Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE ( © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

date: 12 May 2021

Abstract and Keywords

The inevitability and importance of missing data ought to move researchers to prevent, treat, and report the condition. Unfortunately, despite great advances in the field, researchers tend to ignore missing data. We hypothesize that ignoring missing data stems from low interest, unavailable solutions, and higher priorities by most social scientists. Thus, we aimed to remedy those potential mechanisms by providing a clear demonstration of missing-data handling in three distinct data analysis scenarios (psychometric, longitudinal, and covariance models) using R. Each of these exemplar procedures comes with code and data allowing readers to replicate and extend our examples to their own data. By demonstrating the use of missing-data—handling techniques in a freely available statistical package (R), we hope to increase available options and reduce the researcher's burden for handling missing data in common social science data analytic scenarios.

Keywords: Missing data, demonstration, R psychometrics, longitudinal analyses, covariance models

Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.

Please subscribe or login to access full text content.

If you have purchased a print title that contains an access token, please see the token for information about how to register your code.

For questions on access or troubleshooting, please check our FAQs, and if you can''t find the answer there, please contact us.