean :1.939
## 3rd Qu.:1.003e+09 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :1.003e+09 Max. :3.000 Max. :5.000 Max. :2.000
## NA's :2014 NA's :5014 NA's :2400 NA's :2233
## ex20100.5.x ID_cc.5.y e400000.5 e76212y_D.5
## Min. :1 Min. :1.003e+10 Min. :1.000 Min. :2.000
## 1st Qu.:1 1st Qu.:1.003e+10 1st Qu.:3.000 1st Qu.:2.000
## Median :1 Median :1.003e+10 Median :3.000 Median :3.000
## Mean :1 Mean :1.003e+10 Mean :2.918 Mean :3.344
## 3rd Qu.:1 3rd Qu.:1.003e+10 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :1 Max. :1.003e+10 Max. :3.000 Max. :5.000
## NA's :3936 NA's :3936 NA's :4050 NA's :4085
## e762110.5 ex20100.6.x ID_cc.6.y e400000.6
## Min. :1.000 Min. :1 Min. :1.003e+10 Min. :3
## 1st Qu.:2.000 1st Qu.:1 1st Qu.:1.003e+10 1st Qu.:3
## Median :2.000 Median :1 Median :1.003e+10 Median :3
## Mean :1.901 Mean :1 Mean :1.003e+10 Mean :3
## 3rd Qu.:2.000 3rd Qu.:1 3rd Qu.:1.003e+10 3rd Qu.:3
## Max. :2.000 Max. :1 Max. :1.003e+10 Max. :3
## NA's :4047 NA's :4206 NA's :4206 NA's :6312
## e76212y_D.6 e762110.6 ID_cc.3 ex20100.3.y
## Min. :2.000 Min. :1.000 Min. :1.003e+09 Min. :1
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.003e+09 1st Qu.:1
## Median :3.000 Median :2.000 Median :1.003e+09 Median :1
## Mean :3.275 Mean :1.919 Mean :1.003e+09 Mean :1
## 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:1.003e+09 3rd Qu.:1
## Max. :5.000 Max. :2.000 Max. :1.003e+09 Max. :1
## NA's :4313 NA's :4294 NA's :879 NA's :879
## sen_class mig_class ses_class ID_cc.4
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. :1.003e+09
## 1st Qu.: 5.000 1st Qu.: 8.00 1st Qu.: 8.00 1st Qu.:1.003e+09
## Median : 7.000 Median : 21.00 Median : 17.00 Median :1.003e+09
## Mean : 9.664 Mean : 26.55 Mean : 20.72 Mean :1.003e+09
## 3rd Qu.:12.000 3rd Qu.: 40.00 3rd Qu.: 29.00 3rd Qu.:1.003e+09
## Max. :88.000 Max. :100.00 Max. :100.00 Max. :1.003e+09
## NA's :4507 NA's :1388 NA's :2490 NA's :2014
## ex20100.4.y e19001c_D.4 ID_cc.5 ex20100.5.y
## Min. :1 Min. : 0.000 Min. :1.003e+10 Min. :1
## 1st Qu.:1 1st Qu.: 5.000 1st Qu.:1.003e+10 1st Qu.:1
## Median :1 Median : 7.000 Median :1.003e+10 Median :1
## Mean :1 Mean : 9.638 Mean :1.003e+10 Mean :1
## 3rd Qu.:1 3rd Qu.:13.000 3rd Qu.:1.003e+10 3rd Qu.:1
## Max. :1 Max. :35.000 Max. :1.003e+10 Max. :1
## NA's :2014 NA's :4929 NA's :3936 NA's :3936
## e451000_D.5 e79201c_D.5 ID_cc.6 ex20100.6.y
## Min. : 0.00 Min. : 0.00 Min. :1.003e+10 Min. :1
## 1st Qu.: 8.00 1st Qu.: 8.00 1st Qu.:1.003e+10 1st Qu.:1
## Median : 19.00 Median :17.00 Median :1.003e+10 Median :1
## Mean : 25.67 Mean :19.66 Mean :1.003e+10 Mean :1
## 3rd Qu.: 38.00 3rd Qu.:29.00 3rd Qu.:1.003e+10 3rd Qu.:1
## Max. :100.00 Max. :96.00 Max. :1.003e+10 Max. :1
## NA's :4045 NA's :4541 NA's :4206 NA's :4206
## birthday_c testday_c age_c age_p edu
## Min. :2004 Min. :2014 Min. :6.083 Min. :18.00 Min. : 9.00
## 1st Qu.:2006 1st Qu.:2014 1st Qu.:7.420 1st Qu.:35.00 1st Qu.:13.00
## Median :2006 Median :2014 Median :7.671 Median :39.00 Median :15.00
## Mean :2006 Mean :2014 Mean :7.729 Mean :39.07 Mean :14.92
## 3rd Qu.:2006 3rd Qu.:2014 3rd Qu.:8.000 3rd Qu.:43.00 3rd Qu.:18.00
## Max. :2008 Max. :2014 Max. :9.503 Max. :66.00 Max. :18.00
## NA's :1 NA's :62 NA's :63 NA's :1464 NA's :1070
## language read2Z.V1 reasoningZ.V1 eduZ.V1
## Min. :0.0000 Min. :-1.7030 Min. :-2.58924 Min. :-2.5416
## 1st Qu.:0.0000 1st Qu.:-0.7635 1st Qu.:-0.29552 1st Qu.:-0.8242
## Median :0.0000 Median :-0.0589 Median : 0.08677 Median : 0.0345
## Mean :0.1875 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:0.0000 3rd Qu.: 0.6457 3rd Qu.: 0.46906 3rd Qu.: 1.3225
## Max. :1.0000 Max. : 2.9945 Max. : 1.99821 Max. : 1.3225
## NA's :1557 NA's :414 NA's :238 NA's :1070
###select outcome data
datoutcome <- select(data7, ID_t, ID_e, ID_i.4, math2, read2, read2Z)
datoutcome$data7.ID_t <- datoutcome$ID_t
summary(datoutcome)
## ID_t ID_e ID_i.4 math2
## Min. :2000568 Min. :1011403 Min. :1002555 Min. :-4.54340
## 1st Qu.:3005629 1st Qu.:1011639 1st Qu.:1002646 1st Qu.:-0.79733
## Median :3007578 Median :1011849 Median :1002743 Median :-0.02631
## Mean :2924479 Mean :1011871 Mean :1002740 Mean : 0.00461
## 3rd Qu.:3017798 3rd Qu.:1012126 3rd Qu.:1002835 3rd Qu.: 0.76733
## Max. :3023458 Max. :1012342 Max. :1002928 Max. : 4.35296
## NA's :823 NA's :173
## read2 read2Z.V1 data7.ID_t
## Min. : 0.000 Min. :-1.7030 Min. :2000568
## 1st Qu.: 4.000 1st Qu.:-0.7635 1st Qu.:3005629
## Median : 7.000 Median :-0.0589 Median :3007578
## Mean : 7.251 Mean : 0.0000 Mean :2924479
## 3rd Qu.:10.000 3rd Qu.: 0.6457 3rd Qu.:3017798
## Max. :20.000 Max. : 2.9945 Max. :3023458
## NA's :414 NA's :414
###multiple imputation
dataMI <- data.frame(data7$ID_t, data7$sex_c, data7$age_c,
data7$reasoning, data7$reasoningZ, data7$math_grade, data7$reading_grade,
data7$judgMA1, data7$judgRE1,
data7$edu, data7$eduZ, data7$language, data7$sen,
data7$judgMA1T, data7$judgRE1T)
summary(dataMI)
## data7.ID_t data7.sex_c data7.age_c data7.reasoning
## Min. :2000568 Min. :1.000 Min. :6.083 Min. : 0.000
## 1st Qu.:3005629 1st Qu.:1.000 1st Qu.:7.420 1st Qu.: 6.000
## Median :3007578 Median :2.000 Median :7.671 Median : 7.000
## Mean :2924479 Mean :1.511 Mean :7.729 Mean : 6.773
## 3rd Qu.:3017798 3rd Qu.:2.000 3rd Qu.:8.000 3rd Qu.: 8.000
## Max. :3023458 Max. :2.000 Max. :9.503 Max. :12.000
## NA's :1 NA's :63 NA's :238
## data7.reasoningZ data7.math_grade data7.reading_grade data7.judgMA1
## Min. :-2.58924 Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:-0.29552 1st Qu.:1.00 1st Qu.:2.00 1st Qu.:3.000
## Median : 0.08677 Median :2.00 Median :2.00 Median :3.000
## Mean : 0.00000 Mean :1.97 Mean :2.08 Mean :3.577
## 3rd Qu.: 0.46906 3rd Qu.:2.00 3rd Qu.:3.00 3rd Qu.:4.000
## Max. : 1.99821 Max. :5.00 Max. :6.00 Max. :5.000
## NA's :238 NA's :2768 NA's :2773 NA's :1015
## data7.judgRE1 data7.edu data7.eduZ data7.language
## Min. :1.000 Min. : 9.00 Min. :-2.5416 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:13.00 1st Qu.:-0.8242 1st Qu.:0.0000
## Median :3.000 Median :15.00 Median : 0.0345 Median :0.0000
## Mean :3.346 Mean :14.92 Mean : 0.0000 Mean :0.1875
## 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.: 1.3225 3rd Qu.:0.0000
## Max. :5.000 Max. :18.00 Max. : 1.3225 Max. :1.0000
## NA's :1063 NA's :1070 NA's :1070 NA's :1557
## data7.sen data7.judgMA1T data7.judgRE1T
## Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.00 1st Qu.:3.000
## Median :1.000 Median :3.00 Median :3.000
## Mean :1.028 Mean :3.32 Mean :3.197
## 3rd Qu.:1.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :2.000 Max. :5.00 Max. :5.000
## NA's :78 NA's :1043 NA's :949
library(mice)
##
## Attache Paket: 'mice'
## Das folgende Objekt ist maskiert 'package:stats':
##
## filter
## Die folgenden Objekte sind maskiert von 'package:base':
##
## cbind, rbind
pMIss <- function(x){sum(is.na(x))/length(x)*100}
apply(dataMI, 2, pMIss)
## data7.ID_t data7.sex_c data7.age_c data7.reasoning
## 0.00000000 0.01577287 0.99369085 3.75394322
## data7.reasoningZ data7.math_grade data7.reading_grade data7.judgMA1
## 3.75394322 43.65930599 43.73817035 16.00946372
## data7.judgRE1 data7.edu data7.eduZ data7.language
## 16.76656151 16.87697161 16.87697161 24.55835962
## data7.sen data7.judgMA1T data7.judgRE1T
## 1.23028391 16.45110410 14.96845426
apply(dataMI, 1, pMIss)
