What question did this study set out to answer?

To develop a general maximum marginal likelihood projective item response theory (MML-PIRT) approach that overcomes the functional restrictions and computational burdens of existing PIRT estimators.

August 15, 2026Applied Psychological MeasurementOpen Access

A General Approach for Estimating Projective IRT Models

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Authors

RCR. Philip Chalmers CFCarl F. Falk SRSteve P. Reise

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Overview

Methodological study demonstrates a maximum marginal likelihood framework for projective item response theory, highlighting improved flexibility and efficient variance estimation.

Key Points

To develop a general maximum marginal likelihood projective item response theory (MML-PIRT) approach that overcomes the functional restrictions and computational burdens of existing PIRT estimators.
Designed an MML-PIRT estimation framework leveraging expected count information generated during the standard expectation–maximization (EM) algorithm.
Generalized the proxy response function fitting to accommodate focal traits across broader classes of multidimensional IRT models beyond monotonic logistic approximations.
Derived analytical large-sample variability estimates to bypass computationally expensive sampling procedures.
Enables the fitting of projective proxy response functions across broader classes of multidimensional item response models without restricted functional forms.
Produces accurate and computationally efficient large-sample variability estimates for PIRT parameters, facilitating practical application in moderate sample sizes.

Cite This Study

Chalmers et al. (2026) studied this question.

synapsesocial.com/papers/6a8019bb75c2e31742c85dad https://doi.org/10.1177/01466216261474955

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August 15, 2026Applied Psychological MeasurementOpen Access

A General Approach for Estimating Projective IRT Models

View Full Paper

Ask AI

Bookmark

Authors

RCR. Philip Chalmers CFCarl F. Falk SRSteve P. Reise

Discussion

Member takes

Overview

Methodological study demonstrates a maximum marginal likelihood framework for projective item response theory, highlighting improved flexibility and efficient variance estimation.

Key Points

To develop a general maximum marginal likelihood projective item response theory (MML-PIRT) approach that overcomes the functional restrictions and computational burdens of existing PIRT estimators.
Designed an MML-PIRT estimation framework leveraging expected count information generated during the standard expectation–maximization (EM) algorithm.
Generalized the proxy response function fitting to accommodate focal traits across broader classes of multidimensional IRT models beyond monotonic logistic approximations.
Derived analytical large-sample variability estimates to bypass computationally expensive sampling procedures.
Enables the fitting of projective proxy response functions across broader classes of multidimensional item response models without restricted functional forms.
Produces accurate and computationally efficient large-sample variability estimates for PIRT parameters, facilitating practical application in moderate sample sizes.

Cite This Study

Chalmers et al. (2026) studied this question.

synapsesocial.com/papers/6a8019bb75c2e31742c85dad https://doi.org/10.1177/01466216261474955

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