INSTALL PACKAGE
install.packages("seminr")
install.packages("readr")
install.packages("dplyr")
install.packages("psych")
install.packages("corrplot")
install.packages("ggplot2")
LOAD LIBRARY
library(seminr)
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(psych)
library(corrplot)
## corrplot 0.95 loaded
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
IMPORT DATA
data <- read_csv("instagram_usage_lifestyle.csv")
## Rows: 1547896 Columns: 58
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): app_name, gender, country, urban_rural, income_level, employment_...
## dbl (38): user_id, age, exercise_hours_per_week, sleep_hours_per_night, per...
## date (1): last_login_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(data)
## # A tibble: 6 × 58
## user_id app_name age gender country urban_rural income_level
## <dbl> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 1 Instagram 51 Female India Rural High
## 2 2 Instagram 64 Female United Kingdom Urban Middle
## 3 3 Instagram 41 Female Canada Urban Middle
## 4 4 Instagram 27 Non-binary South Korea Urban Middle
## 5 5 Instagram 55 Male India Urban Upper-middle
## 6 6 Instagram 20 Male United States Rural Middle
## # ℹ 51 more variables: employment_status <chr>, education_level <chr>,
## # relationship_status <chr>, has_children <chr>,
## # exercise_hours_per_week <dbl>, sleep_hours_per_night <dbl>,
## # diet_quality <chr>, smoking <chr>, alcohol_frequency <chr>,
## # perceived_stress_score <dbl>, self_reported_happiness <dbl>,
## # body_mass_index <dbl>, blood_pressure_systolic <dbl>,
## # blood_pressure_diastolic <dbl>, daily_steps_count <dbl>, …
str(data)
## spc_tbl_ [1,547,896 × 58] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ user_id : num [1:1547896] 1 2 3 4 5 6 7 8 9 10 ...