## [1] 13.333333 0.000000 0.000000 0.000000 46.666667 0.000000 46.666667
## [8] 0.000000 0.000000 46.666667 13.333333 0.000000 46.666667 0.000000
## [15] 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000 0.000000
## [22] 46.666667 0.000000 20.000000 6.666667 0.000000 0.000000 0.000000
## [29] 0.000000 0.000000 0.000000 40.000000 0.000000 20.000000 20.000000
## [36] 33.333333 20.000000 20.000000 20.000000 33.333333 20.000000 20.000000
## [43] 20.000000 33.333333 0.000000 0.000000 0.000000 0.000000 0.000000
## [50] 0.000000 46.666667 20.000000 13.333333 0.000000 20.000000 13.333333
## [57] 0.000000 0.000000 0.000000 0.000000 6.666667 46.666667 0.000000
## [64] 0.000000 0.000000 0.000000 6.666667 0.000000 0.000000 46.666667
## [71] 0.000000 26.666667 26.666667 13.333333 20.000000 0.000000 46.666667
## [78] 13.333333 46.666667 13.333333 26.666667 0.000000 0.000000 20.000000
## [85] 0.000000 20.000000 20.000000 0.000000 13.333333 0.000000 20.000000
## [92] 20.000000 0.000000 20.000000 0.000000 13.333333 13.333333 0.000000
## [99] 0.000000 0.000000 40.000000 13.333333 13.333333 46.666667 33.333333
## [106] 46.666667 13.333333 20.000000 13.333333 46.666667 0.000000 0.000000
## [113] 46.666667 0.000000 13.333333 13.333333 0.000000 13.333333 20.000000
## [120] 0.000000 20.000000 60.000000 0.000000 0.000000 0.000000 0.000000
## [127] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 46.666667
## [134] 20.000000 6.666667 60.000000 0.000000 26.666667 46.666667 33.333333
## [141] 13.333333 0.000000 0.000000 0.000000 46.666667 46.666667 0.000000
## [148] 0.000000 46.666667 46.666667 20.000000 13.333333 46.666667 20.000000
## [155] 13.333333 46.666667 46.666667 20.000000 46.666667 46.666667 33.333333
## [162] 20.000000 46.666667 0.000000 0.000000 46.666667 0.000000 0.000000
## [169] 0.000000 13.333333 0.000000 20.000000 46.666667 46.666667 6.666667
## [176] 46.666667 20.000000 20.000000 26.666667 20.000000 33.333333 20.000000
## [183] 46.666667 0.000000 0.000000 46.666667 0.000000 0.000000 13.333333
## [190] 13.333333 0.000000 0.000000 0.000000 0.000000 0.000000 60.000000
## [197] 13.333333 46.666667 0.000000 0.000000 26.666667 20.000000 0.000000
## [204] 20.000000 46.666667 0.000000 0.000000 0.000000 40.000000 6.666667
## [211] 0.000000 0.000000 0.000000 13.333333 0.000000 13.333333 20.000000
## [218] 20.000000 13.333333 0.000000 13.333333 0.000000 6.666667 0.000000
## [225] 20.000000 0.000000 46.666667 0.000000 0.000000 0.000000 0.000000
## [232] 13.333333 13.333333 20.000000 13.333333 13.333333 20.000000 13.333333
## [239] 20.000000 13.333333 13.333333 13.333333 20.000000 33.333333 0.000000
## [246] 26.666667 53.333333 0.000000 0.000000 13.333333 20.000000 0.000000
## [253] 26.666667 20.000000 0.000000 13.333333 46.666667 0.000000 46.666667
## [260] 0.000000 13.333333 26.666667 13.333333 40.000000 40.000000 40.000000
## [267] 26.666667 26.666667 40.000000 26.666667 40.000000 40.000000 6.666667
## [274] 33.333333 0.000000 0.000000 46.666667 0.000000 26.666667 20.000000
## [281] 60.000000 13.333333 13.333333 26.666667 13.333333 13.333333 13.333333
## [288] 13.333333 13.333333 0.000000 13.333333 13.333333 13.333333 13.333333
## [295] 46.666667 13.333333 13.333333 13.333333 13.333333 0.000000 20.000000
## [302] 6.666667 53.333333 6.666667 20.000000 6.666667 40.000000 13.333333
## [309] 0.000000 33.333333 46.666667 40.000000 46.666667 60.000000 46.666667
## [316] 40.000000 33.333333 26.666667 33.333333 40.000000 13.333333 13.333333
## [323] 0.000000 0.000000 0.000000 26.666667 0.000000 13.333333 6.666667
## [330] 0.000000 0.000000 0.000000 0.000000 46.666667 0.000000 13.333333
## [337] 13.333333 0.000000 0.000000 46.666667 20.000000 46.666667 13.333333
## [344] 13.333333 46.666667 20.000000 13.333333 26.666667 40.000000 46.666667
## [351] 20.000000 0.000000 0.000000 13.333333 6.666667 13.333333 6.666667
## [358] 0.000000 13.333333 13.333333 0.000000 0.000000 13.333333 0.000000
## [365] 0.000000 20.000000 0.000000 13.333333 13.333333 0.000000 0.000000
## [372] 13.333333 20.000000 0.000000 0.000000 13.333333 26.666667 20.000000
## [379] 20.000000 20.000000 20.000000 0.000000 0.000000 0.000000 20.000000
## [386] 0.000000 0.000000 0.000000 0.000000 46.666667 13.333333 13.333333
## [393] 20.000000 0.000000 20.000000 0.000000 0.000000 0.000000 0.000000
## [400] 20.000000 46.666667 0.000000 0.000000 13.333333 0.000000 0.000000
## [407] 46.666667 0.000000 0.000000 20.000000 13.333333 20.000000 13.333333
## [414] 0.000000 13.333333 0.000000 20.000000 13.333333 0.000000 0.000000
## [421] 0.000000 0.000000 0.000000 13.333333 0.000000 0.000000 20.000000
## [428] 20.000000 33.333333 13.333333 33.333333 0.000000 0.000000 0.000000
## [435] 0.000000 0.000000 13.333333 20.000000 0.000000 46.666667 0.000000
## [442] 20.000000 20.000000 13.333333 13.333333 26.666667 13.333333 20.000000
## [449] 0.000000 20.000000 20.000000 46.666667 0.000000 0.000000 13.333333
## [456] 0.000000 0.000000 6.666667 13.333333 13.333333 0.000000 13.333333
## [463] 0.000000 0.000000 6.666667 0.000000 0.000000 0.000000 0.000000
## [470] 20.000000 0.000000 0.000000 20.000000 0.000000 0.000000 20.000000
## [477] 0.000000 13.333333 0.000000 0.000000 20.000000 13.333333 13.333333
## [484] 0.000000 0.000000 6.666667 26.666667 26.666667 46.666667 20.000000
## [491] 20.000000 20.000000 53.333333 20.000000 13.333333 0.000000 60.000000
## [498] 0.000000 46.666667 13.333333 0.000000 0.000000 0.000000 13.333333
## [505] 13.333333 26.666667 20.000000 46.666667 13.333333 13.333333 0.000000
## [512] 0.000000 0.000000 13.333333 0.000000 13.333333 20.000000 0.000000
## [519] 0.000000 0.000000 46.666667 0.000000 0.000000 26.666667 0.000000
## [526] 40.000000 13.333333 0.000000 13.333333 0.000000 6.666667 0.000000
## [533] 0.000000 6.666667 0.000000 13.333333 0.000000 0.000000 46.666667
## [540] 0.000000 0.000000 53.333333 0.000000 6.666667 26.666667 6.666667
## [547] 13.333333 0.000000 20.000000 20.000000 6.666667 0.000000 0.000000
## [554] 13.333333 0.000000 13.333333 0.000000 13.333333 26.666667 6.666667
## [561] 40.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [568] 6.666667 0.000000 0.000000 0.000000 13.333333 33.333333 46.666667
## [575] 0.000000 0.000000 6.666667 13.333333 0.000000 0.000000 20.000000
## [582] 13.333333 46.666667 0.000000 20.000000 46.666667 0.000000 13.333333
## [589] 13.333333 13.333333 13.333333 13.333333 46.666667 6.666667 0.000000
## [596] 13.333333 20.000000 13.333333 13.333333 13.333333 13.333333 13.333333
## [603] 13.333333 6.666667 46.666667 46.666667 13.333333 40.000000 26.666667
## [610] 13.333333 13.333333 13.333333 0.000000 0.000000 13.333333 0.000000
## [617] 13.333333 0.000000 13.333333 20.000000 13.333333 13.333333 20.000000
## [624] 0.000000 13.333333 13.333333 46.666667 46.666667 20.000000 46.666667
## [631] 0.000000 0.000000 20.000000 33.333333 0.000000 0.000000 0.000000
## [638] 0.000000 33.333333 0.000000 13.333333 13.333333 26.666667 0.000000
## [645] 26.666667 20.000000 46.666667 46.666667 20.000000 6.666667 46.666667
## [652] 20.000000 20.000000 6.666667 13.333333 13.333333 20.000000 0.000000
## [659] 0.000000 0.000000 13.333333 13.333333 60.000000 13.333333 13.333333
## [666] 20.000000 13.333333 13.333333 60.000000 13.333333 13.333333 46.666667
## [673] 13.333333 20.000000 0.000000 6.666667 20.000000 46.666667 13.333333
## [680] 20.000000 46.666667 13.333333 0.000000 0.000000 0.000000 13.333333
## [687] 0.000000 46.666667 46.666667 0.000000 0.000000 13.333333 0.000000
## [694] 0.000000 0.000000 20.000000 0.000000 13.333333 0.000000 20.000000
## [701] 13.333333 0.000000 46.666667 13.333333 0.000000 0.000000 0.000000
## [708] 20.000000 0.000000 13.333333 13.333333 20.000000 0.000000 0.000000
## [715] 13.333333 13.333333 0.000000 0.000000 0.000000 0.000000 0.000000
## [722] 0.000000 13.333333 13.333333 13.333333 13.333333 13.333333 13.333333
## [729] 13.333333 40.000000 13.333333 26.666667 13.333333 13.333333 0.000000
## [736] 13.333333 20.000000 40.000000 6.666667 13.333333 6.666667 0.000000
## [743] 13.333333 13.333333 0.000000 0.000000 0.000000 13.333333 33.333333
## [750] 20.000000 13.333333 0.000000 20.000000 26.666667 0.000000 13.333333
## [757] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## [764] 20.000000 13.333333 0.000000 0.000000 0.000000 0.000000 20.000000
## [771] 46.666667 6.666667 46.666667 0.000000 46.666667 13.333333 20.000000
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## [6231] 60.000000 13.333333 13.333333 20.000000 13.333333 20.000000 26.666667
