HM 2
LMD
2025-05-12
This study employs a cross-sectional observational study design to investigate the social determinants of depression using data from the European Social Survey (ESS) Wave 11. The analysis focuses on the relationship between depressive symptoms, measured through the CES- D8 scale, and two key independent variables: the frequency of social meetings and alcohol consumption. This design allows for an exploration of the associations between the variables at a single point in time, providing valuable insights into their potential relationships within a defined population.
Hypothesis 1: Higher frequency of social meetings is associated with lower levels of depressive symptoms.
Hypothesis 2: Higher alcohol consumption is associated with increased levels of depressive symptoms.
Data sample from Germany
## [1] 2420Distribution of gender and histogram of age distribution
##
## Male Female
## 1214 1206
#### CES-D8 (depresion scale) summary
##
## Cronbach's alpha for the 'df_de[, c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]' data-set
##
## Items: 8
## Sample units: 2420
## alpha: 0.794## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.375 1.625 1.697 1.875 4.000 21
##
## 1 1.125 1.25 1.375 1.5 1.625 1.75 1.875 2 2.125 2.25 2.375 2.5
## 66 148 225 339 339 274 226 216 142 118 77 42 51
## 2.625 2.75 2.875 3 3.125 3.25 3.375 3.5 3.625 3.75 3.875 4
## 31 33 20 13 10 5 6 7 5 2 2 2Depression Scale
likert_df = df[,CES_D8]
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 2.005056plot(likert(likert_df))
Including Plots
## 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 Mean Count
## 1 1.460694 1.425627 39981
## 2 3.366431 1.681515 39983
## 3 4.630532 1.770123 40017
## 4 23.569817 2.920231 39890
## 5 2.285972 1.417377 39983
## 6 25.031346 2.895281 39878
## 7 1.575748 1.555214 39981
## 8 2.005056 1.545546 39949Kruscal test of depression and alcohol frequency
##
## Kruskal-Wallis rank sum test
##
## data: df_de$depression and df_de$alcfreq
## Kruskal-Wallis chi-squared = 53.529, df = 6, p-value = 9.182e-10Kruscal test of depression and alcohol frequency
##
## Kruskal-Wallis rank sum test
##
## data: df_de$depression and df_de$sclmeet
## Kruskal-Wallis chi-squared = 96.496, df = 6, p-value < 2.2e-16Post-stratification weight (pspwght) model was used to weight the linear regression.
Linear regression models for Models with Weights
##
## Call:
## lm(formula = depression ~ sclmeet + alcfreq, data = df_de, weights = pspwght)
##
## Coefficients:
## (Intercept) sclmeetLess than once a month
## 2.04626 0.07449
## sclmeetOnce a month sclmeetSeveral times a month
## -0.23481 -0.27035
## sclmeetOnce a week sclmeetSeveral times a week
## -0.32427 -0.33934
## sclmeetEvery day alcfreqSeveral times a week
## -0.36693 -0.11924
## alcfreqOnce a week alcfreq2-3 times a month
## -0.04405 -0.06883
## alcfreqOnce a month alcfreqLess than once a month
## -0.04693 0.01443
## alcfreqNever
## 0.07744##
## Call:
## lm(formula = depression ~ sclmeet + alcfreq + gndr, data = df_de,
## weights = pspwght)
##
## Coefficients:
## (Intercept) sclmeetLess than once a month
## 2.05021 0.05910
## sclmeetOnce a month sclmeetSeveral times a month
## -0.24970 -0.28862
## sclmeetOnce a week sclmeetSeveral times a week
## -0.34116 -0.35609
## sclmeetEvery day alcfreqSeveral times a week
## -0.38379 -0.13075
## alcfreqOnce a week alcfreq2-3 times a month
## -0.07377 -0.10124
## alcfreqOnce a month alcfreqLess than once a month
## -0.07537 -0.02411
## alcfreqNever gndrFemale
## 0.04080 0.07993Linear regression models for Models WITHOUT Weights
##
## Call:
## lm(formula = depression ~ sclmeet + alcfreq, data = df_de)
##
## Coefficients:
## (Intercept) sclmeetLess than once a month
## 2.08646 -0.16264
## sclmeetOnce a month sclmeetSeveral times a month
## -0.35539 -0.41599
## sclmeetOnce a week sclmeetSeveral times a week
## -0.40837 -0.48466
## sclmeetEvery day alcfreqSeveral times a week
## -0.54007 -0.03723
## alcfreqOnce a week alcfreq2-3 times a month
## -0.02129 0.02296
## alcfreqOnce a month alcfreqLess than once a month
## 0.02111 0.10430
## alcfreqNever
## 0.15361##
## Call:
## lm(formula = depression ~ sclmeet + alcfreq + gndr, data = df_de)
##
## Coefficients:
## (Intercept) sclmeetLess than once a month
## 2.089374 -0.178175
## sclmeetOnce a month sclmeetSeveral times a month
## -0.368777 -0.433938
## sclmeetOnce a week sclmeetSeveral times a week
## -0.424365 -0.502075
## sclmeetEvery day alcfreqSeveral times a week
## -0.554061 -0.049167
## alcfreqOnce a week alcfreq2-3 times a month
## -0.044031 -0.006850
## alcfreqOnce a month alcfreqLess than once a month
## -0.008461 0.071542
## alcfreqNever gndrFemale
## 0.119917 0.075227