# 1. LOAD PACKAGE
library(lavaan)
library(semPlot)
library(psych)
library(dplyr)
library(car)
library(corrplot)
library(MVN)
# 2. MEMBACA DATASET
data <- read.csv("student.csv")
# 3. CEK STRUKTUR DATA
str(data)
## 'data.frame': 149 obs. of 13 variables:
## $ Timestamp : chr "13/08/2022 21:04:40" "13/08/2022 21:05:48" "13/08/2022 21:13:20" "13/08/2022 21:14:14" ...
## $ Age : int 23 22 23 18 19 20 18 22 22 26 ...
## $ gender : chr "Male" "Male" "Male" "Male" ...
## $ Level.of.Education : chr "Under Graduation" "Under Graduation" "Under Graduation" "HSC" ...
## $ Are.you.Playing.Online.Games. : chr "Sometimes" "Yes" "Yes" "Sometimes" ...
## $ How.much.time.spend.in.gaming. : chr "Below 1 hour" "1-2" "1-2" "1-2" ...
## $ When.do.you.play.the.game.most.of.the.time.during.the.day.: chr "Mid-Night" "Mid-Night" "Mid-Night" "Evening" ...
## $ Do.you.feel.Hamper.in.sleep. : chr "Sometimes" "Sometimes" "No" "No" ...
## $ Do..you.feel.the.Headache. : chr "Sometimes" "Yes" "No" "Sometimes" ...
## $ Do.you.feel.mental.Stress. : chr "Yes" "No" "No" "Yes" ...
## $ Do.you.feel.Depression. : chr "Yes" "Yes" "No" "Yes" ...
## $ Your.reading.attention.level.after.the.gaming. : chr "Good" "Good" "Good" "Average" ...
## $ Your.Present.Academic.Result. : chr "Good" "Good" "Good" "Average" ...
head(data)
## Timestamp Age gender Level.of.Education
## 1 13/08/2022 21:04:40 23 Male Under Graduation
## 2 13/08/2022 21:05:48 22 Male Under Graduation
## 3 13/08/2022 21:13:20 23 Male Under Graduation
## 4 13/08/2022 21:14:14 18 Male HSC
## 5 13/08/2022 21:14:39 19 Male Under Graduation
## 6 13/08/2022 21:15:38 20 Male Under Graduation
## Are.you.Playing.Online.Games. How.much.time.spend.in.gaming.
## 1 Sometimes Below 1 hour
## 2 Yes 1-2
## 3 Yes 1-2
## 4 Sometimes 1-2
## 5 Yes 1-2
## 6 No Below 1 hour
## When.do.you.play.the.game.most.of.the.time.during.the.day.
## 1 Mid-Night
## 2 Mid-Night
## 3 Mid-Night
## 4 Evening
## 5 Mid-Night
## 6 Afternoon
## Do.you.feel.Hamper.in.sleep. Do..you.feel.the.Headache.
## 1 Sometimes Sometimes
## 2 Sometimes Yes
## 3 No No
## 4 No Sometimes
## 5 Sometimes Sometimes
## 6 No No
## Do.you.feel.mental.Stress. Do.you.feel.Depression.
## 1 Yes Yes
## 2 No Yes
## 3 No No
## 4 Yes Yes
## 5 No Sometimes
## 6 No No
## Your.reading.attention.level.after.the.gaming. Your.Present.Academic.Result.
## 1 Good Good
## 2 Good Good
## 3 Good Good
## 4 Average Average
## 5 Average Good
## 6 Good Average
dim(data)
## [1] 149 13
# 4. CEK MISSING VALUE
colSums(is.na(data))
