A common way to form scores from multiple-item scales is to sum responses of all items.
Though sum scoring is often contrasted with factor analysis as a competing method, we …

In ever more pressured health-care systems, technological solutions offering scalability of
care and better resource targeting are appealing. Research on machine learning as a …

Although the lack of conceptual clarity has been observed to be a widespread and
fundamental problem in psychology, conceptual clarification plays a mostly marginal role in …

We argue that it is useful to distinguish between three key goals of personality science—
description, prediction and explanation—and that attaining them often requires different …

This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …

Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and
machine learning (ML) for psychological assessment, we provide a conceptual framework …

Most body image studies assess only linear relations between predictors and outcome
variables, relying on techniques such as multiple Linear Regression. These predictor …

There is strong interest in developing a more efficient mental health care system. Digital
interventions and predictive models of treatment prognosis will likely play an important role …

Supervised machine learning (ML) is becoming an influential analytical method in
psychology and other social sciences. However, theoretical ML concepts and predictive …

Careless responding is a bias in survey responses that disregards the actual item content,
constituting a threat to the factor structure, reliability, and validity of psychological …