Pengaruh Game online terhadap gangguan depresi pada mahasiswa- SEM PLS
Caraka Arief Ibrahim dan Alfisyahrina Hapsery
2024-05-23
library(readxl)
library(seminr)
library(readxlsb)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 3.4.2 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::rerun() masks seminr::rerun()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.3.2dat <- read_excel("D:/artikel - game online caraka/Data_Visualisasi.xlsx")
dat## # A tibble: 97 × 42
## Angkatan Asal_Universitas Jenis_Univ Jenis_Prodi Umur Jenis_Kelamin
## <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 20 UNIPA SWASTA Ilmu Sosial dan Hum… 23 LAKI-LAKI
## 2 20 UNIPA SWASTA Ilmu Sosial dan Hum… 22 LAKI-LAKI
## 3 20 UNIPA SWASTA Ilmu Sosial dan Hum… 22 PEREMPUAN
## 4 20 UNIPA SWASTA Ilmu Sosial dan Hum… 22 LAKI-LAKI
## 5 20 UNIPA SWASTA Ilmu Sosial dan Hum… 22 LAKI-LAKI
## 6 21 UNIPA SWASTA Ilmu Sosial dan Hum… 20 LAKI-LAKI
## 7 21 UNIPA SWASTA Ilmu Sosial dan Hum… 22 LAKI-LAKI
## 8 20 UNIPA SWASTA Ilmu Sosial dan Hum… 23 LAKI-LAKI
## 9 20 UNIPA SWASTA Ilmu Sosial dan Hum… 21 PEREMPUAN
## 10 20 UNIPA SWASTA Ilmu Sosial dan Hum… 21 PEREMPUAN
## # ℹ 87 more rows
## # ℹ 36 more variables: Game_Online <chr>, Average_Playing <chr>, Y1 <chr>,
## # Y2 <chr>, Y3 <chr>, Y4 <chr>, Y5 <chr>, Y6 <chr>, Y7 <chr>, Y8 <chr>,
## # Y9 <chr>, Y10 <chr>, Y <chr>, X11 <chr>, X12 <chr>, X13 <chr>, X14 <chr>,
## # X15 <chr>, X16 <chr>, X21 <chr>, X22 <chr>, X23 <chr>, X24 <chr>,
## # X25 <chr>, X26 <chr>, X27 <chr>, X28 <chr>, X31 <chr>, X32 <chr>,
## # X33 <chr>, X34 <chr>, X35 <chr>, X36 <chr>, X37 <chr>, X38 <chr>, …#visual cross tab
#Depresi x Jenis Universitas
cross_1 <- dat %>%
count(Y, Jenis_Univ) %>%
spread(key = Y, value = n, fill = 0)
cross_1## # A tibble: 2 × 5
## Jenis_Univ `Gangguan Depresi Rendah` `Gangguan Depresi Sedang`
## <chr> <dbl> <dbl>
## 1 NEGERI 3 5
## 2 SWASTA 15 49
## # ℹ 2 more variables: `Gangguan Depresi Tinggi` <dbl>,
## # `Tidak Ada Gangguan Depresi` <dbl>#Depresi x Game Online
cross_2 <- dat %>%
count(Y, Game_Online) %>%
spread(key = Y, value = n, fill = 0)
cross_2## # A tibble: 3 × 5
## Game_Online `Gangguan Depresi Rendah` `Gangguan Depresi Sedang`
## <chr> <dbl> <dbl>
## 1 FF 0 4
## 2 MLBB 13 40
## 3 PUBG 5 10
## # ℹ 2 more variables: `Gangguan Depresi Tinggi` <dbl>,
## # `Tidak Ada Gangguan Depresi` <dbl>#Depresi x Umur
cross_3 <- dat %>%
count(Y, Umur) %>%
spread(key = Y, value = n, fill = 0)
cross_3## # A tibble: 5 × 5
## Umur `Gangguan Depresi Rendah` Gangguan Depresi Seda…¹ Gangguan Depresi Tin…²
## <dbl> <dbl> <dbl> <dbl>
## 1 19 3 9 5
## 2 20 9 17 7
## 3 21 2 11 6
## 4 22 1 11 0
## 5 23 3 6 2
## # ℹ abbreviated names: ¹`Gangguan Depresi Sedang`, ²`Gangguan Depresi Tinggi`
## # ℹ 1 more variable: `Tidak Ada Gangguan Depresi` <dbl>min(dat$Umur)## [1] 19max(dat$Umur)## [1] 23#Depresi x Gender
cross_4 <- dat %>%
count(Y, Jenis_Kelamin) %>%
spread(key = Y, value = n, fill = 0)
cross_4## # A tibble: 2 × 5
## Jenis_Kelamin `Gangguan Depresi Rendah` `Gangguan Depresi Sedang`
## <chr> <dbl> <dbl>
## 1 LAKI-LAKI 14 45
## 2 PEREMPUAN 4 9
## # ℹ 2 more variables: `Gangguan Depresi Tinggi` <dbl>,
## # `Tidak Ada Gangguan Depresi` <dbl>#Depresi x Avg
cross_5 <- dat %>%
count(Y, Average_Playing) %>%
spread(key = Y, value = n, fill = 0)
cross_5## # A tibble: 4 × 5
## Average_Playing `Gangguan Depresi Rendah` `Gangguan Depresi Sedang`
## <chr> <dbl> <dbl>
## 1 2 jam lebih hingga 4 jam 5 14
## 2 4 jam lebih hingga 6 jam 4 23
## 3 6 Jam Lebih 4 5
## 4 Kurang lebih 2 jam 5 12
## # ℹ 2 more variables: `Gangguan Depresi Tinggi` <dbl>,
## # `Tidak Ada Gangguan Depresi` <dbl>### visual diagram lingkaran x1
ubaha <- c(2,3,1,4)
ubahb <- c(2,4,1,5,3)
#warna mulai dari kategori 5~1
#sangat setuju(5), setuju(4), kurang setuju(3), tidak setuju(2), sangat tidak setuju (1)
colors <- c("pink", "lightblue", "lightgreen", "yellow","purple")
#x11
ct11 <- table(dat$X11)
dfct11 <- as.data.frame(ct11)
names(dfct11) <- c("Category", "Count")
dfct11 <- dfct11[ubaha,]
piepercent11<- round(100*dfct11$Count/sum(dfct11$Count), 0)
pie(dfct11$Count, labels = paste0(piepercent11, "%"), main = "Indikator X11", col = colors)
legend("right",legend = dfct11$Category, fill = colors[1:length(dfct11$Category)] ,cex = 0.6)
#x12
ct12 <- table(dat$X12)
dfct12 <- as.data.frame(ct12)
names(dfct12) <- c("Category", "Count")
dfct12 <- dfct12[ubaha,]
piepercent12<- round(100*dfct12$Count/sum(dfct12$Count), 0)
pie(dfct12$Count, labels = paste0(piepercent12, "%"),
main = "Indikator X12", col = colors)
legend("right",legend = dfct12$Category, fill = colors[1:length(dfct12$Category)] ,cex = 0.6)
#x13
ct13 <- table(dat$X13)
dfct13 <- as.data.frame(ct13)
names(dfct13) <- c("Category", "Count")
dfct13 <- dfct13[ubaha,]
piepercent13<- round(100*dfct13$Count/sum(dfct13$Count), 0)
pie(dfct13$Count, labels = paste0(piepercent13, "%"),
