Tugas Anmul 126 dan 185
2026-03-05
Load Data dan Library
library(psych)
library(GPArotation)##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, variminlibrary(corrplot)## corrplot 0.95 loadedlibrary(factoextra)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Welcome to factoextra!## Want to learn more? See two factoextra-related books at https://www.datanovia.com/en/product/practical-guide-to-principal-component-methods-in-r/data <- read.csv("gaming_mental_health_10M_40features.csv")
set.seed(123)
data_sample <- data[sample(nrow(data), 2000), ]
data_numeric <- data_sample[, sapply(data_sample, is.numeric)]
cat("Dimensi:", dim(data_numeric), "\n")## Dimensi: 2000 38Imputasi Median
for(i in 1:ncol(data_numeric)){
data_numeric[is.na(data_numeric[,i]), i] <-
median(data_numeric[,i], na.rm=TRUE)
}
cat("Total NA setelah imputasi:", sum(is.na(data_numeric)))## Total NA setelah imputasi: 0Statistik Deskriptif
desc <- describe(data_numeric)
round(desc[,c("mean","sd","skew","kurtosis")],2)## mean sd skew kurtosis
## age 36.10 13.52 0.00 -1.17
## income 77325.38 41241.25 0.02 -1.17
## daily_gaming_hours 4.04 2.92 1.39 2.69
## weekly_sessions 20.72 11.39 -0.09 -1.22
## years_gaming 11.87 7.22 0.02 -1.20
## sleep_hours 6.98 1.49 0.06 -0.09
## caffeine_intake 1.91 1.93 1.95 5.37
## exercise_hours 2.04 2.10 2.07 6.02
## stress_level 5.68 2.84 -0.06 -1.21
## anxiety_score 5.05 1.99 -0.03 -0.25
## depression_score 4.96 2.32 -0.02 -0.47
## social_interaction_score 5.99 1.96 -0.18 -0.23
## relationship_satisfaction 5.92 1.93 -0.08 -0.32
## academic_performance 69.92 14.57 -0.05 -0.28
## work_productivity 69.74 14.78 -0.13 -0.26
## addiction_level 2.83 2.16 0.95 0.65
## multiplayer_ratio 0.50 0.23 -0.01 -0.88
## toxic_exposure 0.28 0.16 0.57 -0.19
## violent_games_ratio 0.40 0.20 0.24 -0.69
## mobile_gaming_ratio 0.50 0.23 -0.04 -0.89
## night_gaming_ratio 0.50 0.22 0.00 -0.85
## weekend_gaming_hours 5.98 4.37 1.33 2.02
## friends_gaming_count 24.66 14.40 0.00 -1.19
## online_friends 249.68 142.09 -0.01 -1.16
## streaming_hours 1.95 1.38 1.24 1.88
## esports_interest 5.04 3.11 -0.02 -1.18
## headset_usage 0.52 0.50 -0.07 -2.00
## microtransactions_spending 492.09 499.94 2.01 5.55
## parental_supervision 5.03 3.14 -0.01 -1.20
## loneliness_score 4.97 2.00 -0.01 -0.21
## aggression_score 5.02 2.04 -0.01 -0.33
## happiness_score 5.97 1.93 -0.17 -0.14
## bmi 24.02 4.00 0.04 -0.03
## screen_time_total 8.09 4.03 0.92 1.14
## eye_strain_score 4.99 1.89 0.00 -0.23
## back_pain_score 4.07 1.95 0.12 -0.20
## competitive_rank 49.10 28.85 0.02 -1.21
## internet_quality 5.61 2.88 -0.05 -1.24data_scaled <- scale(data_numeric)
boxplot(data_scaled,
main="Boxplot Seluruh Variabel",
col="lightblue",
las=2,
cex.axis=0.7) 
hist(data_numeric$screen_time_total,
main="Distribusi Total Screen Time",
xlab="Jam per Hari",
col="steelblue",
border="white")
Uji Asumsi dan Kelayakan
cor_matrix <- cor(data_numeric)
# KMO
KMO(cor_matrix)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor_matrix)
