Depression and Age Analysis

library(foreign)
library(ltm)

## Lade nötiges Paket: MASS

## Lade nötiges Paket: msm

## Lade nötiges Paket: polycor

Load data

df = read.spss(“ESS11_unlabeled.0-10.sav”, to.data.frame = TRUE)

Prepare CES-D8 items

df\(d1 = as.numeric(df\)fltdpr) df\(d2 = as.numeric(df\)flteeff) df\(d3 = as.numeric(df\)fltlnl) df\(d4 = 5 - as.numeric(df\)enjlf) # Reversed item df\(d5 = as.numeric(df\)fltsd) df\(d6 = 5 - as.numeric(df\)wrhpp) # Reversed item df\(d7 = as.numeric(df\)slprl) df\(d8 = as.numeric(df\)cldgng)

Compute Mean Score

df$cesd8 = rowMeans(df[, paste0(“d”, 1:8)], na.rm = TRUE)

#Summary statistics and reliability

summary(df\(cesd8) hist(df\)cesd8, main=“Histogram of CES-D8 Scores”, xlab=“CES-D8”) cronbach.alpha(df[, paste0(“d”, 1:8)], na.rm = TRUE) table(df\(gndr) df\)gndr = factor(df$gndr, labels = c(“Male”, “Female”))

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

summary(df\(cesd8) # hist(df\)cesd8)-didn´t run bc appears error that margins are too large!! cronbach.alpha(df[, paste0(“d”, 1:8)], na.rm = TRUE) table(df\(gndr) df\)gndr = factor(df\(gndr, labels = c("Male", "Female")) lm(formula = cesd8 ~ gndr, data = df) # regression model model_gender = lm(cesd8 ~ gndr, data = df) summary(model_gender) #The regression coefficient for Female indicates the average difference in CES-D8 scores between women and men. A positive coefficient means women report higher depression scores on average than men. # Clean Age Variable (Ensure it is numeric for regression) df\)agea = as.numeric(as.character(df$agea)) #Depression varies with age due to changing biological, psychological, and social conditions. We expect depression to increase with age.

Bivariate Regression

#age model_age = lm(cesd8 ~ agea, data = df) summary(model_age)

#gender df\(gndr = factor(df\)gndr, labels = c(“Male”, “Female”)) model_gender = lm(cesd8 ~ gndr, data = df) summary(model_gender)

#multivariate #employement df\(employed = as.numeric(df\)emplrel == 1)

model_multi = lm(cesd8 ~ agea + gndr, data = df) summary(model_multi)

Female Dummy (assuming gndr: 1 = Male, 2 = Female)

Check your specific data labels, but usually:

df\(gndr = factor(df\)gndr, labels = c(“Male”, “Female”)) df\(female = as.numeric(df\)gndr == “Female”)

Multivariate Regression: Depression ~ Age + Gender

model_multi = lm(cesd8 ~ agea + female, data = df) summary(model_multi) lm(cesd8 ~ agea + gndr, data = df)

Dummy for being employed (1 = employed, 0 = not)

df\(employed = as.numeric(df\)emplrel == 1) model_multi2 = lm(cesd8 ~ agea + female + employed, data = df) summary(model_multi2)

##Intercept: The predicted depression score for a male (female=0) at age zero.Age Coefficient: For every one-year increase in age, the depression score changes by this amount, holding gender constant. #Female Coefficient: The average difference in depression scores between females and males, holding age constant. #The analysis shows a weak but positive association between age and depression. Regression results suggest that depressive symptoms slightly increase with age. Additionally, women report higher CES-D8 scores than men, even after controlling for age.