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#Uji Normalitas semua data

str(data_penelitian_untuk_uji_di_R)
## tibble [34 × 12] (S3: tbl_df/tbl/data.frame)
##  $ Siswa...1        : chr [1:34] "E_01" "E_02" "E_03" "E_04" ...
##  $ eks_pretest      : num [1:34] 16.6 70.8 54.2 45.8 91.7 58.3 16.6 45.8 58.3 33.3 ...
##  $ eks_posttest     : num [1:34] 91.7 83.3 87.5 70.8 95.8 83.3 87.5 91.7 100 75 ...
##  $ ...4             : logi [1:34] NA NA NA NA NA NA ...
##  $ Siswa...5        : chr [1:34] "K_01" "K_02" "K_03" "K_04" ...
##  $ kontrol_pretest  : num [1:34] 58.3 62.5 50 16.6 83.3 58.3 33.3 58.3 54.2 66.7 ...
##  $ kontrol_posttest : num [1:34] 87.5 91.7 70.8 62.5 95.8 79.2 75 66.7 79.2 87.5 ...
##  $ ...8             : logi [1:34] NA NA NA NA NA NA ...
##  $ ...9             : logi [1:34] NA NA NA NA NA NA ...
##  $ No.              : num [1:34] 1 2 3 4 5 6 7 8 9 10 ...
##  $ N-Gain_Eksperimen: num [1:34] 0.9 0.43 0.73 0.46 0.49 0.6 0.85 0.85 1 0.63 ...
##  $ N-Gain_Kontrol   : num [1:34] 0.7 0.78 0.42 0.55 0.75 0.5 0.63 0.2 0.55 0.62 ...
data_penelitian_untuk_uji_di_R$eks_pretest <-
  as.numeric(as.character(data_penelitian_untuk_uji_di_R$eks_pretest))

shapiro.test(data_penelitian_untuk_uji_di_R$eks_pretest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_pretest
## W = 0.93968, p-value = 0.0603
shapiro.test(data_penelitian_untuk_uji_di_R$eks_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest
## W = 0.93921, p-value = 0.05839
shapiro.test(data_penelitian_untuk_uji_di_R$kontrol_pretest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$kontrol_pretest
## W = 0.94363, p-value = 0.07911
shapiro.test(data_penelitian_untuk_uji_di_R$kontrol_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$kontrol_posttest
## W = 0.96055, p-value = 0.2523

#1. Uji Ketuntasan Rata-rata

#Uji Normalitas

shapiro.test(data_penelitian_untuk_uji_di_R$eks_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest
## W = 0.93921, p-value = 0.05839

#Uji t satu sampel

t.test(data_penelitian_untuk_uji_di_R$eks_posttest,y = NULL, 
       alternative = "greater",
       mu = 80, paired = FALSE, var.equal = FALSE,
       conf.level = 0.95)
## 
##  One Sample t-test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest
## t = 6.0703, df = 33, p-value = 3.923e-07
## alternative hypothesis: true mean is greater than 80
## 95 percent confidence interval:
##  85.75691      Inf
## sample estimates:
## mean of x 
##  87.98235

#2. Uji Proporsi Ketuntasan

KKTP <- 80 #Menentukan KKTP
tuntas_eks <- data_penelitian_untuk_uji_di_R$eks_posttest >= KKTP #Status ketuntasan kelas eksperimen (posttest)

n_eks <- length(data_penelitian_untuk_uji_di_R$eks_posttest) #Jumlah siswa

x_eks <- sum(tuntas_eks, na.rm = TRUE) #Jumlah siswa tuntas

# Uji proporsi ketuntasan
prop.test(
  x = x_eks, # jumlah siswa tuntas
  n = n_eks, # jumlah siswa
  p = 0.75, # proporsi target
  alternative = "greater",
  correct = FALSE
)
## 
##  1-sample proportions test without continuity correction
## 
## data:  x_eks out of n_eks, null probability 0.75
## X-squared = 3.1765, df = 1, p-value = 0.03735
## alternative hypothesis: true p is greater than 0.75
## 95 percent confidence interval:
##  0.762269 1.000000
## sample estimates:
##         p 
## 0.8823529

#3. Uji Perbedaan Rata-rata

#Uji Normalitas

shapiro.test(data_penelitian_untuk_uji_di_R$eks_pretest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_pretest
## W = 0.93968, p-value = 0.0603
shapiro.test(data_penelitian_untuk_uji_di_R$eks_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest
## W = 0.93921, p-value = 0.05839
# Menghitung selisih posttest - pretest
selisih <- data_penelitian_untuk_uji_di_R$eks_posttest -
           data_penelitian_untuk_uji_di_R$eks_pretest