## Overall MSA = 0.64
## MSA for each item =
## age income
## 0.56 0.51
## daily_gaming_hours weekly_sessions
## 0.62 0.50
## years_gaming sleep_hours
## 0.53 0.51
## caffeine_intake exercise_hours
## 0.57 0.48
## stress_level anxiety_score
## 0.50 0.48
## depression_score social_interaction_score
## 0.49 0.45
## relationship_satisfaction academic_performance
## 0.53 0.55
## work_productivity addiction_level
## 0.51 0.65
## multiplayer_ratio toxic_exposure
## 0.47 0.49
## violent_games_ratio mobile_gaming_ratio
## 0.53 0.44
## night_gaming_ratio weekend_gaming_hours
## 0.54 0.51
## friends_gaming_count online_friends
## 0.49 0.47
## streaming_hours esports_interest
## 0.49 0.51
## headset_usage microtransactions_spending
## 0.42 0.51
## parental_supervision loneliness_score
## 0.47 0.48
## aggression_score happiness_score
## 0.48 0.51
## bmi screen_time_total
## 0.51 0.86
## eye_strain_score back_pain_score
## 0.47 0.50
## competitive_rank internet_quality
## 0.52 0.53# Bartlett
cortest.bartlett(cor_matrix, n=nrow(data_numeric))## $chisq
## [1] 5274.833
##
## $p.value
## [1] 0
##
## $df
## [1] 703Penentuan Jumlah Komponen(Ekstraksi)
# PCA
pca_model <- prcomp(data_numeric, scale. = TRUE)
# Eigenvalue
library(factoextra)
eigen_values <- get_eigenvalue(pca_model)
print(eigen_values)## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 2.52686825 6.649653 6.649653
## Dim.2 1.27453912 3.354050 10.003704
## Dim.3 1.22685159 3.228557 13.232260
## Dim.4 1.21895233 3.207769 16.440030
## Dim.5 1.18603034 3.121132 19.561162
## Dim.6 1.17623113 3.095345 22.656507
## Dim.7 1.15827382 3.048089 25.704596
## Dim.8 1.15277197 3.033610 28.738207
## Dim.9 1.13425632 2.984885 31.723092
## Dim.10 1.09703436 2.886933 34.610024
## Dim.11 1.08799360 2.863141 37.473165
## Dim.12 1.08045684 2.843307 40.316473
## Dim.13 1.06855937 2.811998 43.128471
## Dim.14 1.05014981 2.763552 45.892023
## Dim.15 1.04176790 2.741494 48.633518
## Dim.16 1.01485620 2.670674 51.304192
## Dim.17 0.99826265 2.627007 53.931199
## Dim.18 0.99592920 2.620866 56.552065
## Dim.19 0.98500618 2.592122 59.144187
## Dim.20 0.97646501 2.569645 61.713832
## Dim.21 0.97434404 2.564063 64.277895
## Dim.22 0.95601492 2.515829 66.793724
## Dim.23 0.94428228 2.484953 69.278677
## Dim.24 0.93417905 2.458366 71.737043
## Dim.25 0.92486962 2.433867 74.170910
## Dim.26 0.91836586 2.416752 76.587663
## Dim.27 0.90993116 2.394556 78.982218
## Dim.28 0.88190918 2.320814 81.303032
## Dim.29 0.87277091 2.296766 83.599797
## Dim.30 0.86318801 2.271547 85.871345
## Dim.31 0.85334205 2.245637 88.116982
## Dim.32 0.85150257 2.240796 90.357778
## Dim.33 0.82953602 2.182990 92.540768
## Dim.34 0.80878004 2.128369 94.669136
## Dim.35 0.78948445 2.077591 96.746727
## Dim.36 0.74602978 1.963236 98.709963
## Dim.37 0.39059025 1.027869 99.737832
## Dim.38 0.09962383 0.262168 100.000000# jumlah komponen (metode Kaiser)
jumlah_pc <- sum(eigen_values$eigenvalue > 1)
jumlah_pc## [1] 16Analisis Komponen Utama (Unrotated PCA)
# Scree Plot
fviz_screeplot(pca_model,
addlabels = TRUE,
main = "Scree Plot PCA") +
theme_minimal()
# Biplot PCA
fviz_pca_biplot(pca_model,
col.var = "steelblue",
col.ind = "gray70",
label = "var",
repel = TRUE,
title = "Biplot PCA") +
theme_minimal()
# Kontribusi variabel PC1
fviz_contrib(pca_model,
