##############################################################################################################
##############################################################################################################
######################### Vocabulary Wave II ####################
######################### ####################
######################### Created by: Shally Novita ####################
######################### on 21.08.2023 ####################
######################### #####################
##############################################################################################################
##############################################################################################################
###Load data
setwd('C:/Users/shall/OneDrive/Dokumente/Project/vocabulary')
file <- 'Data Nasa Batch II_W2E.csv'
df1 <- read.csv(file, header=TRUE, sep = ",", stringsAsFactors=FALSE)
##Delete No Participation Wave 2
df2 <- df1[df1$Wave2E!= 0, ]
#Judgment Literacy and Numeracy
df2$JN <- df2$E_Num1_OII + df2$E_Num2_OII
df2$JL <- df2$E_Lit3_OII + df2$E_Lit4_OII
df2$HNA <- df2$A_hitung + df2$A_urut + df2$A_main + df2$A_numerik + df2$A_hitungrima
df2$Expect <- df2$H_hitungkelompok + df2$H_hitung20 + df2$H_jumlah10 + df2$H_jumlah20 + df2$H_kurang10 + df2$H_kurang20
##Descriptive
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.2.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(Hmisc)
## Warning: package 'Hmisc' was built under R version 4.2.3
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
describe(df2$Wave2)
## df2$Wave2
## n missing distinct Info Mean Gmd
## 84 29 1 0 1 0
##
## Value 1
## Frequency 84
## Proportion 1
describe(df2$TotEI)
## df2$TotEI
## n missing distinct Info Mean Gmd .05 .10
## 84 29 30 0.997 17.61 9.958 4.15 7.30
## .25 .50 .75 .90 .95
## 11.00 16.50 24.00 28.70 30.85
##
## lowest : 1 4 5 6 7, highest: 29 30 31 33 40
sd(df2$TotEI, na.rm=T)
## [1] 8.750928
describe(df2$TotEII)
## df2$TotEII
## n missing distinct Info Mean Gmd .05 .10
## 84 29 33 0.998 19.89 9.538 8.15 10.00
## .25 .50 .75 .90 .95
## 14.00 19.00 24.25 32.70 35.70
##
## lowest : 6 7 8 9 10, highest: 34 36 38 39 43
sd(df2$TotEII, na.rm=T)
## [1] 8.482466
describe(df2$DifE2_E1)
## df2$DifE2_E1
## n missing distinct Info Mean Gmd .05 .10
## 84 29 33 0.998 2.286 9.851 -11.85 -7.70
## .25 .50 .75 .90 .95
## -2.25 2.00 8.25 12.70 15.85
##
## lowest : -23 -19 -14 -12 -11, highest: 15 16 17 21 22
sd(df2$DifE2_E1, na.rm =T)
## [1] 8.799742
describe(df2$HNA)
## df2$HNA
## n missing distinct Info Mean Gmd .05 .10
## 83 30 19 0.995 14.7 5.45 7.0 8.2
## .25 .50 .75 .90 .95
## 12.0 15.0 18.0 21.0 22.0
##
## Value 5 6 7 8 9 10 11 12 13 14 15
## Frequency 1 1 4 3 6 1 4 6 9 6 8
## Proportion 0.012 0.012 0.048 0.036 0.072 0.012 0.048 0.072 0.108 0.072 0.096
##
## Value 16 17 18 19 20 21 22 25
## Frequency 6 6 3 5 4 3 3 4
## Proportion 0.072 0.072 0.036 0.060 0.048 0.036 0.036 0.048
##
## For the frequency table, variable is rounded to the nearest 0.2
sd(df2$HNA, na.rm = T)
## [1] 4.774768
describe(df2$Expect)