## $ app_name : chr [1:1547896] "Instagram" "Instagram" "Instagram" "Instagram" ...
## $ age : num [1:1547896] 51 64 41 27 55 20 33 51 31 35 ...
## $ gender : chr [1:1547896] "Female" "Female" "Female" "Non-binary" ...
## $ country : chr [1:1547896] "India" "United Kingdom" "Canada" "South Korea" ...
## $ urban_rural : chr [1:1547896] "Rural" "Urban" "Urban" "Urban" ...
## $ income_level : chr [1:1547896] "High" "Middle" "Middle" "Middle" ...
## $ employment_status : chr [1:1547896] "Retired" "Full-time employed" "Student" "Unemployed" ...
## $ education_level : chr [1:1547896] "Bachelor’s" "Other" "Bachelor’s" "Master’s" ...
## $ relationship_status : chr [1:1547896] "Single" "Divorced" "In a relationship" "In a relationship" ...
## $ has_children : chr [1:1547896] "No" "No" "No" "No" ...
## $ exercise_hours_per_week : num [1:1547896] 7.2 10.9 5 10.6 7.7 5.2 6.5 10.5 4.2 2.1 ...
## $ sleep_hours_per_night : num [1:1547896] 7.7 8.6 6.7 6.5 6.8 5.8 7.7 7.7 8.3 6.5 ...
## $ diet_quality : chr [1:1547896] "Good" "Very poor" "Good" "Poor" ...
## $ smoking : chr [1:1547896] "No" "No" "No" "Yes" ...
## $ alcohol_frequency : chr [1:1547896] "Rarely" "Rarely" "Rarely" "Never" ...
## $ perceived_stress_score : num [1:1547896] 3 1 4 18 19 1 23 33 12 25 ...
## $ self_reported_happiness : num [1:1547896] 8 1 10 1 1 3 10 3 6 10 ...
## $ body_mass_index : num [1:1547896] 20.8 23.5 28.6 22.5 28.1 25.7 24.5 20.3 22.2 23.3 ...
## $ blood_pressure_systolic : num [1:1547896] 148 133 135 105 146 138 114 118 93 128 ...
## $ blood_pressure_diastolic : num [1:1547896] 86 84 88 73 90 71 72 85 98 75 ...
## $ daily_steps_count : num [1:1547896] 8107 8059 7872 7801 8005 ...
## $ weekly_work_hours : num [1:1547896] 49.9 15.6 31.8 43.4 50.2 36.2 38.5 26 27.4 37.7 ...
## $ hobbies_count : num [1:1547896] 3 0 4 2 2 4 4 1 5 5 ...
## $ social_events_per_month : num [1:1547896] 4 5 5 3 2 3 6 2 3 3 ...
## $ books_read_per_year : num [1:1547896] 7 10 14 13 12 7 9 11 13 12 ...
## $ volunteer_hours_per_month : num [1:1547896] 4.3 4.7 1.5 3.3 4.5 4.7 1.9 5.7 2.2 7 ...
## $ travel_frequency_per_year : num [1:1547896] 0 2 2 4 3 1 4 3 4 2 ...
## $ daily_active_minutes_instagram: num [1:1547896] 5 74 5 233 184 49 125 282 128 196 ...
## $ sessions_per_day : num [1:1547896] 1 5 1 9 14 3 4 14 10 13 ...
## $ posts_created_per_week : num [1:1547896] 3 3 7 5 5 3 7 5 2 5 ...
## $ reels_watched_per_day : num [1:1547896] 42 78 29 241 146 150 112 227 118 176 ...
## $ stories_viewed_per_day : num [1:1547896] 28 54 26 109 113 41 79 144 90 93 ...
## $ likes_given_per_day : num [1:1547896] 28 68 25 132 103 55 80 158 90 131 ...
## $ comments_written_per_day : num [1:1547896] 5 15 6 36 36 9 20 50 16 27 ...
## $ dms_sent_per_week : num [1:1547896] 12 18 12 31 29 13 18 26 20 22 ...
## $ dms_received_per_week : num [1:1547896] 12 10 13 32 37 13 34 37 18 30 ...
## $ ads_viewed_per_day : num [1:1547896] 4 11 4 33 20 16 14 20 23 24 ...
## $ ads_clicked_per_day : num [1:1547896] 1 1 0 3 5 3 5 9 2 1 ...
