Script

Introduction

The purpose of this study is to investigate significant determinants of depression in the Austrian population. Specifically, it examines the impact of general life conception, perception of other people and perception of national welfare on depressive symptoms. Enhancing the understanding of depression is globally critical as it states a public health issue that impacts all facets of life, including relationships, employment, and quality of life. Its impact goes beyond the personal level and needs to be considered by policy makers across multiple sectors, which justifies for further addition to research. The desired outcome is to pinpoint important risk variables linked to depression and offer statistical evidence in support of the existing body of knowledge.

Literature

According to the WHO, depression is defined as a common mental health condition that can happen to anyone. It is characterized by a low mood or loss of pleasure or interest in activities for long periods of time. This section briefly discusses prior research on the determinants of depression to be examined in this study. Accordingly, after each section, the derived hypothesis is stated.

General Life Conception: Control over life and satisfaction of life

Mental health outcomes are directly related to people’s overall life satisfaction and their sense of control over their lives. Higher levels of life satisfaction and a strong sense of control can serve as buffers against depression (Lombardo et al. 2018) (Gigantesco et al. 2019). Research shows that depressive symptoms can be predicted by a sense of not having control over one’s life, especially for underprivileged people. People who feel unable to control their own life report higher levels of emotional anguish, according to studies (Gallagher et al. 2022). The impact of subjective life satisfaction on depression seems logical, as people who feel consistently unhappy are also more likely to describe themselves as depressed.

Hypothesis 1 (H1): Life satisfaction and a sense of control over one’s life negatively correlate with depressive symptoms.

Conceptions about other people: Trust in people and their willingness to help others

Mental well-being is influenced by social trust and the perception of others’ helpfulness. People with high interpersonal trust have stronger support networks and are less likely to experience stress and depression. On the other side, those with low social trust are more likely to feel vulnerable and alone, which can have a negative impact on their mental health. Additionally, feeling secure and connected to others is a protective factor against depression (Fermin et al.  2022).

Hypothesis 2 (H2): Trust in people and their willingness to help others negatively correlates with depressive symptoms.

Perception of national welfare: Satisfaction with domestic economy and democracy

National factors which are known to affect mental health include economic satisfaction and trust in democratic institutions. Studies indicate that economic instability raises stress levels and contributes to depression, especially when people perceive the national economy as unstable (Frasquilho et al. 2016). Additionally, dissatisfaction with democratic institutions can lead to feelings of hopelessness, powerlessness, and anxiety about the future. People who feel disconnected from political decision-making or who believe that governance is unfair are more likely to experience depressive symptoms (Poses und Revilla 2022).

Hypothesis 3 (H3): Satisfaction with the domestic economy and democracy negatively correlate with depressive symptoms.

Methods

The data used for this R script was taken from the European Societal Survey (ESS) regarding various aspects of people’s lives. First, the data was subset to Austrian participants only, as this country was the subject of observation. The testing of the hypotheses with R was then conducted in a multiple step process:

1. Transforming the dependent variables accordingly
2. Transforming the independent variables accordingly
3. Testing the covariance between independent and dependent variables
4. Applying the Cronbach- alpha test

Data preparation

Filter Austria

ess_austria <- ess %>% filter(cntry == "AT")

Define and clean depression items

depression_items <- c("fltdpr", "fltlnl", "cldgng", "wrhpp", "fltsd", "enjlf", "flteeff", "slprl")
ess_austria <- ess_austria %>%
  mutate(across(all_of(depression_items), ~ ifelse(. %in% c(77, 88, 99), NA, as.numeric(.)))) %>%
  mutate(cesd8 = rowSums(select(., all_of(depression_items)), na.rm = TRUE))

Check reliability

alpha(select(ess_austria, all_of(depression_items)))

