But again, some findings are a bit more surprising. It was the trait that revealed the biggest differences between cities.
Factor analysis takes a dataset with a huge number of variables and automatically looks for patterns.
These patterns then help you (in this case, me) summarize what’s happening across this huge set of variables in a more comprehensible, less tedium-filled way.
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So I figured I’d jump into the conversation by making some maps that illustrate how personalities and romantic attitudes differ across the country.
After digging through the numbers, I’ve found that the personality differences between American cities go beyond a single scale of simply being “Red” or “Blue” — you actually need to describe how the personalities of US cities differ from one another.
After running the analysis, we get a set of personality traits that describe how the residents of US cities differ from one another.
The factor analysis doesn’t automatically tell us what each personality trait really “means” — it just gives us a list of point values associated with answers to different questions. These scales are ordered so that the first one explains the biggest differences between cities, and the second describes a little less, and so on and so on.
For this case, our big dataset is the list of how a “typical member” of each MSA responded to each question.
The factor analysis finds patterns based on which responses tend to co-occur in different cities.
I limited this analysis to questions that have been answered over 1 million times from 2014 to 2016.