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A Bootstrapping Assessment on A U.S. Education Indicator Construction Through Multiple Imputation

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  • Jianjun Wang

    (California State University)

Abstract

Under a matrix sampling design, no students complete all test booklets in the National Assessment of Educational Progress (NAEP). To construct an education indicator on what students know and can do, multiple imputation (MI) is conducted to compute plausible values (PV) from student responses to a subset of the questions. Since 2013, NAEP increased the number of imputed PV from five to 20. A purpose of this investigation is to examine the impact of this NAEP change on indicator reporting. R algorithm is created to compute bootstrap standard errors of the PV distribution. The results show that the 20-imputation setting has reduced the standard error and improved normality in comparison to the five-imputation setting. While the bootstrap technique is typically set to generate 1000 resamples, the findings from this study further indicate that an increase of the resampling number is unlikely to reduce the standard error estimate.

Suggested Citation

  • Jianjun Wang, 2021. "A Bootstrapping Assessment on A U.S. Education Indicator Construction Through Multiple Imputation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 431-442, January.
    • Handle: RePEc:spr:soinre:v:153:y:2021:i:2:d:10.1007_s11205-020-02507-4
    DOI: 10.1007/s11205-020-02507-4
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    References listed on IDEAS

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    1. Jianjun Wang & Dallas E. Johnson, 2019. "An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS®," The American Statistician, Taylor & Francis Journals, vol. 73(1), pages 80-88, January.
    2. Robert Mislevy, 1991. "Randomization-based inference about latent variables from complex samples," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 177-196, June.
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