Import Library
library(readr)
## Warning: package 'readr' was built under R version 4.5.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
##
## 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)
## Warning: package 'psych' was built under R version 4.5.3
library(MVN)
## Warning: package 'MVN' was built under R version 4.5.3
##
## Attaching package: 'MVN'
## The following object is masked from 'package:psych':
##
## mardia
library(car)
## Warning: package 'car' was built under R version 4.5.3
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
library(lavaan)
## Warning: package 'lavaan' was built under R version 4.5.3
## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
library(semPlot)
## Warning: package 'semPlot' was built under R version 4.5.3
Load Dataset
data <- read.csv("C:/Users/User/Downloads/smmh.csv")
Data Understanding
head(data)
## Timestamp X1..What.is.your.age. X2..Gender X3..Relationship.Status
## 1 4/18/2022 19:18:47 21 Male In a relationship
## 2 4/18/2022 19:19:28 21 Female Single
## 3 4/18/2022 19:25:59 21 Female Single
## 4 4/18/2022 19:29:43 21 Female Single
## 5 4/18/2022 19:33:31 21 Female Single
## 6 4/18/2022 19:33:48 22 Female Single
## X4..Occupation.Status X5..What.type.of.organizations.are.you.affiliated.with.
## 1 University Student University
## 2 University Student University
## 3 University Student University
## 4 University Student University
## 5 University Student University
## 6 University Student University
## X6..Do.you.use.social.media.
## 1 Yes
## 2 Yes
## 3 Yes
## 4 Yes
## 5 Yes
## 6 Yes
## X7..What.social.media.platforms.do.you.commonly.use.
## 1 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 2 Facebook, Twitter, Instagram, YouTube, Discord, Reddit
## 3 Facebook, Instagram, YouTube, Pinterest
## 4 Facebook, Instagram
## 5 Facebook, Instagram, YouTube
## 6 Facebook, Twitter, Instagram, YouTube, Discord, Pinterest, TikTok
## X8..What.is.the.average.time.you.spend.on.social.media.every.day.
## 1 Between 2 and 3 hours
## 2 More than 5 hours
## 3 Between 3 and 4 hours
## 4 More than 5 hours
## 5 Between 2 and 3 hours
## 6 Between 2 and 3 hours
## X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose.
## 1 5
## 2 4
## 3 3
## 4 4
## 5 3
## 6 4
## X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something.
## 1 3
## 2 3
## 3 2
## 4 2
## 5 5
## 6 4
## X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while.
## 1 2
## 2 2
## 3 1
## 4 1
## 5 4
## 6 2
## X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you.
## 1 5
## 2 4
## 3 2
## 4 3
## 5 4
## 6 3
## X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries.
## 1 2
## 2 5
## 3 5
## 4 5
## 5 5
## 6 4
## X14..Do.you.find.it.difficult.to.concentrate.on.things.
## 1 5
## 2 4
## 3 4
## 4 3
## 5 5
## 6 3
## X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.
## 1 2
## 2 5
## 3 3
## 4 5
## 5 3
## 6 4
## X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking.
## 1 3
## 2 1
## 3 3
## 4 1
## 5 3
## 6 4
## X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media.
## 1 2
## 2 1
## 3 1
## 4 2
## 5 3
## 6 3
## X18..How.often.do.you.feel.depressed.or.down.
## 1 5
## 2 5
## 3 4
## 4 4
## 5 4
## 6 3
## X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate.
## 1 4
## 2 4
## 3 2
## 4 3
## 5 4
## 6 2
## X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep.
## 1 5
## 2 5
## 3 5
## 4 2
## 5 1
## 6 4
str(data)
## 'data.frame': 481 obs. of 21 variables:
## $ Timestamp : chr "4/18/2022 19:18:47" "4/18/2022 19:19:28" "4/18/2022 19:25:59" "4/18/2022 19:29:43" ...
## $ X1..What.is.your.age. : num 21 21 21 21 21 22 21 21 21 20 ...
## $ X2..Gender : chr "Male" "Female" "Female" "Female" ...
## $ X3..Relationship.Status : chr "In a relationship" "Single" "Single" "Single" ...
## $ X4..Occupation.Status : chr "University Student" "University Student" "University Student" "University Student" ...
## $ X5..What.type.of.organizations.are.you.affiliated.with. : chr "University" "University" "University" "University" ...
## $ X6..Do.you.use.social.media. : chr "Yes" "Yes" "Yes" "Yes" ...
## $ X7..What.social.media.platforms.do.you.commonly.use. : chr "Facebook, Twitter, Instagram, YouTube, Discord, Reddit" "Facebook, Twitter, Instagram, YouTube, Discord, Reddit" "Facebook, Instagram, YouTube, Pinterest" "Facebook, Instagram" ...
## $ X8..What.is.the.average.time.you.spend.on.social.media.every.day. : chr "Between 2 and 3 hours" "More than 5 hours" "Between 3 and 4 hours" "More than 5 hours" ...
## $ X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose. : int 5 4 3 4 3 4 4 5 5 1 ...
## $ X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something. : int 3 3 2 2 5 4 3 2 2 1 ...
## $ X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while. : int 2 2 1 1 4 2 2 3 3 1 ...
## $ X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you. : int 5 4 2 3 4 3 2 3 3 1 ...
## $ X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries. : int 2 5 5 5 5 4 4 3 1 1 ...
## $ X14..Do.you.find.it.difficult.to.concentrate.on.things. : int 5 4 4 3 5 3 3 1 1 1 ...
## $ X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.: int 2 5 3 5 3 4 5 1 1 1 ...
## $ X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking. : int 3 1 3 1 3 4 3 3 3 1 ...
## $ X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media. : int 2 1 1 2 3 3 4 1 1 1 ...
## $ X18..How.often.do.you.feel.depressed.or.down. : int 5 5 4 4 4 3 5 5 5 1 ...
## $ X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate. : int 4 4 2 3 4 2 5 5 5 1 ...
## $ X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep. : int 5 5 5 2 1 4 3 1 1 1 ...
summary(data)
## Timestamp X1..What.is.your.age. X2..Gender
## Length:481 Min. :13.00 Length:481
## Class :character 1st Qu.:21.00 Class :character
## Mode :character Median :22.00 Mode :character
## Mean :26.14
## 3rd Qu.:26.00
## Max. :91.00
## X3..Relationship.Status X4..Occupation.Status
## Length:481 Length:481
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## X5..What.type.of.organizations.are.you.affiliated.with.
