A RECAP OF THE MISSING VALUE ANALYSIS (Hair, 2013)

(Nguồn:https://www.researchgate.net/publication/329398079_The_Sin_of_Missing_Data_Is_All_Forgiven_by_Way_of_Imputation/figures?lo=1 )

 

Evaluation of the issues surrounding missing datain the data set can be summarized in four conclusions:

• The missing data process is MCAR. 

All of the diagnostic techniques support the conclusion that no systematic missing data process exists, making the missing data MCAR (missing completely at random). Such a finding provides two advantages to the researcher. First, it should not involve any hidden impact on the results that need to be considered when interpreting the results. Second, any of the imputation methods can be applied as remedies for the missing data. Their selection need not be based on their ability to handle nonrandom processes, but instead on the applicability of the process and its impact on the results.  

• Imputation is the most logical course of action. 

Even given the benefit of deleting cases and variables, the researcher is precluded from the simple solution of using the complete case method, because it results in an inadequate sample size. Some form of imputation is therefore needed to maintain an adequate sample size for any multivariate analysis.

• Imputed correlations differ across techniques.

 When estimating correlations among the variables in the presence of missing data, the researcher can choose from four commonly employed techniques: the complete case method, the all-available information method, the mean substitution method, and the EM method. The researcher is faced in this situation, however, with differences in the results among these methods. The all-available information, mean substitution, and EM approaches lead to generally consistent results. Notable differences, however, are found between these approaches and the complete information approach. Even though the complete information approach would seem the most “safe” and conservative, in this case it is not recommended due to the small sample used (only 26 observations) and its marked differences from the other two methods. The researcher should, if necessary, choose among the other approaches.

• Multiple methods for replacing the missing data are available and appropriate. 

Mean substitution is one acceptable means of generating replacement values for the missing data. The researcher also has available the regression and EM imputation methods, each of which give reasonably consistent estimates for most variables. The presence of several acceptable methods also enables the researcher to combine the estimates into asingle composite, hopefully mitigating any effects strictly due to one of the methods.

Nguồn: 

•  Hair, J. F. (2009). Multivariate data analysis.