Introduction
The happiness is the subjectivity state of feeling so it could be difficult to measure.
However, the need arose measure the development of countries not just per a simple GDP indicator.
A special survey has been created for this purpose The World Happiness Report* and this survey is conducted on respondents from all over the world.
A key factor is the so-called 'ladder score', which is based on the interviewers ask for respondents imagine of the ladder, with the best possible life for them being a 10 and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale.
Also, the research includes additional variables such as: mentioned earlier GDP indicator, social support, healthy life expectancy, freedom, generosity, and perception of corruption.
Correlation case study
Using the raw data from the report, I conducted research to find correlations and investigate relationship between the variables.
All data which I analyzed were quantitative variables so I could used Pearson correlation coefficient.
Such a coefficient is assumed to be between -1 and 1 (where 1 indicates full correlation and 0 no correlation).
Therefore, when the value is closer to 1, the stronger the positive correlation (one phenomenon increases, the other also must ). Conversely, the closer the value is to -1, the stronger the negative correlation (one phenomenon increases and the other decreases).
However, it is worth noting that correlation is not a cause-and-effect relationship.
It can be concluded that correlation defines quantitatively the strength of the relationship between two measurements.
Time for data visualization
In order to present the data collected and the results, I have used PowerBi tool to created a dashboard which I presented below.
Analysis and results
The main indicator 'ladder score' is shown on a map in the world to show a clearer differences between the individual countries.
In turn, two dot plots were used to show how the correlation distributes. And so for the relationship between ladder score and healthy life expectancy correlation is 0,77. This is a strong positive correlation. Therefore, it can be concluded that if life expectancy is longer, the happier society will be (or vice versa). In contrast, the second case shows the relationship between the ladder score and perceptions of corruptions. The correlation coefficient is -0,53 and it means that the correlation is negative and moderately strong. The conclusion is also easy to see that if the phenomenon of corruption increases, the level of happiness in society may be lower (or vice versa).
Another added element of the dashboard is a chart with the average ladder score depending on the continent.
However, in order to increase the attractiveness of the dashboard, I added buttons with continents to make the chart interactive, as can be seen in the example below.
Summary
The correlation coefficient is therefore a fairly simple way to find relationships between variables, but it may be subject to a high risk of mistakes.
It may be that the correlation is completely coincidental and this can lead to very misleading conclusions. But still it is finding relationships between variables that is so exciting.
*The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by the Gallup World Poll data.
Source of data: https://worldhappiness.report/
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