## [6238] 26.666667 13.333333 33.333333 13.333333 26.666667 33.333333 40.000000
## [6245] 40.000000 60.000000 13.333333 60.000000 60.000000 26.666667 46.666667
## [6252] 13.333333 26.666667 20.000000 26.666667 60.000000 13.333333 13.333333
## [6259] 13.333333 26.666667 60.000000 26.666667 13.333333 26.666667 13.333333
## [6266] 46.666667 13.333333 33.333333 13.333333 13.333333 13.333333 60.000000
## [6273] 13.333333 13.333333 13.333333 13.333333 13.333333 13.333333 13.333333
## [6280] 26.666667 26.666667 26.666667 13.333333 13.333333 60.000000 13.333333
## [6287] 13.333333 33.333333 33.333333 13.333333 26.666667 33.333333 33.333333
## [6294] 13.333333 26.666667 13.333333 33.333333 33.333333 13.333333 73.333333
## [6301] 13.333333 26.666667 26.666667 26.666667 13.333333 26.666667 13.333333
## [6308] 66.666667 60.000000 46.666667 13.333333 13.333333 26.666667 26.666667
## [6315] 26.666667 13.333333 13.333333 13.333333 20.000000 60.000000 20.000000
## [6322] 26.666667 13.333333 40.000000 13.333333 13.333333 13.333333 26.666667
## [6329] 46.666667 13.333333 60.000000 13.333333 33.333333 13.333333 26.666667
## [6336] 40.000000 26.666667 33.333333 13.333333 60.000000
#Plot1
md.pattern(dataMI)
## data7.ID_t data7.sex_c data7.age_c data7.sen data7.reasoning
## 2462 1 1 1 1 1
## 13 1 1 1 1 1
## 6 1 1 1 1 1
## 981 1 1 1 1 1
## 171 1 1 1 1 1
## 1 1 1 1 1 1
## 455 1 1 1 1 1
## 15 1 1 1 1 1
## 1 1 1 1 1 1
## 10 1 1 1 1 1
## 2 1 1 1 1 1
## 15 1 1 1 1 1
## 19 1 1 1 1 1
## 14 1 1 1 1 1
## 1 1 1 1 1 1
## 6 1 1 1 1 1
## 1 1 1 1 1 1
## 91 1 1 1 1 1
## 20 1 1 1 1 1
## 8 1 1 1 1 1
## 8 1 1 1 1 1
## 1 1 1 1 1 1
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 1 1 1 1 1 1
## 1 1 1 1 1 1
## 3 1 1 1 1 1
## 4 1 1 1 1 1
## 94 1 1 1 1 1
## 109 1 1 1 1 1
## 30 1 1 1 1 1
## 506 1 1 1 1 1
## 2 1 1 1 1 1
## 4 1 1 1 1 1
## 1 1 1 1 1 1
## 15 1 1 1 1 1
## 21 1 1 1 1 1
## 9 1 1 1 1 1
## 2 1 1 1 1 1
## 2 1 1 1 1 1
## 2 1 1 1 1 1
## 396 1 1 1 1 1
## 2 1 1 1 1 1
## 3 1 1 1 1 1
## 178 1 1 1 1 1
## 21 1 1 1 1 1
## 73 1 1 1 1 1
## 3 1 1 1 1 1
## 2 1 1 1 1 1
## 2 1 1 1 1 1
## 8 1 1 1 1 1
## 5 1 1 1 1 1
## 4 1 1 1 1 1
## 1 1 1 1 1 1
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 3 1 1 1 1 1
## 16 1 1 1 1 1
## 1 1 1 1 1 1
## 18 1 1 1 1 1
## 17 1 1 1 1 1
## 7 1 1 1 1 1
## 97 1 1 1 1 1
## 90 1 1 1 1 0
## 2 1 1 1 1 0
## 30 1 1 1 1 0
## 8 1 1 1 1 0
## 24 1 1 1 1 0
## 1 1 1 1 1 0
## 2 1 1 1 1 0
## 1 1 1 1 1 0
## 1 1 1 1 1 0
## 4 1 1 1 1 0
## 3 1 1 1 1 0
## 37 1 1 1 1 0
## 11 1 1 1 1 0
## 1 1 1 1 1 0
## 6 1 1 1 1 0
## 3 1 1 1 1 0
## 5 1 1 1 1 0
## 19 1 1 1 0 1
## 21 1 1 1 0 1
## 1 1 1 1 0 1
## 6 1 1 1 0 1
## 1 1 1 1 0 1
## 3 1 1 1 0 1
## 1 1 1 1 0 1
## 6 1 1 1 0 1
## 4 1 1 1 0 1
## 1 1 1 1 0 1
## 1 1 1 1 0 1
## 1 1 1 1 0 1
## 3 1 1 1 0 1
## 6 1 1 1 0 1
## 3 1 1 1 0 1
## 20 1 1 0 1 1
## 16 1 1 0 1 1
## 1 1 1 0 1 1
## 2 1 1 0 1 1
## 1 1 1 0 1 1
## 1 1 1 0 1 1
## 5 1 1 0 1 1
## 4 1 1 0 1 1
## 2 1 1 0 1 1
## 1 1 1 0 1 1
## 6 1 1 0 1 0
## 2 1 1 0 1 0
## 1 1 1 0 1 0
## 1 1 1 0 0 1
## 1 1 0 1 1 1
## 0 1 63 78 238
## data7.reasoningZ data7.judgRE1T data7.judgMA1 data7.judgMA1T data7.judgRE1
## 2462 1 1 1 1 1
## 13 1 1 1 1 1
## 6 1 1 1 1 1
## 981 1 1 1 1 1
## 171 1 1 1 1 1
## 1 1 1 1 1 1
## 455 1 1 1 1 1
## 15 1 1 1 1 1
## 1 1 1 1 1 1
## 10 1 1 1 1 1
## 2 1 1 1 1 1
## 15 1 1 1 1 1
## 19 1 1 1 1 0
## 14 1 1 1 1 0
## 1 1 1 1 1 0
## 6 1 1 1 1 0
## 1 1 1 1 1 0
## 91 1 1 1 0 1
## 20 1 1 1 0 1
## 8 1 1 1 0 1
## 8 1 1 1 0 1
## 1 1 1 1 0 1
## 1 1 1 1 0 0
## 2 1 1 0 1 1
## 1 1 1 0 1 1
## 1 1 1 0 1 1
## 3 1 1 0 1 0
## 4 1 1 0 1 0
## 94 1 1 0 1 0
## 109 1 1 0 1 0
## 30 1 1 0 1 0
## 506 1 1 0 1 0
## 2 1 1 0 0 0
## 4 1 1 0 0 0
## 1 1 1 0 0 0
## 15 1 1 0 0 0
## 21 1 0 1 1 1
## 9 1 0 1 1 1
## 2 1 0 1 1 1
## 2 1 0 1 1 1
## 2 1 0 1 1 1
## 396 1 0 1 0 1
## 2 1 0 1 0 1
## 3 1 0 1 0 1
## 178 1 0 1 0 1
## 21 1 0 1 0 1
## 73 1 0 1 0 1
## 3 1 0 1 0 1
## 2 1 0 1 0 1
## 2 1 0 1 0 1
## 8 1 0 1 0 1
## 5 1 0 1 0 0
## 4 1 0 1 0 0
## 1 1 0 1 0 0
## 1 1 0 1 0 0
## 2 1 0 0 1 0
## 3 1 0 0 1 0
## 16 1 0 0 1 0
## 1 1 0 0 0 0
## 18 1 0 0 0 0
## 17 1 0 0 0 0
## 7 1 0 0 0 0
## 97 1 0 0 0 0
## 90 0 1 1 1 1
## 2 0 1 1 1 1
## 30 0 1 1 1 1
## 8 0 1 1 1 1
## 24 0 1 1 1 1
## 1 0 1 1 1 1
## 2 0 1 1 1 1
## 1 0 1 1 0 1
## 1 0 1 0 1 1
## 4 0 1 0 1 0
## 3 0 1 0 1 0
## 37 0 1 0 1 0
## 11 0 0 1 0 1
## 1 0 0 1 0 1
## 6 0 0 1 0 1
## 3 0 0 1 0 1
## 5 0 0 0 0 0
## 19 1 1 1 1 1
## 21 1 1 1 1 1
## 1 1 1 1 1 1
## 6 1 1 1 1 1
## 1 1 1 0 1 0
## 3 1 1 0 1 0
## 1 1 1 0 1 0
## 6 1 1 0 1 0
## 4 1 0 1 0 1
## 1 1 0 1 0 1
## 1 1 0 1 0 1
## 1 1 0 0 1 0
## 3 1 0 0 0 0
## 6 1 0 0 0 0
## 3 1 0 0 0 0
## 20 1 1 1 1 1
## 16 1 1 1 1 1
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 1 1 1 0 1 0
## 1 1 1 0 1 0
## 5 1 1 0 1 0
## 4 1 0 1 0 1
## 2 1 0 1 0 1
## 1 1 0 1 0 1
## 6 0 1 1 1 1
## 2 0 1 1 1 1
## 1 0 1 1 1 1
## 1 1 0 0 0 0
## 1 1 1 1 1 1
## 238 949 1015 1043 1063
## data7.edu data7.eduZ data7.language data7.math_grade data7.reading_grade
## 2462 1 1 1 1 1
## 13 1 1 1 1 0
## 6 1 1 1 0 1
## 981 1 1 1 0 0
## 171 1 1 0 1 1
## 1 1 1 0 0 1
## 455 1 1 0 0 0
## 15 0 0 1 1 1
## 1 0 0 1 0 1
## 10 0 0 1 0 0
## 2 0 0 0 1 1
## 15 0 0 0 0 0
## 19 1 1 1 1 1
## 14 1 1 1 0 0
## 1 1 1 0 1 1
## 6 1 1 0 0 0
## 1 0 0 0 0 0
## 91 1 1 1 1 1
## 20 1 1 1 0 0
## 8 1 1 0 1 1
## 8 1 1 0 0 0
## 1 0 0 1 0 0
## 1 1 1 1 1 1
## 2 1 1 1 1 1
## 1 1 1 1 0 0
## 1 1 1 0 0 0
## 3 1 1 1 1 1
## 4 1 1 1 0 0
## 94 0 0 1 1 1
## 109 0 0 1 0 0
## 30 0 0 0 1 1
## 506 0 0 0 0 0
## 2 0 0 1 1 1
## 4 0 0 1 0 0
## 1 0 0 0 1 1
## 15 0 0 0 0 0
## 21 1 1 1 1 1
## 9 1 1 1 0 0
## 2 1 1 0 1 1
## 2 1 1 0 0 0
## 2 0 0 1 0 0
## 396 1 1 1 1 1
## 2 1 1 1 1 0
## 3 1 1 1 0 1
## 178 1 1 1 0 0
## 21 1 1 0 1 1
## 73 1 1 0 0 0
## 3 0 0 1 1 1
## 2 0 0 1 0 0
## 2 0 0 0 1 1
## 8 0 0 0 0 0
## 5 1 1 1 1 1
## 4 1 1 1 0 0
## 1 1 1 0 0 0
## 1 0 0 1 0 0
## 2 0 0 1 1 1
## 3 0 0 1 0 0
## 16 0 0 0 0 0
## 1 1 1 1 0 0
## 18 0 0 1 1 1
## 17 0 0 1 0 0
## 7 0 0 0 1 1
## 97 0 0 0 0 0
## 90 1 1 1 1 1
## 2 1 1 1 1 0
## 30 1 1 1 0 0
## 8 1 1 0 1 1
## 24 1 1 0 0 0
## 1 0 0 1 1 1
## 2 0 0 1 0 0
## 1 1 1 0 0 0
## 1 0 0 1 0 0
## 4 0 0 1 1 1
## 3 0 0 1 0 0
## 37 0 0 0 0 0
## 11 1 1 1 1 1
## 1 1 1 1 0 1
## 6 1 1 1 0 0
## 3 1 1 0 0 0
## 5 0 0 0 0 0
## 19 1 1 1 1 1
## 21 1 1 1 0 0
## 1 1 1 0 1 1
## 6 1 1 0 0 0
## 1 0 0 1 1 1
## 3 0 0 1 0 0
## 1 0 0 0 1 1
## 6 0 0 0 0 0
## 4 1 1 1 1 1
## 1 1 1 1 0 0
## 1 1 1 0 0 0
## 1 0 0 0 0 0
## 3 0 0 1 1 1
## 6 0 0 1 0 0
## 3 0 0 0 0 0
## 20 1 1 1 1 1
## 16 1 1 1 0 0
## 1 1 1 0 1 1
## 2 1 1 0 0 0
## 1 0 0 1 1 1
## 1 0 0 1 0 0
## 5 0 0 0 0 0
## 4 1 1 1 1 1
## 2 1 1 1 0 0
## 1 1 1 0 0 0
## 6 1 1 1 1 1
## 2 1 1 1 0 0
## 1 0 0 0 0 0
## 1 0 0 1 1 1
## 1 1 1 1 0 0
## 1070 1070 1557 2768 2773
##
## 2462 0
## 13 1
## 6 1
## 981 2
## 171 1
## 1 2
## 455 3
## 15 2
## 1 3
## 10 4
## 2 3
## 15 5
## 19 1
## 14 3
## 1 2
## 6 4
## 1 6
## 91 1
## 20 3
## 8 2
## 8 4
## 1 5
## 1 2
## 2 1
## 1 3
## 1 4
## 3 2
## 4 4
## 94 4
## 109 6
## 30 5
## 506 7
## 2 5
## 4 7
## 1 6
## 15 8
## 21 1
## 9 3
## 2 2
## 2 4
## 2 5
## 396 2
## 2 3
## 3 3
## 178 4
## 21 3
## 73 5
## 3 4
## 2 6
## 2 5
## 8 7
## 5 3
## 4 5
## 1 6
## 1 7
## 2 5
## 3 7
## 16 8
## 1 6
## 18 6
## 17 8
## 7 7
## 97 9
## 90 2
## 2 3
## 30 4
## 8 3
## 24 5
## 1 4
## 2 6
## 1 6
## 1 7
## 4 6
## 3 8
## 37 9
## 11 4
## 1 5
## 6 6
## 3 7
## 5 11
## 19 1
## 21 3
## 1 2
## 6 4
## 1 5
## 3 7
## 1 6
## 6 8
## 4 3
## 1 5
## 1 6
## 1 9
## 3 7
## 6 9
## 3 10
## 20 1
## 16 3
## 1 2
## 2 4
## 1 5
## 1 7
## 5 8
## 4 3
## 2 5
## 1 6
## 6 3
## 2 5
## 1 8
## 1 8
## 1 3
## 13926
#Plot2
library(VIM)
