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A fully conditional specification approach to multilevel imputation of categorical and continuous variables. | LitMetric

AI Article Synopsis

  • - Existing imputation methods for multilevel data in behavioral science are often inadequate in managing complexities like random slopes and categorical variables.
  • - This manuscript presents a new flexible imputation approach that effectively handles various 2-level analysis challenges, using a method known as fully conditional specification with latent variable formulations.
  • - The authors have created a software program to implement this new strategy and provide an example using artificial data to demonstrate its effectiveness, with simulations indicating it yields minimal bias.

Article Abstract

Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential relations at Level-1 and Level-2, and incomplete Level-2 variables. Given the limitations of existing imputation tools, the purpose of this manuscript is to describe a flexible imputation approach that can accommodate a diverse set of 2-level analysis problems that includes any of the aforementioned features. The procedure employs a fully conditional specification (also known as chained equations) approach with a latent variable formulation for handling incomplete categorical variables. Computer simulations suggest that the proposed procedure works quite well, with trivial biases in most cases. We provide a software program that implements the imputation strategy, and we use an artificial data set to illustrate its use. (PsycINFO Database Record

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Source
http://dx.doi.org/10.1037/met0000148DOI Listing

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