Identification of the possibilities of universalization of the methodology of filling in gaps in sociological data

This study addresses the issue of missing data in sociological research and the methods for their imputation. Missing data can significantly distort analysis results and complicate the interpretation of findings. The study systematically evaluates the effectiveness of various imputation algorithms, including mean imputation, median imputation, regression analysis, and the nearest neighbors algorithm. The results indicate that the choice of imputation method should depend on the type and structure of the data, as well as the mechanism of missingness. The most effective algorithms were those that considered the distribution and dependencies within the data. The research emphasizes the need for further development and adaptation of imputation methods to improve the quality of analysis in the presence of incomplete data.