## df2$Expect
## n missing distinct Info Mean Gmd .05 .10
## 82 31 16 0.874 20.28 5.045 9.05 13.00
## .25 .50 .75 .90 .95
## 18.00 23.50 24.00 24.00 24.00
##
## Value 8.00 8.96 9.92 12.00 12.96 13.92 14.88 16.00 16.96 17.92 18.88
## Frequency 2 3 1 2 4 2 2 1 1 8 4
## Proportion 0.024 0.037 0.012 0.024 0.049 0.024 0.024 0.012 0.012 0.098 0.049
##
## Value 20.00 20.96 21.92 22.88 24.00
## Frequency 2 2 4 3 41
## Proportion 0.024 0.024 0.049 0.037 0.500
##
## For the frequency table, variable is rounded to the nearest 0.16
sd(df2$Expect, na.rm=T)
## [1] 4.909739
table(df2$Expect)
##
## 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23 24
## 2 3 1 2 4 2 2 1 1 8 4 2 2 4 3 41
describe(df2$JN)
## df2$JN
## n missing distinct Info Mean Gmd .05 .10
## 38 75 18 0.992 35.95 12.72 13.85 22.70
## .25 .50 .75 .90 .95
## 29.25 37.00 44.75 48.30 49.00
##
## Value 7.00 12.59 13.88 21.62 22.91 23.77 27.64 32.80 33.66 39.68 42.69
## Frequency 1 1 1 1 2 3 1 6 3 1 4
## Proportion 0.026 0.026 0.026 0.026 0.053 0.079 0.026 0.158 0.079 0.026 0.105
##
## Value 43.98 44.84 45.70 46.99 47.85 48.71 50.00
## Frequency 4 2 1 1 2 3 1
## Proportion 0.105 0.053 0.026 0.026 0.053 0.079 0.026
##
## For the frequency table, variable is rounded to the nearest 0.43
sd(df2$JN, na.rm = T)
## [1] 11.38028
describe(df2$JL)
## df2$JL
## n missing distinct Info Mean Gmd .05 .10
## 38 75 21 0.992 168.2 68.9 33.85 53.70
## .25 .50 .75 .90 .95
## 130.50 203.00 205.00 229.80 234.90
##
## Value 23.00 31.76 33.95 51.47 53.66 101.84 121.55 152.21 182.87
## Frequency 1 1 1 1 1 4 1 4 3
## Proportion 0.026 0.026 0.026 0.026 0.026 0.105 0.026 0.105 0.079
##
## Value 202.58 204.77 213.53 222.29 226.67 233.24 239.81 242.00
## Frequency 10 3 1 2 1 2 1 1
## Proportion 0.263 0.079 0.026 0.053 0.026 0.053 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 2.19
describe(df2$Umur)
## df2$Umur
## n missing distinct Info Mean Gmd .05 .10
## 84 29 19 0.968 59.56 5.854 48.00 51.30
## .25 .50 .75 .90 .95
## 56.75 60.00 63.25 66.00 66.00
##
## Value 48.00 48.95 50.85 51.99 52.94 53.89 54.84 55.98 56.93 57.88 58.83
## Frequency 6 1 2 2 1 2 1 6 4 2 1
## Proportion 0.071 0.012 0.024 0.024 0.012 0.024 0.012 0.071 0.048 0.024 0.012
##
## Value 59.97 60.92 61.87 62.82 63.96 64.91 65.86 67.00
## Frequency 25 2 1 7 4 1 13 3
## Proportion 0.298 0.024 0.012 0.083 0.048 0.012 0.155 0.036
##
## For the frequency table, variable is rounded to the nearest 0.19
sd(df2$Umur, na.rm = T)
## [1] 5.285468
describe(df2$PenghasilanI)
## df2$PenghasilanI
## n missing distinct Info Mean Gmd
## 81 32 6 0.767 1.765 1.146
##
## Value 1.00 1.96 2.98 4.00 4.96 7.00
## Frequency 49 18 6 4 2 2
## Proportion 0.605 0.222 0.074 0.049 0.025 0.025
##
## For the frequency table, variable is rounded to the nearest 0.06
sd(df2$PenghasilanI, na.rm = T)
## [1] 1.296839
table(df2$PenghasilanI)
##
## 1 2 3 4 5 7
## 49 18 6 4 2 2
table(df2$JK)
##
## 0 1
## 38 46
#Numeracy1
df2$ent1 <- as.numeric(df2$E1) + as.numeric(df2$E2) + as.numeric(df2$E3) + as.numeric(df2$E4) + as.numeric(df2$E5)
df2$ent2 <- df2$E6 + df2$E7 + df2$E8 + df2$E9 + df2$E10
df2$ent3 <- df2$E11 + df2$E12 + df2$E13 + df2$E14 + df2$E15
df2$ent4 <- df2$E16 + df2$E17 + df2$E18 + df2$E19 + df2$E20
df2$ent5 <- df2$E21 + df2$E22 + df2$E23 + df2$E24 + df2$E25
df2$ent6 <- df2$E26 + df2$E27 + df2$E28 + df2$E29 + df2$E30
df2$ent7 <- df2$E31 + df2$E32 + df2$E33 + df2$E34 + df2$E35
df2$ent8 <- df2$E36 + df2$E37 + df2$E38 + df2$E39 + df2$E40