## Length:481
## Class :character
## Mode :character
##
##
##
## X6..Do.you.use.social.media.
## Length:481
## Class :character
## Mode :character
##
##
##
## X7..What.social.media.platforms.do.you.commonly.use.
## Length:481
## Class :character
## Mode :character
##
##
##
## X8..What.is.the.average.time.you.spend.on.social.media.every.day.
## Length:481
## Class :character
## Mode :character
##
##
##
## X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose.
## Min. :1.000
## 1st Qu.:3.000
## Median :4.000
## Mean :3.553
## 3rd Qu.:4.000
## Max. :5.000
## X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something.
## Min. :1.00
## 1st Qu.:2.00
## Median :3.00
## Mean :3.32
## 3rd Qu.:4.00
## Max. :5.00
## X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while.
## Min. :1.000
## 1st Qu.:2.000
## Median :2.000
## Mean :2.588
## 3rd Qu.:3.000
## Max. :5.000
## X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you.
## Min. :1.000
## 1st Qu.:3.000
## Median :3.000
## Mean :3.349
## 3rd Qu.:4.000
## Max. :5.000
## X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries.
## Min. :1.000
## 1st Qu.:3.000
## Median :4.000
## Mean :3.559
## 3rd Qu.:5.000
## Max. :5.000
## X14..Do.you.find.it.difficult.to.concentrate.on.things.
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :3.245
## 3rd Qu.:4.000
## Max. :5.000
## X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :2.832
## 3rd Qu.:4.000
## Max. :5.000
## X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking.
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :2.775
## 3rd Qu.:3.000
## Max. :5.000
## X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media.
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.455
## 3rd Qu.:3.000
## Max. :5.000
## X18..How.often.do.you.feel.depressed.or.down.
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :3.256
## 3rd Qu.:4.000
## Max. :5.000
## X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate.
## Min. :1.00
## 1st Qu.:2.00
## Median :3.00
## Mean :3.17
## 3rd Qu.:4.00
## Max. :5.00
## X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep.
## Min. :1.000
## 1st Qu.:2.000
## Median :3.000
## Mean :3.202
## 3rd Qu.:5.000
## Max. :5.000
colnames(data)
## [1] "Timestamp"
## [2] "X1..What.is.your.age."
## [3] "X2..Gender"
## [4] "X3..Relationship.Status"
## [5] "X4..Occupation.Status"
## [6] "X5..What.type.of.organizations.are.you.affiliated.with."
## [7] "X6..Do.you.use.social.media."
## [8] "X7..What.social.media.platforms.do.you.commonly.use."
## [9] "X8..What.is.the.average.time.you.spend.on.social.media.every.day."
## [10] "X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose."
## [11] "X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something."
## [12] "X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while."
## [13] "X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you."
## [14] "X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries."
## [15] "X14..Do.you.find.it.difficult.to.concentrate.on.things."
## [16] "X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media."
## [17] "X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking."
## [18] "X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media."
## [19] "X18..How.often.do.you.feel.depressed.or.down."
## [20] "X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate."
## [21] "X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep."
Missing Value Checking
colSums(is.na(data))
## Timestamp
## 0
## X1..What.is.your.age.
## 0
## X2..Gender
## 0
## X3..Relationship.Status
## 0
## X4..Occupation.Status
## 0
## X5..What.type.of.organizations.are.you.affiliated.with.
## 0
## X6..Do.you.use.social.media.
## 0
## X7..What.social.media.platforms.do.you.commonly.use.
## 0
## X8..What.is.the.average.time.you.spend.on.social.media.every.day.
## 0
## X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose.
## 0
## X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something.
## 0
## X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while.
## 0
## X12..On.a.scale.of.1.to.5..how.easily.distracted.are.you.
## 0
## X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries.
## 0
## X14..Do.you.find.it.difficult.to.concentrate.on.things.
## 0
## X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.
## 0
## X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking.
## 0
## X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media.
## 0
## X18..How.often.do.you.feel.depressed.or.down.
## 0
## X19..On.a.scale.of.1.to.5..how.frequently.does.your.interest.in.daily.activities.fluctuate.
## 0
## X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep.
## 0
Variable Selection
