Imagine if you could compare data easily from surveys taken about climate change from all European countries, maybe even from other continents, from different years? If you could work with a sample of not only n=1000, n=4000, or n=10,000 but n=100,000? What type of granularity it would give you about the perception of climate change or supported policy measures? That is exactly what our survey harmonization software allows for you to do.
In the previous example we created a longitudional dataset that contains data on the attitudes European people in various countries, provinces and regions thought climate change was a serious world problem back in 2013, 2015, 2017 and 2019. We will now fix the geographical information for mapping.
We created a longitudinal dataset that contains data on the attitudes European people in various countries, provinces and regions thought climate change was a serious world problem back in 2013, 2015, 2017 and 2019. We join the data with air pollution data so that we can see how serious is the environmental degradation in the smaller area of each (anonymous) respondent.
In this example we are working with data from surveys that were ex ante harmonized to a certain degree – in our tutorials we are choosing questions that were asked in the same way in many natural languages. For example, you can compare what percentage of the European people in various countries, provinces and regions thought climate change was a serious world problem back in 2013, 2015, 2017 and 2019.