# Uji normalitas selisih
shapiro.test(selisih)
## 
##  Shapiro-Wilk normality test
## 
## data:  selisih
## W = 0.95638, p-value = 0.1903

#Uji t berpasangan

t.test(data_penelitian_untuk_uji_di_R$eks_posttest,
  data_penelitian_untuk_uji_di_R$eks_pretest,
  paired = TRUE,
  alternative = "greater",
  conf.level = 0.95)
## 
##  Paired t-test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest and data_penelitian_untuk_uji_di_R$eks_pretest
## t = 10.629, df = 33, p-value = 1.72e-12
## alternative hypothesis: true mean difference is greater than 0
## 95 percent confidence interval:
##  31.63786      Inf
## sample estimates:
## mean difference 
##        37.62941

#4. Uji Perbandingan Ketuntasan Rata-Rata

#Uji Normalitas

shapiro.test(data_penelitian_untuk_uji_di_R$eks_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest
## W = 0.93921, p-value = 0.05839
shapiro.test(data_penelitian_untuk_uji_di_R$kontrol_posttest)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$kontrol_posttest
## W = 0.96055, p-value = 0.2523

#Uji Homogenitas

library(car)

leveneTest(
  data_penelitian_untuk_uji_di_R$eks_posttest,
  data_penelitian_untuk_uji_di_R$kontrol_posttest)
## Warning in leveneTest.default(data_penelitian_untuk_uji_di_R$eks_posttest, :
## data_penelitian_untuk_uji_di_R$kontrol_posttest coerced to factor.
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  8  1.5216 0.1999
##       25

#Uji t independen

t.test(
  data_penelitian_untuk_uji_di_R$eks_posttest,
  data_penelitian_untuk_uji_di_R$kontrol_posttest,
  alternative = "greater",
  var.equal = TRUE,
  conf.level = 0.95)
## 
##  Two Sample t-test
## 
## data:  data_penelitian_untuk_uji_di_R$eks_posttest and data_penelitian_untuk_uji_di_R$kontrol_posttest
## t = 3.0535, df = 66, p-value = 0.00163
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  2.775235      Inf
## sample estimates:
## mean of x mean of y 
##  87.98235  81.86471

#5. Uji Perbandingan Proporsi Ketuntasan

# Menentukan KKTP
KKTP <- 80

# Status ketuntasan
tuntas_eks <- data_penelitian_untuk_uji_di_R$eks_posttest >= KKTP
tuntas_kon <- data_penelitian_untuk_uji_di_R$kontrol_posttest >= KKTP

# Jumlah siswa
n1 <- length(tuntas_eks)
n2 <- length(tuntas_kon)

# Jumlah siswa tuntas
x1 <- sum(tuntas_eks, na.rm = TRUE)
x2 <- sum(tuntas_kon, na.rm = TRUE)

# Proporsi ketuntasan
p1 <- x1 / n1
p2 <- x2 / n2

n1; x1; p1
## [1] 34
## [1] 30
## [1] 0.8823529
n2; x2; p2
## [1] 34
## [1] 18
## [1] 0.5294118

#Uji Z satu sampel

prop.test(x = c(x1, x2),
  n = c(n1, n2),
  alternative = "greater",
  correct = FALSE
)
## 
##  2-sample test for equality of proportions without continuity correction
## 
## data:  c(x1, x2) out of c(n1, n2)
## X-squared = 10.2, df = 1, p-value = 0.0007022
## alternative hypothesis: greater
## 95 percent confidence interval:
##  0.1853547 1.0000000
## sample estimates:
##    prop 1    prop 2 
## 0.8823529 0.5294118

#6. UJI PERBANDINGAN PENINGKATAN KEMAMPUAN BERPIKIR KREATIF KELOMPOK EKSPERIMEN DAN KONTROL

#Uji Normalitas

shapiro.test(data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen`)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen`
## W = 0.93985, p-value = 0.06101
shapiro.test(data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol`)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol`
## W = 0.95578, p-value = 0.1827

#Uji Homogenitas

library(car)

leveneTest(
  data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen`,
  data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol`)
## Warning in
## leveneTest.default(data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen`, :
## data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol` coerced to factor.
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group 21  0.5493 0.8893
##       12

#Uji t Independen

t.test(
  data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen`,
  data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol`,
  alternative = "greater",
  var.equal = TRUE,
  conf.level = 0.95)
## 
##  Two Sample t-test
## 
## data:  data_penelitian_untuk_uji_di_R$`N-Gain_Eksperimen` and data_penelitian_untuk_uji_di_R$`N-Gain_Kontrol`
## t = 3.0557, df = 66, p-value = 0.001619
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  0.06583716        Inf
## sample estimates:
## mean of x mean of y 
## 0.7264706 0.5814706