Methods for Exploratory Assessment of Consent-to-Link in a Household Survey. (7th December 2017)

• Record Type: Journal Article
• Authors: Yang, Daniel; Fricker, Scott; Eltinge, John
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 7:Number 1(2019)
• Page Start: 118
• Page End: 155
• Publication Date: 2017-12-07
• Subjects: Administrative Record Data -- Burden Reduction -- Incomplete Data -- Informed Consent -- Propensity Model; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smx031
• Languages: English
• ISSNs: 2325-0984

Abstract

Abstract: There is increasing interest in linking survey data to administrative records to reduce respondent burden and enhance the amount and quality of information available on sample respondents. In many cases, legal constraints or societal norms require survey organizations to obtain informed consent from sample units before linking survey responses with administrative data. Guiding such efforts is a growing empirical literature examining factors that impact respondents' consent decisions and the success of linkage attempts, as well as evaluations of potential differences between consenting and non-consenting respondents. This paper outlines a range of options that statistical organizations can consider for evaluation and testing of linked datasets. We apply methods for assessing consent propensity and consent bias to data from the U.S. Consumer Expenditure Survey, and investigate the impacts of demographic, socio-economic, and attitudinal variables on respondents' consent-to-link propensities. We then analyze potential consent-to-link biases in mean and quantile estimates of several economic variables, by comparing different propensity-adjusted and unadjusted estimates, and by comparing estimates from consenting and non-consenting respondents. We contrast several estimation approaches, and discuss implications of our findings for consent-propensity assessments and for approaches to minimize risks of consent-to-link bias.