Joint GLMM model for IRT estimation with collateral information¶
Research Method · All research methods
Evidence · 4 claims (4 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 4 claims rest on one study
Description¶
The article applies a multivariate joint model under the generalized linear mixed models framework that "simultaneously models binary item response and continuous RT response on test items that nested within person" (Klein Entink, Fox, & van der Linden, 2009). A Rasch logit submodel for responses and a lognormal submodel for response times are joined, with a covariance parameter ρ modeling ability-speed dependence. Person covariates (cross-test raw score, gender, ethnicity) enter as fixed-effect regressions on the random ability and speed effects, and the full model is fit with SAS PROC GLIMMIX via restricted maximum likelihood.
Accounts¶
- Joint GLMM model regressing person ability and speed on person covariates for IRT estimation with collateral information: The article applies a multivariate joint model under the generalized linear mixed models framework that "simultaneously models binary item response and continuous RT response on test items that nested within person" (Klein Entink, Fox, & van der Linden, 2009). A Rasch logit submodel for responses and a lognormal submodel for response times are joined, with a covariance parameter ρ modeling ability-speed dependence. Person covariates (cross-test raw score, gender, ethnicity) enter as fixed-effect regressions on the random ability and speed effects, and the full model is fit with SAS PROC GLIMMIX via restricted maximum likelihood. (Wang et al. (2011))
Claims¶
- Joint IRT models including collateral information (raw score, gender, ethnicity) fit better than item-only base models for both tests [+M]
- Ability and speed are negatively correlated, and the reading test's ability estimates are more affected by speed than the math test's [+M]
- Adding collateral information has little effect on ability and item parameter estimates but more effect on speed parameter estimates [+W]
- Scores from a different subject test can serve as collateral information: mathematics scores can improve reading item parameter estimation and vice versa [+W]
Related Research Methods¶
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Key Sources¶
- Wang, S., & Jiao, H. (2011). Incorporating Person Covariates and Response Times as Collateral Information to Improve Person and Item Parameter Estimations. Paper presented at the annual meeting of the National Council on Measurement in Education, New Orleans, LA. https://www.nwea.org/research/publication/incorporating-person-covariates-and-response-times-as-collateral-information-to-improve-person-and-item-parameter-estimations/
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
joint-glmm-model-for-irt-estimation-with-collateral-information |
title |
Joint GLMM model for IRT estimation with collateral information |
description |
The article applies a multivariate joint model under the generalized linear mixed models framework that "simultaneously models binary item response and continuous RT response on test items that nested within person" (Klein Entink, Fox, & van der Linden, 2009). |
status |
review |
moved_from |
theory/joint-glmm-collateral-information-model |
generated |
by process:sweep-kinds on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — wang-2011 |
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