main = "Indikator X13", col = colors)
legend("right",legend = dfct13$Category, fill = colors[1:length(dfct13$Category)] ,cex = 0.6)
#x14
ct14 <- table(dat$X14)
dfct14 <- as.data.frame(ct14)
names(dfct14) <- c("Category", "Count")
dfct14 <- dfct14[ubaha,]
piepercent14<- round(100*dfct14$Count/sum(dfct14$Count), 0)
pie(dfct14$Count, labels = paste0(piepercent14, "%"),
main = "Indikator X14", col = colors)
legend("right",legend = dfct14$Category, fill = colors[1:length(dfct14$Category)] ,cex = 0.6)
#x15
ct15 <- table(dat$X15)
dfct15 <- as.data.frame(ct15)
names(dfct15) <- c("Category", "Count")
dfct15 <- dfct15[ubahb,]
piepercent15<- round(100*dfct15$Count/sum(dfct15$Count), 0)
pie(dfct15$Count, labels = paste0(piepercent15, "%"),
main = "Indikator X15", col = colors)
legend("right",legend = dfct15$Category, fill = colors[1:length(dfct15$Category)] ,cex = 0.6)
#x16
ct16 <- table(dat$X16)
dfct16 <- as.data.frame(ct16)
names(dfct16) <- c("Category", "Count")
dfct16 <- dfct16[ubahb,]
piepercent16<- round(100*dfct16$Count/sum(dfct16$Count), 0)
pie(dfct16$Count, labels = paste0(piepercent16, "%"),
main = "Indikator X16", col = colors)
legend("right",legend = dfct16$Category, fill = colors[1:length(dfct16$Category)] ,cex = 0.6)
### visual diagram lingkaran x2
ubaha <- c(2,3,1,4)
ubahb <- c(2,4,1,5,3)
ubahc <- c(2,4,1,3)
#warna mulai dari kategori 5~1
#sangat setuju(5), setuju(4), kurang setuju(3), tidak setuju(2), sangat tidak setuju (1)
colors <- c("pink", "lightblue", "lightgreen", "yellow","purple")
colorsc <- c("pink", "lightblue", "lightgreen","purple")
#x21
ct21 <- table(dat$X21)
dfct21 <- as.data.frame(ct21)
names(dfct21) <- c("Category", "Count")
dfct21 <- dfct21[ubaha,]
piepercent21<- round(100*dfct21$Count/sum(dfct21$Count), 0)
pie(dfct21$Count, labels = paste0(piepercent21, "%"),
main = "Indikator X21", col = colors)
legend("right",legend = dfct21$Category, fill = colors[1:length(dfct21$Category)] ,cex = 0.6)
#x22
ct22 <- table(dat$X22)
dfct22 <- as.data.frame(ct22)
names(dfct22) <- c("Category", "Count")
dfct22 <- dfct22[ubahc,]
piepercent22<- round(100*dfct22$Count/sum(dfct22$Count), 0)
pie(dfct22$Count, labels = paste0(piepercent22, "%"),
main = "Indikator X22", col = colorsc)
legend("right",legend = dfct22$Category, fill = colorsc[1:length(dfct22$Category)] ,cex = 0.6)
#x23
ct23 <- table(dat$X23)
dfct23 <- as.data.frame(ct23)
names(dfct23) <- c("Category", "Count")
dfct23 <- dfct23[ubahc,]
piepercent23<- round(100*dfct23$Count/sum(dfct23$Count), 0)
pie(dfct23$Count, labels = paste0(piepercent23, "%"),
main = "Indikator X23", col = colorsc)
legend("right",legend = dfct23$Category, fill = colorsc[1:length(dfct23$Category)] ,cex = 0.6)
#x24
ct24 <- table(dat$X24)
dfct24 <- as.data.frame(ct24)
names(dfct24) <- c("Category", "Count")
dfct24 <- dfct24[ubaha,]
piepercent24<- round(100*dfct24$Count/sum(dfct24$Count), 0)
pie(dfct24$Count, labels = paste0(piepercent24, "%"),
main = "Indikator X24", col = colors)
legend("right",legend = dfct24$Category, fill = colors[1:length(dfct24$Category)] ,cex = 0.6)
#x25
ct25 <- table(dat$X25)
dfct25 <- as.data.frame(ct25)
names(dfct25) <- c("Category", "Count")
dfct25 <- dfct25[ubaha,]
piepercent25<- round(100*dfct25$Count/sum(dfct25$Count), 0)
pie(dfct25$Count, labels = paste0(piepercent25, "%"),
main = "Indikator X25", col = colors)
legend("right",legend = dfct25$Category, fill = colors[1:length(dfct25$Category)] ,cex = 0.6)
#x26
ct26 <- table(dat$X26)
dfct26 <- as.data.frame(ct26)
names(dfct26) <- c("Category", "Count")
dfct26 <- dfct26[ubahb,]
piepercent26<- round(100*dfct26$Count/sum(dfct26$Count), 0)
pie(dfct26$Count, labels = paste0(piepercent26, "%"),
main = "Indikator X26", col = colors)
legend("right",legend = dfct26$Category, fill = colors[1:length(dfct26$Category)] ,cex = 0.6)
#x27
ct27 <- table(dat$X27)
dfct27 <- as.data.frame(ct27)
names(dfct27) <- c("Category", "Count")
dfct27 <- dfct27[ubahb,]
piepercent27<- round(100*dfct27$Count/sum(dfct27$Count), 0)
pie(dfct27$Count, labels = paste0(piepercent27, "%"),
main = "Indikator X27", col = colors)
legend("right",legend = dfct27$Category, fill = colors[1:length(dfct27$Category)] ,cex = 0.6)
#x28
ct28 <- table(dat$X28)
dfct28 <- as.data.frame(ct28)
names(dfct28) <- c("Category", "Count")
dfct28 <- dfct28[ubahc,]
piepercent28<- round(100*dfct28$Count/sum(dfct28$Count), 0)
pie(dfct28$Count, labels = paste0(piepercent28, "%"),
main = "Indikator X28", col = colorsc)
legend("right",legend = dfct28$Category, fill = colorsc[1:length(dfct28$Category)] ,cex = 0.6)
### visual diagram lingkaran x3
ubaha <- c(2,3,1,4)
ubahb <- c(2,4,1,5,3)
ubahc <- c(2,4,1,3)
#warna mulai dari kategori 5~1
#sangat setuju(5), setuju(4), kurang setuju(3), tidak setuju(2), sangat tidak setuju (1)
colors <- c("pink", "lightblue", "lightgreen", "yellow","purple")
colorsc <- c("pink", "lightblue", "lightgreen","purple")
