Machine Learning and Psychological Research
The Unexplored Effect of Measurement
Bibliographic Data
| ID | 21786323 |
|---|---|
| Authors | Ross Jacobucci (0000-0001-7818-7424, University of Notre Dame, corresponding author), Kevin J Grimm (0000-0002-8576-4469, Arizona State University) |
| Year | 2020 |
| Volume | 15 |
| Issue | 3 |
| Pages | 809-816 |
| Publication date | 2020-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Perspectives on Psychological Science (JOURNAL) |
| Journal identifiers | ISSN: 1745-6916 • E-ISSN: 1745-6924 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/1745691620902467 |
| PMID | 32348703 |
| OpenAlex | W3019193937 |
| Language | EN |
| Citations received | 27 |
| References cited | 33 |
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
Big data · Data mining · Data science · Epistemology · Machine learning · Psychological research · Psychological science · Regression · Cognitive Science and Mapping · Computer Science · Functional Brain Connectivity Studies · Mental Health Research Topics · Psychology · Social Psychology · Artificial Intelligence
Safety and Security
Smartphone Usage
Social Interaction and Interpersonal Relationships
Personality and Individual Differences
Smartphones within Psychological Science
Reliable Trees
Thinking twice about sum scores
Using machine learning techniques for evaluating creativity in Graphic Combination Task (GCT) among Chinese college students
Predicting a few or many friends in schoolchildren
Prediction of differential performance between advanced placement exam scores and class grades using machine learning
Limitations of the Sum-and-Alpha Approach to Measurement in Behavioral Research
Exploratory Factor Analysis Trees
Structural validity and classification performance of the Italian Short Negative Acts Questionnaire
Identifying supportive student factors for mindset interventions
Using machine learning to analyze longitudinal data
To choose or not to choose? Effects of choice in authentic context-based learning environments
Predicting juvenile delinquency and criminal behavior in adulthood using machine learning
Human Values Across the Lifespan
The problem with the internet
Understanding Filipino students’ achievement in Pisa
Relationships between Transportation Expenditures and Built Environment in the United States
Detecting Careless Responding in Survey Data Using Stochastic Gradient Boosting
A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R
On the Selection of Item Scores or Composite Scores for Clinical Prediction
Who Considers Terrorism Justifiable? A Machine Learning Analysis Across 65 Countries
Predicting How Well Adolescents Get Along with Peers and Teachers
Going beyond simplicity
Statistical Theories of Mental Test Scores
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Stan
A systematic review of Bayesian articles in psychology
Measurement models for linking latent variables and indicators
Choosing Prediction Over Explanation in Psychology
Machine Learning Approaches for Clinical Psychology and Psychiatry
Measurement error and the replication crisis
An Agenda for Purely Confirmatory Research
Manifest variable path analysis
Greedy function approximation
Thanks coefficient alpha, we’ll take it from here.
Big data in psychology
Retracted Article
| Citations per year | 4.5 |
|---|---|
| Citation span | 2020 - 2026 (7) |
| Highly cited | No |