The literature on the analysis of incomplete data using models is reviewed in the context of nonresponse in sample surveys. The modeling approach provides a large body of methods for handling unit and item nonresponse, some of which cannot be derived from the randomization theory of inference for surveys. Key concepts from the literature on incomplete data, such as factorizations of the likelihood for special data patterns, the EM algorithm for general data patterns, and ignorability of the response mechanism, are discussed within the survey context. Model-based procedures are related to common methods for handling nonresponse in surveys, such as weighting or imputation of means within subclasses of the population.