Estimation of Teacher Value Added in the Presence of Missing Data.
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| Title: | Estimation of Teacher Value Added in the Presence of Missing Data. |
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| Authors: | Gao, Niu1 (AUTHOR), Semykina, Anastasia2 (AUTHOR) |
| Source: | Journal of Research on Educational Effectiveness. Jul-Sep2021, Vol. 14 Issue 3, p676-695. 20p. |
| Subject Terms: | *Elementary school teachers, *Teachers, Missing data (Statistics), Estimation bias |
| Geographic Terms: | North Carolina |
| Abstract: | Inappropriate treatment of missing data may introduce bias into the value-added estimation. We consider a commonly used value-added model (VAM), which includes the past student test score as a covariate. We formulate a joint model of student achievement and missing data, in which the probability of observing a test score depends on observing the test score in the previous grade. We develop a correction procedure that removes the bias in VAM estimates due to data missing not at random and use it to estimate the value-added of elementary school teachers in North Carolina. We find that errors in VAM and missing data equations are uncorrelated, and corrected estimates of teacher productivity are very similar to their ordinary least squares counterparts. Thus, missingness does not cause bias in our data. We also perform simulations to identify scenarios where a correction may be necessary. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd 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: | Inappropriate treatment of missing data may introduce bias into the value-added estimation. We consider a commonly used value-added model (VAM), which includes the past student test score as a covariate. We formulate a joint model of student achievement and missing data, in which the probability of observing a test score depends on observing the test score in the previous grade. We develop a correction procedure that removes the bias in VAM estimates due to data missing not at random and use it to estimate the value-added of elementary school teachers in North Carolina. We find that errors in VAM and missing data equations are uncorrelated, and corrected estimates of teacher productivity are very similar to their ordinary least squares counterparts. Thus, missingness does not cause bias in our data. We also perform simulations to identify scenarios where a correction may be necessary. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19345747 |
| DOI: | 10.1080/19345747.2021.1894518 |