## $ time_on_feed_per_day : num [1:1547896] 2 31 3 108 78 29 64 167 64 115 ...
## $ time_on_explore_per_day : num [1:1547896] 1 19 1 64 55 6 21 49 38 34 ...
## $ time_on_messages_per_day : num [1:1547896] 1 16 1 52 22 12 26 50 31 27 ...
## $ time_on_reels_per_day : num [1:1547896] 2 19 1 64 55 13 44 87 50 58 ...
## $ followers_count : num [1:1547896] 374 2585 3414 617 1157 ...
## $ following_count : num [1:1547896] 647 3511 6761 1193 1072 ...
## $ uses_premium_features : chr [1:1547896] "No" "No" "No" "No" ...
## $ notification_response_rate : num [1:1547896] 0.34 0.56 0.73 0.73 0.65 0.13 0.79 0.22 0.98 0.6 ...
## $ account_creation_year : num [1:1547896] 2015 2018 2011 2019 2017 ...
## $ last_login_date : Date[1:1547896], format: "2025-11-02" "2025-03-22" ...
## $ average_session_length_minutes: num [1:1547896] 5 14.8 5 25.9 13.1 16.3 31.2 20.1 12.8 15.1 ...
## $ content_type_preference : chr [1:1547896] "Mixed" "Photos" "Mixed" "Stories" ...
## $ preferred_content_theme : chr [1:1547896] "Tech" "Fashion" "Other" "Tech" ...
## $ privacy_setting_level : chr [1:1547896] "Private" "Public" "Public" "Private" ...
## $ two_factor_auth_enabled : chr [1:1547896] "Yes" "No" "Yes" "No" ...
## $ biometric_login_used : chr [1:1547896] "No" "No" "Yes" "No" ...
## $ linked_accounts_count : num [1:1547896] 0 3 1 1 0 1 3 2 2 1 ...
## $ subscription_status : chr [1:1547896] "Free" "Free" "Free" "Free" ...
## $ user_engagement_score : num [1:1547896] 7.83 1.43 9.67 0.94 1.03 1.64 1.12 0.96 0.99 1.02 ...
## - attr(*, "spec")=
## .. cols(
## .. user_id = col_double(),
## .. app_name = col_character(),
## .. age = col_double(),
## .. gender = col_character(),
## .. country = col_character(),
## .. urban_rural = col_character(),
## .. income_level = col_character(),
## .. employment_status = col_character(),
## .. education_level = col_character(),
## .. relationship_status = col_character(),
## .. has_children = col_character(),
## .. exercise_hours_per_week = col_double(),
## .. sleep_hours_per_night = col_double(),
## .. diet_quality = col_character(),
## .. smoking = col_character(),
## .. alcohol_frequency = col_character(),
## .. perceived_stress_score = col_double(),
## .. self_reported_happiness = col_double(),
## .. body_mass_index = col_double(),
## .. blood_pressure_systolic = col_double(),
## .. blood_pressure_diastolic = col_double(),
## .. daily_steps_count = col_double(),
## .. weekly_work_hours = col_double(),
## .. hobbies_count = col_double(),
## .. social_events_per_month = col_double(),
## .. books_read_per_year = col_double(),
## .. volunteer_hours_per_month = col_double(),
## .. travel_frequency_per_year = col_double(),
## .. daily_active_minutes_instagram = col_double(),
## .. sessions_per_day = col_double(),
## .. posts_created_per_week = col_double(),
## .. reels_watched_per_day = col_double(),
## .. stories_viewed_per_day = col_double(),
## .. likes_given_per_day = col_double(),
## .. comments_written_per_day = col_double(),
## .. dms_sent_per_week = col_double(),
## .. dms_received_per_week = col_double(),
## .. ads_viewed_per_day = col_double(),
## .. ads_clicked_per_day = col_double(),
## .. time_on_feed_per_day = col_double(),
## .. time_on_explore_per_day = col_double(),
## .. time_on_messages_per_day = col_double(),
## .. time_on_reels_per_day = col_double(),
## .. followers_count = col_double(),
## .. following_count = col_double(),
## .. uses_premium_features = col_character(),
## .. notification_response_rate = col_double(),
## .. account_creation_year = col_double(),
## .. last_login_date = col_date(format = ""),
## .. average_session_length_minutes = col_double(),
## .. content_type_preference = col_character(),
## .. preferred_content_theme = col_character(),
## .. privacy_setting_level = col_character(),
## .. two_factor_auth_enabled = col_character(),
## .. biometric_login_used = col_character(),
## .. linked_accounts_count = col_double(),
## .. subscription_status = col_character(),
## .. user_engagement_score = col_double()
## .. )
## - attr(*, "problems")=<pointer: 0x0000025bbeae9d00>
FEATURE SELECTION
sem_data <- data %>%
select(
# DOOMSCROLLING
daily_active_minutes_instagram,
sessions_per_day,
reels_watched_per_day,
stories_viewed_per_day,
time_on_feed_per_day,
time_on_explore_per_day,
time_on_reels_per_day,
# ENGAGEMENT
likes_given_per_day,
comments_written_per_day,
dms_sent_per_week,
dms_received_per_week,
# MENTAL HEALTH
perceived_stress_score,
self_reported_happiness,
# LIFESTYLE
daily_steps_count,
exercise_hours_per_week,
hobbies_count
)
REVERSE CODING HAPPINESS
sem_data <- sem_data %>%
mutate(
happiness_reverse =
max(self_reported_happiness, na.rm = TRUE) -
self_reported_happiness
)
RENAME VARIABLE
sem_data <- sem_data %>%
rename(
# DOOMSCROLLING
DOOM1 = daily_active_minutes_instagram,
DOOM2 = sessions_per_day,
DOOM3 = reels_watched_per_day,
DOOM4 = stories_viewed_per_day,
DOOM5 = time_on_feed_per_day,
DOOM6 = time_on_explore_per_day,
DOOM7 = time_on_reels_per_day,
# ENGAGEMENT
ENG1 = likes_given_per_day,
ENG2 = comments_written_per_day,
ENG3 = dms_sent_per_week,
ENG4 = dms_received_per_week,
# MENTAL HEALTH
MH1 = perceived_stress_score,
MH2 = happiness_reverse,
# LIFESTYLE
LIFE1 = daily_steps_count,
LIFE2 = exercise_hours_per_week,
LIFE3 = hobbies_count
)
colnames(sem_data)
## [1] "DOOM1" "DOOM2"
## [3] "DOOM3" "DOOM4"
## [5] "DOOM5" "DOOM6"
## [7] "DOOM7" "ENG1"
## [9] "ENG2" "ENG3"
## [11] "ENG4" "MH1"
## [13] "self_reported_happiness" "LIFE1"
## [15] "LIFE2" "LIFE3"
## [17] "MH2"
HANDLING MISSING VALUE
colSums(is.na(sem_data))
## DOOM1 DOOM2 DOOM3
## 0 0 0
## DOOM4 DOOM5 DOOM6
## 0 0 0
## DOOM7 ENG1 ENG2
## 0 0 0
## ENG3 ENG4 MH1
## 0 0 0
## self_reported_happiness LIFE1 LIFE2
## 0 0 0
## LIFE3 MH2
## 0 0
sem_data <- na.omit(sem_data)
SAMPLING DATA
set.seed(123)
sample_size <- min(5000, nrow(sem_data))
sem_data_sample <- sem_data %>%
sample_n(sample_size)
DESCRIPTIVE STATISTICS
describe(sem_data_sample)
