Face-to-face (FTF) surveys have been the traditional method to gather nationally representative data and remain the dominant data collection mode in resource-poor countries. Conducting these surveys is expensive and time consuming. With the rapid expansion of mobile phone use, Short Message Service presents an opportunity to conduct inexpensive, fast, and scalable surveys. However, these samples are typically not representative of the target population. Standard adjustments to correct for nonrepresentative sampling are insufficient, due to two types of bias: residual sampling bias based on unobserved variables and survey mode effects. We introduce calibrated multilevel regression with poststratification (cMRP), a procedure that corrects for residual bias by incorporating a relatively small sample of FTF data that is known to be unbiased. We apply this method to the problem of estimating financial inclusion (access to formal banking systems) in Uganda. We find that our cMRP approach is effective in replicating estimates from a larger and much more expensive FTF survey. This article includes a description of our methods as well as results from the financial inclusion study and a discussion of limitations and future areas for research.