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

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...

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Bibliographic Details
Main Author: Ferrão, Maria Eugénia (author)
Other Authors: Prata, Paula (author)
Format: bookPart
Language:eng
Published: 2020
Subjects:
Multiple imputation
R programming
Big data
Education research
Online Access:http://hdl.handle.net/10400.6/8731
Country:Portugal
Oai:oai:ubibliorum.ubi.pt:10400.6/8731
Description
Summary: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.

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