data_sem <- data[, c(
"X9..How.often.do.you.find.yourself.using.Social.media.without.a.specific.purpose.",
"X10..How.often.do.you.get.distracted.by.Social.media.when.you.are.busy.doing.something.",
"X11..Do.you.feel.restless.if.you.haven.t.used.Social.media.in.a.while.",
"X13..On.a.scale.of.1.to.5..how.much.are.you.bothered.by.worries.",
"X14..Do.you.find.it.difficult.to.concentrate.on.things.",
"X15..On.a.scale.of.1.5..how.often.do.you.compare.yourself.to.other.successful.people.through.the.use.of.social.media.",
"X16..Following.the.previous.question..how.do.you.feel.about.these.comparisons..generally.speaking.",
"X17..How.often.do.you.look.to.seek.validation.from.features.of.social.media.",
"X18..How.often.do.you.feel.depressed.or.down.",
"X20..On.a.scale.of.1.to.5..how.often.do.you.face.issues.regarding.sleep."
)]
Rename Variables
colnames(data_sem) <- c(
"SM1",
"SM2",
"SM3",
"MH1",
"MH2",
"SC1",
"SC2",
"SM4",
"MH3",
"MH4"
)
head(data_sem)
## SM1 SM2 SM3 MH1 MH2 SC1 SC2 SM4 MH3 MH4
## 1 5 3 2 2 5 2 3 2 5 5
## 2 4 3 2 5 4 5 1 1 5 5
## 3 3 2 1 5 4 3 3 1 4 5
## 4 4 2 1 5 3 5 1 2 4 2
## 5 3 5 4 5 5 3 3 3 4 1
## 6 4 4 2 4 3 4 4 3 3 4
Descriptive Statistics
describe(data_sem)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## SM1 1 481 3.55 1.10 4 3.62 1.48 1 5 4 -0.42 -0.55 0.05
## SM2 2 481 3.32 1.33 3 3.40 1.48 1 5 4 -0.27 -1.10 0.06
## SM3 3 481 2.59 1.26 2 2.50 1.48 1 5 4 0.35 -0.89 0.06
## MH1 4 481 3.56 1.28 4 3.68 1.48 1 5 4 -0.50 -0.87 0.06
## MH2 5 481 3.25 1.35 3 3.31 1.48 1 5 4 -0.24 -1.13 0.06
## SC1 6 481 2.83 1.41 3 2.79 1.48 1 5 4 0.19 -1.25 0.06
## SC2 7 481 2.78 1.06 3 2.76 1.48 1 5 4 0.11 -0.26 0.05
## SM4 8 481 2.46 1.25 2 2.37 1.48 1 5 4 0.32 -1.03 0.06
## MH3 9 481 3.26 1.31 3 3.32 1.48 1 5 4 -0.23 -1.06 0.06
## MH4 10 481 3.20 1.46 3 3.25 1.48 1 5 4 -0.20 -1.38 0.07
Exploratory Data Analysis (EDA)
#Histogram
par(mfrow = c(2,5))
for(i in 1:ncol(data_sem)){
hist(
data_sem[,i],
main = colnames(data_sem)[i],
xlab = colnames(data_sem)[i]
)
}
#Boxplot
par(mfrow = c(2,5))
for(i in 1:ncol(data_sem)){
boxplot(
data_sem[,i],
main = colnames(data_sem)[i]
)
}
#Correlation Matrix
cor_matrix <- cor(data_sem)
round(cor_matrix, 2)
## SM1 SM2 SM3 MH1 MH2 SC1 SC2 SM4 MH3 MH4
## SM1 1.00 0.46 0.36 0.25 0.29 0.15 0.09 0.14 0.32 0.26
## SM2 0.46 1.00 0.51 0.34 0.53 0.29 0.08 0.27 0.38 0.26
## SM3 0.36 0.51 1.00 0.35 0.47 0.32 0.13 0.25 0.33 0.17
## MH1 0.25 0.34 0.35 1.00 0.55 0.41 0.02 0.21 0.59 0.33
## MH2 0.29 0.53 0.47 0.55 1.00 0.37 0.06 0.24 0.51 0.35
## SC1 0.15 0.29 0.32 0.41 0.37 1.00 -0.01 0.42 0.42 0.19
## SC2 0.09 0.08 0.13 0.02 0.06 -0.01 1.00 0.15 0.04 0.07
## SM4 0.14 0.27 0.25 0.21 0.24 0.42 0.15 1.00 0.27 0.13
## MH3 0.32 0.38 0.33 0.59 0.51 0.42 0.04 0.27 1.00 0.37
## MH4 0.26 0.26 0.17 0.33 0.35 0.19 0.07 0.13 0.37 1.00
corr.test(data_sem)
## Call:corr.test(x = data_sem)
## Correlation matrix
## SM1 SM2 SM3 MH1 MH2 SC1 SC2 SM4 MH3 MH4
## SM1 1.00 0.46 0.36 0.25 0.29 0.15 0.09 0.14 0.32 0.26
## SM2 0.46 1.00 0.51 0.34 0.53 0.29 0.08 0.27 0.38 0.26
## SM3 0.36 0.51 1.00 0.35 0.47 0.32 0.13 0.25 0.33 0.17
## MH1 0.25 0.34 0.35 1.00 0.55 0.41 0.02 0.21 0.59 0.33
## MH2 0.29 0.53 0.47 0.55 1.00 0.37 0.06 0.24 0.51 0.35
## SC1 0.15 0.29 0.32 0.41 0.37 1.00 -0.01 0.42 0.42 0.19
## SC2 0.09 0.08 0.13 0.02 0.06 -0.01 1.00 0.15 0.04 0.07
## SM4 0.14 0.27 0.25 0.21 0.24 0.42 0.15 1.00 0.27 0.13
## MH3 0.32 0.38 0.33 0.59 0.51 0.42 0.04 0.27 1.00 0.37
## MH4 0.26 0.26 0.17 0.33 0.35 0.19 0.07 0.13 0.37 1.00
## Sample Size
## [1] 481
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## SM1 SM2 SM3 MH1 MH2 SC1 SC2 SM4 MH3 MH4