#x31
ct31 <- table(dat$X31)
dfct31 <- as.data.frame(ct31)
names(dfct31) <- c("Category", "Count")
dfct31 <- dfct31[ubaha,]
piepercent31<- round(100*dfct31$Count/sum(dfct31$Count), 0)
pie(dfct31$Count, labels = paste0(piepercent31, "%"),
main = "Indikator X31", col = colors)
legend("right",legend = dfct31$Category, fill = colors[1:length(dfct31$Category)] ,cex = 0.6)
#x32
ct32 <- table(dat$X32)
dfct32 <- as.data.frame(ct32)
names(dfct32) <- c("Category", "Count")
dfct32 <- dfct32[ubahc,]
piepercent32<- round(100*dfct32$Count/sum(dfct32$Count), 0)
pie(dfct32$Count, labels = paste0(piepercent32, "%"),
main = "Indikator X32", col = colorsc)
legend("right",legend = dfct32$Category, fill = colorsc[1:length(dfct32$Category)] ,cex = 0.6)
#x33
ct33 <- table(dat$X33)
dfct33 <- as.data.frame(ct33)
names(dfct33) <- c("Category", "Count")
dfct33 <- dfct33[ubahc,]
piepercent33<- round(100*dfct33$Count/sum(dfct33$Count), 0)
pie(dfct33$Count, labels = paste0(piepercent33, "%"),
main = "Indikator X33", col = colorsc)
legend("right",legend = dfct33$Category, fill = colorsc[1:length(dfct33$Category)] ,cex = 0.6)
#x34
ct34 <- table(dat$X34)
dfct34 <- as.data.frame(ct34)
names(dfct34) <- c("Category", "Count")
dfct34 <- dfct34[ubahc,]
piepercent34<- round(100*dfct34$Count/sum(dfct34$Count), 0)
pie(dfct34$Count, labels = paste0(piepercent34, "%"),
main = "Indikator X34", col = colorsc)
legend("right",legend = dfct34$Category, fill = colorsc[1:length(dfct34$Category)] ,cex = 0.6)
#x35
ct35 <- table(dat$X35)
dfct35 <- as.data.frame(ct35)
names(dfct35) <- c("Category", "Count")
dfct35 <- dfct35[ubahb,]
piepercent35<- round(100*dfct35$Count/sum(dfct35$Count), 0)
pie(dfct35$Count, labels = paste0(piepercent35, "%"),
main = "Indikator X35", col = colors)
legend("right",legend = dfct35$Category, fill = colors[1:length(dfct35$Category)] ,cex = 0.6)
#x36
ct36 <- table(dat$X36)
dfct36 <- as.data.frame(ct36)
names(dfct36) <- c("Category", "Count")
dfct36 <- dfct36[ubahb,]
piepercent36<- round(100*dfct36$Count/sum(dfct36$Count), 0)
pie(dfct36$Count, labels = paste0(piepercent36, "%"),
main = "Indikator X36", col = colors)
legend("right",legend = dfct36$Category, fill = colors[1:length(dfct36$Category)] ,cex = 0.6)
#x37
ct37 <- table(dat$X37)
dfct37 <- as.data.frame(ct37)
names(dfct37) <- c("Category", "Count")
dfct37 <- dfct37[ubahb,]
piepercent37<- round(100*dfct37$Count/sum(dfct37$Count), 0)
pie(dfct37$Count, labels = paste0(piepercent37, "%"),
main = "Indikator X37", col = colors)
legend("right",legend = dfct37$Category, fill = colors[1:length(dfct37$Category)] ,cex = 0.6)
#x38
ct38 <- table(dat$X38)
dfct38 <- as.data.frame(ct38)
names(dfct38) <- c("Category", "Count")
dfct38 <- dfct38[ubahb,]
piepercent38<- round(100*dfct38$Count/sum(dfct38$Count), 0)
pie(dfct38$Count, labels = paste0(piepercent38, "%"),
main = "Indikator X38", col = colors)
legend("right",legend = dfct38$Category, fill = colors[1:length(dfct38$Category)] ,cex = 0.6)
#x39
ct39 <- table(dat$X39)
dfct39 <- as.data.frame(ct39)
names(dfct39) <- c("Category", "Count")
dfct39 <- dfct39[ubahb,]
piepercent39<- round(100*dfct39$Count/sum(dfct39$Count), 0)
pie(dfct39$Count, labels = paste0(piepercent39, "%"),
main = "Indikator X39", col = colors)
legend("right",legend = dfct39$Category, fill = colors[1:length(dfct39$Category)] ,cex = 0.6)
#Visualisasai depresi
#Y
ubahy <- c(3,2,1,4)
ubahyy <- c(6,5,4,3,2,1,8,7)
cty <- table(dat$Y)
dfcty <- as.data.frame(cty)
names(dfcty) <- c("Category", "Count")
dfcty <- dfcty[ubahy,]
piepercenty<- round(100*dfcty$Count/sum(dfcty$Count), 0)
pie(dfcty$Count, labels = paste0(piepercenty, "%"),
main = "Kategori Y", col = rainbow(length(dfcty$Count)))
legend("right",legend = dfcty$Category, fill = rainbow(length(dfcty$Count)), cex = 0.6)
#Y1
cty1 <- table(dat$Y1,dat$Y)
dfcty1 <- as.data.frame(cty1)
names(dfcty1) <- c("K1","K", "Count")
dfcty1 <- dfcty1[ubahyy,]
dfcty1 <- dfcty1 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfcty1, aes(x = dfcty1$kategori, y = dfcty1$Count))+
geom_bar(
aes(fill = dfcty1$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfcty1$Count) * 1.1)) + geom_text(aes(label = dfcty1$Count), vjust = -0.5) +
labs(
title = "Indikator Y1",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfcty1$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y2
ctY2 <- table(dat$Y2,dat$Y)
dfctY2 <- as.data.frame(ctY2)
names(dfctY2) <- c("K1","K", "Count")
dfctY2 <- dfctY2[ubahyy,]
dfctY2 <- dfctY2 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY2, aes(x = dfctY2$kategori, y = dfctY2$Count))+
geom_bar(
aes(fill = dfctY2$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY2$Count) * 1.1)) + geom_text(aes(label = dfctY2$Count), vjust = -0.5) +
labs(
title = "Indikator Y2",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY2$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y3
ctY3 <- table(dat$Y3,dat$Y)
dfctY3 <- as.data.frame(ctY3)
names(dfctY3) <- c("K1","K", "Count")
dfctY3 <- dfctY3[ubahyy,]
dfctY3 <- dfctY3 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY3, aes(x = dfctY3$kategori, y = dfctY3$Count))+
geom_bar(
aes(fill = dfctY3$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY3$Count) * 1.1)) + geom_text(aes(label = dfctY3$Count), vjust = -0.5) +
labs(
title = "Indikator Y3",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY3$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y4
ctY4 <- table(dat$Y4,dat$Y)
dfctY4 <- as.data.frame(ctY4)
names(dfctY4) <- c("K1","K", "Count")
dfctY4 <- dfctY4[ubahyy,]
dfctY4 <- dfctY4 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY4, aes(x = dfctY4$kategori, y = dfctY4$Count))+
geom_bar(
aes(fill = dfctY4$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY4$Count) * 1.1)) + geom_text(aes(label = dfctY4$Count), vjust = -0.5) +
labs(
title = "Indikator Y4",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY4$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y5
ctY5 <- table(dat$Y5,dat$Y)
dfctY5 <- as.data.frame(ctY5)
names(dfctY5) <- c("K1","K", "Count")
dfctY5 <- dfctY5[ubahyy,]
dfctY5 <- dfctY5 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY5, aes(x = dfctY5$kategori, y = dfctY5$Count))+
geom_bar(
aes(fill = dfctY5$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY5$Count) * 1.1)) + geom_text(aes(label = dfctY5$Count), vjust = -0.5) +
labs(
title = "Indikator Y5",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY5$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y6
ctY6 <- table(dat$Y6,dat$Y)
dfctY6 <- as.data.frame(ctY6)
names(dfctY6) <- c("K1","K", "Count")
dfctY6 <- dfctY6[ubahyy,]
dfctY6 <- dfctY6 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY6, aes(x = dfctY6$kategori, y = dfctY6$Count))+
geom_bar(
aes(fill = dfctY6$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY6$Count) * 1.1)) + geom_text(aes(label = dfctY6$Count), vjust = -0.5) +
labs(
title = "Indikator Y6",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY6$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y7
ctY7 <- table(dat$Y7,dat$Y)
dfctY7 <- as.data.frame(ctY7)
names(dfctY7) <- c("K1","K", "Count")
dfctY7 <- dfctY7[ubahyy,]
dfctY7 <- dfctY7 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY7, aes(x = dfctY7$kategori, y = dfctY7$Count))+
geom_bar(
aes(fill = dfctY7$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY7$Count) * 1.1)) + geom_text(aes(label = dfctY7$Count), vjust = -0.5) +
labs(
title = "Indikator Y7",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY7$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y8
ctY8 <- table(dat$Y8,dat$Y)
dfctY8 <- as.data.frame(ctY8)
names(dfctY8) <- c("K1","K", "Count")
dfctY8 <- dfctY8[ubahyy,]
dfctY8 <- dfctY8 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY8, aes(x = dfctY8$kategori, y = dfctY8$Count))+
geom_bar(
aes(fill = dfctY8$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY8$Count) * 1.1)) + geom_text(aes(label = dfctY8$Count), vjust = -0.5) +
labs(
title = "Indikator Y8",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY8$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y9
ctY9 <- table(dat$Y9,dat$Y)
dfctY9 <- as.data.frame(ctY9)
names(dfctY9) <- c("K1","K", "Count")
dfctY9 <- dfctY9[ubahyy,]
dfctY9 <- dfctY9 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY9, aes(x = dfctY9$kategori, y = dfctY9$Count))+
geom_bar(
aes(fill = dfctY9$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY9$Count) * 1.1)) + geom_text(aes(label = dfctY9$Count), vjust = -0.5) +
labs(
title = "Indikator Y9",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY9$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
#Y10
ctY10 <- table(dat$Y10,dat$Y)
dfctY10 <- as.data.frame(ctY10)
names(dfctY10) <- c("K1","K", "Count")
dfctY10 <- dfctY10[ubahyy,]
dfctY10 <- dfctY10 %>%
mutate(kategori = c(4, 4, 3, 3, 2, 2, 1, 1))
ggplot(dfctY10, aes(x = dfctY10$kategori, y = dfctY10$Count))+
geom_bar(
aes(fill = dfctY10$K), stat = "identity",color = "black",
position = position_dodge(0.9)
)+ coord_cartesian(ylim = c(0, max(dfctY10$Count) * 1.1)) + geom_text(aes(label = dfctY10$Count), vjust = -0.5) +
labs(
title = "Indikator Y10",
x = "Tingkat Depresi",
y = "Jumlah Indikator Tiap Tingkatan",
fill = "Tingkatan Depresi"
) +
facet_wrap(dfctY10$K1) +
theme_minimal() +
theme(
panel.background = element_rect(fill = "gray90", color = "gray50")
)
data <- read_excel("D:/artikel - game online caraka/Data_Skripsi_Analisis.xlsx")
data## # A tibble: 97 × 24
## Y X11 X12 X13 X14 X15 X16 X21 X22 X23 X24 X25 X26
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 4 4 5 4 4 5 4 5 5 4 4 5
## 2 3 3 5 5 5 5 4 5 4 5 5 4 5
## 3 3 4 5 3 4 5 5 4 3 4 4 3 4
## 4 3 5 4 5 4 5 5 5 3 5 3 3 2
## 5 3 5 5 5 5 1 1 5 3 1 2 5 2
## 6 3 4 4 4 4 4 4 4 4 4 4 4 4
## 7 3 4 3 4 3 3 4 4 4 3 4 3 4
## 8 3 5 5 5 5 5 5 3 5 5 5 4 1
## 9 2 5 5 5 4 5 4 5 4 4 5 4 4
## 10 3 4 4 4 5 4 5 5 5 4 4 5 4
## # ℹ 87 more rows
## # ℹ 11 more variables: X27 <dbl>, X28 <dbl>, X31 <dbl>, X32 <dbl>, X33 <dbl>,
## # X34 <dbl>, X35 <dbl>, X36 <dbl>, X37 <dbl>, X38 <dbl>, X39 <dbl>attach(data)memanggil data
#data model Depresi X Game Online
model_SEM <- constructs(composite("X1", multi_items("X1", 1:6)), composite("X2", multi_items("X2", 1:8)), composite("X3", multi_items("X3", 1:9)), composite("Y", single_item("Y")))
#path SEm
model_SEM_PLS_Skrip <- relationships(paths(from = c("X1","X2","X3"), to = c("Y")))
#estimasi PLS
PLS_Skrip <- estimate_pls(data=data, measurement_model = model_SEM, structural_model = model_SEM_PLS_Skrip)## Generating the seminr model## All 97 observations are valid.pls_model_Skrip <- summary(PLS_Skrip)#setelah model pls didapatkan maka kita akan melakukan uji validitas data outer loading 1
pls_model_Skrip$loadings## X1 X2 X3 Y
## X11 0.903 0.000 0.000 0.000
## X12 0.894 0.000 0.000 0.000
## X13 0.839 0.000 0.000 0.000
## X14 0.899 0.000 0.000 0.000
## X15 -0.145 -0.000 -0.000 0.000
## X16 0.552 0.000 0.000 0.000
## X21 0.000 0.476 0.000 0.000
## X22 0.000 0.733 0.000 0.000
## X23 0.000 0.428 0.000 0.000
## X24 0.000 0.886 0.000 0.000
## X25 0.000 0.796 0.000 0.000
## X26 0.000 0.760 0.000 0.000
## X27 0.000 0.845 0.000 0.000
## X28 0.000 0.708 0.000 0.000
## X31 0.000 0.000 0.238 0.000
## X32 0.000 0.000 0.223 0.000
## X33 0.000 0.000 0.688 0.000
## X34 0.000 0.000 0.757 0.000
## X35 0.000 0.000 0.824 0.000
## X36 0.000 0.000 0.815 0.000
## X37 0.000 0.000 0.711 0.000
## X38 0.000 0.000 0.343 0.000
## X39 0.000 0.000 0.692 0.000
## Y 0.000 0.000 0.000 1.000karena ini menggunakan model reflektif maka setiap perubahan indikator var laten eksogen tidak mempengaruhi var laten eksogen yang lain. hasilnya (x15, x21, x32) dikeluarkan.
#data model Depresi X Game Online
model_SEM <- constructs(composite("X1", multi_items("X1", c(1,2,3,4,6))), composite("X2", multi_items("X2", c(2,3,4,5,6,7,8))), composite("X3", multi_items("X3", c(1,3,4,5,6,7,8,9))), composite("Y", single_item("Y")))
#path SEm
model_SEM_PLS_Skrip <- relationships(paths(from = c("X1","X2","X3"), to = c("Y")))
#estimasi PLS
PLS_Skrip <- estimate_pls(data=data, measurement_model = model_SEM, structural_model = model_SEM_PLS_Skrip)## Generating the seminr model## All 97 observations are valid.pls_model_Skrip <- summary(PLS_Skrip)#setelah model pls didapatkan maka kita akan melakukan uji validitas data outer loading 2
pls_model_Skrip$loadings## X1 X2 X3 Y
## X11 0.903 0.000 0.000 0.000
## X12 0.894 0.000 0.000 0.000
## X13 0.839 0.000 0.000 0.000
## X14 0.899 0.000 0.000 0.000
## X16 0.551 0.000 0.000 0.000
## X22 0.000 0.775 0.000 0.000
## X23 0.000 0.459 0.000 0.000
## X24 0.000 0.900 0.000 0.000
## X25 0.000 0.809 0.000 0.000
## X26 0.000 0.773 0.000 0.000
## X27 0.000 0.845 0.000 0.000
## X28 0.000 0.660 0.000 0.000
## X31 0.000 0.000 0.236 0.000
## X33 0.000 0.000 0.705 0.000
## X34 0.000 0.000 0.760 0.000
## X35 0.000 0.000 0.832 0.000
## X36 0.000 0.000 0.813 0.000
## X37 0.000 0.000 0.710 0.000
## X38 0.000 0.000 0.331 0.000
## X39 0.000 0.000 0.681 0.000
## Y 0.000 0.000 0.000 1.000karena ini menggunakan model reflektif maka setiap perubahan indikator var laten eksogen tidak mempengaruhi var laten eksogen yang lain. hasilnya (x23 dan x31) dikeluarkan.
#data model Depresi X Game Online
model_SEM <- constructs(composite("X1", multi_items("X1", c(1,2,3,4,6))), composite("X2", multi_items("X2", c(2,4,5,6,7,8))), composite("X3", multi_items("X3", c(3,4,5,6,7,8,9))), composite("Y", single_item("Y")))
#path SEm
model_SEM_PLS_Skrip <- relationships(paths(from = c("X1","X2","X3"), to = c("Y")))
#estimasi PLS
PLS_Skrip <- estimate_pls(data=data, measurement_model = model_SEM, structural_model = model_SEM_PLS_Skrip)## Generating the seminr model## All 97 observations are valid.pls_model_Skrip <- summary(PLS_Skrip)#setelah model pls didapatkan maka kita akan melakukan uji validitas data outer loading 3
pls_model_Skrip$loadings## X1 X2 X3 Y
## X11 0.903 0.000 0.000 0.000
## X12 0.894 0.000 0.000 0.000
## X13 0.839 0.000 0.000 0.000
## X14 0.899 0.000 0.000 0.000
## X16 0.551 0.000 0.000 0.000
## X22 0.000 0.770 0.000 0.000
## X24 0.000 0.900 0.000 0.000
## X25 0.000 0.815 0.000 0.000
## X26 0.000 0.773 0.000 0.000
## X27 0.000 0.846 0.000 0.000
## X28 0.000 0.666 0.000 0.000
## X33 0.000 0.000 0.710 0.000
## X34 0.000 0.000 0.760 0.000
## X35 0.000 0.000 0.834 0.000
## X36 0.000 0.000 0.817 0.000
## X37 0.000 0.000 0.710 0.000
## X38 0.000 0.000 0.321 0.000
## X39 0.000 0.000 0.679 0.000
## Y 0.000 0.000 0.000 1.000karena ini menggunakan model reflektif maka setiap perubahan indikator var laten eksogen tidak mempengaruhi var laten eksogen yang lain. hasilnya (x38) dikeluarkan.
