Sample selection bias with multiple dependent selection rules: An application to survey data analysis with multilevel nonresponse

The microdata of surveys are valuable resources for analyzing and modeling relationships between variables of interest. These microdata are often incomplete because of nonresponses in surveys and, if not considered, may lead to model misspecification and biased results. Nonresponse variable is usually assumed as a binary variable, and it is used to construct a sample selection model in many researches. However, this variable is a multilevel variable related to its reasons of occurring. Missing mechanism may differ among the levels of nonresponse, and merging the levels of nonresponse may cause bias in the results of the analysis. In this paper, a method is proposed for analyzing survey data with respect to reasons for the nonresponse based on sample selection model. Each nonresponse level is considered as a selection rule, and classical Heckman model is extended. Simulation studies and an analysis of a real data set from an establishment survey are presented to demonstrate the performance and practical usefulness of the proposed method.