## SM1 0.00 0.00 0 0.00 0.00 0.01 0.38 0.01 0 0.00
## SM2 0.00 0.00 0 0.00 0.00 0.00 0.52 0.00 0 0.00
## SM3 0.00 0.00 0 0.00 0.00 0.00 0.03 0.00 0 0.00
## MH1 0.00 0.00 0 0.00 0.00 0.00 1.00 0.00 0 0.00
## MH2 0.00 0.00 0 0.00 0.00 0.00 0.73 0.00 0 0.00
## SC1 0.00 0.00 0 0.00 0.00 0.00 1.00 0.00 0 0.00
## SC2 0.05 0.09 0 0.65 0.18 0.80 0.00 0.01 1 0.67
## SM4 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0 0.04
## MH3 0.00 0.00 0 0.00 0.00 0.00 0.44 0.00 0 0.00
## MH4 0.00 0.00 0 0.00 0.00 0.00 0.13 0.00 0 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.5.3
## corrplot 0.95 loaded
corrplot(
cor_matrix,
method = "color",
type = "upper",
addCoef.col = "black",
tl.cex = 0.8,
number.cex = 0.7
)
Assumption Testing
Multivariate Normality Test
mvn(data_sem, mvn_test = "mardia")
## $multivariate_normality
## Test Statistic p.value Method MVN
## 1 Mardia Skewness 358.474 <0.001 asymptotic ✗ Not normal
## 2 Mardia Kurtosis 1.708 0.088 asymptotic ✓ Normal
##
## $univariate_normality
## Test Variable Statistic p.value Normality
## 1 Anderson-Darling SM1 18.954 <0.001 ✗ Not normal
## 2 Anderson-Darling SM2 17.287 <0.001 ✗ Not normal
## 3 Anderson-Darling SM3 17.024 <0.001 ✗ Not normal
## 4 Anderson-Darling MH1 21.079 <0.001 ✗ Not normal
## 5 Anderson-Darling MH2 16.632 <0.001 ✗ Not normal
## 6 Anderson-Darling SC1 17.677 <0.001 ✗ Not normal
## 7 Anderson-Darling SC2 22.118 <0.001 ✗ Not normal
## 8 Anderson-Darling SM4 21.092 <0.001 ✗ Not normal
## 9 Anderson-Darling MH3 15.976 <0.001 ✗ Not normal
## 10 Anderson-Darling MH4 21.231 <0.001 ✗ Not normal
##
## $descriptives
## Variable n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## 1 SM1 481 3.553 1.096 4 1 5 3 4 -0.425 2.460
## 2 SM2 481 3.320 1.328 3 1 5 2 4 -0.275 1.907
## 3 SM3 481 2.588 1.257 2 1 5 2 3 0.351 2.121
## 4 MH1 481 3.559 1.283 4 1 5 3 5 -0.505 2.135
## 5 MH2 481 3.245 1.347 3 1 5 2 4 -0.237 1.882
## 6 SC1 481 2.832 1.408 3 1 5 2 4 0.188 1.760
## 7 SC2 481 2.775 1.056 3 1 5 2 3 0.107 2.746
## 8 SM4 481 2.455 1.248 2 1 5 1 3 0.321 1.975
## 9 MH3 481 3.256 1.313 3 1 5 2 4 -0.230 1.944
## 10 MH4 481 3.202 1.462 3 1 5 2 5 -0.196 1.630
##
## $data
## SM1 SM2 SM3 MH1 MH2 SC1 SC2 SM4 MH3 MH4
## 1 5 3 2 2 5 2 3 2 5 5
## 2 4 3 2 5 4 5 1 1 5 5
## 3 3 2 1 5 4 3 3 1 4 5
## 4 4 2 1 5 3 5 1 2 4 2
## 5 3 5 4 5 5 3 3 3 4 1
## 6 4 4 2 4 3 4 4 3 3 4
## 7 4 3 2 4 3 5 3 4 5 3
## 8 5 2 3 3 1 1 3 1 5 1
## 9 5 2 3 1 1 1 3 1 5 1
## 10 1 1 1 1 1 1 1 1 1 1
## 11 5 5 2 3 5 4 2 3 5 5
## 12 4 4 2 3 4 2 2 3 3 2
## 13 3 4 2 4 2 4 2 1 3 5
## 14 5 3 2 4 4 2 3 2 3 3
## 15 5 5 5 5 5 5 5 5 5 5
## 16 3 4 2 4 4 3 2 2 3 2
## 17 2 2 1 4 2 1 3 1 4 3
## 18 5 5 4 3 3 1 4 2 2 2
## 19 3 4 2 5 5 2 3 1 3 1
## 20 2 4 3 4 1 1 5 3 2 2
## 21 4 2 2 3 2 2 2 1 2 2
## 22 3 2 4 4 2 5 2 1 3 1
## 23 3 4 3 4 4 4 3 3 4 4
## 24 3 2 2 4 3 3 2 3 4 1
## 25 3 3 5 5 5 4 1 3 3 5
## 26 4 4 3 3 3 4 4 4 4 2
## 27 4 4 3 3 4 2 3 3 4 4
## 28 2 4 4 4 4 4 4 4 4 5
## 29 3 4 3 5 4 4 3 2 4 1
## 30 4 4 5 5 4 4 4 3 5 1
## 31 4 4 3 5 4 5 2 3 5 3
## 32 3 4 3 3 2 2 2 2 3 2
## 33 3 3 3 3 4 2 3 3 2 3
## 34 4 3 3 4 3 3 2 4 3 2
## 35 5 5 5 5 5 2 3 1 5 4
## 36 3 4 5 4 5 1 3 1 4 1
## 37 5 3 4 2 3 5 3 4 3 1
## 38 2 3 1 1 3 1 3 1 5 5
## 39 4 5 5 4 4 2 3 3 2 5
## 40 4 4 2 3 5 1 3 1 1 4
## 41 1 1 1 1 1 2 1 2 3 1
## 42 3 5 2 3 3 3 4 2 4 2
## 43 1 1 1 3 1 1 3 1 2 1
## 44 3 2 4 5 4 5 2 1 5 2
## 45 5 1 1 3 3 1 3 3 3 3
## 46 4 5 4 5 2 3 3 3 3 3
## 47 5 3 2 2 1 3 4 3 1 2
## 48 4 3 1 5 3 4 2 1 3 1
## 49 3 3 3 2 3 2 3 1 2 1
## 50 3 3 1 2 1 2 2 1 1 1
## 51 4 4 3 3 2 1 3 3 3 4
## 52 5 5 5 4 5 2 2 1 3 1
## 53 3 5 3 5 5 5 5 5 5 5
## 54 3 2 1 2 1 1 3 1 1 1
## 55 4 1 1 2 1 1 3 1 1 2
## 56 2 5 5 4 5 3 3 4 5 3
## 57 3 2 1 4 2 1 3 1 3 2
## 58 3 2 2 4 3 3 3 2 4 5
## 59 4 5 2 4 4 3 3 4 5 2
## 60 1 1 1 2 2 1 3 1 1 5
## 61 4 3 3 4 4 2 2 2 4 4
## 62 2 2 1 5 4 5 2 4 4 4
## 63 4 4 3 4 3 1 3 1 5 5
## 64 3 1 1 2 4 1 3 1 5 5
## 65 2 3 1 3 5 1 3 1 1 4
## 66 3 2 4 2 2 1 3 2 2 4
## 67 5 5 5 5 5 5 2 4 4 1
## 68 2 1 2 5 3 2 3 2 4 4
## 69 4 2 2 5 3 3 4 2 4 4
## 70 4 4 3 3 2 3 2 2 3 4
## 71 2 4 3 2 2 1 1 1 1 2
## 72 5 5 5 5 5 5 5 5 5 5
## 73 4 4 2 4 2 4 2 4 4 5
## 74 5 2 1 2 1 1 3 1 2 2
## 75 4 4 3 4 5 3 2 4 5 4
## 76 2 1 1 2 1 1 1 1 2 4
## 77 4 5 3 3 2 2 3 4 5 2
## 78 4 3 4 5 4 5 1 2 4 2
## 79 4 3 3 5 5 4 3 1 5 5
## 80 2 5 4 5 5 5 