Applied Missing Data Analysis (Methodology In The Social Sciences)

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The Analysis of Social Science Data with Missing Values

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Applied Missing Data Analysis by Craig K Enders

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  • Please note that corrections may take a couple of weeks to filter through the various RePEc services. Economic literature: papers , articles , software , chapters , books. Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable.

    Strategies for Dealing with Missing Data in Clinical Trials: From Design to Analysis

    Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and multiple imputation, are applied to the bivariate problem. General properties of these methods are outlined, and applications to more complex missing-data problems are discussed. The EM algorithm, a convenient method for computing maximum likelihood estimates in missing-data problems, is described and applied to two common models, the multivariate normal model for continuous data and the multinomial model for discrete data.

    Multiple imputation under explicit or implicit models is recommended as a method that retains the advantages of imputation and overcomes its limitations. Roderick J. Rubin, Handle: RePEc:sae:somere:vyip as. More about this item Statistics Access and download statistics. Corrections All material on this site has been provided by the respective publishers and authors.


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