The Precision of Estimates of Nonresponse Bias in Means

Author:

Eckman Stephanie1ORCID,Unangst Jennifer2,Dever Jill A3,Antoun Christopher4

Affiliation:

1. RTI International Fellow with the Survey Research Division, , 701 13th St NW, Suite 750, Washington, DC 20005, USA

2. RTI International Research Statistician with the Division for Statistical and Data Sciences, , 3040 E Cornwallis Rd, PO Box 12194, Research Triangle Park, NC 27709, USA

3. RTI International Senior Director, Division for Statistical and Data Sciences, , 701 13th St NW, Suite 750, Washington, DC 20005, USA

4. Joint Program in Survey Methodology and College of Information Studies Assistant Research Professor with the , 7251 Preinkert Dr, College Park, MD 20742, USA

Abstract

Abstract Survey data producers increasingly provide estimates of nonresponse bias in several variables when they release or analyze data. Researchers understand that sample estimates of population values should be reported with appropriate measures of uncertainty, such as standard errors or confidence intervals. However, few studies acknowledge that nonresponse bias estimates vary across samples. Using simulations, we study the precision of estimates of nonresponse bias in means and how that precision is affected by features such as clustering and response rates. Results show that low response rates and clustering increase the variability of bias estimates. We then evaluate three methods to estimate the sampling variance of nonresponse bias in means: a method developed by Lee, jackknife replication, and linearization. We find that the Lee approach works well for simple random samples but overestimates variability for clustered samples. Linearization and replication work well with all populations studied, and we give an algorithm for the implementation of these approaches. We also apply the three methods to the LISS (Longitudinal Internet Studies for the Social Sciences) panel and the General Social Survey, providing practical confirmation of the simulation results.

Publisher

Oxford University Press (OUP)

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Social Sciences (miscellaneous),Statistics and Probability

Reference50 articles.

1. Nonresponse Bias for Univariate and Multivariate Estimates of Social Activities and Roles;Amaya;Public Opinion Quarterly,2017

2. Simultaneous Estimation of Multiple Sources of Error in a Smartphone-Based Survey;Antoun;Journal of Survey Statistics and Methodology,2019

3. Fitting Linear Mixed-Effects Models Using lme4;Bates;Journal of Statistical Software,2015

4. Introduction to Survey Quality

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