Heterogeneous Attitudinal Profiles Towards Gene Editing: Evidence From Latent Class Analysis

Isaac N. Halstead (Lead / Corresponding author), Jan R. Boehnke, Gary J. Lewis

Research output: Contribution to journalArticlepeer-review

20 Downloads (Pure)

Abstract

Advances in gene-editing technology have important implications for the treatment and prevention of disease. Accordingly, it is important to understand public perceptions towards gene editing, as the public’s willingness to endorse gene editing may be as important as technological breakthroughs themselves. Previous research has almost exclusively examined attitudes towards gene editing on specific issues, but has not addressed how attitudes towards gene editing across a range of issues coalesce in individuals: that is, the degree to which discrete, heterogeneous attitudinal profiles exist versus a simple support/oppose continuum. Here, we addressed this issue using latent class analysis on data from The Pew Research Center (N = 4726; US residents) across a wide range of gene-editing topics. We found that attitudes towards gene editing cohere into 10 distinct latent classes that showed some evidence of a support/oppose continuum, but also for clear qualitative differences between each class, even with support or oppose classes, on a number of issues. The most opposed classes significantly differed from the supporter classes in age, sex, political ideology and self-rated knowledge. These findings provide evidence that attitudes towards gene editing are heterogeneous and public discourse, as well as policy making need to consider a range of arguments when evaluating this technology.

Original languageEnglish
Number of pages16
JournalPublic Understanding of Science
Early online date24 Aug 2022
DOIs
Publication statusE-pub ahead of print - 24 Aug 2022

Keywords

  • biotechnology
  • genetic and reproductive technologies
  • public understanding of science
  • science attitudes and perceptions

Fingerprint

Dive into the research topics of 'Heterogeneous Attitudinal Profiles Towards Gene Editing: Evidence From Latent Class Analysis'. Together they form a unique fingerprint.

Cite this