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Estimating Population Characteristics From Sparse Matrix Samples of Item Responses
Abstract
The multiple‐matrix item sampling designs that provide information about population characteristics most efficiently administer too few responses to students to estimate their proficiencies individually. Marginal estimation procedures, which estimate population characteristics directly from item responses, must be employed to realize the benefits of such a sampling design. Numerical approximations of the appropriate marginal estimation procedures for a broad variety of analyses can be obtained by constructing, from the results of a comprehensive extensive marginal solution, files of plausible values of student proficiencies. This article develops the concepts behind plausible values in a simplified setting, sketches their use in the National Assessment of Educational Progress (NAEP), and illustrates the approach with data from the Scholastic Aptitude Test (SA T).
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Cites
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Self-citationUse of Item Response Theory in the Study of Group Differences in Trace Lines
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Multiple imputation after 18+ years
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Measuring human capital using global learning data
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Metadata
- Authors
- Mislevy, R. J., Beaton, A. E., Kaplan, B., & Sheehan, K. M.
- Venue
- Journal of Educational Measurement
- Publisher
- Wiley
- Published
- 1992-06
- Volume
- 29
- Issue
- 2
- Pages
- 133-161
- ISSN
- 0022-0655 (ISSN-L)
- 1745-3984
- OpenAlex
- W1988703391
- Cited by
- 447
- References
- 19
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
