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‎In many statistical studies some units do not respond to a number or all of the questions‎. ‎This situation causes a problem called non-response‎. ‎Bias and variance inflation are two important consequences of non-response in surveys‎. ‎Although increasing the sample size can prevented variance inflation‎, ‎but cannot necessary adjust for the non-response bias‎. ‎Therefore a number of methods are used for reducing non-response effects‎. ‎In the cases where missing mechanism is at random‎, ‎weighting adjustment is an appropriate method for compensating the effects of unit non-response‎. ‎Propensity score is a weighting method in which weight allocation is accomplished based on the estimates of response probabilities‎. ‎These estimates are obtained by fitting suitable parametric models‎. ‎In this paper‎, ‎the propensity score method and its resulted adjusted estimators are introduced‎. ‎Then we compare the performance of three propensity score adjusted estimators‎. ‎Finally‎, ‎data on Household Income and Expenditure Survey for urban families conducted by Statistical Centre of Iran in spring 1390 are used to compare the adjusted propensity score estimators by two measures of comparisons‎, ‎root relative mean squared error and relative efficiency‎. 

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Evidence-based management and development planning relies on official statistics. There are some obstacles that make it impossible to do a single-mode survey. These obstacles are the sampling frame, time, budget, and accuracy of measurement of each mode. Always we can not use a single-mode survey because of these factors. So we need to use other data collection methods to overcome these obstacles. This method is called the mixed-mode survey, which is a combination of several modes. In this article, we show that mixed-mode surveys can produce more accurate official statistics than single-mode surveys.