Pendahuluan

Analisis ini bertujuan untuk mengetahui pengaruh Work_Location dan Internet_Reliability terhadap Work_Life_Balance dan Sentiment_Score.

Import Data

data <- read.csv(file.choose())

Preprocessing

data$Work_Location <- as.factor(data$Work_Location)
data$Internet_Reliability <- as.factor(data$Internet_Reliability)

Analisis Manova

model_manova <- manova(
  cbind(Work_Life_Balance, Sentiment_Score) ~ 
  Work_Location + Internet_Reliability,
  data = data
)

summary(model_manova, test = "Wilks")
##                         Df   Wilks approx F num Df den Df    Pr(>F)    
## Work_Location            2 0.99550    33.89      4  59986 < 2.2e-16 ***
## Internet_Reliability     3 0.92563   393.88      6  59986 < 2.2e-16 ***
## Residuals            29994                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Analisis Mancova

model_mancova <- manova(
  cbind(Work_Life_Balance, Sentiment_Score) ~ 
  Work_Location + Internet_Reliability + Avg_Working_Hours,
  data = data
)

summary(model_mancova, test = "Wilks")
##                         Df   Wilks approx F num Df den Df    Pr(>F)    
## Work_Location            2 0.99515    36.47      4  59984 < 2.2e-16 ***
## Internet_Reliability     3 0.91959   427.91      6  59984 < 2.2e-16 ***
## Avg_Working_Hours        1 0.89786  1705.92      2  29992 < 2.2e-16 ***
## Residuals            29993                                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA

summary.aov(model_manova)
##  Response Work_Life_Balance :
##                         Df Sum Sq Mean Sq F value Pr(>F)    
## Work_Location            2      7   3.696  2.7744 0.0624 .  
## Internet_Reliability     3    305 101.743 76.3658 <2e-16 ***
## Residuals            29994  39961   1.332                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Sentiment_Score :
##                         Df  Sum Sq Mean Sq F value    Pr(>F)    
## Work_Location            2    15.5   7.746  19.408 3.773e-09 ***
## Internet_Reliability     3   378.9 126.295 316.441 < 2.2e-16 ***
## Residuals            29994 11970.9   0.399                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANCOVA

ancova_model <- aov(
  Work_Life_Balance ~ Work_Location + Internet_Reliability + Avg_Working_Hours,
  data = data
)

summary(ancova_model)
##                         Df Sum Sq Mean Sq  F value Pr(>F)    
## Work_Location            2      7     3.7    2.929 0.0535 .  
## Internet_Reliability     3    305   101.7   80.624 <2e-16 ***
## Avg_Working_Hours        1   2112  2111.7 1673.323 <2e-16 ***
## Residuals            29993  37850     1.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1