Homework 2: Potential Determinants of Depression in the United Kingdom
2025-05-13
New in the Results Section: Likert Scale Frequency Table - Creating Likert Table and Likert Plot
(Using the ESS questions: d20, d21, d22, d23, d24, d25, d26, d27)
1 Introduction
Depression is a prevalent global health issue, experienced by 4-10% of the global population over their lifetime (Chapman et al., 2022). With currently, around 280 million people (3.8%) being affected globally (WHO, 2023), depression ranks among the top contributors to the global health burden (Santomauro et al., 2021). Although adequate and cost-efficient treatments are available across low-, middle-, and high-income countries, and stigma surrounding professional help is decreasing, depression treatment challenges are common occurrences (Ferrari et al., 2024); delayed diagnosis, treatments that are not tailored to individual needs, and inadequate responses to treatment attempts represent such obstacles (Chapman et al., 2022; Ferrari et al., 2024). For instance, in the United Kingdom (UK), 15 30% of individuals still suffer from depression after undergoing two or more treatments. This is relevant, as depression affects various aspects of human’s life, including education, employment, and personal relationships (OECD & European Commission, 2024), while contributing significantly to economic and healthcare costs (Statista, 2024; Vinokur et al., 1996).
1.1 Hypotheses concerning the UK:
H1. The prevalence of depression increases with experienced discrimination based on an individual’s sexuality.
H2. The prevalence of depression increases with experienced discrimination based on an individual’s skin colour or race.
H3. The prevalence of depression decreases with age.
H4. The prevalence of depression among females compared to males is higher.
2 Methods
The present paper aimed to investigate depression in a British population, as 15-30% of individuals do not recover from depression after two or more treatments (Chapman et al., 2022) and therefore a greater understanding of potential contributing factors is crucial for improving recovery outcomes.
2.1 Dependent Variable
Our dependent Variable is “Depression”, which was measured using the Centre for Epidemiological Studies Depression Scale (CES-D8), an 8-item scale.
2.2 Independent Variables
Discrimination based on the respondent’s sexuality (nominal: “marked”, “not marked”), discrimination based on colour or race (nominal: “marked”, “not marked”), age (ratio scale: 15-90), and gender (nominal: “male”, “female”) were considered as potential factors influencing depression.
2.3 Data Analysis
To assess the internal consistency of the depression scale, Cronbach’s alpha was calculated, typically ranging from 0 to 1 (Döring & Bortz, 2016), although it can sometimes be negative (Bühner, 2005).
# calculation of Cronbach's alpha (using df_uk) to check internal consistency ("reliability") of depression items
cronbach.alpha(df_uk[,c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")], na.rm=T)##
## Cronbach's alpha for the 'df_uk[, c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]' data-set
##
## Items: 8
## Sample units: 1684
## alpha: 0.415The degree of agreement between items was high, with an alpha of 0.415, well below the recommended threshold of 0.7 (Hair, 2010), indicating that not all items measure the same underlying construct of depression (Osburn, 2000).
3 Results
3.1 Sample Description
The dataset was subset to include only participants from the United Kingdom, resulting in an initial sample size of n=1684. However, the final sample consisted of n= 1635 valid respondents, aged 15 to 90 years, for the depression variable in the UK sample.
3.2 Calculation of the Depression Score
This section presents information about the depression score.
3.2.1 Descriptive Statistics of Depression
# table
kable_styling(
kable(summary_df,
col.names = c("Statistic", "Value"),
caption = "Summary Statistics of Depression Scores")
)| Statistic | Value |
|---|---|
| Minimum | 1.000 |
| Maximum | 4.000 |
| Median | 1.875 |
| Mean | 1.952 |
The depression scale has a range from 1 (lowest depression level) to 4 (highest depression level). Based on the data, we observe the following: At least one individual answered all items with a score of 1 (indicating the lowest possible depression level), and at least one individual answered all items with a score of 4 (indicating the highest possible depression level). The majority of participants report low to moderate depression levels, as indicated by the median (1.875) and mean (1.952) scores, which are relatively low. This suggests that most participants fall within the lower half of the depression scale.
