Evaluating the performance of personality-based profiling in predicting physical activity
Kentaro Katahira, Keisuke Takano, Takeyuki Oba, Kenta Kimura · 2024
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Description
Kentaro Katahira, Keisuke Takano, Takeyuki Oba, and Kenta Kimura test whether sorting people into personality types helps predict how active they will be. Grouping people into types is a common tool in marketing, medicine, and social science. It makes data simpler, but it throws away fine detail. Whether that loss harms predictions had not been checked in a systematic way. Their 2024 study uses survey data from 20,573 people on activity and psychological traits, including the Big Five.
A four-cluster solution, chosen by the standard rule for deciding the number of clusters, was compared with raw trait scores used as continuous predictors. The cluster approach reached only 60 to 70 percent of the prediction accuracy of the raw scores. The team warns that the number of clusters that looks best by the standard rule may not be the number that predicts real outcomes best.
Catalog record
| CREATOR | Kentaro Katahira, Keisuke Takano, Takeyuki Oba, Kenta Kimura |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |