ArticleApplied psychological measurement2018
Sources of Error in IRT Trait Estimation.
Article in Applied psychological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Parsimonious item response theory modeling with the cauchit link: Revisiting the rationale of the four-parameter logistic model.Behavior research methods · 2025Article
- Accounting for item calibration error in computerized adaptive testing.Behavior research methods · 2025Article
- Parsimonious item response theory modeling with the negative log-log link: The role of inflection point shift.Behavior research methods · 2024Article
- Robustness of Adaptive Measurement of Change to Item Parameter Estimation Error.Educational and psychological measurement · 2022Article
- Modeling Psychometric Relational Data in Social Networks: Latent Interdependence Models.Frontiers in psychology · 2022Article
- More flexible response functions for the PROMIS physical functioning item bank by application of a monotonic polynomial approach.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- Investigating the Impact of Noneffortful Responses on Individual-Level Scores: Can the Effort-Moderated IRT Model Serve as a Solution?Applied psychological measurement · 2021Article
- An Optimized Bayesian Hierarchical Two-Parameter Logistic Model for Small-Sample Item Calibration.Applied psychological measurement · 2020Article
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Abstract
In item response theory (IRT), item response probabilities are a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (a) random error, (b) the trait score estimation method, (c) errors in item parameter estimation, and (d) model misspecification. This study investigated the relative effects of these error sources on the bias and confidence interval coverage rates for trait scores. Our results showed that overall, bias values were close to 0, and coverage rates were fairly accurate for central trait scores and trait estimation methods that did not use a strong Bayesian prior. However, certain types of model misspecifications were found to produce severely biased trait estimates with poor coverage rates, especially at extremes of the latent trait continuum. It is demonstrated that biased trait estimates result from estimated item response functions (IRFs) that exhibit systematic conditional bias, and that these conditionally biased IRFs may not be detected by model or item fit indices. One consequence of these results is that certain types of model misspecifications can lead to estimated trait scores that are nonlinearly related to the data-generating latent trait. Implications for item and trait score estimation and interpretation are discussed.
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