## vars n mean sd median trimmed mad min
## DOOM1 1 5000 188.72 109.05 186.0 186.91 127.50 5.0
## DOOM2 2 5000 10.57 8.12 9.0 9.47 7.41 1.0
## DOOM3 3 5000 176.96 76.07 176.0 177.28 87.47 20.0
## DOOM4 4 5000 101.44 38.49 104.0 103.70 50.41 17.0
## DOOM5 5 5000 94.35 56.00 93.0 92.67 63.75 2.0
## DOOM6 6 5000 37.62 25.07 34.0 35.76 25.20 1.0
## DOOM7 7 5000 56.74 35.13 54.0 55.03 38.55 1.0
## ENG1 8 5000 119.46 55.70 118.0 118.23 63.75 14.0
## ENG2 9 5000 34.32 17.36 34.0 33.80 19.27 1.0
## ENG3 10 5000 28.75 12.09 28.0 28.26 13.34 3.0
## ENG4 11 5000 30.64 14.20 30.0 30.16 16.31 2.0
## MH1 12 5000 19.92 11.79 20.0 19.90 14.83 0.0
## self_reported_happiness 13 5000 5.47 2.85 5.0 5.47 2.97 1.0
## LIFE1 14 5000 8001.43 88.70 8000.0 8001.24 88.96 7608.0
## LIFE2 15 5000 7.18 3.93 6.7 6.91 4.15 0.1
## LIFE3 16 5000 3.03 1.73 3.0 2.92 1.48 0.0
## MH2 17 5000 4.53 2.85 5.0 4.53 2.97 0.0
## max range skew kurtosis se
## DOOM1 495.0 490.0 0.14 -0.81 1.54
## DOOM2 50.0 49.0 1.35 2.11 0.11
## DOOM3 300.0 280.0 -0.01 -1.00 1.08
## DOOM4 150.0 133.0 -0.27 -1.15 0.54
## DOOM5 296.0 294.0 0.26 -0.58 0.79
## DOOM6 133.0 132.0 0.65 -0.02 0.35
## DOOM7 198.0 197.0 0.43 -0.33 0.50
## ENG1 291.0 277.0 0.19 -0.72 0.79
## ENG2 80.0 79.0 0.23 -0.67 0.25
## ENG3 70.0 67.0 0.34 -0.42 0.17
## ENG4 79.0 77.0 0.28 -0.56 0.20
## MH1 40.0 40.0 0.01 -1.21 0.17
## self_reported_happiness 10.0 9.0 0.01 -1.22 0.04
## LIFE1 8325.0 717.0 0.01 0.11 1.25
## LIFE2 20.3 20.2 0.58 -0.13 0.06
## LIFE3 10.0 10.0 0.60 0.46 0.02
## MH2 9.0 9.0 -0.01 -1.22 0.04
CORRELATION MATRIX
cor_matrix <- cor(sem_data_sample)
corrplot(cor_matrix,
method = "color",
type = "upper",
tl.cex = 0.8)
MEASUREMENT MODEL
measurement_model <- constructs(
# DOOMSCROLLING
composite(
"DOOM",
multi_items("DOOM", 1:7)
),
# ENGAGEMENT
composite(
"ENGAGEMENT",
multi_items("ENG", 1:4)
),
# MENTAL HEALTH
composite(
"MENTAL_HEALTH",
multi_items("MH", 1:2)
),
# LIFESTYLE
composite(
"LIFESTYLE",
multi_items("LIFE", 1:3)
)
)
STRUCTURAL MODEL
structural_model <- relationships(
# DOOM -> ENGAGEMENT
paths(from = "DOOM",
to = "ENGAGEMENT"),
# DOOM -> MENTAL HEALTH
paths(from = "DOOM",
to = "MENTAL_HEALTH"),
# ENGAGEMENT -> MENTAL HEALTH
paths(from = "ENGAGEMENT",
to = "MENTAL_HEALTH"),
# MENTAL HEALTH -> LIFESTYLE
paths(from = "MENTAL_HEALTH",
to = "LIFESTYLE")
)
ESTIMATE SEM-PLS MODEL
pls_model <- estimate_pls(
data = sem_data_sample,
measurement_model = measurement_model,
structural_model = structural_model
)
## Generating the seminr model
## All 5000 observations are valid.
MODEL SUMMARY
summary(pls_model)
##
## Results from package seminr (2.4.2)
##
## Path Coefficients:
## ENGAGEMENT MENTAL_HEALTH LIFESTYLE
## R^2 0.940 0.811 0.000
## AdjR^2 0.940 0.811 -0.000
## DOOM 0.970 0.411 .
## ENGAGEMENT . 0.497 .
## MENTAL_HEALTH . . -0.007
##
## Reliability:
## alpha rhoA rhoC AVE
## DOOM 0.970 0.976 0.975 0.851
## ENGAGEMENT 0.965 0.967 0.975 0.906
## MENTAL_HEALTH -0.030 -0.041 0.625 0.495
## LIFESTYLE 0.039 0.000 0.173 0.333
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5
