Multiple Imputation for Missing Data : Making The Most of What You Know  

Oleh:

Cummings, Jonathan M. ; Fichman, Mark

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Organizational Research Methods vol. 6 no. 3 (2003), page 282-308.
Topik: Data; missing data; multivariate analsis; multiple imputation; statistical estimation; internet use
Fulltext: 282.pdf (181.79KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: OO3.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Missing data are a common problem in organizatinal research. Missing data can occur due to attrition in a longitudinal study or non response to questionnaire items in a laboratory or field setting. Improper treatments of missing data (e. g. listwise deletion, mean imputation) can lead to biased statistical inference using complete case analysisi stratistical techniques. This article presents a simulation and data analysis case study using a method for dealing with missing data, multiple imputation, that allows for valid statistical inference with complete case statistical analysis. Software for implementing multiple imputaiton under a multivariate normal model is freely and widely available (e. g. NORM, SAS, SOLAS). It should be routinely considered for imputing missing data. The authors illustrate the application of this technique using data from the home net project.

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