## Timestamp
## 0
## Age
## 0
## gender
## 0
## Level.of.Education
## 0
## Are.you.Playing.Online.Games.
## 0
## How.much.time.spend.in.gaming.
## 0
## When.do.you.play.the.game.most.of.the.time.during.the.day.
## 0
## Do.you.feel.Hamper.in.sleep.
## 0
## Do..you.feel.the.Headache.
## 0
## Do.you.feel.mental.Stress.
## 0
## Do.you.feel.Depression.
## 0
## Your.reading.attention.level.after.the.gaming.
## 0
## Your.Present.Academic.Result.
## 0
# 5. RENAME NAMA KOLOM
names(data) <- c(
"timestamp",
"age",
"gender",
"education",
"play_games",
"gaming_time",
"gaming_period",
"sleep_problem",
"headache",
"mental_stress",
"depression",
"reading_attention",
"academic_result"
)
# 6. MEMILIH VARIABEL
data_sem <- data %>%
select(
gaming_time,
sleep_problem,
headache,
mental_stress,
depression,
reading_attention,
academic_result
)
# 7. ENCODING DATA ORDINAL
encode_factor <- function(x) {
as.numeric(as.factor(x))
}
data_sem <- data_sem %>%
mutate(across(everything(), encode_factor))
# 8. RINGKASAN DATA
summary(data_sem)
## gaming_time sleep_problem headache mental_stress
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :1.000 Median :2.000 Median :2.000
## Mean :2.631 Mean :1.591 Mean :1.819 Mean :1.846
## 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :3.000 Max. :3.000 Max. :3.000
## depression reading_attention academic_result
## Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00
## Median :2.000 Median :2.000 Median :4.00
## Mean :1.832 Mean :2.383 Mean :2.98
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :3.000 Max. :4.000 Max. :4.00
# 9. STANDARISASI DATA
data_sem_scaled <- as.data.frame(scale(data_sem))
summary(data_sem_scaled)
## gaming_time sleep_problem headache mental_stress
## Min. :-1.1842 Min. :-0.7838 Min. :-0.9858 Min. :-1.0214
## 1st Qu.:-1.1842 1st Qu.:-0.7838 1st Qu.:-0.9858 1st Qu.:-1.0214
## Median : 0.2680 Median :-0.7838 Median : 0.2182 Median : 0.1864
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.9942 3rd Qu.: 0.5433 3rd Qu.: 1.4221 3rd Qu.: 1.3943
## Max. : 0.9942 Max. : 1.8705 Max. : 1.4221 Max. : 1.3943
## depression reading_attention academic_result
## Min. :-1.0084 Min. :-1.0290 Min. :-1.5145
## 1st Qu.:-1.0084 1st Qu.:-1.0290 1st Qu.:-1.5145
## Median : 0.2033 Median :-0.2847 Median : 0.7803
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 1.4150 3rd Qu.: 1.2038 3rd Qu.: 0.7803
## Max. : 1.4150 Max. : 1.2038 Max. : 0.7803
# 10. UJI KMO / MSA
kmo_result <- KMO(data_sem_scaled)
kmo_result
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_sem_scaled)
## Overall MSA = 0.66
## MSA for each item =
## gaming_time sleep_problem headache mental_stress
## 0.62 0.67 0.71 0.66
## depression reading_attention academic_result
## 0.64 0.51 0.57
# 11. UJI MULTIKOLINEARITAS
model_vif <- lm(
academic_result ~
gaming_time +
sleep_problem +
headache +
mental_stress +
depression +
reading_attention,
data = data_sem_scaled
)
vif_result <- vif(model_vif)
vif_result
## gaming_time sleep_problem headache mental_stress
## 1.073511 1.418851 1.609433 1.866483
## depression reading_attention
## 1.687467 1.023811
# 12. UJI NORMALITAS MULTIVARIAT
mvn_result <- MVN::mvn(data_sem_scaled)
mvn_result