## Lade nötiges Paket: colorspace
## Lade nötiges Paket: grid
## VIM is ready to use.
## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
##
## Attache Paket: 'VIM'
## Das folgende Objekt ist maskiert 'package:datasets':
##
## sleep
aggr_plot <- aggr(dataMI, col=c('navyblue', 'red'), numbers = TRUE, sortVars = TRUE)
## Warning in plot.aggr(res, ...): not enough vertical space to display frequencies
## (too many combinations)
##
## Variables sorted by number of missings:
## Variable Count
## data7.reading_grade 0.4373817035
## data7.math_grade 0.4365930599
## data7.language 0.2455835962
## data7.edu 0.1687697161
## data7.eduZ 0.1687697161
## data7.judgRE1 0.1676656151
## data7.judgMA1T 0.1645110410
## data7.judgMA1 0.1600946372
## data7.judgRE1T 0.1496845426
## data7.reasoning 0.0375394322
## data7.reasoningZ 0.0375394322
## data7.sen 0.0123028391
## data7.age_c 0.0099369085
## data7.sex_c 0.0001577287
## data7.ID_t 0.0000000000
#Plot3
marginplot(dataMI[c(2,4)])
#Multiple Imputation
data_complete <- mice(dataMI, m=5, maxit=50, meth='cart', seed=NA)
##
## iter imp variable
## 1 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 1 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 1 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 1 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 1 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 2 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 2 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 2 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 2 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 2 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 3 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 3 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 3 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 3 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 3 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 4 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 4 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 4 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 4 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 4 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 5 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 5 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 5 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 5 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 5 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 6 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 6 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 6 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 6 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 6 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 7 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 7 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 7 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 7 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 7 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 8 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 8 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 8 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 8 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 8 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 9 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 9 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 9 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 9 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 9 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 10 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 10 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 10 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 10 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 10 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 11 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 11 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 11 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 11 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 11 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 12 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 12 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 12 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 12 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 12 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 13 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 13 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 13 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 13 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 13 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 14 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 14 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 14 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 14 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 14 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 15 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 15 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 15 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 15 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 15 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 16 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 16 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 16 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 16 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 16 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 17 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 17 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 17 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 17 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 17 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 18 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 18 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 18 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 18 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 18 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 19 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 19 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 19 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 19 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 19 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 20 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 20 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 20 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 20 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 20 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 21 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 21 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 21 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 21 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 21 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 22 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 22 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 22 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 22 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 22 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 23 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 23 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 23 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 23 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 23 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 24 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 24 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 24 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 24 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 24 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 25 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 25 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 25 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 25 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 25 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 26 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 26 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 26 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 26 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 26 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 27 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 27 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 27 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 27 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 27 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 28 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 28 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 28 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 28 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 28 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 29 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 29 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 29 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 29 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 29 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 30 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 30 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 30 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 30 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 30 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 31 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 31 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 31 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 31 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 31 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 32 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 32 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 32 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 32 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 32 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 33 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 33 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 33 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 33 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 33 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 34 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 34 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 34 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 34 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 34 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 35 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 35 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 35 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 35 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 35 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 36 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 36 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 36 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 36 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 36 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 37 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 37 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 37 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 37 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 37 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 38 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 38 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 38 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 38 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 38 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 39 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 39 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 39 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 39 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 39 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 40 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 40 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 40 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 40 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 40 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 41 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 41 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 41 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 41 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 41 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 42 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 42 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 42 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 42 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 42 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 43 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 43 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 43 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 43 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 43 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 44 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 44 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 44 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 44 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 44 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 45 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 45 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 45 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 45 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 45 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 46 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 46 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 46 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 46 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 46 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 47 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 47 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 47 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 47 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 47 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 48 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 48 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 48 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 48 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 48 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 49 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 49 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 49 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 49 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 49 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 50 1 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 50 2 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 50 3 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 50 4 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## 50 5 data7.sex_c data7.age_c data7.reasoning data7.math_grade data7.reading_grade data7.judgMA1 data7.judgRE1 data7.edu data7.language data7.sen data7.judgMA1T data7.judgRE1T
## Warning: Number of logged events: 2
summary(data_complete)
## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## data7.ID_t data7.sex_c data7.age_c data7.reasoning
## "" "cart" "cart" "cart"
## data7.reasoningZ data7.math_grade data7.reading_grade data7.judgMA1
## "" "cart" "cart" "cart"
## data7.judgRE1 data7.edu data7.eduZ data7.language
## "cart" "cart" "" "cart"
## data7.sen data7.judgMA1T data7.judgRE1T
## "cart" "cart" "cart"
## PredictorMatrix:
## data7.ID_t data7.sex_c data7.age_c data7.reasoning
## data7.ID_t 0 1 1 1
## data7.sex_c 1 0 1 1
## data7.age_c 1 1 0 1
## data7.reasoning 1 1 1 0
## data7.reasoningZ 0 0 0 0
## data7.math_grade 1 1 1 1
## data7.reasoningZ data7.math_grade data7.reading_grade
## data7.ID_t 0 1 1
## data7.sex_c 0 1 1
## data7.age_c 0 1 1
## data7.reasoning 0 1 1
## data7.reasoningZ 0 0 0
## data7.math_grade 0 0 1
## data7.judgMA1 data7.judgRE1 data7.edu data7.eduZ
## data7.ID_t 1 1 1 0
## data7.sex_c 1 1 1 0
## data7.age_c 1 1 1 0
## data7.reasoning 1 1 1 0
## data7.reasoningZ 0 0 0 0
## data7.math_grade 1 1 1 0
## data7.language data7.sen data7.judgMA1T data7.judgRE1T
## data7.ID_t 1 1 1 1
## data7.sex_c 1 1 1 1
## data7.age_c 1 1 1 1
## data7.reasoning 1 1 1 1
## data7.reasoningZ 0 0 0 0
## data7.math_grade 1 1 1 1
## Number of logged events: 2
## it im dep meth out
## 1 0 0 collinear data7.reasoningZ
## 2 0 0 collinear data7.eduZ
data_complete <- complete(data_complete, 1)
summary(data_complete)
## data7.ID_t data7.sex_c data7.age_c data7.reasoning
## Min. :2000568 Min. :1.000 Min. :6.084 Min. : 0.00
## 1st Qu.:3005629 1st Qu.:1.000 1st Qu.:7.420 1st Qu.: 6.00
## Median :3007578 Median :2.000 Median :7.671 Median : 7.00
## Mean :2924479 Mean :1.511 Mean :7.728 Mean : 6.76
## 3rd Qu.:3017798 3rd Qu.:2.000 3rd Qu.:8.000 3rd Qu.: 8.00
## Max. :3023458 Max. :2.000 Max. :9.503 Max. :12.00
##
## data7.reasoningZ data7.math_grade data7.reading_grade data7.judgMA1
## Min. :-2.58924 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:-0.29552 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:3.000
## Median : 0.08677 Median :2.00 Median :2.000 Median :3.000
## Mean : 0.00000 Mean :2.06 Mean :2.175 Mean :3.558
## 3rd Qu.: 0.46906 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:4.000
## Max. : 1.99821 Max. :5.00 Max. :6.000 Max. :5.000
## NA's :238
## data7.judgRE1 data7.edu data7.eduZ data7.language
## Min. :1.000 Min. : 9.00 Min. :-2.5416 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:13.00 1st Qu.:-0.8242 1st Qu.:0.0000
## Median :3.000 Median :15.00 Median : 0.0345 Median :0.0000
## Mean :3.317 Mean :14.86 Mean : 0.0000 Mean :0.1975
## 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.: 1.3225 3rd Qu.:0.0000
## Max. :5.000 Max. :18.00 Max. : 1.3225 Max. :1.0000
## NA's :1070
## data7.sen data7.judgMA1T data7.judgRE1T
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:3.000
## Median :1.000 Median :3.000 Median :3.000
## Mean :1.028 Mean :3.323 Mean :3.201
## 3rd Qu.:1.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :2.000 Max. :5.000 Max. :5.000
##
###Merge imputed data with outcome variables
data8 <- data_complete
data9 <- merge(data8, datoutcome, by = "data7.ID_t", all = T)
###Packages
library(lme4)
## Lade nötiges Paket: Matrix
##
## Attache Paket: 'Matrix'
## Die folgenden Objekte sind maskiert von 'package:tidyr':
##
## expand, pack, unpack
library(multilevelTools)
library(lmerTest)
##
## Attache Paket: 'lmerTest'
## Das folgende Objekt ist maskiert 'package:lme4':
##
## lmer
## Das folgende Objekt ist maskiert 'package:stats':
##
## step
library(extraoperators)
##
## Attache Paket: 'extraoperators'
## Das folgende Objekt ist maskiert 'package:Hmisc':
##
## %nin%
library(JWileymisc)
##
## Attache Paket: 'JWileymisc'
## Das folgende Objekt ist maskiert 'package:psych':
##
## cor2cov
###checking the distribution of the dependent variables
mathClass2 <- meanDecompose(math2 ~ ID_e, data = data9)
str(mathClass2, nchar.max = 30)
## List of 2
## $ math2 by ID_e :Classes 'data.table' and 'data.frame': 842 obs. of 2 variables:
## ..$ ID_e: num [1:842] 1011931 1011916| __truncated__ ...
## ..$ X : num [1:842] 0.325 0.343 0.721 0.277 0.853 ...
## ..- attr(*, ".internal.selfref")=<externalptr>
## $ math2 by residual:Classes 'data.table' and 'data.frame': 6340 obs. of 1 variable:
## ..$ X: num [1:6340] 0.4426 -0.6637 | __truncated__ ...
## ..- attr(*, ".internal.selfref")=<externalptr>
plot(testDistribution(mathClass2[["math2 by ID_e"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Between Classes Math2")
plot(testDistribution(mathClass2[["math2 by residual"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Within Classes Math2")
mathSchool2 <- meanDecompose(math2 ~ ID_i.4, data = data9)
str(mathSchool2, nchar.max = 30)
## List of 2
## $ math2 by ID_i.4 :Classes 'data.table' and 'data.frame': 361 obs. of 2 variables:
## ..$ ID_i.4: num [1:361] 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## ..$ X : num [1:361] 0.3525 0.0112 0| __truncated__ ...
