Mengaktifkan Packages yang Dibutuhkan
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.3
library(MVN)
## Warning: package 'MVN' was built under R version 4.4.3
library(MVTests)
## Warning: package 'MVTests' was built under R version 4.4.3
##
## Attaching package: 'MVTests'
## The following object is masked from 'package:datasets':
##
## iris
library(profileR)
## Warning: package 'profileR' was built under R version 4.4.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.4.3
## Loading required package: RColorBrewer
## Loading required package: reshape
## Warning: package 'reshape' was built under R version 4.4.3
## Loading required package: lavaan
## Warning: package 'lavaan' was built under R version 4.4.3
## This is lavaan 0.6-20
## lavaan is FREE software! Please report any bugs.
Mengubah Variabel Dependen Menjadi Format Matriks
y1 <- as.matrix(data$EnglishScore, nrow=42, ncol=1)
y2 <- as.matrix(data$MathScore, nrow=42, ncol=1)
y3 <- as.matrix(data$ChemistryScore, nrow=42, ncol=1)
y4 <- as.matrix(data$PhysicsScore, nrow=42, ncol=1)
y5 <- as.matrix(data$BiologyScore, nrow=42, ncol=1)
Menggabungkan Semua Variabel Menjadi Satu Data Frame
datafix = data.frame(perlakuan,y1,y2,y3,y4,y5)
datafix
## perlakuan y1 y2 y3 y4 y5
## 1 Differentiated Instruction 85 88 90 87 89
## 2 Differentiated Instruction 87 90 92 89 91
## 3 Differentiated Instruction 89 85 88 86 88
## 4 Differentiated Instruction 84 87 85 88 87
## 5 Differentiated Instruction 88 89 90 90 89
## 6 Differentiated Instruction 90 92 91 91 92
## 7 Differentiated Instruction 85 86 87 85 86
## 8 Differentiated Instruction 87 88 89 87 88
## 9 Differentiated Instruction 88 85 86 88 87
## 10 Differentiated Instruction 86 87 88 86 89
## 11 Differentiated Instruction 89 88 90 89 90
## 12 Differentiated Instruction 87 89 91 87 88
## 13 Differentiated Instruction 85 90 92 90 91
## 14 Differentiated Instruction 88 87 89 86 87
## 15 Lecture-based Instruction 78 75 77 76 78
## 16 Lecture-based Instruction 76 77 79 75 77
## 17 Lecture-based Instruction 80 78 80 77 79
## 18 Lecture-based Instruction 77 76 78 76 78
## 19 Lecture-based Instruction 79 77 76 78 77
## 20 Lecture-based Instruction 75 79 77 75 76
## 21 Lecture-based Instruction 78 78 79 76 77
## 22 Lecture-based Instruction 80 75 78 77 78
## 23 Lecture-based Instruction 77 76 75 78 77
## 24 Lecture-based Instruction 79 78 76 75 78
## 25 Lecture-based Instruction 78 77 79 76 77
## 26 Lecture-based Instruction 76 78 77 75 78
## 27 Lecture-based Instruction 80 76 78 77 76
## 28 Lecture-based Instruction 77 75 79 76 78
## 29 Technology-based Learning 82 85 83 84 86
## 30 Technology-based Learning 84 87 85 83 85
## 31 Technology-based Learning 81 82 80 81 83
## 32 Technology-based Learning 83 86 84 85 84
## 33 Technology-based Learning 85 88 86 87 86
## 34 Technology-based Learning 86 89 87 88 87
## 35 Technology-based Learning 82 84 85 83 84
## 36 Technology-based Learning 83 85 84 86 83
## 37 Technology-based Learning 84 87 86 85 85
## 38 Technology-based Learning 85 88 87 87 86
## 39 Technology-based Learning 86 86 85 88 85
## 40 Technology-based Learning 82 87 84 85 86
## 41 Technology-based Learning 83 88 86 87 85
## 42 Technology-based Learning 84 89 87 88 86
Mengecek Ketersediaan Fungsi mvn
exists("mvn", where = "package:MVN")
## [1] TRUE
Uji Normalitas Multivariat
norm.test = mvn(data = datafix, subset = "perlakuan", mvn_test = "mardia")
norm.test$multivariate_normality
## Group Test Statistic p.value MVN
## 1 Differentiated Instruction Mardia Skewness 41.490 0.209 ✓ Normal
## 2 Differentiated Instruction Mardia Kurtosis -0.732 0.464 ✓ Normal
## 3 Lecture-based Instruction Mardia Skewness 45.791 0.105 ✓ Normal
## 4 Lecture-based Instruction Mardia Kurtosis -0.517 0.605 ✓ Normal
## 5 Technology-based Learning Mardia Skewness 47.765 0.074 ✓ Normal
## 6 Technology-based Learning Mardia Kurtosis -0.442 0.658 ✓ Normal
Uji Homogenitas Matriks Ragam Peragam
ujiboxm <- BoxM(data = datafix[,2:6], datafix$perlakuan)
summary(ujiboxm)
## Box's M Test
##
## Chi-Squared Value = 42.95087 , df = 30 and p-value: 0.0592
Membangun Model MANOVA
ujimanova <- manova(cbind(y1,y2,y3,y4,y5) ~ perlakuan, data=datafix)
Pengujian MANOVA dengan Berbagai Statistik Uji
summary(ujimanova, test="Pillai")
## Df Pillai approx F num Df den Df Pr(>F)
## perlakuan 2 1.4367 18.366 10 72 3.92e-16 ***
## Residuals 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Roy")
## Df Roy approx F num Df den Df Pr(>F)
## perlakuan 2 15.238 109.71 5 36 < 2.2e-16 ***
## Residuals 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Wilks")
## Df Wilks approx F num Df den Df Pr(>F)
## perlakuan 2 0.030896 32.824 10 70 < 2.2e-16 ***
## Residuals 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(ujimanova, test="Hotelling-Lawley")
## Df Hotelling-Lawley approx F num Df den Df Pr(>F)
## perlakuan 2 16.231 55.186 10 68 < 2.2e-16 ***
## Residuals 39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pengujian ANOVA Setiap Variabel
summary.aov(ujimanova)
## Response y1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 597.33 298.667 108.71 < 2.2e-16 ***
## Residuals 39 107.14 2.747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 1029.33 514.67 160.85 < 2.2e-16 ***
## Residuals 39 124.79 3.20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 935.29 467.64 136.61 < 2.2e-16 ***
## Residuals 39 133.50 3.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y4 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 1051.62 525.81 173.47 < 2.2e-16 ***
## Residuals 39 118.21 3.03
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response y5 :
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 928.90 464.45 261.7 < 2.2e-16 ***
## Residuals 39 69.21 1.77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot Analisis Profil
profil <- pbg(datafix[,2:6], datafix[,1], profile.plot = TRUE)
Uji Analisis Profil
summary(profil)
## Call:
## pbg(data = datafix[, 2:6], group = datafix[, 1], profile.plot = TRUE)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.3199533 6.911063 8 72 1.029239e-06
## 2 Pillai 0.8464523 6.787481 8 74 1.199769e-06
## 3 Hotelling-Lawley 1.6053625 7.023461 8 70 9.132202e-07
## 4 Roy 1.1551069 10.684739 4 37 7.394009e-06
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 890.2 445.1 259.7 <2e-16 ***
## Residuals 39 66.8 1.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 3.931507 4 36 0.009500572