choice = "var",
axes = 1,
title = "Kontribusi Variabel ke PC1")
# Kontribusi variabel PC2
fviz_contrib(pca_model,
choice = "var",
axes = 2,
title = "Kontribusi Variabel ke PC2")
pca_unrot <- principal(data_numeric,
nfactors = jumlah_pc,
rotate = "none",
scores = TRUE)
print(pca_unrot$loadings)##
## Loadings:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## age -0.257 -0.131 0.332 -0.153
## income -0.124 0.140 0.267 0.147 -0.181 -0.296
## daily_gaming_hours 0.951
## weekly_sessions 0.317 -0.269
## years_gaming 0.187 0.183 0.189 -0.205 0.250
## sleep_hours -0.278 0.162 0.223
## caffeine_intake 0.216 -0.201 -0.279 -0.191 0.100
## exercise_hours 0.148 0.393 -0.366 0.133
## stress_level 0.108 -0.344 0.166 0.123 0.110 0.178
## anxiety_score -0.210 0.110 0.136 -0.394 -0.117
## depression_score 0.148 0.316 0.418
## social_interaction_score -0.153 -0.106 0.254
## relationship_satisfaction 0.149 -0.416 0.321 0.170
## academic_performance -0.112 0.277 -0.157
## work_productivity -0.398 0.115
## addiction_level 0.931
## multiplayer_ratio -0.172 -0.300 0.101
## toxic_exposure -0.159 -0.181 -0.295 0.131
## violent_games_ratio 0.234 0.105 -0.286
## mobile_gaming_ratio 0.135 -0.166 0.375 0.156
## night_gaming_ratio 0.193 0.115
## weekend_gaming_hours -0.139 -0.106 -0.107 -0.360
## friends_gaming_count 0.297 0.126 -0.205
## online_friends 0.117 0.256 0.338 -0.368
## streaming_hours 0.116 -0.311 0.172 -0.192
## esports_interest 0.330 0.117 0.102
## headset_usage -0.184 -0.184 0.153
## microtransactions_spending -0.278 -0.136 -0.129 0.306 -0.176
## parental_supervision 0.223 0.151 0.378 -0.198
## loneliness_score 0.291 -0.144 -0.231
## aggression_score -0.242 -0.177 0.117 0.125 0.307
## happiness_score 0.222 0.121 -0.146
## bmi 0.310 0.342 -0.301 0.230 0.109
## screen_time_total 0.844
## eye_strain_score 0.380 -0.277 -0.189
## back_pain_score 0.253 0.262 -0.202 0.231
## competitive_rank 0.179 0.315 0.180 0.282
## internet_quality 0.273 0.283 0.202
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## age -0.134 0.135 0.269 0.225 -0.154 0.157
## income 0.173 0.211 -0.260 -0.119
## daily_gaming_hours
## weekly_sessions 0.227 -0.143 -0.150 0.181 -0.302
## years_gaming -0.160 0.213 -0.111
## sleep_hours -0.167 0.208 0.312 -0.227
## caffeine_intake 0.212 0.154 0.200 -0.116
## exercise_hours 0.117 -0.203 0.221
## stress_level 0.424 -0.220
## anxiety_score -0.107 -0.294 0.198 -0.266 0.249
## depression_score -0.344 0.107 -0.323
## social_interaction_score -0.101 -0.330 0.281
## relationship_satisfaction 0.215 -0.118 0.135 0.128
## academic_performance 0.352 0.304 0.132
## work_productivity 0.147 -0.301 0.279 -0.187 -0.192
## addiction_level
## multiplayer_ratio -0.118 -0.239 -0.278 -0.269
## toxic_exposure 0.254 0.163 -0.280 0.111 0.201
## violent_games_ratio -0.262 0.116 -0.191 0.282 -0.144
## mobile_gaming_ratio 0.276 0.110 -0.198 -0.287 -0.198
## night_gaming_ratio 0.398 -0.240 0.146 0.328 -0.239 -0.128
## weekend_gaming_hours 0.161 0.348 0.170 0.178
## friends_gaming_count 0.306 0.165 -0.137
## online_friends 0.124 -0.113 0.281
## streaming_hours 0.182 -0.359 -0.157 -0.142 0.190 -0.155
