Abstract
One of the most difficult problems with performing multiple imputation relates to having too many variables in the imputation model. In many instances, the problems associated with having too many variables in the model can be avoided by using the strategies suggested in the previous chapter. Still, situations arise in which more variables need to be included in the model than can feasibly be handled by the current software. In this chapter, we reiterate the “Think FIML” approach to multiple imputation, which will help you avoid many pitfalls in this regard. Also, for situations in which the Think FIML approach is not enough, we describe two other strategies for dealing with this problem. The first strategy involves reducing the number of variables by imputing whole scales rather than the individual items making up the scales. The second strategy involves dividing up the variables into two or more sets that can be imputed separately with minimal bias.
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Notes
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One could consider other approaches to making these judgments about unidimensionality and homogeneity. For example, one could consider using tools of exploratory factor analysis (e.g., the scree test) to verify that the items making up a scale do indeed form a single factor. One could also use the SEM framework to help make judgments about whether the scale items tap a single factor, and whether it is reasonable in a statistical sense to treat the factor loadings to be equal, that is, to be homogeneous.
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Graham, J.W., Van Horn, M.L., Taylor, B.J. (2012). Dealing with the Problem of Having Too Many Variables in the Imputation Model. In: Missing Data. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4018-5_9
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DOI: https://doi.org/10.1007/978-1-4614-4018-5_9
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