J4 ›› 2013, Vol. 12 ›› Issue (10): 1-5.

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Imputation Method Study with Missing Data in Random Experiment Design

  

  1. College of Mathematics and Computer, Dali University, Dali, Yunnan 671003, China
  • Received:2013-03-12 Revised:2013-06-24 Online:2013-10-15 Published:2013-10-15

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Abstract:

In random experiment design, missing data often exist due to some reason. There are four methods to deal with the missing data：delete the missing data，mean imputation，formula imputation and Yate's imputation. It is an interesting question to compare the four methods. This article presents how to use simulation study to carry out this comparison. First, built a 4×5 random experiment design; m denotes the numbers of missing data which equals from 1 to 6; Second，find out all missing values' location combinations. For each combination, these 4 methods are executed separately, and standard error, square R and adjust square R for each method are recorded. Last, the simulation study shows Yate's inputaton method performance is better than other 3 methods. The real example also
proves simulation results.

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