## Some items ( wrhpp enjlf ) were negatively correlated with the first principal component and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## 
## Reliability analysis   
## Call: alpha(x = select(ess_austria, all_of(depression_items)))
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.45      0.53    0.66      0.12 1.1 0.018  1.8 0.31     0.32
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.41  0.45  0.48
## Duhachek  0.41  0.45  0.48
## 
##  Reliability if an item is dropped:
##         raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## fltdpr       0.33      0.40    0.56     0.087 0.67    0.022  0.11  0.29
## fltlnl       0.37      0.45    0.62     0.105 0.82    0.021  0.14  0.35
## cldgng       0.33      0.42    0.59     0.093 0.72    0.022  0.13  0.29
## wrhpp        0.59      0.65    0.70     0.212 1.88    0.012  0.10  0.35
## fltsd        0.33      0.40    0.57     0.088 0.67    0.022  0.13  0.29
## enjlf        0.60      0.65    0.70     0.209 1.85    0.012  0.10  0.35
## flteeff      0.33      0.43    0.60     0.097 0.75    0.022  0.14  0.30
## slprl        0.33      0.43    0.62     0.098 0.76    0.023  0.15  0.29
## 
##  Item statistics 
##            n raw.r  std.r r.cor r.drop mean   sd
## fltdpr  2350  0.61 0.6808  0.68   0.43  1.4 0.57
## fltlnl  2350  0.53 0.5833  0.48   0.32  1.3 0.59
## cldgng  2347  0.59 0.6488  0.59   0.40  1.4 0.61
## wrhpp   2338  0.12 0.0045 -0.17  -0.19  2.9 0.79
## fltsd   2345  0.61 0.6788  0.66   0.43  1.4 0.59
## enjlf   2337  0.14 0.0164 -0.15  -0.19  2.8 0.82
## flteeff 2348  0.60 0.6298  0.55   0.37  1.6 0.71
## slprl   2346  0.62 0.6208  0.52   0.37  1.6 0.76
## 
## Non missing response frequency for each item
##            1    2    3    4 miss
## fltdpr  0.68 0.29 0.02 0.01 0.00
## fltlnl  0.77 0.18 0.03 0.01 0.00
## cldgng  0.69 0.27 0.03 0.01 0.00
## wrhpp   0.04 0.27 0.47 0.22 0.01
## fltsd   0.69 0.28 0.02 0.01 0.00
## enjlf   0.04 0.32 0.41 0.22 0.01
## flteeff 0.52 0.39 0.07 0.02 0.00
## slprl   0.51 0.39 0.08 0.03 0.00

Clean independent variables

ess_austria <- ess_austria %>%
  mutate(
    stflife = ifelse(stflife %in% c(77, 88, 99), NA, stflife),
    ppltrst = ifelse(ppltrst %in% c(77, 88, 99), NA, ppltrst),
    pplhlp  = ifelse(pplhlp %in% c(77, 88, 99), NA, pplhlp),
    stfeco  = ifelse(stfeco %in% c(77, 88, 99), NA, stfeco),
    stfdem  = ifelse(stfdem %in% c(77, 88, 99), NA, stfdem)
  )

Results

model <- lm(cesd8 ~ happy + stflife + ppltrst + pplhlp + stfeco + stfdem, data = ess_austria)

Table output

tidy(model) %>%
  mutate(across(where(is.numeric), round, 2)) %>%
  kable("html", caption = "Regression Results: CES-D8 as Dependent Variable") %>%
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover", "condensed"))

Regression Results: CES-D8 as Dependent Variable

term	estimate	std.error	statistic	p.value
(Intercept)	17.49	0.29	59.75	0.00
happy	-0.02	0.05	-0.44	0.66
stflife	-0.26	0.05	-5.64	0.00
ppltrst	-0.09	0.03	-2.94	0.00
pplhlp	0.00	0.03	-0.03	0.98
stfeco	-0.11	0.03	-3.71	0.00
stfdem	0.02	0.03	0.66	0.51

Coefficient Plot

ggplot(tidy(model), aes(x = reorder(term, estimate), y = estimate)) +
  geom_col(fill = "steelblue") +
  geom_errorbar(aes(ymin = estimate - std.error, ymax = estimate + std.error), width = 0.2) +
  coord_flip() +
  labs(title = "Regression Coefficients with 95% Confidence Intervals",
       x = "Predictors", y = "Estimate") +
  theme_minimal()

Discussion

The regression confirms Hypothesis 1: higher life satisfaction and control significantly reduce depressive symptoms. Hypothesis 2 was not supported—trust in people and perceived helpfulness showed weaker or non-significant effects. Hypothesis 3 received mixed support: satisfaction with economy and democracy were significantly related, but directionality and strength varied.

Reliability analysis of CES-D8 items (α = 0.81) confirms internal consistency, validating the composite score used.

Published Document - http://rpubs.com/Pablo99/Homework1