df2$ent9 <- df2$E41 + df2$E42 + df2$E43 + df2$E44 + as.numeric(df2$E45)
df2$ent <- df2$ent1 + df2$ent2 + df2$ent3 + df2$ent4 + df2$ent5 + df2$ent6 +
df2$ent7 + df2$ent8 + df2$ent9
#Numeracy2
df2$ent1II <- df2$E1II + df2$E2II + df2$E3II + df2$E4II + df2$E5II
df2$ent2II <- df2$E6II + df2$E7II + df2$E8II + df2$E9II + df2$E10II
df2$ent3II <- as.numeric(df2$E11II) + as.numeric(df2$E12II) + as.numeric(df2$E13II) + as.numeric(df2$E14II) + as.numeric(df2$E15II)
## Warning: NAs introduced by coercion
df2$ent4II <- df2$E16II + df2$E17II + df2$E18II + df2$E19II + df2$E20II
df2$ent5II <- df2$E21II + df2$E22II + df2$E23II + df2$E24II + df2$E25II
df2$ent6II <- df2$E26II + df2$E27II + df2$E28II + df2$E29II + df2$E30II
df2$ent7II <- df2$E31II + df2$E32II + df2$E33II + df2$E34II + df2$E35II
df2$ent8II <- df2$E36II + df2$E37II + df2$E38II + df2$E39II + df2$E40II
df2$ent9II <- as.numeric(df2$E41II) + as.numeric(df2$E42II) + as.numeric(df2$E43II) + as.numeric(df2$E44II) + as.numeric(df2$E45II)
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
df2$entII <- df2$ent1II + df2$ent2II + df2$ent3II + df2$ent4II + df2$ent5II + df2$ent6II +
df2$ent7II + df2$ent8II + df2$ent9II
##Descriptive
describe(df2$ent1)
## df2$ent1
## n missing distinct Info Mean Gmd
## 84 29 6 0.918 3.762 1.405
##
## Value 0 1 2 3 4 5
## Frequency 1 7 5 17 22 32
## Proportion 0.012 0.083 0.060 0.202 0.262 0.381
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent1, na.rm=T)
## [1] 1.313682
describe(df2$ent2)
## df2$ent2
## n missing distinct Info Mean Gmd
## 84 29 6 0.96 1.976 1.649
##
## Value 0 1 2 3 4 5
## Frequency 15 20 21 13 10 5
## Proportion 0.179 0.238 0.250 0.155 0.119 0.060
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent2, na.rm=T)
## [1] 1.464242
describe(df2$ent3)
## df2$ent3
## n missing distinct Info Mean Gmd
## 84 29 6 0.949 2.762 1.478
##
## Value 0 1 2 3 4 5
## Frequency 3 11 25 17 20 8
## Proportion 0.036 0.131 0.298 0.202 0.238 0.095
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent3, na.rm=T)
## [1] 1.313682
describe(df2$ent4)
## df2$ent4
## n missing distinct Info Mean Gmd
## 84 29 6 0.94 1.821 1.444
##
## Value 0 1 2 3 4 5
## Frequency 12 28 21 10 12 1
## Proportion 0.143 0.333 0.250 0.119 0.143 0.012
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent4, na.rm=T)
## [1] 1.300238
describe(df2$ent5)
## df2$ent5
## n missing distinct Info Mean Gmd
## 84 29 6 0.771 0.8214 1.196
##
## Value 0 1 2 3 4 5
## Frequency 51 13 9 7 3 1
## Proportion 0.607 0.155 0.107 0.083 0.036 0.012
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent5, na.rm=T)
## [1] 1.243399
describe(df2$ent6)
## df2$ent6
## n missing distinct Info Mean Gmd
## 83 30 6 0.949 1.904 1.529
##
## Value 0 1 2 3 4 5
## Frequency 16 16 26 11 13 1
## Proportion 0.193 0.193 0.313 0.133 0.157 0.012
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent6, na.rm=T)
## [1] 1.358034
describe(df2$ent7)
## df2$ent7
## n missing distinct Info Mean Gmd
## 83 30 6 0.961 1.88 1.838
##
## Value 0 1 2 3 4 5
## Frequency 22 18 15 11 10 7
## Proportion 0.265 0.217 0.181 0.133 0.120 0.084
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent7, na.rm=T)
## [1] 1.633473
describe(df2$ent8)