#data model Depresi X Game Online
model_SEM <- constructs(composite("X1", multi_items("X1", c(1,2,3,4,6))), composite("X2", multi_items("X2", c(2,4,5,6,7,8))), composite("X3", multi_items("X3", c(3,4,5,6,7,9))), composite("Y", single_item("Y")))
#path SEm
model_SEM_PLS_Skrip <- relationships(paths(from = c("X1","X2","X3"), to = c("Y")))
#estimasi PLS
PLS_Skrip <- estimate_pls(data=data, measurement_model = model_SEM, structural_model = model_SEM_PLS_Skrip)## Generating the seminr model## All 97 observations are valid.pls_model_Skrip <- summary(PLS_Skrip)
PLS_Skrip$construct_scores## X1 X2 X3 Y
## [1,] -0.252592036 0.34712914 0.98264341 0.1067147
## [2,] -0.181054481 0.60254383 1.21595914 0.1067147
## [3,] -0.476928584 -0.91506259 0.04117420 0.1067147
## [4,] 0.179391485 -2.10401526 -0.42960631 0.1067147
## [5,] 0.152220633 -1.42028370 0.54772767 0.1067147
## [6,] -0.715308732 -0.26196094 0.21820873 0.1067147
## [7,] -1.395727116 -0.57067637 0.21820873 0.1067147
## [8,] 0.859809870 0.03524541 0.33756275 0.1067147
## [9,] 0.349796400 0.18625057 0.39524326 -1.1872010
## [10,] -0.205295262 0.23955131 0.67086760 0.1067147
## [11,] -1.225322201 -1.18620976 1.25827321 0.1067147
## [12,] 0.179391485 0.45095444 1.21595914 0.1067147
## [13,] -2.304613398 -2.55222678 0.03360684 -1.1872010
## [14,] 0.002494176 -0.05700112 -1.89503523 0.1067147
## [15,] -1.457352419 0.59038083 -0.05110320 0.1067147
## [16,] -0.429489345 -1.15786098 -1.28597606 0.1067147
## [17,] 0.573990484 0.64375126 1.21595914 1.4006304
## [18,] 0.859809870 1.10405608 1.63010076 1.4006304
## [19,] -0.538411422 -3.00755968 0.09001947 0.1067147
## [20,] -0.283325211 -0.26196094 0.21820873 1.4006304
## [21,] -0.082187121 1.10405608 -1.36166558 -1.1872010
## [22,] -1.001128118 0.60254383 0.71708666 1.4006304
## [23,] 0.002494176 0.62801878 0.60426384 0.1067147
## [24,] -0.793338680 -0.05062751 -1.68603002 0.1067147
## [25,] -1.827710637 -1.88339265 -1.86306455 -1.1872010
## [26,] 0.397093174 -2.06852753 -1.33597112 0.1067147
## [27,] 0.179391485 -0.02449863 -1.01412772 0.1067147
## [28,] -3.579726548 -2.59623834 -2.20175627 -2.4811168
## [29,] -3.688648625 -2.54578347 -4.01746735 -2.4811168
## [30,] 0.226688259 0.13579571 -1.23737586 0.1067147
## [31,] -0.106427901 0.20191336 -0.85132075 0.1067147
## [32,] -0.824230808 0.23955131 -0.70903292 -1.1872010
## [33,] -2.545513545 -2.99399499 -2.60557532 -2.4811168
## [34,] 0.512507645 -0.06916412 -1.19368329 -1.1872010
## [35,] 0.512507645 1.10405608 1.37876611 0.1067147
## [36,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [37,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [38,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [39,] 0.859809870 0.60254383 0.63235035 0.1067147
## [40,] 0.859809870 0.62801878 0.95419375 1.4006304
## [41,] -0.538411422 -0.78060737 0.21820873 0.1067147
## [42,] -0.283325211 -0.26196094 0.38101570 1.4006304
## [43,] -0.082187121 1.10405608 -1.69396053 -1.1872010
## [44,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [45,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [46,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [47,] 0.859809870 0.60254383 0.63235035 0.1067147
## [48,] 0.859809870 0.62801878 0.95419375 1.4006304
## [49,] -1.001128118 0.60254383 0.63235035 1.4006304
## [50,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [51,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [52,] 0.859809870 1.10405608 0.21820873 0.1067147
## [53,] 0.859809870 0.60254383 0.63235035 0.1067147
## [54,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [55,] 0.002494176 0.62801878 0.95419375 0.1067147
## [56,] -0.538411422 -0.78060737 0.21820873 0.1067147
## [57,] -0.283325211 -0.26196094 0.38101570 1.4006304
## [58,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [59,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [60,] 0.859809870 0.62801878 0.95419375 1.4006304
## [61,] 0.859809870 0.60254383 0.63235035 0.1067147
## [62,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [63,] 0.859809870 0.62801878 0.95419375 1.4006304
## [64,] 0.512507645 -0.26196094 0.38101570 0.1067147
## [65,] 0.859809870 1.10405608 0.21820873 0.1067147
## [66,] 0.859809870 0.60254383 0.63235035 0.1067147
## [67,] 0.859809870 0.62801878 0.95419375 1.4006304
## [68,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [69,] 0.512507645 -0.26196094 0.38101570 0.1067147
## [70,] 0.859809870 1.10405608 0.21820873 0.1067147
## [71,] 0.859809870 0.60254383 0.63235035 0.1067147
## [72,] 0.859809870 0.62801878 0.95419375 1.4006304
## [73,] -1.001128118 -0.65971759 -0.57052110 -2.4811168
## [74,] 0.512507645 -0.26196094 0.38101570 0.1067147
## [75,] 0.859809870 1.10405608 0.21820873 0.1067147
## [76,] 0.859809870 0.60254383 0.63235035 0.1067147
## [77,] 0.859809870 0.62801878 0.95419375 1.4006304
## [78,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [79,] 0.859809870 0.62801878 0.95419375 1.4006304
## [80,] 0.859809870 0.62801878 0.95419375 1.4006304
## [81,] 0.859809870 0.60254383 0.63235035 0.1067147
## [82,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [83,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [84,] -1.001128118 -0.65971759 -0.57052110 -2.4811168
## [85,] 0.859809870 1.10405608 0.21820873 0.1067147
## [86,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [87,] 0.859809870 0.62801878 0.95419375 1.4006304
## [88,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [89,] 0.859809870 0.62801878 0.95419375 1.4006304
## [90,] 0.859809870 1.10405608 0.21820873 0.1067147
## [91,] -1.490463129 -3.26507246 -2.12469550 0.1067147
## [92,] 0.859809870 0.62801878 0.95419375 1.4006304
## [93,] 0.859809870 -0.20221690 0.21820873 0.1067147
## [94,] -0.824230808 0.23955131 -0.85384179 -1.1872010
## [95,] 0.859809870 1.10405608 0.21820873 0.1067147
## [96,] 0.859809870 0.62801878 0.95419375 1.4006304
## [97,] 0.859809870 1.10405608 0.21820873 0.1067147#setelah model pls didapatkan maka kita akan melakukan uji validitas data outer loading 4
pls_model_Skrip$loadings## X1 X2 X3 Y
