ArticleApplied psychological measurement2020
An Optimized Bayesian Hierarchical Two-Parameter Logistic Model for Small-Sample Item Calibration.
Article in Applied psychological measurement, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Accounting for item calibration error in computerized adaptive testing.Behavior research methods · 2025Article
- Optimizing Large-Scale Educational Assessment with a "Divide-and-Conquer" Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models.Psychometrika · 2024Article
- HBMIRT: A SAS macro for estimating uni- and multidimensional 1- and 2-parameter item response models in small (and large!) samples.Behavior research methods · 2024Article
- Benefits of the Curious Behavior of Bayesian Hierarchical Item Response Theory Models-An in-Depth Investigation and Bias Correction.Applied psychological measurement · 2024Article
- Robustness of the performance of the optimized hierarchical two-parameter logistic IRT model for small-sample item calibration.Behavior research methods · 2023Article
- Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions.PharmacoEconomics · 2022Article
- Bayesian Hierarchical Multidimensional Item Response Modeling of Small Sample, Sparse Data for Personalized Developmental Surveillance.Educational and psychological measurement · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate item calibration in models of item response theory (IRT) requires rather large samples. For instance,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.