## esports_interest 0.116 0.371 0.256
## headset_usage 0.149 -0.170 -0.153 0.428 -0.235 0.246
## microtransactions_spending -0.242 0.165 0.213
## parental_supervision 0.164 -0.359 0.113 0.224
## loneliness_score 0.278 0.184 0.287 -0.188 0.288 -0.143
## aggression_score 0.277 0.156 0.297 0.168
## happiness_score 0.465 -0.144 -0.129 0.327
## bmi -0.106 -0.109 -0.116 -0.197 0.141
## screen_time_total
## eye_strain_score 0.116 -0.122 0.284 -0.221 -0.179
## back_pain_score -0.144 -0.105 -0.111 0.299 0.256
## competitive_rank 0.112 -0.155 0.248 0.176 0.280
## internet_quality -0.344
## PC15 PC16
## age -0.200 0.147
## income -0.148 0.104
## daily_gaming_hours
## weekly_sessions 0.116 0.117
## years_gaming -0.179
## sleep_hours 0.137 -0.123
## caffeine_intake -0.115
## exercise_hours 0.158
## stress_level -0.121
## anxiety_score
## depression_score -0.141
## social_interaction_score 0.323 -0.208
## relationship_satisfaction 0.119
## academic_performance 0.336 -0.105
## work_productivity 0.108 0.188
## addiction_level
## multiplayer_ratio 0.100 0.292
## toxic_exposure -0.260 -0.234
## violent_games_ratio 0.161 0.154
## mobile_gaming_ratio -0.116
## night_gaming_ratio
## weekend_gaming_hours
## friends_gaming_count -0.467
## online_friends -0.197
## streaming_hours
## esports_interest 0.287 0.166
## headset_usage 0.151
## microtransactions_spending -0.163 0.235
## parental_supervision 0.396
## loneliness_score 0.314
## aggression_score 0.179
## happiness_score
## bmi -0.148 -0.238
## screen_time_total
## eye_strain_score 0.268 -0.315
## back_pain_score 0.127
## competitive_rank -0.136
## internet_quality 0.176 0.112
##
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
## SS loadings 2.527 1.275 1.227 1.219 1.186 1.176 1.158 1.153 1.134 1.097
## Proportion Var 0.066 0.034 0.032 0.032 0.031 0.031 0.030 0.030 0.030 0.029
## Cumulative Var 0.066 0.100 0.132 0.164 0.196 0.227 0.257 0.287 0.317 0.346
## PC11 PC12 PC13 PC14 PC15 PC16
## SS loadings 1.088 1.080 1.069 1.050 1.042 1.015
## Proportion Var 0.029 0.028 0.028 0.028 0.027 0.027
## Cumulative Var 0.375 0.403 0.431 0.459 0.486 0.513skor_pca <- as.data.frame(pca_unrot$scores)
head(skor_pca)## PC1 PC2 PC3 PC4 PC5 PC6
## 969167 -0.1735429 -0.576083399 -0.04677546 0.02473745 0.09277985 0.05438539
## 188942 -0.4133187 0.053124779 -1.15991931 1.56052373 1.51386590 -0.98009723
## 134058 -0.2996580 0.009501433 -0.35689080 0.77478626 -0.82011129 -0.15984599
## 124022 -0.9657914 -0.916095386 -0.49528105 -1.39734989 -0.40457242 -0.77957198
## 685285 -0.4991855 0.538801217 -0.73634182 -1.84262468 0.20525690 0.27864787
## 226318 0.3942790 -1.532115504 -0.43218915 -2.19227239 1.55296288 1.23378472
## PC7 PC8 PC9 PC10 PC11 PC12
## 969167 -1.0520466 -0.1893227 0.3381657 0.3956740 0.006055845 -0.01362691
## 188942 -0.1703374 1.1739741 0.9451856 -0.0962916 0.236573087 1.13003738
## 134058 0.7354931 -0.3049681 1.9540400 -0.4833779 1.221293151 -0.78623073
## 124022 -0.6314017 -0.6375343 -0.6775547 0.2333630 -0.745148771 0.44404384
## 685285 0.3444215 -0.9179188 0.7203258 0.1557850 -0.710958455 2.92871096
## 226318 0.4996327 -0.4328555 -1.9267028 1.0800242 -1.526400088 -0.59772689
## PC13 PC14 PC15 PC16
## 969167 1.3634246 -0.6153250 1.42665282 -0.4290958
## 188942 0.7690762 -1.3399541 -2.39919424 0.2032196
## 134058 0.7233293 -2.0932042 1.54650704 -0.5728923
## 124022 0.0734412 -0.6647065 0.01655439 -1.4685173
## 685285 1.4803849 1.7196718 -2.89119642 1.7242594
## 226318 -0.9495389 -0.7950670 1.20710533 0.1506474Rotasi Komponen (PCA with Varimax)
pca <- principal(data_numeric,
