Response Propensity Score Matching Imputation Method And Application Research

Imputation is currently an important way to deal with non-response.propensity score matching imputation method based on counterfactual thinking,constructing unmatched counterfactual matching groups results in unpleasant imputation values.However,this method relies on the construction of the matching model for non-response and is more sensitive to the differences of sample size between no-answer and answer-groups.This paper presents a response propensity score matching imputation method.The observational data response variable observations are ranked in order of magnitude,converted to a value between 0 and 1 by rank normalization,and a propensity score model between explanatory variables is established,which matches the score of no answer and answer to determine the interpolation value without answer.The simulation results show that the imputation effect of response propensity score matching imputation method is superior to the propensity score matching imputation method,nearest neighbor imputation method and regression imputation method.Under the completely random non-response mechanism and the random non-response mechanism,as the imputation weight increases,the absolute value of the deviation and the mean square error of response propensity score matched imputation show an increasing trend.In practice,imputation weight number can be 5.Finally,the Sparrows dataset was used for analysis,and the non-response response propensity score matching imputation method was used for imputation.The processing results showed that the imputation effect was better.The empirical analysis is consistent with the simulation results.Response propensity score matching imputation method based on propensity score matching imputation method effectively improves the fitting effect of the model and improves the reliability of the imputation value.