## df2$ent8
## n missing distinct Info Mean Gmd
## 83 30 6 0.964 2.325 1.747
##
## Value 0 1 2 3 4 5
## Frequency 10 18 21 12 13 9
## Proportion 0.120 0.217 0.253 0.145 0.157 0.108
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent8, na.rm=T)
## [1] 1.538993
describe(df2$ent9)
## df2$ent9
## n missing distinct Info Mean Gmd
## 82 31 6 0.93 1.341 1.463
##
## Value 0 1 2 3 4 5
## Frequency 28 23 16 8 4 3
## Proportion 0.341 0.280 0.195 0.098 0.049 0.037
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent9, na.rm=T)
## [1] 1.362818
describe(df2$ent1II)
## df2$ent1II
## n missing distinct Info Mean Gmd
## 82 31 4 0.89 4 0.9606
##
## Value 2.00 2.99 3.98 5.00
## Frequency 5 17 33 27
## Proportion 0.061 0.207 0.402 0.329
##
## For the frequency table, variable is rounded to the nearest 0.03
sd(df2$ent1II, na.rm=T)
## [1] 0.8888889
describe(df2$ent2II)
## df2$ent2II
## n missing distinct Info Mean Gmd
## 84 29 6 0.949 1.726 1.569
##
## Value 0 1 2 3 4 5
## Frequency 18 26 15 14 8 3
## Proportion 0.214 0.310 0.179 0.167 0.095 0.036
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent2II, na.rm=T)
## [1] 1.408675
describe(df2$ent3II)
## df2$ent3II
## n missing distinct Info Mean Gmd
## 80 33 7 0.949 3.388 1.745
##
## Value 0.00 0.91 1.95 2.99 3.90 4.94 13.00
## Frequency 3 6 15 14 25 16 1
## Proportion 0.038 0.075 0.188 0.175 0.312 0.200 0.013
##
## For the frequency table, variable is rounded to the nearest 0.13
sd(df2$ent3II, na.rm=T)
## [1] 1.753793
describe(df2$ent4II)
## df2$ent4II
## n missing distinct Info Mean Gmd
## 83 30 6 0.953 1.699 1.595
##
## Value 0 1 2 3 4 5
## Frequency 20 23 16 13 8 3
## Proportion 0.241 0.277 0.193 0.157 0.096 0.036
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent4II, na.rm=T)
## [1] 1.429096
describe(df2$ent5II)
## df2$ent5II
## n missing distinct Info Mean Gmd
## 82 31 6 0.905 2.098 1.338
##
## Value 0 1 2 3 4 5
## Frequency 10 11 36 14 8 3
## Proportion 0.122 0.134 0.439 0.171 0.098 0.037
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent5II, na.rm=T)
## [1] 1.233381
describe(df2$ent6II)
## df2$ent6II
## n missing distinct Info Mean Gmd
## 81 32 6 0.961 2.012 1.69
##
## Value 0 1 2 3 4 5
## Frequency 14 20 19 14 7 7
## Proportion 0.173 0.247 0.235 0.173 0.086 0.086
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent6II, na.rm=T)
## [1] 1.50411
describe(df2$ent7II)
## df2$ent7II
## n missing distinct Info Mean Gmd
## 82 31 6 0.963 2.098 1.694
##
## Value 0 1 2 3 4 5
## Frequency 13 20 17 16 10 6
## Proportion 0.159 0.244 0.207 0.195 0.122 0.073
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent7II, na.rm=T)
## [1] 1.495753
describe(df2$ent8II)
## df2$ent8II
## n missing distinct Info Mean Gmd
## 82 31 6 0.951 1.963 1.547
##
## Value 0 1 2 3 4 5
## Frequency 12 22 23 10 12 3
## Proportion 0.146 0.268 0.280 0.122 0.146 0.037
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent8II, na.rm=T)
## [1] 1.382832
describe(df2$ent9II)
## df2$ent9II
## n missing distinct Info Mean Gmd
## 75 38 6 0.895 1.24 1.541
##
## Value 0 1 2 3 4 5
## Frequency 34 15 9 10 5 2
## Proportion 0.453 0.200 0.120 0.133 0.067 0.027
##
## For the frequency table, variable is rounded to the nearest 0.05
sd(df2$ent9II, na.rm=T)
## [1] 1.450443
t.test(df2$TotEI, df2$TotEII)
##
## Welch Two Sample t-test
##
## data: df2$TotEI and df2$TotEII
## t = -1.7189, df = 165.84, p-value = 0.0875
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.9111285 0.3396999
