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The analysis of sample survey data often requires adjustments for missing values in variables of interest. Standard adjustments based on item imputation or propensity weighting factors rely on the availability of auxiliary variables for both responding and non-responding units. However, the application of these can be challenging when the auxiliary variables are themselves subject to incomplete-data issues. This paper will demonstrate how low rank matrix approximation can be applied to impute missing auxiliary variables. The performance depends on the rank of the auxiliary variable matrix and the extent to missingness rates. We will evaluate the method in terms of bias and mean squared error.