Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia

Population Health Metrics

Research

Eric Vittinghoff¹  Robert A. Hiatt¹  Heidi Moseson²  Christine Dehlendorf³  Caitlin Gerdts⁴ 

[1] Department of Epidemiology & Biostatistics, University of California, San Francisco, USA;Department of Epidemiology & Biostatistics, University of California, San Francisco, USA;Ibis Reproductive Health, 1330 Broadway St, Suite 1100, 94612, Oakland, CA, USA;Department of Family & Community Medicine, University of California, San Francisco, USA;Ibis Reproductive Health, 1330 Broadway St, Suite 1100, 94612, Oakland, CA, USA;

关键词: Abortion;    List experiment;    Item count technique;    Multivariable regression analysis;    Design effect;    Liberia;    Family planning;    Methods;   

DOI  :  10.1186/s12963-017-0157-x

 received in 2017-03-02, accepted in 2017-12-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe list experiment is a promising measurement tool for eliciting truthful responses to stigmatized or sensitive health behaviors. However, investigators may be hesitant to adopt the method due to previously untestable assumptions and the perceived inability to conduct multivariable analysis. With a recently developed statistical test that can detect the presence of a design effect – the absence of which is a central assumption of the list experiment method – we sought to test the validity of a list experiment conducted on self-reported abortion in Liberia. We also aim to introduce recently developed multivariable regression estimators for the analysis of list experiment data, to explore relationships between respondent characteristics and having had an abortion – an important component of understanding the experiences of women who have abortions.MethodsTo test the null hypothesis of no design effect in the Liberian list experiment data, we calculated the percentage of each respondent “type,” characterized by response to the control items, and compared these percentages across treatment and control groups with a Bonferroni-adjusted alpha criterion. We then implemented two least squares and two maximum likelihood models (four total), each representing different bias-variance trade-offs, to estimate the association between respondent characteristics and abortion.ResultsWe find no clear evidence of a design effect in list experiment data from Liberia (p = 0.18), affirming the first key assumption of the method. Multivariable analyses suggest a negative association between education and history of abortion. The retrospective nature of measuring lifetime experience of abortion, however, complicates interpretation of results, as the timing and safety of a respondent’s abortion may have influenced her ability to pursue an education.ConclusionOur work demonstrates that multivariable analyses, as well as statistical testing of a key design assumption, are possible with list experiment data, although with important limitations when considering lifetime measures. We outline how to implement this methodology with list experiment data in future research.

【 授权许可】

CC BY   
© The Author(s). 2017