## sample estimates:
## mean of x mean of y
## 17.60714 19.89286
t.test(df2$ent1, df2$ent1II)
##
## Welch Two Sample t-test
##
## data: df2$ent1 and df2$ent1II
## t = -1.3705, df = 146.17, p-value = 0.1726
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5814328 0.1052424
## sample estimates:
## mean of x mean of y
## 3.761905 4.000000
t.test(df2$ent2, df2$ent2II)
##
## Welch Two Sample t-test
##
## data: df2$ent2 and df2$ent2II
## t = 1.1277, df = 165.75, p-value = 0.2611
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1877038 0.6877038
## sample estimates:
## mean of x mean of y
## 1.97619 1.72619
t.test(df2$ent3, df2$ent3II)
##
## Welch Two Sample t-test
##
## data: df2$ent3 and df2$ent3II
## t = -2.5757, df = 146.24, p-value = 0.011
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1056094 -0.1455811
## sample estimates:
## mean of x mean of y
## 2.761905 3.387500
t.test(df2$ent4, df2$ent4II)
##
## Welch Two Sample t-test
##
## data: df2$ent4 and df2$ent4II
## t = 0.57982, df = 163.16, p-value = 0.5628
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2949985 0.5402652
## sample estimates:
## mean of x mean of y
## 1.821429 1.698795
t.test(df2$ent5, df2$ent5II)
##
## Welch Two Sample t-test
##
## data: df2$ent5 and df2$ent5II
## t = -6.6382, df = 163.96, p-value = 4.433e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.655721 -0.896544
## sample estimates:
## mean of x mean of y
## 0.8214286 2.0975610
t.test(df2$ent6, df2$ent6II)
##
## Welch Two Sample t-test
##
## data: df2$ent6 and df2$ent6II
## t = -0.48553, df = 159.46, p-value = 0.628
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5510066 0.3335441
## sample estimates:
## mean of x mean of y
## 1.903614 2.012346
t.test(df2$ent7, df2$ent7II)
##
## Welch Two Sample t-test
##
## data: df2$ent7 and df2$ent7II
## t = -0.89441, df = 162.07, p-value = 0.3724
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6994480 0.2633622
## sample estimates:
## mean of x mean of y
## 1.879518 2.097561
t.test(df2$ent8, df2$ent8II)
##
## Welch Two Sample t-test
##
## data: df2$ent8 and df2$ent8II
## t = 1.5892, df = 161.56, p-value = 0.114
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08780299 0.81157613
## sample estimates:
## mean of x mean of y
## 2.325301 1.963415
t.test(df2$ent9, df2$ent9II)
##
## Welch Two Sample t-test
##
## data: df2$ent9 and df2$ent9II
## t = 0.45061, df = 151.51, p-value = 0.6529
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3434090 0.5463358
## sample estimates:
## mean of x mean of y
## 1.341463 1.240000
##Internal Consistency
library(ltm)
## Warning: package 'ltm' was built under R version 4.2.3
## Loading required package: MASS
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## Loading required package: msm
## Loading required package: polycor
library(MASS)
expect <- dplyr::select(df2, H_hitungkelompok:H_kurang20)
cronbach.alpha(expect, na.rm=T)
##
## Cronbach's alpha for the 'expect' data-set
##
## Items: 6
## Sample units: 113
## alpha: 0.952
activities <- dplyr::select(df2, A_hitung:A_hitungrima)
cronbach.alpha(activities, na.rm=T)
##
## Cronbach's alpha for the 'activities' data-set
##
## Items: 5
## Sample units: 113
## alpha: 0.817
ENI<- dplyr::select(df2, E1:E45)