## ..- attr(*, ".internal.selfref")=<externalptr>
## $ math2 by residual:Classes 'data.table' and 'data.frame': 6340 obs. of 1 variable:
## ..$ X: num [1:6340] 0.415 -0.332 0.| __truncated__ ...
## ..- attr(*, ".internal.selfref")=<externalptr>
plot(testDistribution(mathSchool2[["math2 by ID_i.4"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Between Schools Math2")
plot(testDistribution(mathSchool2[["math2 by residual"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Within Schools Math2")
readClass2 <- meanDecompose(read2 ~ ID_e, data = data9)
str(readClass2, nchar.max = 30)
## List of 2
## $ read2 by ID_e :Classes 'data.table' and 'data.frame': 842 obs. of 2 variables:
## ..$ ID_e: num [1:842] 1011931 1011916| __truncated__ ...
## ..$ X : num [1:842] 7.33 5.33 9.5 8.25 10 ...
## ..- attr(*, ".internal.selfref")=<externalptr>
## $ read2 by residual:Classes 'data.table' and 'data.frame': 6340 obs. of 1 variable:
## ..$ X: num [1:6340] -3.33 -2.33 5.5 -3.25 4 ...
## ..- attr(*, ".internal.selfref")=<externalptr>
plot(testDistribution(readClass2[["read2 by ID_e"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Between Classes Read2")
plot(testDistribution(readClass2[["read2 by residual"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Within Classes Read2")
readSchool2 <- meanDecompose(read2 ~ ID_i.4, data = data9)
str(readSchool2, nchar.max = 30)
## List of 2
## $ read2 by ID_i.4 :Classes 'data.table' and 'data.frame': 361 obs. of 2 variables:
## ..$ ID_i.4: num [1:361] 1e+06 1e+06 1e+06 1e+06 1e+06 ...
## ..$ X : num [1:361] 6.28 6.8 11.67 7.83 8.81 ...
## ..- attr(*, ".internal.selfref")=<externalptr>
## $ read2 by residual:Classes 'data.table' and 'data.frame': 6340 obs. of 1 variable:
## ..$ X: num [1:6340] -2.28 -3.8 3.33 -2.83 5.19 ...
## ..- attr(*, ".internal.selfref")=<externalptr>
plot(testDistribution(readSchool2[["read2 by ID_i.4"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Between Schools Read2")
plot(testDistribution(readSchool2[["read2 by residual"]]$X,
extremevalues = "theoretical", ev.perc = .001),
varlab = "Within Schools Read2")
####Correlations
library(dplyr)
library(psych)
datacor <- select(data9, data7.sex_c, data7.age_c, data7.sen, data7.language, data7.edu,
data7.judgMA1, data7.judgMA1T, data7.judgRE1, data7.judgRE1T,
math2, read2, data7.reasoning, data7.math_grade, data7.reading_grade)
cor(datacor, method= "pearson", use="complete.obs")
## data7.sex_c data7.age_c data7.sen data7.language
## data7.sex_c 1.000000000 -0.085251770 -0.04966675 -0.002574901
## data7.age_c -0.085251770 1.000000000 0.07023478 -0.012404965
## data7.sen -0.049666749 0.070234785 1.00000000 0.065050650
## data7.language -0.002574901 -0.012404965 0.06505065 1.000000000
## data7.edu -0.010838068 -0.112422950 -0.07898101 -0.247137619
## data7.judgMA1 -0.180018458 0.008543706 -0.07245685 0.046158899
## data7.judgMA1T -0.147264937 -0.011034460 -0.13777890 -0.061669295
## data7.judgRE1 0.065830068 -0.046506510 -0.15013067 -0.025040330
## data7.judgRE1T 0.095795214 -0.038149278 -0.17372680 -0.131550033
## math2 -0.157031788 0.030639110 -0.12402208 -0.108459179
## read2 0.057336322 0.001263608 -0.10535352 -0.061169563
## data7.reasoning 0.067406159 0.006643082 -0.10197490 -0.032223374
## data7.math_grade 0.053138725 0.036938503 0.14969599 0.029816311
## data7.reading_grade -0.179173589 0.066650411 0.19584172 0.050068105
## data7.edu data7.judgMA1 data7.judgMA1T data7.judgRE1
## data7.sex_c -0.01083807 -0.180018458 -0.14726494 0.06583007
## data7.age_c -0.11242295 0.008543706 -0.01103446 -0.04650651
## data7.sen -0.07898101 -0.072456855 -0.13777890 -0.15013067
## data7.language -0.24713762 0.046158899 -0.06166929 -0.02504033
## data7.edu 1.00000000 0.091576968 0.23903350 0.18884574
## data7.judgMA1 0.09157697 1.000000000 0.43167770 0.28146002
## data7.judgMA1T 0.23903350 0.431677704 1.00000000 0.30279016
## data7.judgRE1 0.18884574 0.281460015 0.30279016 1.00000000
## data7.judgRE1T 0.31005907 0.233434034 0.63088443 0.44263042
## math2 0.28382071 0.360917597 0.48167670 0.25565276
## read2 0.22319762 0.181986810 0.36600110 0.38522698
## data7.reasoning 0.16396137 0.197181590 0.29966263 0.16043822
## data7.math_grade -0.26097816 -0.385338506 -0.51575604 -0.30091013
## data7.reading_grade -0.26946816 -0.204229907 -0.40917938 -0.39444677
## data7.judgRE1T math2 read2 data7.reasoning
## data7.sex_c 0.09579521 -0.15703179 0.057336322 0.067406159
## data7.age_c -0.03814928 0.03063911 0.001263608 0.006643082
## data7.sen -0.17372680 -0.12402208 -0.105353518 -0.101974901
## data7.language -0.13155003 -0.10845918 -0.061169563 -0.032223374
## data7.edu 0.31005907 0.28382071 0.223197622 0.163961372
## data7.judgMA1 0.23343403 0.36091760 0.181986810 0.197181590
## data7.judgMA1T 0.63088443 0.48167670 0.366001096 0.299662626
## data7.judgRE1 0.44263042 0.25565276 0.385226977 0.160438216
## data7.judgRE1T 1.00000000 0.39611754 0.455006555 0.273672382
## math2 0.39611754 1.00000000 0.456965530 0.392202345
## read2 0.45500656 0.45696553 1.000000000 0.271856299
## data7.reasoning 0.27367238 0.39220235 0.271856299 1.000000000
## data7.math_grade -0.44212816 -0.44498236 -0.337852749 -0.295924266
## data7.reading_grade -0.52099262 -0.33935816 -0.404241655 -0.270696661
## data7.math_grade data7.reading_grade
## data7.sex_c 0.05313872 -0.17917359
## data7.age_c 0.03693850 0.06665041
## data7.sen 0.14969599 0.19584172
## data7.language 0.02981631 0.05006810
## data7.edu -0.26097816 -0.26946816
## data7.judgMA1 -0.38533851 -0.20422991
## data7.judgMA1T -0.51575604 -0.40917938
## data7.judgRE1 -0.30091013 -0.39444677
## data7.judgRE1T -0.44212816 -0.52099262
## math2 -0.44498236 -0.33935816
## read2 -0.33785275 -0.40424165
## data7.reasoning -0.29592427 -0.27069666
## data7.math_grade 1.00000000 0.58397679
## data7.reading_grade 0.58397679 1.00000000
cor_test_mat <- corr.test(datacor)$p
cor_test_mat
## data7.sex_c data7.age_c data7.sen data7.language
## data7.sex_c 0.000000e+00 5.567397e-11 4.974455e-04 1.000000e+00
## data7.age_c 1.988356e-12 0.000000e+00 8.037419e-13 1.000000e+00
## data7.sen 3.109034e-05 2.771524e-14 0.000000e+00 8.174426e-06
## data7.language 9.182890e-01 2.873955e-01 3.768728e-07 0.000000e+00
## data7.edu 4.832938e-01 8.090123e-21 2.456679e-12 1.862587e-96
## data7.judgMA1 1.931843e-42 9.311271e-01 6.877196e-12 8.291309e-04
## data7.judgMA1T 4.116223e-29 1.550669e-01 4.012854e-36 8.501077e-09
## data7.judgRE1 3.334195e-08 2.205071e-06 8.467291e-35 1.773508e-02
## data7.judgRE1T 1.707553e-14 3.809058e-05 1.226394e-49 1.390669e-28
## math2 3.763929e-31 6.564020e-02 2.949018e-26 1.839624e-20
## read2 9.030920e-06 9.494254e-01 5.680639e-17 2.603656e-06
## data7.reasoning 3.715648e-07 6.995700e-01 1.947178e-20 3.581196e-03
## data7.math_grade 2.104894e-04 4.098781e-05 3.154261e-36 8.681158e-03
## data7.reading_grade 5.004066e-45 3.877114e-10 3.287172e-57 2.996910e-06
## data7.edu data7.judgMA1 data7.judgMA1T data7.judgRE1
## data7.sex_c 1.000000e+00 8.306924e-41 1.564165e-27 7.668648e-07
## data7.age_c 2.831543e-19 1.000000e+00 1.000000e+00 4.410143e-05
## data7.sen 6.633033e-11 1.788071e-10 1.645270e-34 3.386916e-33
## data7.language 1.043049e-94 9.949571e-03 2.040258e-07 1.596157e-01
## data7.edu 0.000000e+00 1.644465e-13 2.753700e-89 1.460254e-55
## data7.judgMA1 5.304725e-15 0.000000e+00 3.439230e-299 8.385264e-121
## data7.judgMA1T 5.006727e-91 4.194183e-301 0.000000e+00 4.222265e-141
## data7.judgRE1 2.920508e-57 1.352462e-122 6.397371e-143 0.000000e+00
## data7.judgRE1T 1.907556e-153 3.558773e-88 0.000000e+00 6.745645e-316
## math2 5.863877e-126 5.268551e-192 0.000000e+00 1.020863e-99
## read2 3.641384e-68 1.237016e-44 1.356324e-187 9.631614e-210
## data7.reasoning 8.670391e-44 3.696573e-63 6.430458e-150 2.163169e-44
## data7.math_grade 3.353289e-106 2.421689e-239 0.000000e+00 3.133441e-139
## data7.reading_grade 1.524266e-117 1.470413e-73 1.433669e-284 6.938127e-246
## data7.judgRE1T math2 read2 data7.reasoning
## data7.sex_c 5.122659e-13 1.467932e-29 1.535256e-04 8.174426e-06
## data7.age_c 5.713587e-04 5.251216e-01 1.000000e+00 1.000000e+00
## data7.sen 5.886690e-48 1.061646e-24 1.817805e-15 6.425687e-19
## data7.language 5.145476e-27 6.254722e-19 4.946946e-05 3.939316e-02
## data7.edu 1.316214e-151 3.694243e-124 1.893520e-66 3.814972e-42
## data7.judgMA1 1.921738e-86 3.846042e-190 5.690275e-43 1.885252e-61
## data7.judgMA1T 0.000000e+00 0.000000e+00 9.765532e-186 4.372711e-148
## data7.judgRE1 5.733798e-314 5.921005e-98 7.127394e-208 9.734263e-43
## data7.judgRE1T 0.000000e+00 8.307461e-249 2.536938e-302 1.090962e-125
## math2 1.051577e-250 0.000000e+00 1.426108e-293 8.093393e-235
## read2 3.056551e-304 1.760627e-295 0.000000e+00 1.466104e-97
## data7.reasoning 1.704628e-127 1.064920e-236 2.572113e-99 0.000000e+00
## data7.math_grade 0.000000e+00 3.062380e-309 1.386195e-158 3.706237e-144
## data7.reading_grade 0.000000e+00 2.172003e-180 1.269857e-235 5.147824e-122
## data7.math_grade data7.reading_grade
## data7.sex_c 2.736363e-03 2.351911e-43
## data7.age_c 5.738293e-04 9.692786e-09
## data7.sen 1.324790e-34 1.610714e-55
## data7.language 8.681158e-02 5.394437e-05
## data7.edu 1.978440e-104 9.145596e-116
## data7.judgMA1 1.864701e-237 7.793189e-72
## data7.judgMA1T 0.000000e+00 1.146935e-282
## data7.judgRE1 2.036736e-137 5.411739e-244
## data7.judgRE1T 0.000000e+00 0.000000e+00
## math2 2.572399e-307 1.542122e-178
## read2 9.703368e-157 9.523931e-234
## data7.reasoning 2.483179e-142 3.140173e-120
## data7.math_grade 0.000000e+00 0.000000e+00
## data7.reading_grade 0.000000e+00 0.000000e+00
####Cohen's Kappa
cohen.kappa(x=cbind(datacor$data7.judgMA1,datacor$data7.judgMA1T))