2 1 5 5
## 81 4 4 4 5 5 3 3 1 4 5
## 82 4 4 2 4 4 3 3 2 1 4
## 83 5 5 3 4 5 2 3 2 4 2
## 84 3 5 5 3 5 4 4 3 4 5
## 85 4 4 3 5 4 3 2 4 2 2
## 86 4 3 2 5 2 3 2 2 4 1
## 87 3 4 3 1 3 1 2 2 2 4
## 88 4 3 2 2 5 2 4 1 1 1
## 89 5 4 2 1 3 1 3 5 3 2
## 90 4 5 4 5 5 4 2 4 5 4
## 91 3 5 2 4 4 2 3 2 4 3
## 92 4 5 4 5 5 2 3 2 4 2
## 93 4 3 3 5 4 1 3 2 4 1
## 94 4 4 3 2 4 3 2 2 3 5
## 95 4 5 3 4 4 3 3 3 4 4
## 96 5 5 1 2 5 2 1 1 3 5
## 97 5 5 2 2 3 1 3 1 1 5
## 98 4 2 1 1 1 2 2 1 4 2
## 99 4 4 4 4 5 5 3 4 5 3
## 100 4 5 2 3 4 3 1 4 2 3
## 101 3 4 3 4 3 2 2 3 3 3
## 102 5 4 3 4 2 1 1 1 5 5
## 103 3 4 3 4 3 2 3 3 4 3
## 104 4 3 4 5 5 3 3 4 3 5
## 105 2 3 4 3 3 3 4 4 1 3
## 106 2 3 4 3 3 3 4 3 2 5
## 107 3 2 2 2 2 1 3 3 3 5
## 108 1 1 1 3 2 1 3 2 1 1
## 109 3 2 1 2 3 2 1 3 2 5
## 110 4 3 3 2 2 2 3 3 3 3
## 111 3 3 4 4 4 4 3 3 2 3
## 112 4 5 4 5 3 5 4 3 4 3
## 113 4 3 4 3 3 2 3 1 2 4
## 114 2 4 3 3 3 3 3 2 2 4
## 115 2 5 4 3 2 5 1 4 4 1
## 116 5 5 4 3 1 3 3 3 2 4
## 117 5 5 5 5 5 3 1 1 3 5
## 118 5 4 5 5 4 3 3 4 3 4
## 119 5 5 5 5 4 5 5 4 5 5
## 120 4 5 3 3 5 2 3 3 4 5
## 121 2 4 1 2 1 1 3 2 1 1
## 122 4 3 1 4 2 4 2 1 3 3
## 123 4 5 5 2 5 2 3 1 5 3
## 124 4 5 4 5 5 3 1 1 4 2
## 125 5 5 3 2 3 4 5 4 1 1
## 126 4 4 3 4 3 2 2 2 4 3
## 127 1 1 3 4 4 3 3 2 4 4
## 128 3 2 4 5 3 5 1 4 4 5
## 129 5 2 1 1 1 5 1 1 4 3
## 130 3 1 2 3 2 2 4 1 3 2
## 131 2 1 1 3 2 1 4 3 1 4
## 132 4 2 2 2 1 4 2 3 3 2
## 133 3 3 2 4 3 2 4 2 3 4
## 134 3 2 3 2 1 3 1 1 2 1
## 135 2 1 1 2 2 1 4 1 2 1
## 136 5 5 3 4 3 3 3 3 4 5
## 137 3 3 3 3 2 1 3 1 1 4
## 138 3 3 1 1 1 1 1 1 1 1
## 139 4 3 2 3 4 3 3 3 3 3
## 140 4 3 4 5 4 3 4 3 4 2
## 141 3 2 2 2 2 3 4 1 1 1
## 142 4 5 2 5 5 5 2 3 3 4
## 143 4 2 1 4 3 3 3 3 4 1
## 144 3 1 1 2 1 1 3 5 2 2
## 145 4 3 4 3 2 5 2 3 5 5
## 146 4 4 3 2 2 4 2 3 3 3
## 147 4 4 3 4 4 5 1 4 4 4
## 148 3 4 3 5 4 2 2 1 2 2
## 149 3 3 3 2 2 3 3 3 2 2
## 150 4 3 2 2 2 2 4 2 2 4
## 151 3 2 1 2 2 2 4 3 2 2
## 152 5 2 4 2 1 1 5 1 1 5
## 153 3 1 2 2 2 3 3 2 2 3
## 154 1 1 2 2 2 1 1 3 3 2
## 155 5 1 1 4 2 1 3 3 4 5
## 156 2 2 1 4 5 3 3 2 3 4
## 157 1 1 2 3 4 2 3 1 2 4
## 158 3 2 1 4 4 1 1 1 1 4
## 159 3 1 2 1 3 1 2 2 4 4
## 160 5 4 4 5 4 2 5 4 4 5
## 161 4 2 1 2 1 4 2 2 2 5
## 162 3 1 1 1 1 5 3 2 1 2
## 163 4 5 5 4 5 3 3 2 3 4
## 164 3 2 3 4 3 2 3 2 3 4
## 165 3 2 3 5 5 2 5 2 3 4
## 166 2 2 2 2 2 4 3 2 2 2
## 167 5 1 1 2 1 1 1 1 2 4
## 168 2 4 2 2 1 3 5 3 2 4
## 169 2 2 1 1 1 2 3 1 1 2
## 170 2 3 2 3 3 2 3 1 2 1
## 171 5 5 5 2 4 3 3 1 3 4
## 172 4 5 4 5 5 5 2 2 3 4
## 173 5 5 2 4 5 3 2 4 5 5
## 174 4 4 4 4 2 4 3 4 3 1
## 175 4 3 5 4 4 5 4 3 4 5
## 176 4 3 3 4 3 1 2 1 1 1
## 177 3 2 1 1 1 1 3 4 1 1
## 178 5 3 3 3 5 1 3 3 4 5
## 179 3 5 4 5 4 3 2 1 3 5
## 180 3 2 5 4 1 3 2 4 4 1
## 181 5 4 4 5 2 5 2 2 4 5
## 182 4 2 2 2 2 2 3 3 3 4
## 183 3 1 1 4 2 3 3 2 1 3
## 184 3 4 5 5 4 2 4 4 5 2
## 185 5 5 4 4 3 2 3 3 3 4
## 186 3 2 3 3 3 3 2 2 2 2
## 187 4 3 2 3 2 2 3 3 3 1
## 188 4 5 2 3 1 3 3 1 3 1
## 189 5 3 4 4 5 3 3 3 4 4
## 190 5 5 5 1 5 2 5 5 2 5
## 191 3 2 2 2 2 1 3 3 4 1
## 192 3 4 3 3 3 3 5 3 4 1
## 193 3 3 2 2 4 3 2 1 4 4
## 194 5 3 3 5 3 2 3 4 5 4
## 195 4 5 4 5 4 1 3 4 4 5
## 196 3 1 1 1 1 1 3 1 1 1
## 197 5 3 2 1 3 2 4 1 3 2
## 198 3 3 3 3 2 3 4 3 2 2
## 199 2 2 2 2 1 1 2 1 1 2
## 200 3 1 3 2 1 1 3 1 1 1
## 201 3 3 2 4 5 4 2 2 2 2
## 202 4 2 2 4 3 2 4 2 3 5
## 203 5 5 3 3 3 1 3 1 3 4
## 204 4 5 3 5 5 5 2 4 3 3
## 205 5 5 5 5 5 1 3 1 5 5
## 206 3 2 3 3 2 3 2 3 2 4
## 207 4 4 4 4 3 2 2 1 1 2
## 208 5 5 5 1 5 5 4 1 5 4
## 209 3 2 1 1 1 1 1 1 1 1
## 210 2 1 1 5 3 5 4 3 4 4
## 211 3 3 3 4 3 4 4 4 3 2
## 212 5 1 1 1 1 1 3 1 1 1
## 213 5 3 2 3 4 4 4 5 5 5
## 214 1 1 1 1 1 1 1 1 1 1
## 215 3 3 1 5 3 2 3 5 4 1
## 216 4 2 1 3 1 1 3 1 3 1
## 217 1 1 1 1 1 5 5 3 1 1
## 218 4 4 1 4 4 4 4 3 2 2
## 219 3 3 2 4 4 4 4 3 5 4
## 220 3 2 3 3 5 4 5 5 2 3
## 221 4 4 4 5 5 4 2 3 3 4
## 222 4 4 1 2 2 1 2 1 2 1
## 223 4 3 1 4 3 5 2 3 3 3
## 224 3 3 3 3 2 2 3 3 3 3
## 225 3 1 4 3 4 3 4 3 4 3
## 226 2 5 3 3 4 5 3 3 2 3
## 227 5 4 5 5 4 5 1 5 4 3
## 228 5 5 3 3 5 1 3 2 2 5
## 229 2 1 1 1 1 1 2 1 1 4
## 230 4 5 3 4 4 3 3 4 4 5
## 231 2 3 1 4 3 4 4 3 4 1
## 232 3 1 1 2 2 5 1 2 1 1
## 233 5 5 1 2 3 4 5 3 5 5
## 234 2 1 1 2 1 1 1 1 1 1
## 235 4 4 3 4 4 4 2 1 4 3
## 