Author: Jennifer Ouellette
New sorting algorithm yields more robust, replicable results than other methods.
People love taking online quizzes; just ask Buzzfeed and Facebook. A new study has sifted through some of the largest online data sets of personality quizzes and identified four distinct "types" therein. The new methodology used for this study—described in detail in a new paper in Nature Human Behavior—is rigorous and replicable, which could help move personality typing analysis out of the dubious self-help section in your local bookstore and into serious scientific journals.
Frankly, personality "type" is not the ideal nomenclature here; personality "clusters" might be more accurate. Paper co-author William Revelle (Northwestern University) bristles a bit at the very notion of distinct personality types, like those espoused by the hugely popular Myers-Briggs Type Indicator. Revelle is an adamant "anti-fan" of the Myers-Briggs, and he is not alone. Most scientists who study personality prefer to think of it as a set of continuous dimensions, in which people shift where they fall on the spectrum of various traits as they mature.
What's new here is the identification of four dominant clusters in the overall distribution of traits. Revelle prefers to think of them as "lumps in the batter" and suggests that a good analogy would be how people tend to concentrate in cities in the United States.
Divide the country into four regions—north, south, east, and west—and then look at how the population density is distributed. You will likely find the highest concentration of people living in dense cities like New York, Chicago, Los Angeles, or Houston. "But to describe everyone as living in one of those four cities is a mistake," he says. Similarly, "What we're describing is the likelihood of being at certain parts of that distribution; we're not saying that everyone is in one of those four categories."
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