14
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Imputing Cross-Sectional Missing Data: Comparison of Common Techniques

      1 , 1 , 1
      Australian & New Zealand Journal of Psychiatry
      Informa UK Limited

      Read this article at

      ScienceOpenPublisherPubMed
      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.

          Related collections

          Most cited references15

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

          Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective.

          Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced anew generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m > 1 plausible values. The rn versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997a) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from the Adolescent Alcohol Prevention Trial (Hansen & Graham, 199 I).
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Application of random-effects pattern-mixture models for missing data in longitudinal studies.

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

              Missing data in Likert ratings: A comparison of replacement methods.

              The effects of using two methods (item mean and person mean) for replacing missing data in Likert scales were studied. The results showed that both methods were good representations of the original data when both the number of respondents with missing data and the number of items missing were 20% or less. As the numbers of missing items and of respondents with missing data increased for the person mean substitution method, a spurious increase in the inter-item correlations (and, therefore, reliability) for the sale was produced. The item mean substitution reduced the reliability estimates of the scale. These results suggest caution in the use of the person mean substitution method as the numbers of missing items and respondents increase.
                Bookmark

                Author and article information

                Journal
                Australian & New Zealand Journal of Psychiatry
                Aust N Z J Psychiatry
                Informa UK Limited
                0004-8674
                1440-1614
                June 26 2016
                July 2005
                June 26 2016
                July 2005
                : 39
                : 7
                : 583-590
                Affiliations
                [1 ]Department of Psychiatry, Australian Centre for Posttraumatic Mental Health, The University of Melbourne, PO Box 5444, West Heidelberg, Melbourne, Victoria, 3081, Australia
                Article
                10.1080/j.1440-1614.2005.01630.x
                15996139
                b9b4f699-7fab-4029-bdf0-d9cda992526f
                © 2005

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

                History

                Comments

                Comment on this article