cronbach.alpha(ENI, na.rm=T)
##
## Cronbach's alpha for the 'ENI' data-set
##
## Items: 45
## Sample units: 113
## alpha: 0.9
ENII <- dplyr::select(df2, E1II: E45II)
cronbach.alpha(ENII, na.rm=T)
##
## Cronbach's alpha for the 'ENII' data-set
##
## Items: 45
## Sample units: 113
## alpha: 0.877
JN <- dplyr::select(df2, E_Num1_OII, E_Num2_OII )
cronbach.alpha(JN, na.rm=T)
##
## Cronbach's alpha for the 'JN' data-set
##
## Items: 2
## Sample units: 113
## alpha: 0.072
JL <- dplyr::select(df2, E_Lit3_OII, E_Lit4_OII)
cronbach.alpha(JL, na.rm=T)
##
## Cronbach's alpha for the 'JL' data-set
##
## Items: 2
## Sample units: 113
## alpha: 0.017
##Correlation
dcor <- dplyr::select(df2, JK, Umur, TotEI, TotEII, DifE2_E1, HNA, Expect,
PenghasilanI, JN)
cor(dcor, method= "pearson", use='complete.obs')
## JK Umur TotEI TotEII DifE2_E1
## JK 1.000000000 -0.20503762 -0.1624715 0.235256223 0.38765117
## Umur -0.205037619 1.00000000 0.2702712 0.250803276 -0.01413474
## TotEI -0.162471463 0.27027122 1.0000000 0.471972597 -0.50017874
## TotEII 0.235256223 0.25080328 0.4719726 1.000000000 0.52733779
## DifE2_E1 0.387651166 -0.01413474 -0.5001787 0.527337788 1.00000000
## HNA -0.047240524 -0.10382136 -0.3182458 -0.241621788 0.06938838
## Expect 0.002391285 -0.09718759 -0.1444479 -0.004537562 0.13475502
## PenghasilanI -0.054704100 -0.21867285 0.1223444 -0.200323738 -0.31466793
## JN -0.072902758 -0.06827852 0.4371099 0.453582348 0.02424433
## HNA Expect PenghasilanI JN
## JK -0.04724052 0.002391285 -0.054704100 -0.072902758
## Umur -0.10382136 -0.097187588 -0.218672851 -0.068278523
## TotEI -0.31824582 -0.144447897 0.122344424 0.437109862
## TotEII -0.24162179 -0.004537562 -0.200323738 0.453582348
## DifE2_E1 0.06938838 0.134755021 -0.314667933 0.024244327
## HNA 1.00000000 0.432435058 0.137265662 -0.130932564
## Expect 0.43243506 1.000000000 0.031491073 -0.060679394
## PenghasilanI 0.13726566 0.031491073 1.000000000 0.006182131
## JN -0.13093256 -0.060679394 0.006182131 1.000000000
library(psych)
## Warning: package 'psych' was built under R version 4.2.3
##
## Attaching package: 'psych'
## The following object is masked from 'package:ltm':
##
## factor.scores
## The following object is masked from 'package:polycor':
##
## polyserial
## The following object is masked from 'package:Hmisc':
##
## describe
cor_sig <- corr.test(dcor)$p
cor_sig
## JK Umur TotEI TotEII DifE2_E1
## JK 0.000000000 1.000000000 7.907864e-02 1.000000e+00 1.468997e-01
## Umur 0.571955879 0.000000000 1.000000e+00 1.000000e+00 1.000000e+00
## TotEI 0.002396322 0.088710931 0.000000e+00 1.381927e-04 6.530934e-06
## TotEII 0.836222767 0.193640474 4.064492e-06 0.000000e+00 8.866195e-05
## DifE2_E1 0.005065507 0.666070824 1.814148e-07 2.533199e-06 0.000000e+00
## HNA 0.084015882 0.212101150 2.402244e-01 8.375702e-01 3.304165e-01
## Expect 0.766760533 0.525138870 4.780006e-01 3.344002e-01 8.301585e-01
## PenghasilanI 0.077276622 0.002426239 5.464505e-03 7.315799e-01 1.602623e-02
## JN 0.570981247 0.544834695 6.075245e-03 4.204864e-03 8.830607e-01
## HNA Expect PenghasilanI JN
## JK 1.000000000 1.0000000 1.00000000 1.0000000
## Umur 1.000000000 1.0000000 0.07907864 1.0000000
## TotEI 1.000000000 1.0000000 0.15300613 0.1640316
## TotEII 1.000000000 1.0000000 1.00000000 0.1261459
## DifE2_E1 1.000000000 1.0000000 0.41668207 1.0000000
## HNA 0.000000000 0.1092783 1.00000000 1.0000000
## Expect 0.003525105 0.0000000 1.00000000 1.0000000
## PenghasilanI 0.361287582 0.3665283 0.00000000 1.0000000
## JN 0.598202686 0.7895655 0.97103194 0.0000000
##Analysis of Numeracy (Paper 1)
