ArticleBehavior research methods2023
Parsimonious asymmetric item response theory modeling with the complementary log-log link.
Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 24 citations in OpenAlex.
- Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.Psychometrika · 2026Article
- Joint modeling with generalized item response theory model family and response time model: Enhancing model structural flexibility and data-fitting adequacy.Behavior research methods · 2025Article
- Parsimonious item response theory modeling with the cauchit link: Revisiting the rationale of the four-parameter logistic model.Behavior research methods · 2025Article
- Spatial extrapolation of cadmium concentration in terrestrial mosses using multiple linear regression model predictions across French biogeographical regions.Environmental science and pollution research international · 2025Article
- A Definition of a Heywood Case in Item Response Theory Based on Fisher Information.Entropy (Basel, Switzerland) · 2024Article
- Unipolar IRT and the Author Recognition Test (ART).Behavior research methods · 2024Article
- Parsimonious item response theory modeling with the negative log-log link: The role of inflection point shift.Behavior research methods · 2024Article
- Exploration and analysis of a generalized one-parameter item response model with flexible link functions.Frontiers in psychology · 2023Article
- On the Choice of the Item Response Model for Scaling PISA Data: Model Selection Based on Information Criteria and Quantifying Model Uncertainty.Entropy (Basel, Switzerland) · 2022Article
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3 authors at 1 institution in 1 country.
Funding
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Abstract
Traditional item response theory (IRT) models assume a symmetric error distribution and rely on symmetric (logit or probit) link functions to model the response probabilities. As an alternative, we investigated the one-parameter complementary log-log model (CLLM), which is founded on an asymmetric error distribution and results in an asymmetric item response function with important psychometric properties. In a series of simulation studies, we demonstrate that the CLLM (a) is estimable in small sample sizes, (b) facilitates item-weighted scoring, and (c) accounts for the effect of guessing, despite the presence of a single parameter. We then provide further evidence for these claims by applying the CLLM to empirical data. Finally, we discuss how this work contributes to the growing psychometric literature on model complexity.
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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.