ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

›› 2011, Vol. 19 ›› Issue (7): 1083-1090.

• 研究方法 • Previous Articles    

Handling Missing Data: Expectation-Maximization Algorithm and Markov Chain Monte Carlo Algorithm

MU Shou-Kuan;ZHOU Wei   

  1. Department of Education, Zhangzhou Normal University, Zhangzhou 363000, China
  • Received:2010-10-29 Revised:1900-01-01 Online:2011-07-15 Published:2011-07-15
  • Contact: MU Shou-Kuan;ZHOU Wei

Abstract: Dataset with missing data is quite common in psychological research, which usually creates major problems in statistical inference. Maximum Likelihood Estimator and Multiple Imputation based on Bayesian Estimator are most important methods of handling missing data. Expectation-Maximization Algorithm, included in Maximum Likelihood Estimator is quite advantageous to flexible use and accurate results, while Markov Chain Monte Carlo Algorithm may achieve multiple imputation more easily and can be applied to handling missing data in complex situations. Finally, suitable statistical software is discussed in the field of psychological study.

Key words: missing data, Expectation-Maximization algorithm, Markov Chain Monte Carlo algorithm, Maximum Likelihood Estimator, multiple imputation