## Call: cohen.kappa1(x = x, w = w, n.obs = n.obs, alpha = alpha, levels = levels)
##
## Cohen Kappa and Weighted Kappa correlation coefficients and confidence boundaries
## lower estimate upper
## unweighted kappa 0.15 0.17 0.19
## weighted kappa 0.33 0.43 0.52
##
## Number of subjects = 6340
cohen.kappa(x=cbind(datacor$data7.judgRE1,datacor$data7.judgRE1T))
## Call: cohen.kappa1(x = x, w = w, n.obs = n.obs, alpha = alpha, levels = levels)
##
## Cohen Kappa and Weighted Kappa correlation coefficients and confidence boundaries
## lower estimate upper
## unweighted kappa 0.14 0.15 0.17
## weighted kappa 0.31 0.43 0.56
##
## Number of subjects = 6340
###Model1: judgments in grade 1
##Unconditional, zero model
l3_ma2 <-
lmer(math2 ~ 1 + (1|ID_e) + (1|ID_i.4),
data = data9, REML = FALSE, na.action=na.omit)
summary(l3_ma2)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: math2 ~ 1 + (1 | ID_e) + (1 | ID_i.4)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 16664.0 16690.3 -8328.0 16656.0 5372
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7149 -0.6667 -0.0512 0.6068 3.9478
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.07397 0.2720
## ID_i.4 (Intercept) 0.14941 0.3865
## Residual 1.17184 1.0825
## Number of obs: 5376, groups: ID_e, 833; ID_i.4, 336
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01433 0.02868 318.93748 0.5 0.618
l3_re2 <-
lmer(read2Z ~ 1 + (1|ID_e) + (1|ID_i.4),
data = data9, REML = FALSE, na.action=na.omit)
summary(l3_re2)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: read2Z ~ 1 + (1 | ID_e) + (1 | ID_i.4)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 14307.3 14333.5 -7149.7 14299.3 5139
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2457 -0.6842 -0.1158 0.5353 3.3817
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.05881 0.2425
## ID_i.4 (Intercept) 0.07901 0.2811
## Residual 0.85818 0.9264
## Number of obs: 5143, groups: ID_e, 835; ID_i.4, 339
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.00152 0.02284 301.83099 -0.067 0.947
##Conditional model
#Background variables math
f_math2 <- lmer(math2~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + (1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2 ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 12218.0 12281.7 -6099.0 12198.0 4328
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5186 -0.6644 -0.0223 0.6160 4.4416
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.05554 0.2357
## ID_i.4 (Intercept) 0.07600 0.2757
## Residual 0.88679 0.9417
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.20640 0.33792 4321.71348 0.611 0.541359
## data7.age_c 0.14359 0.04119 4321.58571 3.486 0.000495 ***
## data7.sex_c -0.42200 0.02990 4206.97555 -14.113 < 2e-16 ***
## data7.reasoningZ 0.43109 0.01589 4294.15284 27.124 < 2e-16 ***
## data7.language -0.09517 0.04016 4321.75059 -2.370 0.017840 *
## data7.sen -0.57581 0.10270 4296.20516 -5.607 2.19e-08 ***
## data7.eduZ 0.25969 0.01626 4305.24140 15.975 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn
## data7.age_c -0.936
## data7.sex_c -0.240 0.093
## dat7.rsnngZ 0.004 -0.024 -0.068
## data7.langg -0.034 0.028 0.003 -0.015
## data7.sen -0.261 -0.060 0.061 0.086 -0.050
## data7.eduZ -0.142 0.121 0.056 -0.131 0.242 0.053
library(MuMIn)
r.squaredGLMM(f_math2)
## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.
## R2m R2c
## [1,] 0.2462087 0.3435807
#centering the predictors
data9$data7.judgMA1_c <- data9$data7.judgMA1 - mean(data9$data7.judgMA1)
data9$data7.judgMA1T_c <- data9$data7.judgMA1T - mean(data9$data7.judgMA1T)
data9$data7.judgRE1_c <- data9$data7.judgRE1 - mean(data9$data7.judgRE1)
data9$data7.judgRE1T_c <- data9$data7.judgRE1T - mean(data9$data7.judgRE1T)
#standardized regression weight
data9$math2Z <- scale(data9$math2)
data9$data7.age_cZ <- scale(data9$data7.age_c)
data9$data7.sex_cZ <- scale(data9$data7.sex_c)
data9$data7.languageZ <- scale(data9$data7.language)
data9$data7.senZ <- scale(data9$data7.sen)
data9$data7.judgMA1T_cZ <- scale(data9$data7.judgMA1T_c)
data9$data7.judgMA1_cZ <- scale(data9$data7.judgMA1_c)
f_math2Z <- lmer(math2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + (1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10826.8 10890.5 -5403.4 10806.8 4328
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5186 -0.6644 -0.0223 0.6160 4.4416
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04030 0.2008
## ID_i.4 (Intercept) 0.05515 0.2348
## Residual 0.64349 0.8022
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.05363 0.02013 290.40208 2.664 0.008163 **
## data7.age_cZ 0.04698 0.01348 4321.58579 3.486 0.000495 ***
## data7.sex_cZ -0.17971 0.01273 4206.97556 -14.113 < 2e-16 ***
## data7.reasoningZ 0.36722 0.01354 4294.15284 27.124 < 2e-16 ***
## data7.languageZ -0.03228 0.01362 4321.75059 -2.370 0.017840 *
## data7.senZ -0.08125 0.01449 4296.20516 -5.607 2.19e-08 ***
## data7.eduZ 0.22121 0.01385 4305.24140 15.975 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ
## data7.ag_cZ 0.004
## data7.sx_cZ 0.008 0.093
## dat7.rsnngZ -0.028 -0.024 -0.068
## data7.lnggZ 0.017 0.028 0.003 -0.015
## data7.senZ 0.026 -0.060 0.061 0.086 -0.050
## data7.eduZ 0.026 0.121 0.056 -0.131 0.242 0.053
r.squaredGLMM(f_math2Z)
## R2m R2c
## [1,] 0.2462087 0.3435807
#Background variables reading
f_read2 <- lmer(read2Z~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + (1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11672.3 11736.1 -5826.2 11652.3 4319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3529 -0.7078 -0.1336 0.5967 3.4235
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04711 0.2171
## ID_i.4 (Intercept) 0.05286 0.2299
## Residual 0.79370 0.8909
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.472e-01 3.222e-01 4.308e+03 -0.457 0.647704
## data7.age_c 6.912e-02 3.905e-02 4.310e+03 1.770 0.076791 .
## data7.sex_c 1.026e-01 2.825e-02 4.218e+03 3.631 0.000286 ***
## data7.reasoningZ 2.066e-01 1.510e-02 4.301e+03 13.685 < 2e-16 ***
## data7.language -1.147e-03 3.809e-02 4.316e+03 -0.030 0.975974
## data7.sen -4.981e-01 1.002e-01 4.304e+03 -4.971 6.92e-07 ***
## data7.eduZ 1.903e-01 1.531e-02 4.273e+03 12.430 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn
## data7.age_c -0.934
## data7.sex_c -0.241 0.094
## dat7.rsnngZ 0.012 -0.033 -0.057
## data7.langg -0.040 0.036 0.001 -0.015
## data7.sen -0.279 -0.049 0.063 0.075 -0.047
## data7.eduZ -0.143 0.123 0.052 -0.130 0.241 0.051
r.squaredGLMM(f_read2)
## R2m R2c
## [1,] 0.09908674 0.19987
#standardized regression weight
data9$data7.judgRE1T_cZ <- scale(data9$data7.judgRE1T_c)
data9$data7.judgRE1_cZ <- scale(data9$data7.judgRE1_c)
f_read2Z <- lmer(read2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + (1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11672.3 11736.1 -5826.2 11652.3 4319
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3529 -0.7078 -0.1336 0.5967 3.4235
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04711 0.2171
## ID_i.4 (Intercept) 0.05286 0.2299
## Residual 0.79370 0.8909
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.948e-02 2.114e-02 2.900e+02 1.394 0.164277
## data7.age_cZ 2.655e-02 1.500e-02 4.310e+03 1.770 0.076791 .
## data7.sex_cZ 5.128e-02 1.412e-02 4.218e+03 3.631 0.000286 ***
## data7.reasoningZ 2.066e-01 1.510e-02 4.301e+03 13.685 < 2e-16 ***
## data7.languageZ -4.567e-04 1.516e-02 4.316e+03 -0.030 0.975974
## data7.senZ -8.251e-02 1.660e-02 4.304e+03 -4.971 6.92e-07 ***
## data7.eduZ 1.903e-01 1.531e-02 4.273e+03 12.430 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ
## data7.ag_cZ 0.006
## data7.sx_cZ 0.008 0.094
## dat7.rsnngZ -0.039 -0.033 -0.057
## data7.lnggZ 0.021 0.036 0.001 -0.015
## data7.senZ 0.035 -0.049 0.063 0.075 -0.047
## data7.eduZ 0.019 0.123 0.052 -0.130 0.241 0.051
r.squaredGLMM(f_read2Z)
## R2m R2c
## [1,] 0.09908674 0.19987
#Teacher judgments math
f_math2T1 <- lmer(math2~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgMA1T_c +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2T1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2 ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgMA1T_c + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11491.6 11561.7 -5734.8 11469.6 4327
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7311 -0.6457 -0.0199 0.5810 4.8897
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.06896 0.2626
## ID_i.4 (Intercept) 0.08438 0.2905
## Residual 0.73025 0.8545
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.21542 0.31044 4292.57515 -0.694 0.487764
## data7.age_c 0.12892 0.03779 4290.13146 3.412 0.000652 ***
## data7.sex_c -0.27221 0.02784 4168.91600 -9.777 < 2e-16 ***
## data7.reasoningZ 0.30566 0.01521 4276.75394 20.097 < 2e-16 ***
## data7.language -0.09269 0.03683 4289.65031 -2.517 0.011886 *
## data7.sen -0.29782 0.09474 4270.21627 -3.144 0.001681 **
## data7.eduZ 0.17703 0.01521 4312.68621 11.638 < 2e-16 ***
## data7.judgMA1T_c 0.43681 0.01542 4264.14358 28.329 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ
## data7.age_c -0.934
## data7.sex_c -0.245 0.089
## dat7.rsnngZ 0.018 -0.020 -0.119
## data7.langg -0.032 0.027 0.004 -0.015
## data7.sen -0.264 -0.062 0.080 0.051 -0.049
## data7.eduZ -0.129 0.119 0.019 -0.067 0.238 0.034
## dt7.jdMA1T_ -0.053 -0.010 0.191 -0.289 -0.002 0.106 -0.187
r.squaredGLMM(f_math2T1)
## R2m R2c
## [1,] 0.3498183 0.4626535
f_math2T1Z <- lmer(math2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgMA1T_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2T1Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgMA1T_cZ + (1 | ID_i.4) +
## (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10100.4 10170.5 -5039.2 10078.4 4327
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7311 -0.6457 -0.0199 0.5810 4.8897
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.05004 0.2237
## ID_i.4 (Intercept) 0.06123 0.2474
## Residual 0.52990 0.7279
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03440 0.02027 288.19667 1.697 0.090697 .