236 5 4 4 4 3 4 4 4 4 1
## 237 2 1 1 3 1 3 3 1 2 1
## 238 5 5 3 5 2 3 1 2 5 5
## 239 5 5 5 5 5 5 5 5 5 5
## 240 3 4 2 5 1 5 1 5 4 5
## 241 4 2 1 4 3 5 1 4 4 2
## 242 3 4 2 5 5 5 4 4 2 4
## 243 3 2 1 4 3 3 3 2 3 5
## 244 5 3 2 5 5 2 3 3 3 5
## 245 3 5 2 2 2 1 4 2 3 3
## 246 5 5 3 4 4 4 2 3 4 2
## 247 4 3 1 4 3 4 2 2 4 1
## 248 4 2 1 4 1 1 5 1 2 3
## 249 2 5 1 1 5 2 3 5 1 1
## 250 5 5 3 5 3 3 4 3 4 4
## 251 2 2 1 1 1 2 1 1 1 1
## 252 5 5 3 5 5 3 3 3 5 4
## 253 4 5 2 1 5 2 3 4 5 5
## 254 5 4 2 3 2 1 3 2 2 3
## 255 4 2 1 5 2 1 4 2 2 2
## 256 4 5 3 5 5 5 1 3 3 5
## 257 2 5 4 5 5 5 1 5 5 5
## 258 4 1 2 1 1 1 2 1 1 3
## 259 3 3 2 3 4 3 3 4 4 2
## 260 2 3 4 3 4 2 4 2 3 4
## 261 4 1 1 2 1 1 3 2 2 3
## 262 3 3 2 5 4 3 3 3 5 4
## 263 2 2 2 2 3 4 3 3 3 2
## 264 4 4 3 2 4 4 3 4 3 3
## 265 5 2 5 4 4 4 3 2 2 2
## 266 3 3 3 4 3 4 3 3 3 2
## 267 4 4 1 3 2 2 4 1 2 4
## 268 3 3 2 5 4 2 3 2 4 4
## 269 4 5 3 5 5 2 3 1 3 5
## 270 3 2 1 3 1 1 3 1 3 2
## 271 3 2 1 2 3 1 3 1 2 5
## 272 4 5 5 5 5 5 1 4 5 5
## 273 5 4 3 4 3 2 3 4 3 4
## 274 2 2 1 5 5 1 3 1 5 5
## 275 4 4 4 5 4 4 2 3 3 3
## 276 5 4 2 5 4 4 3 3 5 5
## 277 5 5 3 5 5 5 5 5 5 5
## 278 5 5 5 5 5 5 5 1 5 5
## 279 4 4 2 5 3 1 3 4 5 2
## 280 3 4 3 3 3 5 3 4 5 4
## 281 4 3 3 5 4 5 4 4 3 5
## 282 2 4 2 5 4 5 4 4 4 3
## 283 1 1 1 1 1 2 2 1 2 2
## 284 4 5 3 5 3 4 2 1 5 5
## 285 4 4 3 4 4 3 3 3 4 4
## 286 4 4 2 4 5 3 2 2 3 4
## 287 4 3 2 2 3 2 1 3 2 2
## 288 3 4 4 5 4 5 2 4 5 4
## 289 3 3 2 4 2 2 3 4 4 2
## 290 2 1 1 4 2 1 1 3 3 4
## 291 3 4 1 2 3 3 3 3 3 4
## 292 3 2 1 4 3 3 3 2 4 3
## 293 4 5 3 4 5 2 3 3 4 4
## 294 4 4 4 4 4 5 5 4 5 5
## 295 3 3 1 1 1 1 3 5 1 1
## 296 2 1 3 4 4 3 3 1 2 1
## 297 2 1 4 5 2 4 3 1 4 2
## 298 4 2 3 5 4 4 3 3 4 5
## 299 3 4 2 4 4 2 2 1 3 2
## 300 2 3 2 3 3 4 3 3 5 3
## 301 3 4 5 5 3 3 3 3 5 4
## 302 4 2 2 5 4 1 1 2 5 5
## 303 4 5 3 2 5 2 3 4 4 2
## 304 4 5 4 3 5 4 3 1 4 5
## 305 2 4 1 4 3 4 2 2 3 2
## 306 4 5 4 5 4 3 3 3 3 2
## 307 5 5 5 4 3 5 2 2 5 3
## 308 5 3 2 2 2 1 3 5 1 1
## 309 3 2 2 4 2 1 3 3 2 4
## 310 3 3 2 4 2 2 3 2 2 1
## 311 4 3 1 5 3 3 3 1 5 5
## 312 5 3 2 5 5 1 3 1 3 5
## 313 2 5 4 3 5 2 2 3 2 2
## 314 2 3 1 5 4 3 3 1 5 5
## 315 5 1 4 4 4 3 4 2 4 4
## 316 4 3 3 4 5 4 4 3 3 4
## 317 2 1 1 4 1 1 3 1 3 3
## 318 4 5 4 3 5 3 3 2 5 4
## 319 5 5 5 5 5 5 1 4 5 5
## 320 5 5 5 5 5 5 5 5 5 5
## 321 3 4 5 5 4 5 5 3 5 5
## 322 3 3 2 4 4 4 3 3 4 4
## 323 5 4 1 4 3 1 3 3 3 5
## 324 3 2 1 4 2 2 3 4 2 5
## 325 5 4 2 5 5 1 1 1 5 5
## 326 4 2 3 5 4 4 3 5 5 1
## 327 5 5 4 5 4 5 5 3 5 5
## 328 4 5 4 5 5 2 2 5 5 4
## 329 4 4 1 2 2 2 2 2 4 2
## 330 5 4 3 4 5 3 2 2 4 4
## 331 4 2 2 3 1 2 5 1 1 4
## 332 4 3 2 3 3 4 3 1 4 2
## 333 5 5 2 1 1 2 4 1 3 3
## 334 5 5 4 5 1 2 2 3 5 4
## 335 3 3 3 3 3 3 3 3 3 3
## 336 4 2 4 5 4 1 3 2 4 2
## 337 4 4 1 3 1 1 1 1 1 2
## 338 5 5 2 4 5 5 2 4 4 5
## 339 4 3 2 4 3 2 4 1 3 3
## 340 3 3 2 5 3 1 3 4 5 2
## 341 5 3 5 4 5 1 3 1 3 1
## 342 3 4 3 5 5 4 4 4 3 3
## 343 5 3 1 5 3 2 2 1 5 5
## 344 4 4 3 5 2 4 2 2 2 5
## 345 3 5 4 5 4 1 5 1 5 5
## 346 5 4 3 4 4 2 3 1 5 2
## 347 2 1 1 1 1 1 3 3 1 1
## 348 5 5 3 4 4 4 1 1 4 1
## 349 4 5 3 5 5 1 3 1 3 1
## 350 5 5 3 5 4 1 3 1 5 4
## 351 4 3 1 4 4 2 3 1 4 5
## 352 2 2 1 3 2 2 3 1 3 4
## 353 4 4 2 4 5 4 2 3 3 5
## 354 2 2 1 5 4 4 2 1 5 4
## 355 3 3 2 3 2 2 1 3 2 2
## 356 2 3 2 4 4 2 2 4 3 5
## 357 4 5 4 5 4 3 3 1 4 2
## 358 3 2 2 2 2 2 4 2 3 3
## 359 5 5 5 4 4 2 3 2 3 1
## 360 4 4 1 5 3 5 2 2 4 5
## 361 3 3 4 3 3 4 3 3 3 2
## 362 4 4 1 3 3 2 3 1 2 4
## 363 4 5 3 3 3 5 1 4 4 5
## 364 4 2 2 1 1 1 3 1 2 2
## 365 3 2 1 1 3 1 1 2 1 2
## 366 5 4 2 5 5 5 1 4 5 5
## 367 4 5 4 3 5 4 3 2 4 4
## 368 5 5 5 5 5 5 5 5 5 5
## 369 4 5 5 1 3 3 4 2 1 1
## 370 3 3 3 4 4 4 4 3 5 4
## 371 5 2 2 4 2 2 3 3 2 5
## 372 5 5 2 5 5 5 1 5 4 5
## 373 3 2 1 2 1 1 2 2 3 3
## 374 4 4 2 4 4 2 3 2 3 5
## 375 4 4 3 4 2 4 2 3 2 1
## 376 3 2 2 5 3 5 2 3 5 4
## 377 3 2 2 4 3 1 3 2 5 4
## 378 4 5 3 5 5 4 2 5 4 4
## 379 3 4 2 5 4 5 5 5 5 2
## 380 4 3 3 2 2 3 3 1 1 2
## 381 5 5 3 4 5 5 3 3 5 5
## 382 4 3 2 5 5 5 5 5 5 1
## 383 4 2 2 5 3 5 2 3 4 5
## 384 3 4 1 3 2 3 3 4 5 3
## 385 4 5 3 4 4 5 1 2 3 3
## 386 5 4 2 3 4 3 4 1 2 4
## 387 5 5 2 5 3 5 3 2 3 4
## 388 4 4 2 4 4 5 2 4 2 4
## 389 3 4 3 4 4 5 1 2 4 2
## 390 5 5 5 5 5 2 3 2 4 