## $multivariate_normality
## Test Statistic p.value Method MVN
## 1 Henze-Zirkler 2.242 <0.001 asymptotic ✗ Not normal
##
## $univariate_normality
## Test Variable Statistic p.value Normality
## 1 Anderson-Darling gaming_time 17.752 <0.001 ✗ Not normal
## 2 Anderson-Darling sleep_problem 18.228 <0.001 ✗ Not normal
## 3 Anderson-Darling headache 14.078 <0.001 ✗ Not normal
## 4 Anderson-Darling mental_stress 13.501 <0.001 ✗ Not normal
## 5 Anderson-Darling depression 13.622 <0.001 ✗ Not normal
## 6 Anderson-Darling reading_attention 15.491 <0.001 ✗ Not normal
## 7 Anderson-Darling academic_result 21.205 <0.001 ✗ Not normal
##
## $descriptives
## Variable n Mean Std.Dev Median Min Max 25th 75th Skew
## 1 gaming_time 149 0 1 0.268 -1.184 0.994 -1.184 0.994 -0.142
## 2 sleep_problem 149 0 1 -0.784 -0.784 1.870 -0.784 0.543 0.831
## 3 headache 149 0 1 0.218 -0.986 1.422 -0.986 1.422 0.348
## 4 mental_stress 149 0 1 0.186 -1.021 1.394 -1.021 1.394 0.293
## 5 depression 149 0 1 0.203 -1.008 1.415 -1.008 1.415 0.319
## 6 reading_attention 149 0 1 -0.285 -1.029 1.204 -1.029 1.204 0.134
## 7 academic_result 149 0 1 0.780 -1.514 0.780 -1.514 0.780 -0.727
## Kurtosis
## 1 1.180
## 2 2.240
## 3 1.544
## 4 1.528
## 5 1.548
## 6 1.231
## 7 1.717
##
## $data
## gaming_time sleep_problem headache mental_stress depression
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## 149 -1.1842168 -0.7838234 -0.9857706 -1.0213858 0.2033025
## reading_attention academic_result
## 1 1.2037818 0.7803326
## 2 1.2037818 0.7803326
## 3 1.2037818 0.7803326
## 4 -1.0289587 -1.5144613
## 5 -1.0289587 0.7803326
## 6 1.2037818 -1.5144613
## 7 -1.0289587 0.7803326
## 8 -1.0289587 0.7803326
## 9 0.4595350 -1.5144613
## 10 -0.2847119 -1.5144613
## 11 0.4595350 0.7803326
## 12 -1.0289587 0.7803326
## 13 -0.2847119 -0.7495300
## 14 -0.2847119 -1.5144613
## 15 1.2037818 0.0154013
## 16 -1.0289587 0.7803326
## 17 0.4595350 -1.5144613
## 18 1.2037818 0.7803326
## 19 0.4595350 0.7803326
## 20 0.4595350 0.0154013
## 21 -1.0289587 -1.5144613
## 22 1.2037818 0.7803326
## 23 -1.0289587 -1.5144613
## 24 -1.0289587 0.7803326
## 25 1.2037818 0.7803326
## 26 1.2037818 0.7803326
## 27 1.2037818 -1.5144613
## 28 -0.2847119 -1.5144613
## 29 1.2037818 -1.5144613
## 30 0.4595350 0.7803326
## 31 -1.0289587 0.0154013
## 32 -0.2847119 -1.5144613
## 33 -0.2847119 0.7803326
## 34 -1.0289587 0.7803326
## 35 1.2037818 0.7803326
## 36 1.2037818 0.7803326
## 37 0.4595350 -1.5144613
## 38 1.2037818 0.7803326
## 39 1.2037818 0.7803326
## 40 -0.2847119 -1.5144613
## 41 -1.0289587 0.7803326
## 42 0.4595350 0.0154013
## 43 -1.0289587 0.7803326
## 44 -1.0289587 -1.5144613
## 45 -1.0289587 -1.5144613
## 46 -1.0289587 0.7803326
## 47 -1.0289587 -1.5144613
## 48 1.2037818 0.0154013
## 49 -1.0289587 0.7803326
## 50 -1.0289587 0.7803326
## 51 -1.0289587 0.7803326
## 52 -1.0289587 -0.7495300
## 53 1.2037818 0.7803326
## 54 -1.0289587 0.7803326
## 55 -1.0289587 -1.5144613
## 56 -1.0289587 -1.5144613
## 57 -1.0289587 0.7803326
## 58 -1.0289587 0.7803326
## 59 -1.0289587 -1.5144613
## 60 0.4595350 0.0154013
## 61 0.4595350 -1.5144613
## 62 1.2037818 0.7803326
## 63 0.4595350 -1.5144613
## 64 1.2037818 0.0154013
## 65 -1.0289587 0.7803326
## 