The Library
Multiple imputation of missing data in educational production functions
Tools
Elasra, Amira (2022) Multiple imputation of missing data in educational production functions. Computation, 10 (4). e49. doi:10.3390/computation10040049 ISSN 2079-3197.
|
PDF
computation-10-00049.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2605Kb) | Preview |
Official URL: https://doi.org/10.3390/computation10040049
Abstract
Educational production functions rely mostly on longitudinal data that almost always exhibit missing data. This paper contributes to a number of avenues in the literature on the economics of education and applied statistics by reviewing the theoretical foundation of missing data analysis with a special focus on the application of multiple imputation to educational longitudinal studies. Multiple imputation is one of the most prominent methods to surmount this problem. Not only does it account for all available information in the predictors, but it also takes into account the uncertainty generated by the missing data themselves. This paper applies a multiple imputation technique using a fully conditional specification method based on an iterative Markov chain Monte Carlo (MCMC) simulation using a Gibbs sampler algorithm. Previous attempts to use MCMC simulation were applied on relatively small datasets with small numbers of variables. Therefore, another contribution of this paper is its application and comparison of the imputation technique on a large longitudinal English educational study for three iteration specifications. The results of the simulation proved the convergence of the algorithm.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | H Social Sciences > HA Statistics L Education > LB Theory and practice of education Q Science > QA Mathematics |
||||||
Divisions: | Faculty of Social Sciences > Economics | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Multiple imputation (Statistics), Monte Carlo method , Markov processes, Missing observations (Statistics), Education -- Research -- Statistical methods, Educational statistics, Statistics | ||||||
Journal or Publication Title: | Computation | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2079-3197 | ||||||
Official Date: | 24 March 2022 | ||||||
Dates: |
|
||||||
Volume: | 10 | ||||||
Number: | 4 | ||||||
Article Number: | e49 | ||||||
DOI: | 10.3390/computation10040049 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 28 March 2022 | ||||||
Date of first compliant Open Access: | 28 March 2022 | ||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year