Obtaining Predictions from Models Fit to Multiply Imputed Data

Andrew Miles

Sociological Methods & Research, 2016, vol. 45, issue 1, 175-185

Abstract: Obtaining predictions from regression models fit to multiply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how predictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how predictions can be obtained using Rubin’s rules, that is, by being estimated separately in each imputed data set and then combined. It then demonstrates that predictions can also be calculated directly from the final analysis model. Both approaches yield identical results when predictions rely solely on linear transformations of the coefficients and calculate standard errors using the delta method and diverge only slightly when using nonlinear transformations. However, calculation from the final model is faster, easier to implement, and generates predictions with a clearer relationship to model coefficients. These principles are illustrated using data from the General Social Survey and with a simulation.

Keywords: multiple imputation; prediction; missing data; linear transformation; nonlinear transformation (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124115610345 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:45:y:2016:i:1:p:175-185

DOI: 10.1177/0049124115610345

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().