66 1.2037818 0.7803326
## 67 -1.0289587 0.0154013
## 68 1.2037818 0.7803326
## 69 -1.0289587 -1.5144613
## 70 -1.0289587 0.7803326
## 71 -1.0289587 -1.5144613
## 72 -1.0289587 0.7803326
## 73 -1.0289587 0.0154013
## 74 1.2037818 0.7803326
## 75 -1.0289587 -1.5144613
## 76 -1.0289587 -1.5144613
## 77 -0.2847119 -1.5144613
## 78 1.2037818 0.7803326
## 79 -1.0289587 -1.5144613
## 80 -1.0289587 0.7803326
## 81 1.2037818 0.7803326
## 82 -0.2847119 0.7803326
## 83 1.2037818 0.7803326
## 84 -1.0289587 -1.5144613
## 85 -0.2847119 0.7803326
## 86 -1.0289587 -1.5144613
## 87 -1.0289587 0.0154013
## 88 1.2037818 0.7803326
## 89 1.2037818 0.7803326
## 90 0.4595350 0.7803326
## 91 0.4595350 0.0154013
## 92 -1.0289587 0.7803326
## 93 1.2037818 0.0154013
## 94 -1.0289587 -1.5144613
## 95 1.2037818 0.7803326
## 96 -0.2847119 0.7803326
## 97 -1.0289587 0.7803326
## 98 -1.0289587 0.7803326
## 99 1.2037818 0.7803326
## 100 -1.0289587 0.7803326
## 101 0.4595350 -1.5144613
## 102 1.2037818 0.7803326
## 103 1.2037818 0.7803326
## 104 -1.0289587 -1.5144613
## 105 -1.0289587 0.7803326
## 106 0.4595350 0.7803326
## 107 1.2037818 -1.5144613
## 108 1.2037818 0.0154013
## 109 -1.0289587 -1.5144613
## 110 1.2037818 -1.5144613
## 111 0.4595350 -1.5144613
## 112 -1.0289587 0.7803326
## 113 1.2037818 -1.5144613
## 114 1.2037818 0.0154013
## 115 1.2037818 0.7803326
## 116 0.4595350 0.0154013
## 117 -1.0289587 0.7803326
## 118 1.2037818 0.7803326
## 119 1.2037818 0.7803326
## 120 1.2037818 0.7803326
## 121 -1.0289587 0.7803326
## 122 0.4595350 0.0154013
## 123 -1.0289587 0.0154013
## 124 -1.0289587 0.0154013
## 125 1.2037818 0.0154013
## 126 -1.0289587 0.7803326
## 127 -0.2847119 0.7803326
## 128 -1.0289587 0.7803326
## 129 -1.0289587 0.7803326
## 130 1.2037818 0.0154013
## 131 1.2037818 0.7803326
## 132 -1.0289587 -1.5144613
## 133 -1.0289587 0.7803326
## 134 1.2037818 0.7803326
## 135 -1.0289587 0.7803326
## 136 -1.0289587 0.7803326
## 137 0.4595350 -1.5144613
## 138 -1.0289587 0.7803326
## 139 -1.0289587 0.7803326
## 140 0.4595350 0.0154013
## 141 -1.0289587 0.0154013
## 142 1.2037818 0.7803326
## 143 1.2037818 0.7803326
## 144 1.2037818 0.7803326
## 145 1.2037818 0.7803326
## 146 -0.2847119 -1.5144613
## 147 1.2037818 0.7803326
## 148 1.2037818 0.7803326
## 149 -1.0289587 -1.5144613
##
## $subset
## NULL
##
## $outlierMethod
## [1] "none"
##
## attr(,"class")
## [1] "mvn"
# 13. STATISTIK DESKRIPTIF
describe(data_sem_scaled)
## vars n mean sd median trimmed mad min max range skew
## gaming_time 1 149 0 1 0.27 0.02 1.08 -1.18 0.99 2.18 -0.14
## sleep_problem 2 149 0 1 -0.78 -0.13 0.00 -0.78 1.87 2.65 0.82
## headache 3 149 0 1 0.22 -0.05 1.78 -0.99 1.42 2.41 0.34
## mental_stress 4 149 0 1 0.19 -0.04 1.79 -1.02 1.39 2.42 0.29
## depression 5 149 0 1 0.20 -0.05 1.80 -1.01 1.41 2.42 0.32
## reading_attention 6 149 0 1 -0.28 -0.02 1.10 -1.03 1.20 2.23 0.13
## academic_result 7 149 0 1 0.78 0.08 0.00 -1.51 0.78 2.29 -0.72
## kurtosis se
## gaming_time -1.84 0.08
## sleep_problem -0.79 0.08
## headache -1.48 0.08
## mental_stress -1.49 0.08
## depression -1.47 0.08
## reading_attention -1.79 0.08
## academic_result -1.31 0.08
# 14. VISUALISASI KORELASI
cor_matrix <- cor(data_sem_scaled)