5
## 391 4 4 4 2 3 2 3 4 3 4
## 392 3 3 3 5 5 4 3 1 5 3
## 393 5 4 4 5 5 2 3 1 5 5
## 394 4 5 5 5 5 5 2 4 5 1
## 395 5 4 2 3 2 1 3 3 2 2
## 396 4 2 1 3 1 2 3 4 2 1
## 397 5 4 4 4 4 2 4 3 5 3
## 398 4 4 3 4 5 4 3 4 5 5
## 399 2 1 4 2 2 3 3 2 1 4
## 400 3 4 1 4 5 3 3 4 5 5
## 401 4 2 3 4 3 2 2 2 4 3
## 402 2 2 3 3 2 3 2 2 3 4
## 403 5 4 3 4 4 3 3 2 3 5
## 404 1 1 1 1 1 1 1 1 1 1
## 405 1 1 1 1 1 1 1 1 1 1
## 406 4 5 2 5 3 5 5 1 5 5
## 407 5 5 5 5 5 5 1 1 5 5
## 408 3 5 2 3 5 4 2 4 4 3
## 409 4 5 5 5 5 2 2 1 4 1
## 410 5 2 4 5 1 3 2 4 4 5
## 411 4 4 3 5 5 5 1 1 3 1
## 412 3 4 4 3 3 2 3 1 3 3
## 413 5 3 3 5 5 5 3 1 5 5
## 414 2 4 3 5 5 3 2 1 5 5
## 415 4 4 2 3 4 3 3 2 4 4
## 416 4 5 4 5 4 4 3 3 5 5
## 417 2 1 1 3 2 1 3 1 2 2
## 418 5 3 4 4 4 1 3 1 3 5
## 419 3 4 2 5 4 2 3 2 4 3
## 420 4 5 3 2 5 1 2 3 3 1
## 421 4 4 3 5 4 4 3 2 3 5
## 422 2 2 3 3 3 3 4 3 2 4
## 423 4 3 3 5 4 4 1 4 5 4
## 424 4 5 3 5 4 5 2 4 4 5
## 425 4 3 1 4 2 2 3 3 1 3
## 426 4 1 3 5 4 5 5 1 4 1
## 427 4 3 5 3 5 4 2 3 3 3
## 428 5 5 5 5 4 3 3 4 4 3
## 429 1 5 1 3 3 2 4 3 3 2
## 430 4 5 4 5 5 5 1 3 5 4
## 431 4 3 2 4 4 3 3 3 5 5
## 432 5 5 2 4 3 4 4 4 5 5
## 433 3 5 3 5 5 5 5 5 5 5
## 434 4 3 2 5 4 5 1 3 5 5
## 435 3 1 2 4 2 2 1 1 4 5
## 436 3 2 2 5 3 5 2 5 3 5
## 437 1 5 2 5 2 1 2 2 3 4
## 438 4 3 3 3 3 4 2 4 2 2
## 439 5 4 2 2 3 2 3 1 2 5
## 440 4 3 2 4 3 4 4 4 4 4
## 441 4 4 5 2 4 3 3 4 2 4
## 442 3 4 4 3 3 2 5 3 1 2
## 443 4 2 1 3 2 3 3 1 3 2
## 444 4 5 3 4 4 3 3 4 4 3
## 445 5 3 2 4 2 2 4 3 3 3
## 446 2 1 1 5 3 3 4 1 3 4
## 447 3 5 3 5 5 5 3 3 5 5
## 448 5 3 4 5 5 5 1 4 5 3
## 449 4 4 4 4 5 4 1 4 4 5
## 450 3 5 2 3 3 1 3 1 2 5
## 451 2 2 1 4 4 2 1 1 2 2
## 452 4 3 1 5 4 1 3 1 4 5
## 453 1 1 1 3 5 1 1 1 3 3
## 454 1 2 1 1 1 3 3 3 1 1
## 455 3 4 3 4 3 3 3 3 3 2
## 456 2 1 1 1 1 3 4 1 1 1
## 457 1 1 1 1 1 1 1 1 1 2
## 458 3 2 3 2 3 4 3 3 2 4
## 459 3 3 3 3 2 2 3 3 4 2
## 460 3 5 3 3 2 3 3 1 2 3
## 461 1 1 1 1 1 1 1 1 1 1
## 462 2 2 1 5 5 1 4 1 3 2
## 463 5 4 1 4 5 2 2 2 5 2
## 464 3 4 1 3 1 1 3 1 3 4
## 465 3 4 2 3 2 1 3 1 2 1
## 466 4 4 3 2 4 2 3 2 2 2
## 467 2 4 2 4 4 1 3 1 3 3
## 468 3 3 1 2 1 1 2 1 1 4
## 469 2 3 1 5 3 5 1 2 5 1
## 470 1 1 1 3 3 3 3 3 1 1
## 471 2 2 1 5 3 5 1 2 5 5
## 472 5 4 2 5 3 1 3 2 4 4
## 473 3 2 2 3 2 1 2 2 2 4
## 474 4 4 3 4 4 4 5 3 4 4
## 475 2 4 4 4 5 4 2 2 4 1
## 476 5 5 3 5 5 5 1 4 5 4
## 477 3 4 3 3 4 4 2 4 3 4
## 478 2 1 2 4 4 4 2 4 4 1
## 479 3 3 4 2 3 3 3 4 2 2
## 480 2 3 2 3 2 2 3 4 4 4
## 481 2 3 1 1 1 3 3 2 2 3
##
## $subset
## NULL
##
## $outlierMethod
## [1] "none"
##
## attr(,"class")
## [1] "mvn"
Multivariate Outlier Detection
center <- colMeans(data_sem)
cov_matrix <- cov(data_sem)
md <- mahalanobis(
data_sem,
center,
cov_matrix
)
cutoff <- qchisq(
0.999,
df = ncol(data_sem)
)
outlier <- which(md > cutoff)
length(outlier)
## [1] 2
Uji Multikolinearitas
model_vif <- lm(
MH3 ~ SM1 + SM2 + SM3 + SM4,
data = data_sem
)
vif(model_vif)
## SM1 SM2 SM3 SM4
## 1.306564 1.586931 1.416350 1.097212
Confirmatory Factor Analysis (CFA)
CFA Model
model_cfa <- '
Social_Media_Addiction =~ SM1 + SM2 + SM3
Mental_Health =~ MH1 + MH2 + MH3 + MH4
'
CFA Model Estimation
fit_cfa <- cfa(
model_cfa,
data = data_sem
)
summary(
fit_cfa,
standardized = TRUE,
fit.measures = TRUE
)
## lavaan 0.6-21 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Number of observations 481
##
## Model Test User Model:
##
## Test statistic 62.282
## Degrees of freedom 13
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1019.135
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951
## Tucker-Lewis Index (TLI) 0.920
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5163.270
## Loglikelihood unrestricted model (H1) -5132.129
##
## Akaike (AIC) 10356.540
## Bayesian (BIC) 10419.178
## Sample-size adjusted Bayesian (SABIC) 10371.569
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.089
## 90 Percent confidence interval - lower 0.067
## 90 Percent confidence interval - upper 0.111
## P-value H_0: RMSEA <= 0.050 0.002
## P-value H_0: RMSEA >= 0.080 0.763
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## Social_Media_Addiction =~
