Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning. EdWorkingPaper No. 23-868
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| Title: | Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning. EdWorkingPaper No. 23-868 |
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| Language: | English |
| Authors: | Joshua B. Gilbert, James S. Kim, Luke W. Miratrix, Annenberg Institute for School Reform at Brown University |
| Source: | Annenberg Institute for School Reform at Brown University. 2024. |
| Availability: | Annenberg Institute for School Reform at Brown University. Brown University Box 1985, Providence, RI 02912. Tel: 401-863-7990; Fax: 401-863-1290; e-mail: annenberg@brown.edu; Web site: https://annenberg.brown.edu/ |
| Peer Reviewed: | N |
| Page Count: | 42 |
| Publication Date: | 2024 |
| Document Type: | Reports - Research |
| Education Level: | Elementary Education Early Childhood Education Grade 1 Primary Education Grade 2 Grade 3 |
| Descriptors: | Vocabulary Development, Item Response Theory, Test Items, Student Development, Longitudinal Studies, Simulation, Monte Carlo Methods, Error of Measurement, Reading, Learner Engagement, Randomized Controlled Trials, Elementary School Students, Grade 1, Grade 2, Grade 3, Language Tests |
| Abstract: | Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary "within" persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of "item parameter drift" (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items or subscales at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED672233 |
| Database: | ERIC |
| Abstract: | Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary "within" persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of "item parameter drift" (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items or subscales at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time. |
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