Do-It-Yourself Multiple Imputation: Mode Effect Correction in a Public Opinion Survey

In this talk, I am demonstrating how a multiple imputation procedure can be built by a user from scratch. The motivating example comes from a public opinion survey in which the sampled respondents were providing their responses on the web or by phone. As is known in survey methodology literature, presence of an interviewer on the phone produces higher reports of socially desirable behaviors, such as number of friends or political engagement, or lower reports of undesirable behaviors, such as illicit drug use. Treating these less accurate responses as partially missing data, I develop a non-standard multiple imputation model that is driven by a concept of utility from choice and decision literature in economics. My implementation is aligned to supply the data to Stata -mi- suite, in the sense that I create the imputations, and -mi- combines them using Rubin's rules. As a side track, the workflow of the mode effect detection also featured multiple testing corrections. They required extensive -post- operations and exchanging the lists of variables between the do-files of the project. My work on this is also demonstrated.