Abstract
This article shows how to conduct multiple imputation in big identifiable data for educational research purposes. The R statistical package and procedures to handle missing data applied for the purpose of this study were “BaylorEdPsych” and “mi”. Firstly, we checked that every dataset rejected the null hypothesis for Missing Completely At Random (MCAR), using the function “LittleMCAR”. Simulated and real data analyses were conducted. Results suggest that the improvement of the quality of imputation requires alternative methods to be developed.
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Acknowledgements
This work was partially funded by FCT- Fundação para a Ciência e a Tecnologia through project number CEMAPRE - UID/MULTI/00491/2019 and by FCT/MEC through national funds and when applicable co-funded by FEDER – PT2020 partnership agreement under the project UID/EEA/50008/2019.
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Ferrão, M.E., Prata, P. (2019). Computing Topics on Multiple Imputation in Big Identifiable Data Using R: An Application to Educational Research. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_2
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