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ERIC ED599384: A Fully Conditional Specification Approach to Multil... |
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by ERIC |
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Specialized imputation routines for multilevel data are |
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widely available in software packages, but these methods |
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are generally not equipped to handle a wide range of |
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complexities that are typical of behavioral science data. |
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In particular, existing imputation schemes differ in |
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their ability to handle random slopes, categorical |
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variables, differential relations at level-1 and level-2, |
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and incomplete level-2 variables. Given the limitations |
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of existing imputation tools, the purpose of this |
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manuscript is to describe a flexible imputation approach |
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that can accommodate a diverse set of two-level analysis |
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problems that includes any of the aforementioned |
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features. The procedure employs a fully conditional |
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specification (also known as chained equations) approach |
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with a latent variable formulation for handling |
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incomplete categorical variables. Computer simulations |
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suggest that the proposed procedure works quite well, |
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with trivial biases in most cases. We provide a software |
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program that implements the imputation strategy, and we |
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use an artificial data set to illustrate its use. [This |
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paper was published in "Psychological Methods" v23 n2 |
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p298-317 2018.] |
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Date Published: 2022-07-18 06:43:02 |
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Identifier: ERIC_ED599384 |
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Item Size: 36022964 |
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Language: english |
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Media Type: texts |
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# Topics |
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ERIC Archive; ERIC; Enders, Craig K.\... |
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# Uploaded by |
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@chris85 |
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