A Group Fit Statistic for the Multilevel Item Response Model
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| Title: | A Group Fit Statistic for the Multilevel Item Response Model |
|---|---|
| Language: | English |
| Authors: | Yishan Ding (ORCID |
| 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: | 27 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Hierarchical Linear Modeling, Item Response Theory, Goodness of Fit, Test Wiseness, Behavior Patterns, Error Patterns, Prediction, Cheating, Test Items |
| DOI: | 10.1111/jedm.70024 |
| ISSN: | 0022-0655 1745-3984 |
| Abstract: | Aberrant behaviors among test-takers in large-scale assessments are often more prevalent within specific groups or testing sites. While various techniques have been developed to detect individual-level test-takers' aberrant behaviors, research in detecting those behaviors at the group level is rare. We propose a group fit statistic l[subscript z][subscript 2] by extending the l[subscript z] statistic to a multilevel item response model. This new statistic demonstrates adequate power and effectively controls the Type I error rate, particularly when true latent variable values are used or when group sizes are large, such as 500. When latent variable estimates are employed, an adjustment to the l[subscript z][subscript 2] based on the posterior predictive checking approach can offer improved control over the Type I error rate. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1501255 |
| Database: | ERIC |
| Abstract: | Aberrant behaviors among test-takers in large-scale assessments are often more prevalent within specific groups or testing sites. While various techniques have been developed to detect individual-level test-takers' aberrant behaviors, research in detecting those behaviors at the group level is rare. We propose a group fit statistic l[subscript z][subscript 2] by extending the l[subscript z] statistic to a multilevel item response model. This new statistic demonstrates adequate power and effectively controls the Type I error rate, particularly when true latent variable values are used or when group sizes are large, such as 500. When latent variable estimates are employed, an adjustment to the l[subscript z][subscript 2] based on the posterior predictive checking approach can offer improved control over the Type I error rate. |
|---|---|
| ISSN: | 0022-0655 1745-3984 |
| DOI: | 10.1111/jedm.70024 |