OUTER LOADINGS
pls_model$outer_loadings
## DOOM ENGAGEMENT MENTAL_HEALTH LIFESTYLE
## DOOM1 0.9868135 0.0000000 0.0000000 0.0000000
## DOOM2 0.7906904 0.0000000 0.0000000 0.0000000
## DOOM3 0.9000194 0.0000000 0.0000000 0.0000000
## DOOM4 0.9593909 0.0000000 0.0000000 0.0000000
## DOOM5 0.9699931 0.0000000 0.0000000 0.0000000
## DOOM6 0.8926092 0.0000000 0.0000000 0.0000000
## DOOM7 0.9431764 0.0000000 0.0000000 0.0000000
## ENG1 0.0000000 0.9743490 0.0000000 0.0000000
## ENG2 0.0000000 0.9565554 0.0000000 0.0000000
## ENG3 0.0000000 0.9297477 0.0000000 0.0000000
## ENG4 0.0000000 0.9453620 0.0000000 0.0000000
## MH1 0.0000000 0.0000000 0.9215866 0.0000000
## MH2 0.0000000 0.0000000 0.3746866 0.0000000
## LIFE1 0.0000000 0.0000000 0.0000000 0.8518077
## LIFE2 0.0000000 0.0000000 0.0000000 0.2531336
## LIFE3 0.0000000 0.0000000 0.0000000 -0.4589828
R-SQUARE
summary(pls_model)$r_squared
## NULL
RELIABILITY & VALIDITY
summary(pls_model)$reliability
## alpha rhoA rhoC AVE
## DOOM 0.970 0.976 0.975 0.851
## ENGAGEMENT 0.965 0.967 0.975 0.906
## MENTAL_HEALTH -0.030 -0.041 0.625 0.495
## LIFESTYLE 0.039 0.000 0.173 0.333
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5
BOOTSTRAPPING
boot_model <- bootstrap_model(
seminr_model = pls_model,
nboot = 1000,
cores = parallel::detectCores() - 1
)
## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrapped
BOOTSTRAP SUMMARY
summary(boot_model)
##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD
## DOOM -> ENGAGEMENT 0.970 0.970 0.001
## DOOM -> MENTAL_HEALTH 0.411 0.411 0.023
## ENGAGEMENT -> MENTAL_HEALTH 0.497 0.497 0.024
## MENTAL_HEALTH -> LIFESTYLE -0.007 -0.008 0.025
## T Stat. 2.5% CI 97.5% CI Bootstrap P Val
## DOOM -> ENGAGEMENT 1225.650 0.968 0.971 0.000
## DOOM -> MENTAL_HEALTH 17.558 0.367 0.456 0.000
## ENGAGEMENT -> MENTAL_HEALTH 21.016 0.450 0.541 0.000
## MENTAL_HEALTH -> LIFESTYLE -0.295 -0.042 0.039 0.676
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## DOOM1 -> DOOM 0.171 0.171 0.000 387.391
## DOOM2 -> DOOM 0.127 0.127 0.001 184.376
## DOOM3 -> DOOM 0.144 0.144 0.001 282.567
## DOOM4 -> DOOM 0.164 0.164 0.000 403.841
## DOOM5 -> DOOM 0.167 0.167 0.000 420.278
## DOOM6 -> DOOM 0.148 0.148 0.000 306.647
## DOOM7 -> DOOM 0.160 0.160 0.000 444.894
## ENG1 -> ENGAGEMENT 0.275 0.275 0.001 463.205
## ENG2 -> ENGAGEMENT 0.265 0.265 0.001 527.633
## ENG3 -> ENGAGEMENT 0.251 0.251 0.001 461.025
## ENG4 -> ENGAGEMENT 0.258 0.259 0.001 502.563
## MH1 -> MENTAL_HEALTH 0.927 0.927 0.009 102.021
## MH2 -> MENTAL_HEALTH 0.388 0.388 0.011 36.706
## LIFE1 -> LIFESTYLE 0.850 0.288 0.504 1.685
## LIFE2 -> LIFESTYLE 0.243 0.254 0.511 0.475
## LIFE3 -> LIFESTYLE -0.467 0.207 0.543 -0.862
## 2.5% CI 97.5% CI Bootstrap P Val
## DOOM1 -> DOOM 0.170 0.172 0.000
## DOOM2 -> DOOM 0.126 0.128 0.000
## DOOM3 -> DOOM 0.143 0.145 0.000
## DOOM4 -> DOOM 0.163 0.165 0.000
## DOOM5 -> DOOM 0.166 0.167 0.000
## DOOM6 -> DOOM 0.147 0.149 0.000
## DOOM7 -> DOOM 0.159 0.160 0.000
## ENG1 -> ENGAGEMENT 0.274 0.277 0.000
## ENG2 -> ENGAGEMENT 0.264 0.266 0.000
## ENG3 -> ENGAGEMENT 0.250 0.252 0.000
## ENG4 -> ENGAGEMENT 0.257 0.260 0.000
## MH1 -> MENTAL_HEALTH 0.909 0.944 0.000
## MH2 -> MENTAL_HEALTH 0.368 0.408 0.000
## LIFE1 -> LIFESTYLE -0.766 0.967 0.600
## LIFE2 -> LIFESTYLE -0.797 0.963 0.628
## LIFE3 -> LIFESTYLE -0.782 0.978 0.732
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## DOOM1 -> DOOM 0.987 0.987 0.000 2822.393
