For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
30
views
15
references
 Top references
cited by
324
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      240
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The prevention and handling of the missing data

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: found
          • Article: not found

          Missing data analysis: making it work in the real world.

          John W Graham (2009)
          This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.
          Article 0 comments Cited 607 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Inference and missing data

            Donald B Rubin (1976)
            Article 0 comments Cited 600 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Multiple Imputation after 18+ Years

              Donald B Rubin (1996)
              Article 0 comments Cited 568 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Korean J Anesthesiol
                Journal ID (iso-abbrev): Korean J Anesthesiol
                Journal ID (publisher-id): KJAE
                Title: Korean Journal of Anesthesiology
                Publisher: The Korean Society of Anesthesiologists
                ISSN (Print): 2005-6419
                ISSN (Electronic): 2005-7563
                Publication date (Print): May 2013
                Publication date (Electronic): 24 May 2013
                Volume: 64
                Issue: 5
                Pages: 402-406
                Affiliations
                Department of Anesthesiology and Pain Medicine, Chung-Ang Universtiy College of Medicine, Seoul, Korea.
                Author notes
                Corresponding author: Hyun Kang, M.D., Ph.D., Department of Anesthesiology and Pain Medicine, Chung-Ang Universtiy College of Medicine, 224-1, Heuksuk-dong, Dongjak-gu, Seoul 156-756, Korea. Tel: 82-2-6299-2571, Fax: 82-2-6299-2585, roman00@ 123456naver.com
                Article
                DOI: 10.4097/kjae.2013.64.5.402
                PMC ID: 3668100
                PubMed ID: 23741561
                SO-VID: b8ff2289-09eb-4bc2-8ea8-5f7148c18edf
                Copyright © Copyright © the Korean Society of Anesthesiologists, 2013
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 13 February 2013
                Date accepted : 20 February 2013
                Categories
                Subject: Review Article

                ScienceOpen disciplines: Anesthesiology & Pain management
                Keywords: expectation-maximization,imputation,missing data,sensitivity analysis
                Data availability:
                ScienceOpen disciplines: Anesthesiology & Pain management
                Keywords: expectation-maximization, imputation, missing data, sensitivity analysis

                Comments

                Comment on this article

                scite_

                Similar content240

                See all similar

                Cited by317

                See all cited by