corrplot(
cor_matrix,
method = "color",
type = "upper",
addCoef.col = "black",
tl.cex = 0.8,
number.cex = 0.7
)
# 15. MODEL CB-SEM
model_cbsem <- '
# ==================================================
# MEASUREMENT MODEL
# ==================================================
Mental_Health =~ sleep_problem + headache + mental_stress + depression
Academic_Performance =~ reading_attention + academic_result
# ==================================================
# STRUCTURAL MODEL
# ==================================================
Mental_Health ~ a*gaming_time
Academic_Performance ~ b*Mental_Health + c*gaming_time
# ==================================================
# RESIDUAL COVARIANCE
# ==================================================
mental_stress ~~ depression
sleep_problem ~~ headache
# ==================================================
# INDIRECT EFFECT
# ==================================================
indirect_gaming := a*b
# ==================================================
# TOTAL EFFECT
# ==================================================
total_gaming := c + (a*b)
'
cat(model_cbsem)
##
##
## # ==================================================
## # MEASUREMENT MODEL
## # ==================================================
##
## Mental_Health =~ sleep_problem + headache + mental_stress + depression
##
## Academic_Performance =~ reading_attention + academic_result
##
## # ==================================================
## # STRUCTURAL MODEL
## # ==================================================
##
## Mental_Health ~ a*gaming_time
##
## Academic_Performance ~ b*Mental_Health + c*gaming_time
##
## # ==================================================
## # RESIDUAL COVARIANCE
## # ==================================================
##
## mental_stress ~~ depression
##
## sleep_problem ~~ headache
##
## # ==================================================
## # INDIRECT EFFECT
## # ==================================================
##
## indirect_gaming := a*b
##
## # ==================================================
## # TOTAL EFFECT
## # ==================================================
##
## total_gaming := c + (a*b)
# 16. ESTIMASI MODEL
fit_cbsem <- sem(
model_cbsem,
data = data_sem_scaled,
estimator = "MLR",
missing = "fiml"
)
# 17. RINGKASAN HASIL SEM
summary(
fit_cbsem,
fit.measures = TRUE,
standardized = TRUE,
rsquare = TRUE
)
## lavaan 0.6-21 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 149
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 4.966 5.382
## Degrees of freedom 10 10
## P-value (Chi-square) 0.893 0.864
## Scaling correction factor 0.923
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 179.275 155.679
## Degrees of freedom 21 21
## P-value 0.000 0.000
## Scaling correction factor 1.152
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.067 1.072
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.063
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1178.367 -1178.367
## Scaling correction factor 0.994
## for the MLR correction
## Loglikelihood unrestricted model (H1) -1175.884 -1175.884
## Scaling correction factor 0.972
## for the MLR correction
##
## Akaike (AIC) 2402.734 2402.734
## Bayesian (BIC) 2471.824 2471.824
## Sample-size adjusted Bayesian (SABIC) 2399.036 2399.036
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.040 0.051