3.2.2 Frequency Distribution of Depression
Here is a comprehensive overview of the frequency distribution:
table(df_uk$depression)##
## 1 1.125 1.25 1.375 1.5 1.625 1.75 1.875 2 2.125 2.25 2.375 2.5
## 11 1 6 17 110 178 297 304 221 176 97 72 48
## 2.625 2.75 2.875 3 3.125 3.25 3.375 3.75 4
## 25 29 13 10 6 9 3 1 1# frequency distribution of the new variable (depression)3.3 Create Likert Table and Likert Plot
3.3.1 Create Basic Frequencies
# create table directly from long format data
likert(likert_df)## Item None or almost none of the time Some of the time Most of the time
## 1 fltdpr 64.915835 29.06631 4.557165
## 2 flteeff 48.395568 38.42383 9.814171
## 3 slprl 43.873854 39.87056 11.625059
## 4 wrhpp 4.003510 23.53973 48.886939
## 5 fltlnl 68.136458 24.27532 5.302253
## 6 enjlf 5.338783 24.82572 44.804153
## 7 fltsd 52.489933 41.07451 4.859808
## 8 cldgng 55.673484 36.10353 6.217928
## All or almost all of the time
## 1 1.460694
## 2 3.366431
## 3 4.630532
## 4 23.569817
## 5 2.285972
## 6 25.031346
## 7 1.575748
## 8 2.0050563.3.2 Create a Basic Plot
# append means and counts to table
likert_table = likert(likert_df)$results # we save the "inner" data frame of the likert structure ...
likert_table$Mean = unlist(likert_means) # ... and append new columns to the data frame
likert_table$Count = unlist(likert_counts)# set new item labels
likert_table$Item = c(
d20="How often during the last week participants felt depressed",
d21="How often during the last week participants felt that everything they did was an effort",
d22="How often during the last week participants's sleep was restless",
d23="How often during the last week participants were happy",
d24="How often during the last week participants felt lonely",
d25="How often during the last week participants enjoyed life",
d26="How often during the last week participants felt sad",
d27="How often during the last week participants could not get going (felt lethargic and lacked motivation)"
)# round all percetage values to 1 decimal digit
likert_table[,2:5] = round(likert_table[,2:5],1)
# round means to 3 decimal digits
likert_table[,6] = round(likert_table[,6],3)# create formatted table
kable_styling(kable(likert_table,
caption = "Distribution of Answers Regarding Depression (ESS Round 11, Based on Data From the United Kingdom)"
)
)| Item | None or almost none of the time | Some of the time | Most of the time | All or almost all of the time | Mean | Count |
|---|---|---|---|---|---|---|
| How often during the last week participants felt depressed | 64.9 | 29.1 | 4.6 | 1.5 | 1.426 | 39981 |
| How often during the last week participants felt that everything they did was an effort | 48.4 | 38.4 | 9.8 | 3.4 | 1.682 | 39983 |
| How often during the last week participants’s sleep was restless | 43.9 | 39.9 | 11.6 | 4.6 | 1.770 | 40017 |
| How often during the last week participants were happy | 4.0 | 23.5 | 48.9 | 23.6 | 2.920 | 39890 |
| How often during the last week participants felt lonely | 68.1 | 24.3 | 5.3 | 2.3 | 1.417 | 39983 |
| How often during the last week participants enjoyed life | 5.3 | 24.8 | 44.8 | 25.0 | 2.895 | 39878 |
| How often during the last week participants felt sad | 52.5 | 41.1 | 4.9 | 1.6 | 1.555 | 39981 |
| How often during the last week participants could not get going (felt lethargic and lacked motivation) | 55.7 | 36.1 | 6.2 | 2.0 | 1.546 | 39949 |
# create basic plot (code also valid)
plot(likert(summary=likert_table[,1:4])) # limit to columns 1:4 to skip mean and count
3.4 Multivariate Regression (based on UK data) - Unweighted
# final model and show extended summary (unweighted)
# 824 male (48,93%); 860 female (51,67%)
# = 0.5 / (824/1684) ≈ 0.5 / 0.4893 ≈ 1.022 (male)
# = 0.5 / (860/1684) ≈ 0.5 / 0.5107 ≈ 0.979 (female)
model = lm(depression ~ dscrsex + female, data=df_uk)
summary(model)##
