Computing Topics on Multiple Imputation in Big Identifiable Data Using R: An Application to Educational Research

Ferrão, Maria Eugénia; Prata, Paula

http://hdl.handle.net/10400.6/8731

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Authors

Ferrão, Maria Eugénia

Prata, Paula

Abstract(s)

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 “Bay-lorEdPsych” 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 sug-gest that the improvement of the quality of imputation requires alternative methods to be developed.

Keywords

Multiple imputation R programming Big data Education research

URI

http://hdl.handle.net/10400.6/8731

Citation

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. (eds) Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science, vol 11621. Springer, Cham