Modeling Missing Response Data in Item Response Theory: Addressing Missing Not at Random Mechanism with Monotone Missing Characteristics
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| Title: | Modeling Missing Response Data in Item Response Theory: Addressing Missing Not at Random Mechanism with Monotone Missing Characteristics |
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| Language: | English |
| Authors: | Jiwei Zhang, Jing Lu, Zhaoyuan Zhang |
| Source: | Journal of Educational Measurement. 2026 63(1). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 34 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Secondary Education |
| Descriptors: | Item Response Theory, Research Problems, Models, Bayesian Statistics, Evaluation Criteria, Sampling, Algorithms, Achievement Tests, Foreign Countries, International Assessment, Secondary School Students |
| Assessment and Survey Identifiers: | Program for International Student Assessment |
| DOI: | 10.1111/jedm.12428 |
| ISSN: | 0022-0655 1745-3984 |
| Abstract: | Item nonresponses frequently occurs in educational and psychological assessments, and if not appropriately handled, it can undermine the reliability of the results. This study introduces a missing data model based on the missing not at random (MNAR) mechanism, incorporating the monotonic missingness assumption to capture individual-level missingness patterns and behavioral dynamics. In specific, the cumulative number of missing indicators allows to consider the tendency of current item's missingness based on the previous missingnesses, which reduces the number of nuisance parameters for modeling missing data mechanisms. Two Bayesian model evaluation criteria were developed to distinguish between missing at random (MAR) and MNAR mechanisms by imposing specific parameter constraints. Additionally, the study introduces a highly efficient Bayesian slice sampling algorithm to estimate the model parameters. Four simulation studies were conducted to show the performance of the proposed model. The PISA 2015 science data was carried out to further illustrate the application of the proposed approach. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1501423 |
| Database: | ERIC |
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| Abstract: | Item nonresponses frequently occurs in educational and psychological assessments, and if not appropriately handled, it can undermine the reliability of the results. This study introduces a missing data model based on the missing not at random (MNAR) mechanism, incorporating the monotonic missingness assumption to capture individual-level missingness patterns and behavioral dynamics. In specific, the cumulative number of missing indicators allows to consider the tendency of current item's missingness based on the previous missingnesses, which reduces the number of nuisance parameters for modeling missing data mechanisms. Two Bayesian model evaluation criteria were developed to distinguish between missing at random (MAR) and MNAR mechanisms by imposing specific parameter constraints. Additionally, the study introduces a highly efficient Bayesian slice sampling algorithm to estimate the model parameters. Four simulation studies were conducted to show the performance of the proposed model. The PISA 2015 science data was carried out to further illustrate the application of the proposed approach. |
|---|---|
| ISSN: | 0022-0655 1745-3984 |
| DOI: | 10.1111/jedm.12428 |
