Multiple imputation for missing data:A cautionary tale  

Oleh:

Allison, Paul D.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 28 no. 03 (Feb. 2000), page 301-309.
Topik: regression coefficients; Multiple imputation; Missing data

Ketersediaan

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

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Isi artikel

Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Software using a propensity score classifier with the approximate Bayesian bootstrap produces badly biased estimates of regression coefficients when data on predictor variables are missing at random or missing completely at random. On the other hand, a regression-based method employing the data augmentation algorithm produces estimates with little or no bias.

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