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Comparing and Combining IRTree Models and Anchoring Vignettes in Addressing Response Styles.

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Title: Comparing and Combining IRTree Models and Anchoring Vignettes in Addressing Response Styles.
Authors: Xue, Mingfeng1,2 (AUTHOR), Chen, Ping2 (AUTHOR)
Source: Journal of Educational Measurement. Jun2025, Vol. 62 Issue 2, p225-247. 23p.
Subject Terms: Psychometrics, Vignettes
Abstract: Response styles pose great threats to psychological measurements. This research compares IRTree models and anchoring vignettes in addressing response styles and estimating the target traits. It also explores the potential of combining them at the item level and total‐score level (ratios of extreme and middle responses to vignettes). Four models were evaluated: three multidimensional IRTree models with different levels of using vignette data and a nominal response model (NRM) addressing extreme and midpoint response styles with item‐level vignette responses. Simulation results indicated that the IRTree model using item‐level vignette responses outperformed others in estimating the target trait and response styles to different extents, with performance improving as the number of vignettes increased. Empirical findings further demonstrated that models using item‐level vignette information yielded higher reliability and closely aligned target trait estimates. These results underscore the value of integrating anchoring vignettes with IRTree models to enhance estimation accuracy and control for response styles. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Comparing and Combining IRTree Models and Anchoring Vignettes in Addressing Response Styles.
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  Data: <searchLink fieldCode="AR" term="%22Xue%2C+Mingfeng%22">Xue, Mingfeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Ping%22">Chen, Ping</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: Response styles pose great threats to psychological measurements. This research compares IRTree models and anchoring vignettes in addressing response styles and estimating the target traits. It also explores the potential of combining them at the item level and total‐score level (ratios of extreme and middle responses to vignettes). Four models were evaluated: three multidimensional IRTree models with different levels of using vignette data and a nominal response model (NRM) addressing extreme and midpoint response styles with item‐level vignette responses. Simulation results indicated that the IRTree model using item‐level vignette responses outperformed others in estimating the target trait and response styles to different extents, with performance improving as the number of vignettes increased. Empirical findings further demonstrated that models using item‐level vignette information yielded higher reliability and closely aligned target trait estimates. These results underscore the value of integrating anchoring vignettes with IRTree models to enhance estimation accuracy and control for response styles. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jedm.12437
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              Text: Jun2025
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