  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.