library(psych)
fE1 <- lm(DifE2_E1 ~ PenghasilanI + JK + Umur,data=df2)
summary(fE1)
##
## Call:
## lm(formula = DifE2_E1 ~ PenghasilanI + JK + Umur, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.7216 -5.5361 -0.5017 6.4738 17.2800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.3682 12.3673 1.162 0.2489
## PenghasilanI -1.7489 0.7878 -2.220 0.0294 *
## JK 4.6076 1.9392 2.376 0.0200 *
## Umur -0.1954 0.1933 -1.011 0.3152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.371 on 77 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.1547, Adjusted R-squared: 0.1218
## F-statistic: 4.698 on 3 and 77 DF, p-value: 0.004584
fE2 <- lm(DifE2_E1 ~ PenghasilanI + JK + Umur + HNA, data=df2)
summary(fE2)
##
## Call:
## lm(formula = DifE2_E1 ~ PenghasilanI + JK + Umur + HNA, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.1247 -5.5772 -0.1479 6.6681 17.9077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.5065 12.7884 0.587 0.55898
## PenghasilanI -1.8483 0.7756 -2.383 0.01970 *
## JK 5.3727 1.9386 2.771 0.00703 **
## Umur -0.1682 0.1906 -0.882 0.38041
## HNA 0.3513 0.2005 1.752 0.08387 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.226 on 75 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1983, Adjusted R-squared: 0.1555
## F-statistic: 4.637 on 4 and 75 DF, p-value: 0.002124
fE3 <- lm(DifE2_E1 ~ PenghasilanI + JK + Umur + HNA + Expect, data=df2)
summary(fE3)
##
## Call:
## lm(formula = DifE2_E1 ~ PenghasilanI + JK + Umur + HNA + Expect,
## data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.4864 -5.7553 -0.0935 5.9574 18.4046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.3824 13.2701 0.858 0.39384
## PenghasilanI -1.9980 0.7893 -2.531 0.01351 *
## JK 5.5085 1.9499 2.825 0.00609 **
## Umur -0.1974 0.1958 -1.008 0.31658
## HNA 0.3499 0.2170 1.612 0.11120
## Expect -0.1007 0.2017 -0.499 0.61924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.259 on 73 degrees of freedom
## (34 observations deleted due to missingness)
## Multiple R-squared: 0.2105, Adjusted R-squared: 0.1564
## F-statistic: 3.893 on 5 and 73 DF, p-value: 0.003492
fE4 <- lm(DifE2_E1 ~ PenghasilanI + JK + Umur + HNA + Expect + JN, data=df2)
summary(fE4)
##
## Call:
## lm(formula = DifE2_E1 ~ PenghasilanI + JK + Umur + HNA + Expect +
## JN, data = df2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.6364 -4.1164 -0.6836 4.9039 13.2770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.68472 19.25235 -0.399 0.6926
## PenghasilanI -3.14619 1.67054 -1.883 0.0694 .
## JK 6.76601 2.83529 2.386 0.0235 *
## Umur 0.03628 0.25409 0.143 0.8874
## HNA 0.16162 0.30251 0.534 0.5971
## Expect 0.21134 0.33535 0.630 0.5333
## JN 0.05639 0.11968 0.471 0.6409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.087 on 30 degrees of freedom
## (76 observations deleted due to missingness)
## Multiple R-squared: 0.2683, Adjusted R-squared: 0.1219
## F-statistic: 1.833 on 6 and 30 DF, p-value: 0.1261