Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model

Rapid-guessing behavior in data can compromise our ability to estimate item and person parameters accurately. Consequently, it is crucial to model data with rapid-guessing patterns in a way that can produce unbiased ability estimates. This study proposes and evaluates three alternative modeling approaches that follow the logic of the effort-moderated item response theory model (EM-IRT) to analyze response data with rapid-guessing responses. One is the two-step EM-IRT model, which utilizes the item parameters estimated by respondents without rapid-guessing behavior and was initially proposed by Rios and Soland without further investigation. The other two models are effort-moderated multidimensional models (EM-MIRT), which we introduce in this study and vary as both between-item and within-item structures. The advantage of the EM-MIRT model is to account for the underlying relationship between rapid-guessing propensity and ability. The three models were compared with the traditional EM-IRT model regarding the accuracy of parameter recovery in various simulated conditions. Results demonstrated that the two-step EM-IRT and between-item EM-MIRT model consistently outperformed the traditional EM-IRT model under various conditions, with the two-step EM-IRT estimation generally delivering the best performance, especially for ability and item difficulty parameters estimation. In addition, different rapid-guessing patterns (i.e., difficulty-based, changing state, and decreasing effort) did not affect the performance of the two-step EM-IRT model. Overall, the findings suggest that the EM-IRT model with the two-step parameter estimation method can be applied in practice for estimating ability in the presence of rapid-guessing responses due to its accuracy and efficiency. The between-item EM-MIRT model can be used as an alternative model when there is no significant mean difference in the ability estimates between examinees who exhibit rapid-guessing behavior and those who do not.

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Publication Details

Journal
Educational and Psychological Measurement
Published
2024-10-06
DOI
https://doi.org/10.1177/00131644241280727
Citations
3
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
1.47
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article

Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model

Jiawei Xiong, Huan Kuang, Anne Corinne Huggins‐Manley, Bowen Wang
3 citations
Educational and Psychological Measurement
Psychometric Methodologies and Testing
1.47
article

Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model

Jiawei Xiong, Huan Kuang, Anne Corinne Huggins‐Manley, Bowen Wang
article en
3 citations

Abstract

Rapid-guessing behavior in data can compromise our ability to estimate item and person parameters accurately. Consequently, it is crucial to model data with rapid-guessing patterns in a way that can produce unbiased ability estimates. This study proposes and evaluates three alternative modeling approaches that follow the logic of the effort-moderated item response theory model (EM-IRT) to analyze response data with rapid-guessing responses. One is the two-step EM-IRT model, which utilizes the item parameters estimated by respondents without rapid-guessing behavior and was initially proposed by Rios and Soland without further investigation. The other two models are effort-moderated multidimensional models (EM-MIRT), which we introduce in this study and vary as both between-item and within-item structures. The advantage of the EM-MIRT model is to account for the underlying relationship between rapid-guessing propensity and ability. The three models were compared with the traditional EM-IRT model regarding the accuracy of parameter recovery in various simulated conditions. Results demonstrated that the two-step EM-IRT and between-item EM-MIRT model consistently outperformed the traditional EM-IRT model under various conditions, with the two-step EM-IRT estimation generally delivering the best performance, especially for ability and item difficulty parameters estimation. In addition, different rapid-guessing patterns (i.e., difficulty-based, changing state, and decreasing effort) did not affect the performance of the two-step EM-IRT model. Overall, the findings suggest that the EM-IRT model with the two-step parameter estimation method can be applied in practice for estimating ability in the presence of rapid-guessing responses due to its accuracy and efficiency. The between-item EM-MIRT model can be used as an alternative model when there is no significant mean difference in the ability estimates between examinees who exhibit rapid-guessing behavior and those who do not.

Educational and Psychological MeasurementVol. 85(2)
Florida State University (US), University of Florida (US)
Openalex Percentile: Top 16%
Psychometric Methodologies and Testing
1.47
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