Machine Learning and Psychological Research: The Unexplored Effect of Measurement

Ross Jacobucci, Kevin J. Grimm

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We argue that this phenomenon results from measurement errors that prevent machine-learning algorithms from accurately modeling nonlinear relationships, if indeed they exist. This shortcoming is showcased across a set of simulated examples, demonstrating that model selection between a machine-learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.

Original languageEnglish (US)
Pages (from-to)809-816
Number of pages8
JournalPerspectives on Psychological Science
Volume15
Issue number3
DOIs
StatePublished - May 1 2020

Keywords

  • data mining
  • machine learning
  • measurement error
  • psychometrics
  • structural-equation modeling

ASJC Scopus subject areas

  • Psychology(all)

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