nfactors = 5,
rotate = "varimax",
scores = TRUE)
print(pca$loadings)##
## Loadings:
## RC1 RC2 RC5 RC3 RC4
## age -0.166 -0.245
## income 0.349
## daily_gaming_hours 0.950
## weekly_sessions 0.116 0.309 -0.126 -0.238
## years_gaming 0.274 0.174
## sleep_hours -0.159 -0.251
## caffeine_intake 0.147 -0.369
## exercise_hours 0.400 -0.260 0.286
## stress_level 0.411
## anxiety_score 0.267
## depression_score 0.344
## social_interaction_score -0.290 0.110
## relationship_satisfaction 0.543
## academic_performance -0.104 -0.319
## work_productivity -0.223 -0.297 0.129
## addiction_level 0.928
## multiplayer_ratio 0.121 -0.255 -0.217
## toxic_exposure -0.347 -0.151
## violent_games_ratio 0.217 0.169
## mobile_gaming_ratio -0.186 0.303 0.242
## night_gaming_ratio
## weekend_gaming_hours -0.130 -0.117 -0.113
## friends_gaming_count -0.170 0.251
## online_friends 0.275
## streaming_hours 0.253 -0.152 -0.156
## esports_interest -0.211 0.269
## headset_usage -0.287
## microtransactions_spending 0.139 -0.304
## parental_supervision 0.433 0.160
## loneliness_score 0.262 0.105 -0.182
## aggression_score -0.277 -0.188
## happiness_score 0.271 0.132
## bmi 0.506 -0.308
## screen_time_total 0.844
## eye_strain_score 0.427 -0.194
## back_pain_score 0.355 -0.191
## competitive_rank 0.176 0.266 0.231
## internet_quality 0.108 0.222 0.153
##
## RC1 RC2 RC5 RC3 RC4
## SS loadings 2.516 1.234 1.231 1.229 1.223
## Proportion Var 0.066 0.032 0.032 0.032 0.032
## Cumulative Var 0.066 0.099 0.131 0.163 0.196ev <- eigen(cor_matrix)$values
jumlah_pc <- sum(ev > 1)
jumlah_pc## [1] 16data.frame(
Variabel = colnames(data_numeric),
h2 = round(pca$communality,3)
)## Variabel h2
## age age 0.093
## income income 0.129
## daily_gaming_hours daily_gaming_hours 0.906
## weekly_sessions weekly_sessions 0.182
## years_gaming years_gaming 0.112
## sleep_hours sleep_hours 0.090
## caffeine_intake caffeine_intake 0.171
## exercise_hours exercise_hours 0.311
## stress_level stress_level 0.173
## anxiety_score anxiety_score 0.077
## depression_score depression_score 0.128
## social_interaction_score social_interaction_score 0.100
## relationship_satisfaction relationship_satisfaction 0.304
## academic_performance academic_performance 0.120
## work_productivity work_productivity 0.163
## addiction_level addiction_level 0.867
## multiplayer_ratio multiplayer_ratio 0.127
## toxic_exposure toxic_exposure 0.146
## violent_games_ratio violent_games_ratio 0.079
## mobile_gaming_ratio mobile_gaming_ratio 0.188
## night_gaming_ratio night_gaming_ratio 0.009
## weekend_gaming_hours weekend_gaming_hours 0.044
## friends_gaming_count friends_gaming_count 0.106
## online_friends online_friends 0.086
## streaming_hours streaming_hours 0.112
## esports_interest esports_interest 0.124
## headset_usage headset_usage 0.094
## microtransactions_spending microtransactions_spending 0.123
## parental_supervision parental_supervision 0.218
## loneliness_score loneliness_score 0.113
## aggression_score aggression_score 0.114
## happiness_score happiness_score 0.093
## bmi bmi 0.358
## screen_time_total screen_time_total 0.714
## eye_strain_score eye_strain_score 0.227
## back_pain_score back_pain_score 0.176
## competitive_rank competitive_rank 0.166
## internet_quality internet_quality 0.092head(pca$scores)## RC1 RC2 RC5 RC3 RC4
## 969167 -0.1640023 -0.4580843 -0.3330218 -0.05405484 0.1511083
## 188942 -0.5208790 -0.6849911 0.4877612 2.07064978 0.9899810
## 134058 -0.3989368 0.7013576 -0.7661614 0.45624093 0.2095716
## 124022 -0.8695872 -0.9109335 -0.7287508 -0.64599154 -1.2645901
## 685285 -0.3807554 -0.4767106 0.6543285 -0.20345766 -1.9176339
## 226318 0.6254229 -2.7609626 0.2868990 -0.65401361 -1.1761208Analisis Faktor (Exploratory Factor Analysis - EFA)
data.frame(
Variabel = colnames(data_numeric),
h2 = round(pca$communality,3)
)## Variabel h2
## age age 0.093
## income income 0.129
## daily_gaming_hours daily_gaming_hours 0.906
## weekly_sessions weekly_sessions 0.182
## years_gaming years_gaming 0.112
## sleep_hours sleep_hours 0.090
## caffeine_intake caffeine_intake 0.171
## exercise_hours exercise_hours 0.311
## stress_level stress_level 0.173
## anxiety_score anxiety_score 0.077
## depression_score depression_score 0.128
## social_interaction_score social_interaction_score 0.100
## relationship_satisfaction relationship_satisfaction 0.304
## academic_performance academic_performance 0.120
## work_productivity work_productivity 0.163
## addiction_level addiction_level 0.867
## multiplayer_ratio multiplayer_ratio 0.127
## toxic_exposure toxic_exposure 0.146
## violent_games_ratio violent_games_ratio 0.079
## mobile_gaming_ratio mobile_gaming_ratio 0.188
## night_gaming_ratio night_gaming_ratio 0.009
## weekend_gaming_hours weekend_gaming_hours 0.044
## friends_gaming_count friends_gaming_count 0.106
## online_friends online_friends 0.086
## streaming_hours streaming_hours 0.112
## esports_interest esports_interest 0.124
## headset_usage headset_usage 0.094
## microtransactions_spending microtransactions_spending 0.123
## parental_supervision parental_supervision 0.218
## loneliness_score loneliness_score 0.113
## aggression_score aggression_score 0.114
## happiness_score happiness_score 0.093
## bmi bmi 0.358
## screen_time_total screen_time_total 0.714
## eye_strain_score eye_strain_score 0.227
## back_pain_score back_pain_score 0.176
## competitive_rank competitive_rank 0.166
## internet_quality internet_quality 0.092#``````
fa_result <- fa(data_numeric,
nfactors = 5,
rotate = "varimax",
fm = "pa",
scores = "regression")
print(fa_result$loadings, cutoff=0.4)##