## DOOM2 -> DOOM 0.791 0.791 0.004 184.837
## DOOM3 -> DOOM 0.900 0.900 0.002 376.694
## DOOM4 -> DOOM 0.959 0.959 0.001 927.413
## DOOM5 -> DOOM 0.970 0.970 0.001 1304.441
## DOOM6 -> DOOM 0.893 0.893 0.003 337.281
## DOOM7 -> DOOM 0.943 0.943 0.001 656.484
## ENG1 -> ENGAGEMENT 0.974 0.974 0.001 1481.717
## ENG2 -> ENGAGEMENT 0.957 0.957 0.001 786.206
## ENG3 -> ENGAGEMENT 0.930 0.930 0.002 496.785
## ENG4 -> ENGAGEMENT 0.945 0.945 0.001 691.189
## MH1 -> MENTAL_HEALTH 0.922 0.922 0.004 205.953
## MH2 -> MENTAL_HEALTH 0.375 0.374 0.022 17.005
## LIFE1 -> LIFESTYLE 0.852 0.294 0.501 1.699
## LIFE2 -> LIFESTYLE 0.253 0.263 0.508 0.499
## LIFE3 -> LIFESTYLE -0.459 0.212 0.540 -0.850
## 2.5% CI 97.5% CI Bootstrap P Val
## DOOM1 -> DOOM 0.986 0.988 0.000
## DOOM2 -> DOOM 0.783 0.799 0.000
## DOOM3 -> DOOM 0.895 0.905 0.000
## DOOM4 -> DOOM 0.957 0.961 0.000
## DOOM5 -> DOOM 0.969 0.971 0.000
## DOOM6 -> DOOM 0.887 0.898 0.000
## DOOM7 -> DOOM 0.940 0.946 0.000
## ENG1 -> ENGAGEMENT 0.973 0.976 0.000
## ENG2 -> ENGAGEMENT 0.954 0.959 0.000
## ENG3 -> ENGAGEMENT 0.926 0.933 0.000
## ENG4 -> ENGAGEMENT 0.943 0.948 0.000
## MH1 -> MENTAL_HEALTH 0.913 0.930 0.000
## MH2 -> MENTAL_HEALTH 0.332 0.416 0.000
## LIFE1 -> LIFESTYLE -0.753 0.965 0.594
## LIFE2 -> LIFESTYLE -0.784 0.962 0.608
## LIFE3 -> LIFESTYLE -0.772 0.979 0.722
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI
## DOOM -> ENGAGEMENT 0.999 0.999 0.001 0.998
## DOOM -> MENTAL_HEALTH 4.858 6.450 6.324 2.845
## DOOM -> LIFESTYLE 0.066 0.123 0.051 0.052
## ENGAGEMENT -> MENTAL_HEALTH 4.915 6.526 6.399 2.864
## ENGAGEMENT -> LIFESTYLE 0.053 0.114 0.050 0.042
## MENTAL_HEALTH -> LIFESTYLE 0.604 1.307 1.431 0.317
## 97.5% CI Bootstrap P Val
## DOOM -> ENGAGEMENT 1.001 0.442
## DOOM -> MENTAL_HEALTH 19.290 0.000
## DOOM -> LIFESTYLE 0.243 0.000
## ENGAGEMENT -> MENTAL_HEALTH 19.517 0.000
## ENGAGEMENT -> LIFESTYLE 0.239 0.000
## MENTAL_HEALTH -> LIFESTYLE 4.658 0.950
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI
## DOOM -> ENGAGEMENT 0.970 0.970 0.001 0.968
## DOOM -> MENTAL_HEALTH 0.893 0.893 0.003 0.887
## DOOM -> LIFESTYLE -0.007 -0.007 0.022 -0.038
## ENGAGEMENT -> MENTAL_HEALTH 0.497 0.497 0.024 0.450
## ENGAGEMENT -> LIFESTYLE -0.004 -0.004 0.012 -0.021
## MENTAL_HEALTH -> LIFESTYLE -0.007 -0.008 0.025 -0.042
## 97.5% CI
## DOOM -> ENGAGEMENT 0.971
## DOOM -> MENTAL_HEALTH 0.897
## DOOM -> LIFESTYLE 0.034
## ENGAGEMENT -> MENTAL_HEALTH 0.541
## ENGAGEMENT -> LIFESTYLE 0.020
## MENTAL_HEALTH -> LIFESTYLE 0.039
VISUALISASI MODEL
plot(pls_model)