## Call:
## lm(formula = depression ~ dscrsex + female, data = df_uk)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98402 -0.23402 -0.03865 0.14098 2.01598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.91365 0.01259 152.029 < 2e-16 ***
## dscrsexMarked 0.08674 0.05793 1.497 0.135
## female 0.07038 0.01746 4.030 5.83e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3526 on 1632 degrees of freedom
## (49 observations deleted due to missingness)
## Multiple R-squared: 0.0109, Adjusted R-squared: 0.009691
## F-statistic: 8.995 on 2 and 1632 DF, p-value: 0.00013033.5 Multivariate Regression (based on UK data) - Weighted
# final model and show extended summary (weighted)
df_uk$weight = ifelse(df_uk$female == 1, 0.979, 1.022)
model = lm(depression ~ dscrsex + female, data = df_uk, weights = weight)
summary(model)##
## Call:
## lm(formula = depression ~ dscrsex + female, data = df_uk, weights = weight)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -0.97367 -0.23159 -0.03913 0.13946 1.99466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.91370 0.01245 153.761 < 2e-16 ***
## dscrsexMarked 0.08485 0.05773 1.470 0.142
## female 0.07035 0.01745 4.032 5.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3524 on 1632 degrees of freedom
## (49 observations deleted due to missingness)
## Multiple R-squared: 0.01087, Adjusted R-squared: 0.009658
## F-statistic: 8.967 on 2 and 1632 DF, p-value: 0.0001339A linear regression was conducted to predict depression scores based on dscrsexMarked (reported or experienced disrimination because of one’s sex) and female variables. Intercept: is estimated at 1.91365 (p < 2e-16, highly significant).
dscrsexMarked: This variable has a coefficient estimate of 0.08674 but is not statistically significant (p = 0.135), suggesting no strong evidence that this variable predicts depression in this model.
female: The coefficient is 0.07038, statistically significant (p = 5.83e-05), indicating that females tend to have slightly higher depression scores by about 0.07 units compared to the reference group.
The overall model explains a small proportion of variance in depression scores: Multiple R-squared = 0.0109 (about 1.1% variance explained), which means, that there must be additional factors influencing depression that are not included in this model.
4 References
Bühner, M. (2005). Einführung in die Test- und Fragebogenkonstruktion (Nachdr.). Pearson Studium.
Chapman, N., Browning, M., Baghurst, D., Hotopf, M., Willis, D., Haylock, S., Zakaria, S., Speechley, J., Withey, J., Brooks, E., Chan, F., Pappa, S., Geddes, J., Insole, L., Mohammed, Z., Kessler, D., Jones, P. B., Mansoori, P., & Difficult to Treat Depression Research Priority Setting Group. (2022). Setting national research priorities for difficult-to-treat depression in the UK between 2021-2026. Journal of Global Health, 12, 09004. https://doi.org/10.7189/jogh.12.09004
Döring, N., & Bortz, J. (with Pöschl-Günther, S.). (2016). Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften (5. vollst. überarb. Aufl). Springer. 13
Ferrari, A. J., Santomauro, D. F., Aali, A., Abate, Y. H., Abbafati, C., Abbastabar, H., Abd ElHafeez, S., Abdelmasseh, M., Abd-Elsalam, S., Abdollahi, A., Abdullahi, A., Abegaz, K. H., Abeldaño Zuñiga, R. A., Aboagye, R. G., Abolhassani, H., Abreu, L. G., Abualruz, H., Abu-Gharbieh, E., Abu-Rmeileh, N. M., … Murray, C. J. L. (2024). Global incidence, prevalence, years lived with disability (YLDs), disability adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990 2021: A systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 2133–2161. https://doi.org/10.1016/S0140-6736(24)00757-8
OECD & European Commission. (2024). Health at a Glance: Europe 2024: State of Health in the EU Cycle. OECD. https://doi.org/10.1787/b3704e14-en
Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., Bang-Jensen, B. L., Bertolacci, G. J., Bloom, S. S., Castellano, R., Castro, E., Chakrabarti, S.,
Vinokur, A. D., Price, R. H., & Caplan, R. D. (1996). Hard times and hurtful partners: How financial strain affects depression and relationship satisfaction of unemployed persons and their spouses. Journal of Personality and Social Psychology, 71(1), 166–179. https://doi.org/10.1037/0022-3514.71.1.166
WHO. (2023, March 31). Depressive disorder (depression). https://www.who.int/news room/fact-sheets/detail/depression