 the questions that differentiate this group from the rest of the population, seems like we could qualify them as the conservatives.
Let’s now see their vote intention. Seems like they are divided between Ciudadanos and PP. But some of them want to vote for Podemos, which is against our intuition because Podemos is a leftist party. Who are these people? Well, with one click, we can easily find out the reason.
Seems like their main differentiation is their “Vote Reason”. They vote mainly “to punish the established parties”.
We can also see other variables that define them, such as demographics. These people mostly hold Technical education degrees, and are around 35-45 years old.
This is an example of an insight we were not expecting, and a question we did not have at the beginning. Graphext is allowing us to have a dialogue with the data so we could understand its story.
But there is much more we could do with Graphext. Instead of using unsupervised clusters, we can also create our own filters to define and compare data segments. For example, let’s investigate how people change their vote intention from one year to the next. Let’s take people who voted for PSOE in the last elections. Who do they intend to vote for now? Apparently, those who have decided are divided into “being loyal to PSOE”, and “changing to Podemos”.
Using those filters, we can manually create data segments (called “Collections”) for further anaysis.
Let’s now compare those groups, and check their opinion patterns. The “Compare” section allows us to identify automatically the questions that most differentiate these two groups. Participants who decided to switch to Podemos do not agree with “The 978 constitution is the appropriate framework for our society” while loyals to PSOE are in favour of that question. Also bullfighting is a hot topic dividing these two groups, seems like people who decided to vote for Podemos are very in favour of “Bullfights in Spain should be banned”, whereas PSOE loyal opinion is very evenly divided.
Those are some examples of complex analyses that can be done very quickly in Graphext.
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