TREATMENTS OF MISSING DATA

This paper presented a critical review of the most commonly used treatments of missing data: traditional, such as case deletion and single imputation (mean imputation, imputation by regression, hot deck imputation), and modern, such as multiple imputation methods and maximum likelihood methods (EM algorithm, FIML method). We described their advantages and disadvantages and recommendations regarding selection of treatment in relation to the missing data mechanism, the type and measurement level of variables, sample size, etc. Also, paper included an overview of treatment practices of categorical and numerical missing data in psychology articles published in top journals. It was concluded that the most commonly used treatment is the traditional listwise deletion, while multiple imputation is a slightly less used method. Thus, we provided an example of implementation of multiple imputation in SPSS.