Idiographic Personality Gaussian Process for Psychological Assessment

TL;DR

This paper introduces the idiographic personality Gaussian process (IPGP), a novel model that captures both shared and individual-specific personality traits, improving prediction and personalized diagnosis in psychometrics.

Contribution

The paper proposes the IPGP framework, combining Gaussian process coregionalization with non-Gaussian ordinal data handling, enabling scalable individualized personality assessment.

Findings

IPGP outperforms benchmarks in response prediction

IPGP accurately estimates personalized trait structures

IPGP identifies meaningful personality clusters

Abstract

We develop a novel measurement framework based on a Gaussian process coregionalization model to address a long-lasting debate in psychometrics: whether psychological features like personality share a common structure across the population, vary uniquely for individuals, or some combination. We propose the idiographic personality Gaussian process (IPGP) framework, an intermediate model that accommodates both shared trait structure across a population and "idiographic" deviations for individuals. IPGP leverages the Gaussian process coregionalization model to handle the grouped nature of battery responses, but adjusted to non-Gaussian ordinal data. We further exploit stochastic variational inference for efficient latent factor estimation required for idiographic modeling at scale. Using synthetic and real data, we show that IPGP improves both prediction of actual responses and estimation of individualized factor structures relative to existing benchmarks. In a third study, we show that IPGP also identifies unique clusters of personality taxonomies in real-world data, displaying great potential in advancing individualized approaches to psychological diagnosis and treatment.