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A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model

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Yehu Chen, Jacob Montgomery, Roman Garnett

Methodology · Submitted 3 Apr 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate.

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arXiv ID2504.02643 (version 1)
Pith IDBZFPSRUF, Pith's permanent record of this paper and its review
CitationJacob Montgomery, Roman Garnett, Yehu Chen. "A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model." arXiv:2504.02643 (2025). https://pith.science/paper/BZFPSRUF
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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.05 · human participants 0.62 · controlled comparison 0.62 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.95 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Social scientists are often interested in using ordinal indicators to estimate latent traits that change over time. Frequently, this is done with item response theoretic (IRT) models that describe the relationship between those latent traits and observed indicators. We combine recent advances in Bayesian nonparametric IRT, which makes minimal assumptions on shapes of item response functions, and Gaussian process time series methods to capture dynamic structures in latent traits from longitudinal observations. We propose a generalized dynamic Gaussian process item response theory (GD-GPIRT) as well as a Markov chain Monte Carlo sampling algorithm for estimation of both latent traits and response functions. We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Social scientists are often interested in using ordinal indicators to estimate latent traits that change over time. background 1.00 0.03 0.02 asserted 0.90
s1 Frequently, this is done with item response theoretic (IRT) models that describe the relationship between those latent traits and observed indicators. background 1.00 0.05 0.02 asserted 0.98
s2 We combine recent advances in Bayesian nonparametric IRT, which makes minimal assumptions on shapes of item response functions, and Gaussian process time series methods to capture dynamic structures in latent traits from longitudinal observations. method 0.63 0.46 0.09 asserted 0.97
s3 We propose a generalized dynamic Gaussian process item response theory (GD-GPIRT) as well as a Markov chain Monte Carlo sampling algorithm for estimation of both latent traits and response functions. contribution 0.92 0.95 0.18 asserted 1.00
s4 We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate. contribution 0.73 0.95 0.24 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.78.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Social scientists are often interested in using ordinal indicators to estimate latent traits that change over time. Frequently, this is done with item response theoretic (IRT) models that describe the relationship between those latent traits and observed indicators. We combine recent advances in Bayesian nonparametric IRT, which makes minimal assumptions on shapes of item response functions, and Gaussian process time series methods to capture dynamic structures in latent traits from longitudinal observations. We propose a generalized dynamic Gaussian process item response theory (GD-GPIRT) as well as a Markov chain Monte Carlo sampling algorithm for estimation of both latent traits and response functions. We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate.

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