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

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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… Click to show full 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.

Keywords: imputation; education indicator; multiple imputation; indicator; bootstrapping assessment

Journal Title: Social Indicators Research
Year Published: 2020

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