## P-value H_0: RMSEA <= 0.050 0.967 0.948
## P-value H_0: RMSEA >= 0.080 0.005 0.009
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.042
## P-value H_0: Robust RMSEA <= 0.050 0.965
## P-value H_0: Robust RMSEA >= 0.080 0.005
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.019 0.019
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mental_Health =~
## sleep_problem 1.000 0.548 0.550
## headache 1.363 0.355 3.842 0.000 0.747 0.750
## mental_stress 1.100 0.567 1.939 0.053 0.603 0.605
## depression 0.876 0.447 1.960 0.050 0.481 0.482
## Academic_Performance =~
## reading_attntn 1.000 0.435 0.436
## academic_reslt 0.952 0.852 1.117 0.264 0.414 0.416
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mental_Health ~
## gaming_tim (a) 0.139 0.063 2.198 0.028 0.254 0.253
## Academic_Performance ~
## Mentl_Hlth (b) -0.160 0.198 -0.806 0.420 -0.202 -0.202
## gaming_tim (c) 0.125 0.089 1.399 0.162 0.287 0.286
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mental_stress ~~
## .depression 0.330 0.159 2.072 0.038 0.330 0.477
## .sleep_problem ~~
## .headache 0.104 0.227 0.460 0.646 0.104 0.190
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .sleep_problem -0.000 0.081 -0.000 1.000 -0.000 -0.000
## .headache 0.000 0.080 0.000 1.000 0.000 0.000
## .mental_stress -0.000 0.081 -0.000 1.000 -0.000 -0.000
## .depression 0.000 0.081 0.000 1.000 0.000 0.000
## .reading_attntn -0.000 0.081 -0.000 1.000 -0.000 -0.000
## .academic_reslt -0.000 0.081 -0.000 1.000 -0.000 -0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .sleep_problem 0.693 0.186 3.716 0.000 0.693 0.697
## .headache 0.435 0.343 1.266 0.206 0.435 0.437
## .mental_stress 0.630 0.214 2.944 0.003 0.630 0.634
## .depression 0.762 0.130 5.863 0.000 0.762 0.768
## .reading_attntn 0.804 0.186 4.323 0.000 0.804 0.810
## .academic_reslt 0.822 0.169 4.850 0.000 0.822 0.827
## .Mental_Health 0.281 0.166 1.693 0.090 0.936 0.936
## .Acadmc_Prfrmnc 0.172 0.176 0.973 0.331 0.907 0.907
##
## R-Square:
## Estimate
## sleep_problem 0.303
## headache 0.563
## mental_stress 0.366
## depression 0.232
## reading_attntn 0.190
## academic_reslt 0.173
## Mental_Health 0.064
## Acadmc_Prfrmnc 0.093
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect_gamng -0.022 0.026 -0.866 0.386 -0.051 -0.051
## total_gaming 0.102 0.085 1.206 0.228 0.235 0.235
# 18. MODEL FIT
fitMeasures(
fit_cbsem,
c(
"chisq",
"df",
"pvalue",
"cfi",
"tli",
"rmsea",
"srmr"
)
)
## chisq df pvalue cfi tli rmsea srmr
## 4.966 10.000 0.893 1.000 1.067 0.000 0.019
# 19. LOADING FACTOR
parameterEstimates(
fit_cbsem,
standardized = TRUE
) %>%
filter(op == "=~") %>%
select(
lhs,
rhs,
est,
se,
z,
pvalue,
std.all
)
## lhs rhs est se z pvalue std.all
## 1 Mental_Health sleep_problem 1.000 0.000 NA NA 0.550
## 2 Mental_Health headache 1.363 0.355 3.842 0.000 0.750
## 3 Mental_Health mental_stress 1.100 0.567 1.939 0.053 0.605
## 4 Mental_Health depression 0.876 0.447 1.960 0.050 0.482
## 5 Academic_Performance reading_attention 1.000 0.000 NA NA 0.436
## 6 Academic_Performance academic_result 0.952 0.852 1.117 0.264 0.416
# 20. KOEFISIEN JALUR
parameterEstimates(