## data7.age_cZ 0.04218 0.01236 4290.13122 3.412 0.000652 ***
## data7.sex_cZ -0.11592 0.01186 4168.91598 -9.777 < 2e-16 ***
## data7.reasoningZ 0.26038 0.01296 4276.75394 20.097 < 2e-16 ***
## data7.languageZ -0.03144 0.01249 4289.65031 -2.517 0.011886 *
## data7.senZ -0.04203 0.01337 4270.21627 -3.144 0.001681 **
## data7.eduZ 0.15080 0.01296 4312.68621 11.638 < 2e-16 ***
## data7.judgMA1T_cZ 0.37519 0.01324 4264.14358 28.329 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ
## data7.ag_cZ 0.004
## data7.sx_cZ 0.002 0.089
## dat7.rsnngZ -0.014 -0.020 -0.119
## data7.lnggZ 0.015 0.027 0.004 -0.015
## data7.senZ 0.020 -0.062 0.080 0.051 -0.049
## data7.eduZ 0.031 0.119 0.019 -0.067 0.238 0.034
## dt7.jMA1T_Z -0.032 -0.010 0.191 -0.289 -0.002 0.106 -0.187
r.squaredGLMM(f_math2T1Z)
## R2m R2c
## [1,] 0.3498183 0.4626535
#Parent judgments math
f_math2P1 <- lmer(math2~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgMA1_c +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2P1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2 ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgMA1_c + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11730.6 11800.8 -5854.3 11708.6 4327
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5295 -0.6501 -0.0253 0.5981 4.6870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.05918 0.2433
## ID_i.4 (Intercept) 0.07438 0.2727
## Residual 0.78415 0.8855
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05898 0.31939 4311.90742 -0.185 0.853493
## data7.age_c 0.13274 0.03891 4311.49921 3.412 0.000651 ***
## data7.sex_c -0.28601 0.02883 4204.47651 -9.921 < 2e-16 ***
## data7.reasoningZ 0.35572 0.01536 4290.65986 23.159 < 2e-16 ***
## data7.language -0.16514 0.03806 4309.91867 -4.339 1.46e-05 ***
## data7.sen -0.42991 0.09722 4287.54120 -4.422 1.00e-05 ***
## data7.eduZ 0.23836 0.01539 4309.92215 15.486 < 2e-16 ***
## data7.judgMA1_c 0.37780 0.01658 4206.71883 22.790 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ
## data7.age_c -0.935
## data7.sex_c -0.242 0.089
## dat7.rsnngZ 0.012 -0.021 -0.110
## data7.langg -0.030 0.028 -0.014 0.003
## data7.sen -0.262 -0.061 0.073 0.069 -0.055
## data7.eduZ -0.139 0.121 0.043 -0.114 0.245 0.050
## dt7.jdgMA1_ -0.039 -0.010 0.208 -0.214 -0.083 0.067 -0.058
r.squaredGLMM(f_math2P1)
## R2m R2c
## [1,] 0.3178146 0.4171002
f_math2P1Z <- lmer(math2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgMA1_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_math2P1Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## math2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgMA1_cZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10339.4 10409.5 -5158.7 10317.4 4327
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5295 -0.6501 -0.0253 0.5981 4.6870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04295 0.2072
## ID_i.4 (Intercept) 0.05397 0.2323
## Residual 0.56901 0.7543
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.04716 0.01966 286.49297 2.399 0.017082 *
## data7.age_cZ 0.04343 0.01273 4311.49931 3.412 0.000651 ***
## data7.sex_cZ -0.12180 0.01228 4204.47652 -9.921 < 2e-16 ***
## data7.reasoningZ 0.30302 0.01308 4290.65986 23.159 < 2e-16 ***
## data7.languageZ -0.05601 0.01291 4309.91867 -4.339 1.46e-05 ***
## data7.senZ -0.06066 0.01372 4287.54121 -4.422 1.00e-05 ***
## data7.eduZ 0.20305 0.01311 4309.92215 15.486 < 2e-16 ***
## data7.judgMA1_cZ 0.28842 0.01266 4206.71883 22.790 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ
## data7.ag_cZ 0.004
## data7.sx_cZ 0.005 0.089
## dat7.rsnngZ -0.023 -0.021 -0.110
## data7.lnggZ 0.017 0.028 -0.014 0.003
## data7.senZ 0.024 -0.061 0.073 0.069 -0.055
## data7.eduZ 0.026 0.121 0.043 -0.114 0.245 0.050
## dt7.jdMA1_Z -0.013 -0.010 0.208 -0.214 -0.083 0.067 -0.058
r.squaredGLMM(f_math2P1Z)
## R2m R2c
## [1,] 0.3178146 0.4171002
#Adult judgments math
f_math2int1 <- lmer(math2~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgMA1T_c + data7.judgMA1_c +
(1|ID_i.4) + (1|ID_e),
data=data9)
summary(f_math2int1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## math2 ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgMA1T_c + data7.judgMA1_c +
## (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## REML criterion at convergence: 11328.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6965 -0.6449 -0.0408 0.5822 4.9618
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.06427 0.2535
## ID_i.4 (Intercept) 0.08370 0.2893
## Residual 0.70034 0.8369
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.29633 0.30406 4284.58464 -0.975 0.329829
## data7.age_c 0.12478 0.03701 4282.09736 3.372 0.000753 ***
## data7.sex_c -0.21631 0.02756 4167.83119 -7.850 5.26e-15 ***
## data7.reasoningZ 0.28320 0.01498 4268.54663 18.906 < 2e-16 ***
## data7.language -0.13701 0.03621 4279.66250 -3.784 0.000157 ***
## data7.sen -0.26189 0.09279 4258.39345 -2.822 0.004790 **
## data7.eduZ 0.18024 0.01490 4305.75184 12.097 < 2e-16 ***
## data7.judgMA1T_c 0.34881 0.01636 4264.39311 21.320 < 2e-16 ***
## data7.judgMA1_c 0.23797 0.01705 4201.35071 13.955 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ d7.MA1T
## data7.age_c -0.933
## data7.sex_c -0.245 0.087
## dat7.rsnngZ 0.020 -0.019 -0.133
## data7.langg -0.030 0.027 -0.009 -0.006
## data7.sen -0.264 -0.062 0.083 0.048 -0.051
## data7.eduZ -0.129 0.119 0.021 -0.068 0.235 0.034
## dt7.jdMA1T_ -0.041 -0.006 0.118 -0.224 0.032 0.087 -0.178
## dt7.jdgMA1_ -0.020 -0.008 0.145 -0.107 -0.088 0.028 0.016 -0.385
r.squaredGLMM(f_math2int1)
## R2m R2c
## [1,] 0.3730509 0.4824125
f_math2int1Z <- lmer(math2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgMA1T_cZ + data7.judgMA1_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9)
summary(f_math2int1Z)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## math2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgMA1T_cZ + data7.judgMA1_cZ +
## (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## REML criterion at convergence: 9949.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6965 -0.6449 -0.0408 0.5822 4.9618
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04664 0.2160
## ID_i.4 (Intercept) 0.06074 0.2465
## Residual 0.50820 0.7129
## Number of obs: 4338, groups: ID_e, 811; ID_i.4, 333
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03421 0.01996 285.66908 1.714 0.087681 .
## data7.age_cZ 0.04083 0.01211 4282.09732 3.372 0.000753 ***
## data7.sex_cZ -0.09212 0.01174 4167.83117 -7.850 5.26e-15 ***
## data7.reasoningZ 0.24124 0.01276 4268.54663 18.906 < 2e-16 ***
## data7.languageZ -0.04647 0.01228 4279.66250 -3.784 0.000157 ***
## data7.senZ -0.03696 0.01309 4258.39345 -2.822 0.004790 **
## data7.eduZ 0.15354 0.01269 4305.75184 12.097 < 2e-16 ***
## data7.judgMA1T_cZ 0.29961 0.01405 4264.39311 21.320 < 2e-16 ***
## data7.judgMA1_cZ 0.18167 0.01302 4201.35071 13.955 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ d7.MA1T
## data7.ag_cZ 0.004
## data7.sx_cZ 0.002 0.087
## dat7.rsnngZ -0.014 -0.019 -0.133
## data7.lnggZ 0.015 0.027 -0.009 -0.006
## data7.senZ 0.020 -0.062 0.083 0.048 -0.051
## data7.eduZ 0.031 0.119 0.021 -0.068 0.235 0.034
## dt7.jMA1T_Z -0.029 -0.006 0.118 -0.224 0.032 0.087 -0.178
## dt7.jdMA1_Z -0.001 -0.008 0.145 -0.107 -0.088 0.028 0.016 -0.385
r.squaredGLMM(f_math2int1Z)
## R2m R2c
## [1,] 0.3730509 0.4824125
library(car)
## Lade nötiges Paket: carData
##
## Attache Paket: 'car'
## Das folgende Objekt ist maskiert 'package:psych':
##
## logit
## Das folgende Objekt ist maskiert 'package:dplyr':
##
## recode
linearHypothesis(f_math2int1Z,"data7.judgMA1_cZ = data7.judgMA1T_cZ")
## Linear hypothesis test
##
## Hypothesis:
## - data7.judgMA1T_cZ + data7.judgMA1_cZ = 0
##
## Model 1: restricted model
## Model 2: math2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgMA1T_cZ + data7.judgMA1_cZ +
## (1 | ID_i.4) + (1 | ID_e)
##
## Df Chisq Pr(>Chisq)
## 1
## 2 1 27.383 1.669e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Teacher's judgments reading
f_read2T1 <- lmer(read2Z~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgRE1T_c +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2T1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgRE1T_c + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10835.3 10905.4 -5406.6 10813.3 4318
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9222 -0.6778 -0.0989 0.5738 4.3610
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.07244 0.2691
## ID_i.4 (Intercept) 0.05722 0.2392
## Residual 0.62965 0.7935
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.27749 0.29137 4259.24637 -0.952 0.3410
## data7.age_c 0.05301 0.03532 4262.17254 1.501 0.1335
## data7.sex_c 0.02443 0.02557 4142.17190 0.956 0.3393
## data7.reasoningZ 0.10630 0.01401 4258.93265 7.590 3.92e-14 ***
## data7.language 0.05456 0.03450 4272.02127 1.582 0.1138
## data7.sen -0.18269 0.09126 4262.03134 -2.002 0.0454 *
## data7.eduZ 0.07455 0.01438 4295.02325 5.183 2.28e-07 ***
## data7.judgRE1T_c 0.41380 0.01347 4263.30382 30.723 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ
## data7.age_c -0.933
## data7.sex_c -0.237 0.095
## dat7.rsnngZ 0.014 -0.027 -0.033
## data7.langg -0.040 0.033 -0.002 -0.028
## data7.sen -0.277 -0.052 0.052 0.046 -0.040
## data7.eduZ -0.134 0.122 0.078 -0.060 0.219 0.022
## dt7.jdRE1T_ -0.015 -0.015 -0.100 -0.227 0.052 0.115 -0.258
r.squaredGLMM(f_read2T1)
## R2m R2c
## [1,] 0.2460503 0.3747949
f_read2T1Z <- lmer(read2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgRE1T_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2T1Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgRE1T_cZ + (1 | ID_i.4) +
## (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10835.3 10905.4 -5406.6 10813.3 4318
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9222 -0.6778 -0.0989 0.5738 4.3610
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.07244 0.2691
## ID_i.4 (Intercept) 0.05722 0.2392
## Residual 0.62965 0.7935
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.012e-03 2.140e-02 3.029e+02 -0.374 0.7084
## data7.age_cZ 2.036e-02 1.357e-02 4.262e+03 1.501 0.1335
## data7.sex_cZ 1.222e-02 1.278e-02 4.142e+03 0.956 0.3393
## data7.reasoningZ 1.063e-01 1.401e-02 4.259e+03 7.590 3.92e-14 ***
## data7.languageZ 2.172e-02 1.374e-02 4.272e+03 1.582 0.1138
## data7.senZ -3.026e-02 1.512e-02 4.262e+03 -2.002 0.0454 *
## data7.eduZ 7.455e-02 1.438e-02 4.295e+03 5.183 2.28e-07 ***
## data7.judgRE1T_cZ 4.484e-01 1.460e-02 4.263e+03 30.723 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ
## data7.ag_cZ 0.006
## data7.sx_cZ 0.013 0.095
## dat7.rsnngZ -0.020 -0.027 -0.033
## data7.lnggZ 0.014 0.033 -0.002 -0.028
## data7.senZ 0.025 -0.052 0.052 0.046 -0.040
## data7.eduZ 0.033 0.122 0.078 -0.060 0.219 0.022
## dt7.jRE1T_Z -0.055 -0.015 -0.100 -0.227 0.052 0.115 -0.258
r.squaredGLMM(f_read2T1Z)
## R2m R2c
## [1,] 0.2460503 0.3747949
#Parents' judgments reading
f_read2P1 <- lmer(read2Z~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgRE1_c +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2P1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgRE1_c + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11034.5 11104.6 -5506.2 11012.5 4318
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8874 -0.6940 -0.1099 0.5892 3.8019
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04894 0.2212
## ID_i.4 (Intercept) 0.05408 0.2325
## Residual 0.67610 0.8223
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.35019 0.29908 4296.79943 -1.171 0.2417
