How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method

@article{Chen2003HowTD,
  title={How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method},
  author={G. Chen and T. {\AA}stebro},
  journal={Organizational Research Methods},
  year={2003},
  volume={6},
  pages={309 - 327}
}
The authors analyze the efficiency of six missing data techniques for categorical item nonresponse under the assumption that data are missing at random or missing completely at random. By efficiency, the authors mean a procedure that produces an unbiased estimate of true sample properties that is also easy to implement. The investigated techniques include listwise deletion, mode substitution, random imputation, two regression imputations, and a Bayesian model-based procedure. The authors… Expand

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References

SHOWING 1-10 OF 40 REFERENCES
The use of multiple imputation for the analysis of missing data.
A Monte Carlo Analysis of Missing Data Techniques in a HRM Setting
Multiple Imputation After 18+ Years
The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.
  • C. Enders
  • Mathematics, Medicine
  • Psychological methods
  • 2001
Missing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques
A comparison of inclusive and restrictive strategies in modern missing data procedures.
INFERENCE AND MISSING DATA
Weighted estimating equations with nonignorably missing response data.
Missing data: our view of the state of the art.
...
1
2
3
4
...