Obtaining Predictions from Models Fit to Multiply Imputed Data  

Oleh:

Miles, Andrew

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 45 no. 01 (Feb. 2016), page 175-185.
Topik: multiple imputation; prediction; missing data; linear transformation; non-linear transformation

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Obtaining predictions from regression models fit to multyply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how presictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how prediction can be obtained using Rubin's rules, this 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, calculating 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 Gneral Social Survey and with a simulation.

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