How Many Plausible Values?
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| Title: | How Many Plausible Values? |
|---|---|
| Authors: | Jewsbury, Paul A.1 (AUTHOR), McCaffrey, Daniel F.1 (AUTHOR), Jia, Yue1 (AUTHOR), Gonzalez, Eugenio J.2 (AUTHOR) |
| Source: | Journal of Educational Measurement. Dec2025, Vol. 62 Issue 4, p531-558. 28p. |
| Subject Terms: | *Data analysis, Multiple imputation (Statistics), Monte Carlo method, Questionnaires, Latent variables, Statistical power analysis |
| Company/Entity: | National Assessment of Educational Progress (Project) |
| Abstract: | Large‐scale survey assessments (LSAs) such as NAEP, TIMSS, PIRLS, IELS, and NAPLAN produce plausible values of student proficiency for estimating population statistics. Plausible values are imputed values for latent proficiency variables. While prominently used for LSAs, they are applicable to a wide range of latent variable modelling contexts such as surveys about psychological dispositions or beliefs. Following the practice of multiple imputation, LSAs produce multiple sets of plausible values for each survey. The criteria used to determine the number of plausible values remains unresolved and is inconsistent in practice. We show analytically and via simulation that the number of plausible values used determines the amount of Monte Carlo error on point estimates and standard errors as a function of the fraction of missing information. We derive expressions to determine the number of plausible values required to reach a given level of precision. We analyze real data from a LSA to provide guidelines supported by theory, simulation, and real data on the number of plausible values. Finally, we illustrate the impact with a power analysis. Our results show there is meaningful benefit to the use of greater numbers of plausible values than currently generated by LSAs. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Large‐scale survey assessments (LSAs) such as NAEP, TIMSS, PIRLS, IELS, and NAPLAN produce plausible values of student proficiency for estimating population statistics. Plausible values are imputed values for latent proficiency variables. While prominently used for LSAs, they are applicable to a wide range of latent variable modelling contexts such as surveys about psychological dispositions or beliefs. Following the practice of multiple imputation, LSAs produce multiple sets of plausible values for each survey. The criteria used to determine the number of plausible values remains unresolved and is inconsistent in practice. We show analytically and via simulation that the number of plausible values used determines the amount of Monte Carlo error on point estimates and standard errors as a function of the fraction of missing information. We derive expressions to determine the number of plausible values required to reach a given level of precision. We analyze real data from a LSA to provide guidelines supported by theory, simulation, and real data on the number of plausible values. Finally, we illustrate the impact with a power analysis. Our results show there is meaningful benefit to the use of greater numbers of plausible values than currently generated by LSAs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00220655 |
| DOI: | 10.1111/jedm.70000 |
