New sorting algorithm yields more robust, replicable results than other methods.
The four “types”
Revelle admits that when his Northwestern colleague and co-author Luis Amaral came to him with the initial findings using traditional clustering algorithms, he had found 16 distinct clusters. Revelle was instantly skeptical: “That was ridiculous,” he says. He didn’t think there were any types at all lurking in the data, and challenged Amaral and another co-author, Martin Gerlach, to better refine their analysis.
“These statistical learning algorithms do not automatically produce the right answer,” says Revelle. “You need to then compare it to random solutions.” That second step made all the difference, by imposing extra constraints to winnow down the results. The researchers ended up with four distinct personality clusters:
Average: These people score high in neuroticism and extraversion, but score low in openness. It is the most typical category, with women being more likely than men to fit into it.
Reserved: This type of person is stable emotionally without being especially open or neurotic. They tend to score lower on extraversion but tend to be somewhat agreeable and conscientious.
Role Models: These people score high in every trait except neuroticism, and the likelihood that someone fits into this category increases dramatically as they age. “These are people who are dependable and open to new ideas,” says Amaral. “These are good people to be in charge of things.” Women are more likely than men to be role models.
Self-Centered: These people score very high in extraversion, but score low in openness, agreeableness, and conscientiousness. Most teenage boys would fall into this category, according to Revelle, before (hopefully) maturing out of it. The number of people who fall into this category decreases dramatically with age.Original Source