## X11 0.903 0.000 0.000 0.000
## X12 0.894 0.000 0.000 0.000
## X13 0.839 0.000 0.000 0.000
## X14 0.899 0.000 0.000 0.000
## X16 0.551 0.000 0.000 0.000
## X22 0.000 0.770 0.000 0.000
## X24 0.000 0.900 0.000 0.000
## X25 0.000 0.815 0.000 0.000
## X26 0.000 0.773 0.000 0.000
## X27 0.000 0.846 0.000 0.000
## X28 0.000 0.666 0.000 0.000
## X33 0.000 0.000 0.724 0.000
## X34 0.000 0.000 0.769 0.000
## X35 0.000 0.000 0.838 0.000
## X36 0.000 0.000 0.818 0.000
## X37 0.000 0.000 0.711 0.000
## X39 0.000 0.000 0.661 0.000
## Y 0.000 0.000 0.000 1.000pls_model_Skrip$validity## $vif_items
## X1 :
## X11 X12 X13 X14 X16
## 2.971 3.191 2.605 3.398 1.477
##
## X2 :
## X22 X24 X25 X26 X27 X28
## 2.287 2.521 2.663 2.162 2.835 1.798
##
## X3 :
## X33 X34 X35 X36 X37 X39
## 2.872 2.830 2.769 2.631 1.442 1.531
##
## Y :
## Y
## 1
##
##
## $htmt
## X1 X2 X3 Y
## X1 . . . .
## X2 0.704 . . .
## X3 0.737 0.577 . .
## Y 0.652 0.343 0.746 .
##
## $fl_criteria
## X1 X2 X3 Y
## X1 0.828 . . .
## X2 0.630 0.798 . .
## X3 0.664 0.527 0.756 .
## Y 0.630 0.384 0.718 1.000
##
## FL Criteria table reports square root of AVE on the diagonal and construct correlations on the lower triangle.
##
## $cross_loadings
## X1 X2 X3 Y
## X11 0.903 0.557 0.591 0.653
## X12 0.894 0.592 0.566 0.530
## X13 0.839 0.412 0.558 0.542
## X14 0.899 0.612 0.667 0.521
## X16 0.551 0.466 0.301 0.268
## X22 0.417 0.770 0.269 0.239
## X24 0.667 0.900 0.557 0.486
## X25 0.478 0.815 0.405 0.264
## X26 0.417 0.773 0.417 0.259
## X27 0.507 0.846 0.454 0.247
## X28 0.446 0.666 0.286 0.070
## X33 0.503 0.442 0.724 0.429
## X34 0.413 0.375 0.769 0.402
## X35 0.579 0.437 0.838 0.634
## X36 0.414 0.408 0.818 0.445
## X37 0.512 0.304 0.711 0.672
## X39 0.524 0.438 0.661 0.539
## Y 0.630 0.384 0.718 1.000karena seluruh nilai loading sudah berada diatas atau lebih dari sama dengan 0,7 maka kita lanjut ke metode selanjunya
#uji reliabilitas
pls_model_Skrip$reliability## alpha rhoC AVE rhoA
## X1 0.880 0.914 0.686 0.922
## X2 0.891 0.913 0.638 0.995
## X3 0.850 0.888 0.572 0.860
## Y 1.000 1.000 1.000 1.000
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5pls_model_Skrip$paths## Y
## R^2 0.567
## AdjR^2 0.553
## X1 0.335
## X2 -0.123
## X3 0.561Nilai Uji Coba Resampling
#test 1
boot_skrip1 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 20, cores = 2, seed = 29)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedboot_pls1 <- summary(boot_skrip1)
boot_pls1$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.307 0.122 2.738 0.126 0.518
## X2 -> Y -0.123 -0.086 0.074 -1.662 -0.215 0.022
## X3 -> Y 0.561 0.558 0.075 7.519 0.432 0.675print("resampling 20")## [1] "resampling 20"#test 2
boot_skrip2 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 40, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls2 <- summary(boot_skrip2)
boot_pls2$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.342 0.123 2.729 0.135 0.544
## X2 -> Y -0.123 -0.111 0.086 -1.436 -0.253 0.044
## X3 -> Y 0.561 0.554 0.076 7.348 0.424 0.690print("resampling 40")## [1] "resampling 40"#test 3
boot_skrip3 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 60, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls3 <- summary(boot_skrip3)
boot_pls3$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.327 0.121 2.756 0.109 0.572
## X2 -> Y -0.123 -0.111 0.094 -1.308 -0.275 0.050
## X3 -> Y 0.561 0.567 0.078 7.147 0.422 0.709print("resampling 60")## [1] "resampling 60"#test 4
boot_skrip4 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 80, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls4 <- summary(boot_skrip4)
boot_pls4$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.325 0.115 2.906 0.110 0.544
## X2 -> Y -0.123 -0.113 0.094 -1.304 -0.276 0.044
## X3 -> Y 0.561 0.569 0.077 7.280 0.419 0.701print("resampling 80")## [1] "resampling 80"#test 5
boot_skrip5 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 100, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls5 <- summary(boot_skrip5)
boot_pls5$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.330 0.109 3.072 0.113 0.535
## X2 -> Y -0.123 -0.119 0.089 -1.383 -0.271 0.042
## X3 -> Y 0.561 0.569 0.078 7.167 0.422 0.709print("resampling 100")## [1] "resampling 100"#test 6
boot_skrip6 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 120, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls6 <- summary(boot_skrip6)
boot_pls6$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.330 0.111 3.003 0.110 0.526
## X2 -> Y -0.123 -0.118 0.095 -1.290 -0.284 0.044
## X3 -> Y 0.561 0.568 0.081 6.909 0.424 0.717print("resampling 120")## [1] "resampling 120"#test 7
boot_skrip7 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 140, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls7 <- summary(boot_skrip7)
boot_pls7$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.327 0.116 2.891 0.106 0.562
