By Eoghan Daly, Taiming Zhang, Payton McGlory, Hannah Tarbrake, Kyle Grimaldi, Tiansheng Jiang
Eoghan Daly, Taiming Zhang, Payton McGlory, Hannah Tarbrake, Kyle Grimaldi, Tiansheng Jiang
| Variables | Description | Scale |
|---|---|---|
| How happy would you say you are? | Self-rated happiness | 1 = Very unhappy to 10 = Very happy |
| Life satisfaction | General life satisfaction | 1 = Very dissatisfied to 10 = Very satisfied |
| I have felt particularly tense | Perceived stress level | 1 = All the time to 6 = At no time |
| I woke up feeling fresh and rested | Sleep quality or restfulness | 1 = All the time to 6 = At no time |
| Respondent employment status | Used to flag unemployed individuals | 6/7 = Unemployed |
| Satisfaction with the state of the economy | National economic satisfaction perception | 1 = Very dissatisfied to 10 = Very satisfied |
| Support if feeling depressed | Whether the respondent has someone to talk to when feeling low | 4 = No support; others = Support exists |
| Response Counts by Category | ||||
|---|---|---|---|---|
| Unemployment Status | All of the time | Majority of the time | Some of the time | At no time |
| Unemployed | 185 | 1028 | 237 | 21 |
| Employed | 10 | 35 | 12 | 0 |
| Retired | 114 | 500 | 135 | 14 |
Insight
The respondents who reported the most unhappiness (or lowest levels of cheer) were those in the 'Unable to Work' category. These are the respondents who are disabled or ill, who cannot work.
In this section, I isolate respondents who reported feeling lonely often (scores 1–3 on a 6-point scale) and visualize where they live.
| Lonely Respondents by Locality | ||
|---|---|---|
| Respondents scoring 1–3 (Lonely Often) | ||
| Locality | Respondents | % |
| Large Town | 186 | 53.8 |
| Rural Area | 70 | 20.2 |
| Small/Mid Town | 90 | 26.0 |
Insight
Most respondents who report feeling lonely often live in large towns, suggesting a potential correlation between urban living and isolation.
Insight
Examine the prevalence of diagnosed mental health conditions and how they relate to economic conditions across countries. The grouped bar chart visualizes how diagnosed mental health conditions—such as Depression, Anxiety, PTSD, and others—are distributed across countries categorized by GDP growth tiers: Low, Medium, and High. Each bar represents the number of people diagnosed with a specific condition in countries within a certain GDP tier.
General Description: Selected columns from European Quality of Life Survey focusing year of 2016. Each row is the response of survey from 1 person.
Source and Citation: European Foundation for the Improvement of Living and Working Conditions. 2023. European Quality of Life Survey Integrated Data File, 2003–2016. 3rd ed. UK Data Service. SN 7348. https://doi.org/10.5255/UKDA-SN-7348-3.
Important to know: If the work you are dealing with, does not involve any economic indicators, simply replace everywhere you see a “data” with “data_EU28_imputed” in your codes This will enrich your data set from 2 countries of 2502 observations to 28 EU countries of 30809 observations. All values of 98, 99, 998, are already cleaned for “data”; “data_EU28_imputed”, or data set with a suffix imputed; These values indicate refusal of response, or no response collected The way of cleaning is to replace the original value with a column average.
About the two joined data set named data: data is the one that is fully processed, directly usable, with various column groups of interesting characteristics, with economic indicators. There are 5 added columns of economic data for the corresponding countries and year. Economic indicators are calculated as the arithmetic average of the three quarters the data_original_eco has. Caution: It only contains 2 countries’ samples, from UK and France, due to incomplete data of economic indicators we have.
E- About data_EU28_imputed: data_EU28_imputed is the one that is fully processed, directly usable, with various column groups of interesting characteristics, without economic indicators, for 28 Countries of European Union; Back to the time when UK is still part of it.
-About global_data: This is the very original data set we chosen; Mental_Health_Lifestyle_Dataset; A different data set from above
For all the other datas: All Can Be Ignored data_backup_do_not_use Just ignore; A backup in case data is contaminated data_eco Just ignore; the calculated average economic indicator used to join with data_imputed data_EU28 Just ignore; the one with N/A, refusal, no-response data_imputed Just ignore; the one used to join with data_eco, to have data data_joined Just ignore; the mother version of data data_selected Just ignore data_original; data_original_eco Just ignore; the original data sets, unprocessed, impossible to process by yourself in one day
Good to know: CLICK selected_eqls_integrated_trend_2003-2016_ukda_data_dictionary.rtf, in the project folder, to download the variable dictionary This is Taiming Zhang, who is responsible for the data set processing and general assistance about the data itself