fit_cbsem,
standardized = TRUE
) %>%
filter(op == "~") %>%
select(
lhs,
rhs,
est,
se,
z,
pvalue,
std.all
)
## lhs rhs est se z pvalue std.all
## 1 Mental_Health gaming_time 0.139 0.063 2.198 0.028 0.253
## 2 Academic_Performance Mental_Health -0.160 0.198 -0.806 0.420 -0.202
## 3 Academic_Performance gaming_time 0.125 0.089 1.399 0.162 0.286
# 21. INDIRECT EFFECT
parameterEstimates(
fit_cbsem,
standardized = TRUE
) %>%
filter(op == ":=") %>%
select(
lhs,
est,
se,
z,
pvalue,
std.all
)
## lhs est se z pvalue std.all
## 1 indirect_gaming -0.022 0.026 -0.866 0.386 -0.051
## 2 total_gaming 0.102 0.085 1.206 0.228 0.235
# 22. R-SQUARE
inspect(fit_cbsem, "r2")
## sleep_problem headache mental_stress
## 0.303 0.563 0.366
## depression reading_attention academic_result
## 0.232 0.190 0.173
## Mental_Health Academic_Performance
## 0.064 0.093
# 23. MODIFICATION INDICES
modindices(
fit_cbsem,
sort. = TRUE,
maximum.number = 10
)
## lhs op rhs mi epc sepc.lv sepc.all
## 42 headache ~~ mental_stress 3.079 -0.154 -0.154 -0.295
## 38 sleep_problem ~~ mental_stress 2.232 0.101 0.101 0.153
## 43 headache ~~ depression 1.907 0.102 0.102 0.176
## 39 sleep_problem ~~ depression 1.188 -0.062 -0.062 -0.085
## 36 Academic_Performance =~ mental_stress 0.799 -0.220 -0.096 -0.096
## 46 mental_stress ~~ reading_attention 0.740 -0.052 -0.052 -0.073
## 35 Academic_Performance =~ headache 0.613 0.233 0.101 0.102
## 47 mental_stress ~~ academic_result 0.335 -0.035 -0.035 -0.049
## 48 depression ~~ reading_attention 0.287 0.033 0.033 0.042
## 40 sleep_problem ~~ reading_attention 0.229 0.033 0.033 0.044
## sepc.nox
## 42 -0.295
## 38 0.153
## 43 0.176
## 39 -0.085
## 36 -0.096
## 46 -0.073
## 35 0.102
## 47 -0.049
## 48 0.042
## 40 0.044
# 24. VISUALISASI SEM
semPaths(
fit_cbsem,
what = "std",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeLat = 8,
sizeMan = 6,
edge.label.cex = 0.8,
label.cex = 0.9,
residuals = FALSE,
intercepts = FALSE
)
# 25. BOOTSTRAPPING
fit_boot <- sem(
model_cbsem,
data = data_sem_scaled,
estimator = "ML",
se = "bootstrap",
bootstrap = 500,
missing = "fiml"
)
summary(
fit_boot,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE
)
## lavaan 0.6-21 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 149
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 4.966
## Degrees of freedom 10
## P-value (Chi-square) 0.893
##
## Model Test Baseline Model:
##
## Test statistic 179.275
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.067
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.067
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1178.367
## Loglikelihood unrestricted model (H1) -1175.884
##
## Akaike (AIC) 2402.734
## Bayesian (BIC) 2471.824
## Sample-size adjusted Bayesian (SABIC) 2399.036
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.040
## P-value H_0: RMSEA <= 0.050 0.967
## P-value H_0: RMSEA >= 0.080 0.005
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.040
## P-value H_0: Robust RMSEA <= 0.050 0.967
## P-value H_0: Robust RMSEA >= 0.080 0.005
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.019