## X2 -> Y -0.123 -0.116 0.099 -1.239 -0.287 0.050
## X3 -> Y 0.561 0.568 0.085 6.611 0.406 0.720print("resampling 140")## [1] "resampling 140"#test 8
boot_skrip8 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 160, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls8 <- summary(boot_skrip8)
boot_pls8$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.334 0.113 2.966 0.110 0.544
## X2 -> Y -0.123 -0.117 0.098 -1.252 -0.291 0.057
## X3 -> Y 0.561 0.563 0.085 6.628 0.394 0.717print("resampling 160")## [1] "resampling 160"#test 9
boot_skrip9 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 180, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls9 <- summary(boot_skrip9)
boot_pls9$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.331 0.112 2.986 0.113 0.537
## X2 -> Y -0.123 -0.111 0.101 -1.214 -0.293 0.082
## X3 -> Y 0.561 0.562 0.084 6.680 0.400 0.714print("resampling 180")## [1] "resampling 180"#test 10
boot_skrip10 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 200, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls10 <- summary(boot_skrip10)
boot_pls10$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.331 0.114 2.939 0.105 0.544
## X2 -> Y -0.123 -0.111 0.101 -1.220 -0.291 0.082
## X3 -> Y 0.561 0.562 0.083 6.762 0.406 0.711print("resampling 200")## [1] "resampling 200"#test 11
boot_skrip11 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 220, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls11 <- summary(boot_skrip11)
boot_pls11$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.331 0.117 2.859 0.103 0.571
## X2 -> Y -0.123 -0.113 0.100 -1.228 -0.293 0.077
## X3 -> Y 0.561 0.563 0.086 6.561 0.400 0.714print("resampling 220")## [1] "resampling 220"#test 12
boot_skrip12 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 240, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls12 <- summary(boot_skrip12)
boot_pls12$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.329 0.117 2.868 0.102 0.570
## X2 -> Y -0.123 -0.108 0.102 -1.199 -0.291 0.084
## X3 -> Y 0.561 0.563 0.086 6.544 0.406 0.714print("resampling 240")## [1] "resampling 240"#test 13
boot_skrip13 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 260, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls13 <- summary(boot_skrip13)
boot_pls13$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.327 0.115 2.904 0.103 0.557
## X2 -> Y -0.123 -0.108 0.101 -1.221 -0.287 0.082
## X3 -> Y 0.561 0.565 0.084 6.655 0.409 0.712print("resampling 260")## [1] "resampling 260"#test 14
boot_skrip14 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 280, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls14 <- summary(boot_skrip14)
boot_pls14$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.327 0.115 2.903 0.105 0.570
## X2 -> Y -0.123 -0.108 0.101 -1.216 -0.291 0.083
## X3 -> Y 0.561 0.566 0.083 6.775 0.412 0.711print("resampling 280")## [1] "resampling 280"#test 15
boot_skrip15 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 300, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls15 <- summary(boot_skrip15)
boot_pls15$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.329 0.117 2.861 0.103 0.571
## X2 -> Y -0.123 -0.110 0.102 -1.208 -0.293 0.086
## X3 -> Y 0.561 0.563 0.082 6.834 0.409 0.711print("resampling 300")## [1] "resampling 300"#test 16
boot_skrip16 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 320, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls16 <- summary(boot_skrip16)
boot_pls16$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.331 0.117 2.868 0.102 0.570
## X2 -> Y -0.123 -0.111 0.101 -1.220 -0.291 0.083
## X3 -> Y 0.561 0.563 0.081 6.901 0.412 0.710print("resampling 320")## [1] "resampling 320"#test 17
boot_skrip17 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 340, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls17 <- summary(boot_skrip17)
boot_pls17$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.328 0.117 2.861 0.101 0.557
## X2 -> Y -0.123 -0.110 0.101 -1.215 -0.293 0.082
## X3 -> Y 0.561 0.564 0.081 6.954 0.412 0.711print("resampling 340")## [1] "resampling 340"#test 18
boot_skrip18 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 360, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls18 <- summary(boot_skrip18)
boot_pls18$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.326 0.117 2.860 0.092 0.544
## X2 -> Y -0.123 -0.110 0.100 -1.234 -0.291 0.082
## X3 -> Y 0.561 0.566 0.081 6.930 0.412 0.714print("resampling 360")## [1] "resampling 360"#test 19
boot_skrip19 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 380, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls19 <- summary(boot_skrip19)
boot_pls19$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.326 0.118 2.837 0.091 0.553
## X2 -> Y -0.123 -0.110 0.099 -1.237 -0.288 0.082
## X3 -> Y 0.561 0.565 0.081 6.905 0.409 0.712print("resampling 380")## [1] "resampling 380"#test 20
boot_skrip20 <- bootstrap_model(seminr_model = PLS_Skrip, nboot = 400, cores = 2, seed = 29)## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedboot_pls20 <- summary(boot_skrip20)
boot_pls20$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1 -> Y 0.335 0.325 0.118 2.834 0.092 0.558
## X2 -> Y -0.123 -0.108 0.101 -1.220 -0.285 0.097
## X3 -> Y 0.561 0.565 0.081 6.937 0.412 0.714print("resampling 400")## [1] "resampling 400"Dari hasil resampling diatas di dapat nilai original estimate dan bootstrap mean paling mendekati pada resampling sebesar 160. maka digunakan resampling sebanyak 160 sebagai hasil penelitian ini