## SM1 1.000 0.621
## SM2 1.680 0.159 10.551 0.000 1.043
## SM3 1.337 0.134 9.995 0.000 0.830
## Mental_Health =~
## MH1 1.000 0.933
## MH2 1.100 0.077 14.223 0.000 1.027
## MH3 1.021 0.074 13.758 0.000 0.953
## MH4 0.737 0.080 9.242 0.000 0.688
## Std.all
##
## 0.567
## 0.786
## 0.661
##
## 0.728
## 0.763
## 0.727
## 0.471
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## Social_Media_Addiction ~~
## Mental_Health 0.425 0.054 7.924 0.000 0.733
## Std.all
##
## 0.733
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SM1 0.814 0.061 13.376 0.000 0.814 0.679
## .SM2 0.672 0.083 8.136 0.000 0.672 0.382
## .SM3 0.888 0.075 11.876 0.000 0.888 0.563
## .MH1 0.772 0.067 11.523 0.000 0.772 0.470
## .MH2 0.757 0.072 10.579 0.000 0.757 0.418
## .MH3 0.812 0.070 11.556 0.000 0.812 0.472
## .MH4 1.658 0.114 14.532 0.000 1.658 0.778
## Socl_Md_Addctn 0.385 0.065 5.964 0.000 1.000 1.000
## Mental_Health 0.871 0.104 8.396 0.000 1.000 1.000
Goodness of Fit Evaluation
fitMeasures(
fit_cfa,
c(
"chisq",
"df",
"pvalue",
"cfi",
"tli",
"rmsea",
"srmr"
)
)
## chisq df pvalue cfi tli rmsea srmr
## 62.282 13.000 0.000 0.951 0.920 0.089 0.040
Structural Equation Modeling (SEM)
SEM Model Specification
model_sem <- '
# Measurement Model
Social_Media_Addiction =~ SM1 + SM2 + SM3
Mental_Health =~ MH1 + MH2 + MH3 + MH4
# Structural Model
Mental_Health ~ Social_Media_Addiction
'
SEM Model Estimation
fit_sem <- sem(
model_sem,
data = data_sem,
estimator = "MLR"
)
summary(
fit_sem,
standardized = TRUE,
fit.measures = TRUE,
rsquare = TRUE
)
## lavaan 0.6-21 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Number of observations 481
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 62.282 58.404
## Degrees of freedom 13 13
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.066
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1019.135 865.454
## Degrees of freedom 21 21
## P-value 0.000 0.000
## Scaling correction factor 1.178
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951 0.946
## Tucker-Lewis Index (TLI) 0.920 0.913
##
## Robust Comparative Fit Index (CFI) 0.951
## Robust Tucker-Lewis Index (TLI) 0.921
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5163.270 -5163.270
## Scaling correction factor 0.960
## for the MLR correction
## Loglikelihood unrestricted model (H1) -5132.129 -5132.129
## Scaling correction factor 1.010
## for the MLR correction
##
## Akaike (AIC) 10356.540 10356.540
## Bayesian (BIC) 10419.178 10419.178
## Sample-size adjusted Bayesian (SABIC) 10371.569 10371.569
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.089 0.085
## 90 Percent confidence interval - lower 0.067 0.064
## 90 Percent confidence interval - upper 0.111 0.107
## P-value H_0: RMSEA <= 0.050 0.002 0.003
## P-value H_0: RMSEA >= 0.080 0.763 0.678
##
## Robust RMSEA 0.088
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.112
## P-value H_0: Robust RMSEA <= 0.050 0.003
## P-value H_0: Robust RMSEA >= 0.080 0.739
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040 0.040
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## Social_Media_Addiction =~
## SM1 1.000 0.621
## SM2 1.680 0.160 10.503 0.000 1.043
## SM3 1.337 0.146 9.184 0.000 0.830
## Mental_Health =~
## MH1 1.000 0.933
## MH2 1.100 0.088 12.441 0.000 1.027
## MH3 1.021 0.062 16.439 0.000 0.953
## MH4 0.737 0.076 9.653 0.000 0.688
## Std.all
##
## 0.567
## 0.786
## 0.661
##
## 0.728
## 0.763
## 0.727
## 0.471
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mental_Health ~
## Socl_Md_Addctn 1.103 0.130 8.490 0.000 0.733 0.733
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .SM1 0.814 0.063 12.999 0.000 0.814 0.679
## .SM2 0.672 0.095 7.089 0.000 0.672 0.382
## .SM3 0.888 0.071 12.425 0.000 0.888 0.563
## .MH1 0.772 0.091 8.463 0.000 0.772 0.470
## .MH2 0.757 0.099 7.634 0.000 0.757 0.418
## .MH3 0.812 0.081 10.004 0.000 0.812 0.472
## .MH4 1.658 0.099 16.736 0.000 1.658 0.778
## Socl_Md_Addctn 0.385 0.070 5.466 0.000 1.000 1.000
## .Mental_Health 0.403 0.078 5.143 0.000 0.462 0.462
##
## R-Square:
## Estimate
## SM1 0.321
## SM2 0.618
## SM3 0.437
## MH1 0.530
## MH2 0.582
## MH3 0.528
## MH4 0.222
## Mental_Health 0.538
SEM Visualization
semPaths(
fit_sem,
what = "std",
layout = "tree",
edge.label.cex = 1,
curvePivot = TRUE
)
