Missing data appear in all areas of research, and the client is in the field of social sciences. As such, they can reduce the statistical power of research and produce biased estimates, which can result in inadequate conclusions. This paper provides an overview of the patterns and mechanisms by which missing data may be missing in research. In addition to the above, the paper presents traditional and modern methods that can be used to eliminate missing data and points out the advantages and disadvantages of both groups of methods. It is recommended to use modern methods – such as multiple imputation methods, due to the high bias in the assessment of parameters caused by traditional methods of missing data treatment, such as methods of deleting missing data in whole or in pairs or single imputation methods.With that in mind, this paper gives an example of conducting multiple imputation in the SPSS program.