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9 MULTIPLE IMPUTATION OF INCOMPLETE CATEGORICAL DATA USING LATENT CLASS ANALYSIS
| Content Provider | CiteSeerX |
|---|---|
| Author | Ginkel, Joost R. Van Vermunt, Jeroen K. Ark, L. Andries Van Der Ginkel, Van Sijtsma Sijtsma, Klaas Ark, Van Der |
| Abstract | We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete cate-gorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large im-putation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood We would like to thank Paul Allison and Jay Magidson, as well as the editor and the three anonymous reviewers, for their comments, which very much helped to improve this article. We would also like to thank Greg Richards for providing the |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multiple Imputation Greg Richards Large Im-putation Model Unknown Model Parameter Nonparametric Bootstrap Procedure Latent Class Analysis Multiple Imputation Method Simulated Data Example Latent Gold Software Well-established Method Jay Magidson Paul Allison Latent Class Model Common Full Bayesian Approach Incomplete Cate-gorical Data Imputation Model Log-linear Analysis Categorical Variable Complex Association Structure |
| Content Type | Text |
