Comparison of abortion incidence estimates derived from direct survey questions versus the list experiment among women in Ohio

• Authors: Robert B Hood, Emory University; Heidi Moseson, Ibis Reproductive Health; Mikaela Smith, The Ohio State University, Columbus,; Payal Chakraborty, The Ohio State University, Columbus,; Alison H Norris, The Ohio State University, Columbus,; Maria F Gallo, The Ohio State University, Columbus,
• Journal Title: PLoS One
• Volume: Volume 17, Number 6
• Publisher: Public Library of Science (PLoS) | 2022-06-03
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0269476
• Subjects: Health Sciences, Epidemiology
• Keywords: abortion incidence; direct survey questions; women's health

Abstract

Abortion is highly stigmatized in the United States which prevents its accurate measurement in surveys. The list experiment aims to improve the reporting of abortion history. We evaluated whether a list experiment resulted in higher reporting of abortion experiences than did two direct questions. Utilizing data from a representative survey of adult women of reproductive age in Ohio, we examined abortion history using two direct questions and a double list experiment. Through the double list experiment, we asked respondents to report how many of two lists of health items they had experienced; one list included abortion. We compared weighted history of abortion between these measures and by respondent demographic characteristics (age and socioeconomic status). Estimates of abortion history were similar between direct and list experiment questions. When measured with the two different direct question of abortion history, 8.4% and 8.0% of all respondents indicated ever having an abortion and with the list experiment, 8.5% indicated ever having an abortion. In a Midwestern state-level survey, the list experiment did not lead to increases in abortion reporting as compared to the direct questions. Subgroup analyses require larger samples, and future iterations should incorporate related but non-stigmatized control items to reduce misclassification and under-powering of such subgroup analyses.