Doctoral Dissertation Research: Investigating the Bias of Alternative Statistical Inference Methods in Sequential Mixed-Mode Surveys

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Sequential mixed-mode surveys use a mix of modes or data collection methods such as mail, telephone, in-person, and web to increase the number of people who respond to a survey. In sequential designs, there is usually no control in assigning subgroups of respondents to modes. As a result, nonrandom assignment of modes is an inherent characteristic of sequential mixed-mode surveys. This design is important since there are usually limited funds to probe people to respond. While the goal of using mixed modes is clear, one compelling research question is how the nonrandom mix of mode impacts survey data and how these effects should be handled in estimating survey population characteristics such as mean income, and health insurance coverage. To date, since the nonrandom mix of modes poses a challenge in evaluating the mode effects, the existing inference methods assume that mode effects can be ignored in sequential mixed-mode surveys despite their unknown impact on the quality of the survey estimates. This research develops and evaluates the statistical inference methods accounting for nonrandom mode effects to test the comparability of the survey estimates from the different modes. In parallel, this project also develops statistical inference methods accounting for both nonresponse and nonrandom mode effects in the presence of nonignorable mode effects. The public-use Current Population Survey (CPS), 1973, and Social Security Records Exact Match, and the nonpublic-use American Community Survey (ACS) data will be used to conduct empirical and simulation evaluations. This research provides federal agencies, survey organizations, research centers, and other data producers assessment and inferential methods that adjust for both nonresponse and nonrandom mode effects in the context of sequential mixed-mode surveys. Some large surveys have employed some variation of mixed-mode surveys in order to meet budget constraints. On the other hand, in the presence of nonignorable mode effects, the bias properties for the survey population characteristics are not known and the existing assessment and inferential methods do not control for the nonrandom mode effects. This research produces sequential mixed-mode assessment methods which will test the ignorability of the mode effects which can be a threat for the quality of survey data. In parallel, this research also produces methods of inference which will yield higher quality survey estimates in the presence of nonignorable mode effects.