## data7.age_c 0.06967 0.03624 4300.02026 1.922 0.0546 .
## data7.sex_c 0.04896 0.02625 4194.23964 1.865 0.0623 .
## data7.reasoningZ 0.16125 0.01411 4285.32849 11.432 <2e-16 ***
## data7.language -0.03228 0.03537 4305.61666 -0.913 0.3614
## data7.sen -0.23973 0.09350 4289.22883 -2.564 0.0104 *
## data7.eduZ 0.12710 0.01443 4292.73422 8.808 <2e-16 ***
## data7.judgRE1_c 0.43685 0.01661 4232.31438 26.303 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ
## data7.age_c -0.934
## data7.sex_c -0.237 0.094
## dat7.rsnngZ 0.015 -0.032 -0.047
## data7.langg -0.039 0.035 0.005 -0.011
## data7.sen -0.280 -0.049 0.055 0.061 -0.050
## data7.eduZ -0.136 0.121 0.065 -0.106 0.243 0.034
## dt7.jdgRE1_ -0.026 0.001 -0.078 -0.119 -0.034 0.106 -0.165
r.squaredGLMM(f_read2P1)
## R2m R2c
## [1,] 0.2141562 0.318061
f_read2P1Z <- lmer(read2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgRE1_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2P1Z)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgRE1_cZ + (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 11034.5 11104.6 -5506.2 11012.5 4318
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8874 -0.6940 -0.1099 0.5892 3.8019
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.04894 0.2212
## ID_i.4 (Intercept) 0.05408 0.2325
## Residual 0.67610 0.8223
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.00929 0.02060 292.43167 0.451 0.6524
## data7.age_cZ 0.02676 0.01392 4300.02036 1.922 0.0546 .
## data7.sex_cZ 0.02447 0.01312 4194.23963 1.865 0.0623 .
## data7.reasoningZ 0.16125 0.01410 4285.32849 11.432 <2e-16 ***
## data7.languageZ -0.01285 0.01408 4305.61666 -0.913 0.3614
## data7.senZ -0.03971 0.01549 4289.22883 -2.564 0.0104 *
## data7.eduZ 0.12710 0.01443 4292.73422 8.808 <2e-16 ***
## data7.judgRE1_cZ 0.36228 0.01377 4232.31438 26.303 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ
## data7.ag_cZ 0.006
## data7.sx_cZ 0.010 0.094
## dat7.rsnngZ -0.032 -0.032 -0.047
## data7.lnggZ 0.021 0.035 0.005 -0.011
## data7.senZ 0.030 -0.049 0.055 0.061 -0.050
## data7.eduZ 0.025 0.121 0.065 -0.106 0.243 0.034
## dt7.jdRE1_Z -0.036 0.001 -0.078 -0.119 -0.034 0.106 -0.165
r.squaredGLMM(f_read2P1Z)
## R2m R2c
## [1,] 0.2141562 0.318061
#Adult judgments reading
f_read2int1 <- lmer(read2Z~data7.age_c + data7.sex_c + data7.reasoningZ +
data7.language + data7.sen +
data7.eduZ + data7.judgRE1_c + data7.judgRE1T_c +
(1|ID_i.4) + (1|ID_e),
data=data9, REML=F)
summary(f_read2int1)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgRE1_c + data7.judgRE1T_c +
## (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## AIC BIC logLik deviance df.resid
## 10560.5 10637.0 -5268.3 10536.5 4317
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0046 -0.6707 -0.0876 0.5668 3.9569
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.06585 0.2566
## ID_i.4 (Intercept) 0.05727 0.2393
## Residual 0.59049 0.7684
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.808e-01 2.823e-01 4.259e+03 -1.349 0.1775
## data7.age_c 5.671e-02 3.422e-02 4.262e+03 1.658 0.0975 .
## data7.sex_c 6.715e-03 2.478e-02 4.141e+03 0.271 0.7864
## data7.reasoningZ 9.888e-02 1.357e-02 4.257e+03 7.287 3.76e-13 ***
## data7.language 2.149e-02 3.347e-02 4.272e+03 0.642 0.5208
## data7.sen -8.280e-02 8.857e-02 4.260e+03 -0.935 0.3499
## data7.eduZ 5.867e-02 1.397e-02 4.298e+03 4.201 2.71e-05 ***
## data7.judgRE1_c 2.875e-01 1.700e-02 4.208e+03 16.908 < 2e-16 ***
## data7.judgRE1T_c 3.201e-01 1.417e-02 4.271e+03 22.594 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_ dt7.s_ dt7.rZ dt7.ln dt7.sn dt7.dZ d7.RE1_
## data7.age_c -0.933
## data7.sex_c -0.236 0.095
## dat7.rsnngZ 0.015 -0.027 -0.031
## data7.langg -0.038 0.032 0.000 -0.026
## data7.sen -0.278 -0.051 0.049 0.044 -0.044
## data7.eduZ -0.132 0.121 0.080 -0.058 0.222 0.017
## dt7.jdgRE1_ -0.021 0.006 -0.043 -0.032 -0.059 0.067 -0.067
## dt7.jdRE1T_ -0.005 -0.016 -0.075 -0.196 0.071 0.079 -0.211 -0.391
r.squaredGLMM(f_read2int1)
## R2m R2c
## [1,] 0.2873785 0.4103291
f_read2int1Z <- lmer(read2Z~data7.age_cZ + data7.sex_cZ + data7.reasoningZ +
data7.languageZ + data7.senZ +
data7.eduZ + data7.judgRE1T_cZ + data7.judgRE1_cZ +
(1|ID_i.4) + (1|ID_e),
data=data9)
summary(f_read2int1Z)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## read2Z ~ data7.age_cZ + data7.sex_cZ + data7.reasoningZ + data7.languageZ +
## data7.senZ + data7.eduZ + data7.judgRE1T_cZ + data7.judgRE1_cZ +
## (1 | ID_i.4) + (1 | ID_e)
## Data: data9
##
## REML criterion at convergence: 10596.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0016 -0.6699 -0.0868 0.5672 3.9527
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID_e (Intercept) 0.06589 0.2567
## ID_i.4 (Intercept) 0.05777 0.2404
## Residual 0.59164 0.7692
## Number of obs: 4329, groups: ID_e, 811; ID_i.4, 335
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.328e-02 2.099e-02 3.015e+02 -0.633 0.5273
## data7.age_cZ 2.179e-02 1.316e-02 4.253e+03 1.656 0.0978 .
## data7.sex_cZ 3.362e-03 1.240e-02 4.133e+03 0.271 0.7863
## data7.reasoningZ 9.886e-02 1.358e-02 4.249e+03 7.278 4.02e-13 ***
## data7.languageZ 8.571e-03 1.334e-02 4.263e+03 0.643 0.5206
## data7.senZ -1.372e-02 1.469e-02 4.251e+03 -0.934 0.3502
## data7.eduZ 5.865e-02 1.398e-02 4.289e+03 4.195 2.78e-05 ***
## data7.judgRE1T_cZ 3.470e-01 1.537e-02 4.262e+03 22.573 < 2e-16 ***
## data7.judgRE1_cZ 2.384e-01 1.411e-02 4.200e+03 16.891 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dt7.g_Z dt7.s_Z dt7.rZ dt7.lZ dt7.sZ dt7.dZ d7.RE1T
## data7.ag_cZ 0.006
## data7.sx_cZ 0.014 0.095
## dat7.rsnngZ -0.020 -0.027 -0.031
## data7.lnggZ 0.014 0.032 0.000 -0.026
## data7.senZ 0.024 -0.051 0.049 0.044 -0.044
## data7.eduZ 0.034 0.121 0.080 -0.058 0.222 0.017
## dt7.jRE1T_Z -0.044 -0.016 -0.075 -0.196 0.071 0.079 -0.211
## dt7.jdRE1_Z -0.014 0.006 -0.043 -0.032 -0.059 0.067 -0.067 -0.391
r.squaredGLMM(f_read2int1Z)
## R2m R2c
## [1,] 0.286891 0.410177
linearHypothesis(f_read2int1,"data7.judgRE1_c = data7.judgRE1T_c")
## Linear hypothesis test
##
## Hypothesis:
## data7.judgRE1_c - data7.judgRE1T_c = 0
##
## Model 1: restricted model
## Model 2: read2Z ~ data7.age_c + data7.sex_c + data7.reasoningZ + data7.language +
## data7.sen + data7.eduZ + data7.judgRE1_c + data7.judgRE1T_c +
## (1 | ID_i.4) + (1 | ID_e)
##
## Df Chisq Pr(>Chisq)
## 1
## 2 1 1.5751 0.2095
#ICC/VPC math
InterceptSchoolM <- VarCorr(l3_ma2)$ID_i.4
InterceptClassM <- VarCorr(l3_ma2)$ID_e
ResidualVarM <- attr( VarCorr(l3_ma2), "sc")^2
ICCMath1 <- (InterceptClassM)/(InterceptClassM + InterceptSchoolM + ResidualVarM)
ICCMath1
## (Intercept)
## (Intercept) 0.05301554
## attr(,"stddev")
## (Intercept)
## 0.2719716
## attr(,"correlation")
## (Intercept)
## (Intercept) 1
ICCMath2 <- (InterceptSchoolM)/(InterceptClassM + InterceptSchoolM + ResidualVarM)
ICCMath2
## (Intercept)
## (Intercept) 0.1070879
## attr(,"stddev")
## (Intercept)
## 0.386538
## attr(,"correlation")
## (Intercept)
## (Intercept) 1
#ICC/VPC reading
InterceptSchoolR <- VarCorr(l3_re2)$ID_i.4
InterceptClassR <- VarCorr(l3_re2)$ID_e
ResidualVarR <- attr( VarCorr(l3_re2), "sc")^2
ICCRead1 <- (InterceptClassR)/(InterceptClassR + InterceptSchoolR + ResidualVarR)
ICCRead1
## (Intercept)
## (Intercept) 0.0590495
## attr(,"stddev")
## (Intercept)
## 0.2425144
## attr(,"correlation")
## (Intercept)
## (Intercept) 1
ICCRead2 <- (InterceptSchoolR)/(InterceptClassR + InterceptSchoolR + ResidualVarR)
ICCRead2
## (Intercept)
## (Intercept) 0.07932714
## attr(,"stddev")
## (Intercept)
## 0.2810867
## attr(,"correlation")
## (Intercept)
## (Intercept) 1