## Loadings:
## PA1 PA3 PA5 PA4 PA2
## age
## income
## daily_gaming_hours 0.983
## weekly_sessions
## years_gaming
## sleep_hours
## caffeine_intake
## exercise_hours
## stress_level
## anxiety_score
## depression_score
## social_interaction_score
## relationship_satisfaction
## academic_performance
## work_productivity
## addiction_level 0.900
## multiplayer_ratio
## toxic_exposure
## violent_games_ratio
## mobile_gaming_ratio
## night_gaming_ratio
## weekend_gaming_hours
## friends_gaming_count
## online_friends
## streaming_hours
## esports_interest
## headset_usage
## microtransactions_spending
## parental_supervision
## loneliness_score
## aggression_score
## happiness_score
## bmi
## screen_time_total 0.711
## eye_strain_score
## back_pain_score
## competitive_rank
## internet_quality
##
## PA1 PA3 PA5 PA4 PA2
## SS loadings 2.298 0.303 0.294 0.279 0.272
## Proportion Var 0.060 0.008 0.008 0.007 0.007
## Cumulative Var 0.060 0.068 0.076 0.084 0.091#``````
data.frame(
Variabel = colnames(data_numeric),
h2 = round(fa_result$communality,3),
Psi = round(fa_result$uniquenesses,3)
)## Variabel h2 Psi
## age age 0.014 0.986
## income income 0.017 0.983
## daily_gaming_hours daily_gaming_hours 0.972 0.028
## weekly_sessions weekly_sessions 0.037 0.963
## years_gaming years_gaming 0.022 0.978
## sleep_hours sleep_hours 0.019 0.981
## caffeine_intake caffeine_intake 0.040 0.960
## exercise_hours exercise_hours 0.110 0.890
## stress_level stress_level 0.041 0.959
## anxiety_score anxiety_score 0.008 0.992
## depression_score depression_score 0.033 0.967
## social_interaction_score social_interaction_score 0.015 0.985
## relationship_satisfaction relationship_satisfaction 0.102 0.898
## academic_performance academic_performance 0.018 0.982
## work_productivity work_productivity 0.036 0.964
## addiction_level addiction_level 0.825 0.175
## multiplayer_ratio multiplayer_ratio 0.017 0.983
## toxic_exposure toxic_exposure 0.018 0.982
## violent_games_ratio violent_games_ratio 0.010 0.990
## mobile_gaming_ratio mobile_gaming_ratio 0.045 0.955
## night_gaming_ratio night_gaming_ratio 0.002 0.998
## weekend_gaming_hours weekend_gaming_hours 0.011 0.989
## friends_gaming_count friends_gaming_count 0.018 0.982
## online_friends online_friends 0.012 0.988
## streaming_hours streaming_hours 0.012 0.988
## esports_interest esports_interest 0.021 0.979
## headset_usage headset_usage 0.015 0.985
## microtransactions_spending microtransactions_spending 0.020 0.980
## parental_supervision parental_supervision 0.032 0.968
## loneliness_score loneliness_score 0.028 0.972
## aggression_score aggression_score 0.018 0.982
## happiness_score happiness_score 0.021 0.979
## bmi bmi 0.146 0.854
## screen_time_total screen_time_total 0.510 0.490
## eye_strain_score eye_strain_score 0.078 0.922
## back_pain_score back_pain_score 0.036 0.964
## competitive_rank competitive_rank 0.048 0.952
## internet_quality internet_quality 0.019 0.981#``````
head(fa_result$scores)## PA1 PA3 PA5 PA4 PA2
## 969167 -0.1144214 -0.02389559 -0.37509588 -0.2274070 0.02321295
## 188942 -0.7114270 0.89072753 0.09561537 -0.2752606 0.37192903
## 134058 -0.3714190 0.30249829 -0.36190539 0.1786933 0.01140296
## 124022 -0.8133638 -0.52568973 -0.38336001 -0.2682179 -0.60551235
## 685285 -0.9698832 -0.11192388 0.24926422 -0.3734783 -0.93085663
## 226318 0.2253806 -0.37846757 0.20994099 -1.3028050 -0.40851999