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 500
## Number of successful bootstrap draws 387
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mental_Health =~
## sleep_problem 1.000 0.548 0.550
## headache 1.363 1.180 1.155 0.248 0.747 0.750
## mental_stress 1.100 1.435 0.766 0.444 0.603 0.605
## depression 0.876 1.277 0.686 0.493 0.481 0.482
## Academic_Performance =~
## reading_attntn 1.000 0.435 0.436
## academic_reslt 0.952 3.004 0.317 0.751 0.414 0.416
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mental_Health ~
## gaming_tim (a) 0.139 0.068 2.037 0.042 0.254 0.253
## Academic_Performance ~
## Mentl_Hlth (b) -0.160 0.243 -0.658 0.511 -0.202 -0.202
## gaming_tim (c) 0.125 0.081 1.539 0.124 0.287 0.286
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mental_stress ~~
## .depression 0.330 0.350 0.944 0.345 0.330 0.477
## .sleep_problem ~~
## .headache 0.104 0.563 0.185 0.853 0.104 0.190
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .sleep_problem -0.000 0.084 -0.000 1.000 -0.000 -0.000
## .headache 0.000 0.083 0.000 1.000 0.000 0.000
## .mental_stress -0.000 0.076 -0.000 1.000 -0.000 -0.000
## .depression 0.000 0.077 0.000 1.000 0.000 0.000
## .reading_attntn -0.000 0.081 -0.000 1.000 -0.000 -0.000
## .academic_reslt -0.000 0.080 -0.000 1.000 -0.000 -0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .sleep_problem 0.693 0.417 1.662 0.097 0.693 0.697
## .headache 0.435 0.890 0.488 0.625 0.435 0.437
## .mental_stress 0.630 0.441 1.428 0.153 0.630 0.634
## .depression 0.762 0.296 2.578 0.010 0.762 0.768
## .reading_attntn 0.804 0.584 1.377 0.169 0.804 0.810
## .academic_reslt 0.822 0.651 1.262 0.207 0.822 0.827
## .Mental_Health 0.281 0.398 0.707 0.480 0.936 0.936
## .Acadmc_Prfrmnc 0.172 0.579 0.296 0.767 0.907 0.907
##
## R-Square:
## Estimate
## sleep_problem 0.303
## headache 0.563
## mental_stress 0.366
## depression 0.232
## reading_attntn 0.190
## academic_reslt 0.173
## Mental_Health 0.064
## Acadmc_Prfrmnc 0.093
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect_gamng -0.022 0.027 -0.817 0.414 -0.051 -0.051
## total_gaming 0.102 0.077 1.326 0.185 0.235 0.235
# 26. BOOTSTRAP CONFIDENCE INTERVAL
parameterEstimates(
fit_boot,
standardized = TRUE,
boot.ci.type = "bca.simple"
) %>%
filter(op %in% c("~", ":=", "=~")) %>%
select(
lhs,
op,
rhs,
est,
ci.lower,
ci.upper,
pvalue,
std.all
)
## lhs op rhs est ci.lower ci.upper pvalue
## 1 Mental_Health =~ sleep_problem 1.000 1.000 1.000 NA
## 2 Mental_Health =~ headache 1.363 0.852 2.842 0.248
## 3 Mental_Health =~ mental_stress 1.100 0.129 3.282 0.444
## 4 Mental_Health =~ depression 0.876 0.202 3.279 0.493
## 5 Academic_Performance =~ reading_attention 1.000 1.000 1.000 NA
## 6 Academic_Performance =~ academic_result 0.952 0.127 10.575 0.751
## 7 Mental_Health ~ gaming_time 0.139 0.012 0.293 0.042
## 8 Academic_Performance ~ Mental_Health -0.160 -0.690 0.262 0.511
## 9 Academic_Performance ~ gaming_time 0.125 -0.013 0.274 0.124
## 10 indirect_gaming := a*b -0.022 -0.102 0.018 0.414
## 11 total_gaming := c+(a*b) 0.102 -0.019 0.251 0.185
## std.all
## 1 0.550
## 2 0.750
## 3 0.605
## 4 0.482
## 5 0.436
## 6 0.416
## 7 0.253
## 8 -0.202
## 9 0.286
## 10 -0.051
## 11 0.235