Sequential mixed-mode surveys combine different data collection modes in succession to reduce nonresponse bias under certain cost constraints. However, as a result of nonignorable mode effects, nonrandom mixes of modes may yield unknown bias properties for population estimates such as means, proportions and totals. The existing inference methods for sequential mixed-mode surveys generally assume that mode effects are ignorable. The objective of this paper is to describe and empirically evaluate some proposed multiple imputation estimation methods that account for both nonresponse and nonrandom mixtures of modes in a sequential mixed-mode survey. In particular, the multiple selection imputation models allow imputation of responses for alternative modes as if they responded in a given mode by controlling nonrandom mixes of mode. For example, if personal and telephone interviews are used, one step in the process is to impute values for the telephone cases as if they had responded by personal interview (PI) to produce a completed PI data set. Similarly, a completed telephone interview (TI) data set is created. The completed PI and TI data sets are combined for inference. Through simulations, the method is evaluated in terms of the bias reduction for varying degrees of mode effects and model fit. The American Community Survey (ACS) or the 1973 public-use Current Population Survey and Social Security Records Exact Match data will be used to conduct empirical and simulation evaluations. The focus of the empirical evaluations and simulations will be mean family income.