Using Online Implicit Association Tests in Opinion Polling

TL;DR

This paper explores the use of online implicit association tests to detect socially desirable responding in opinion polls, demonstrating their potential to improve polling accuracy and inclusivity, especially during COVID-19 restrictions.

Contribution

It introduces the integration of online IATs into opinion polling to identify shy voters and improve data validity, a novel approach in survey methodology.

Findings

Shy voters can be effectively identified using online IATs.

Inclusion of IATs can enhance polling accuracy and diversity.

Online IATs are feasible and reliable during pandemic restrictions.

Abstract

Opinion polls have now become a very important component of society because they are now a defacto component of our daily news cycle and because their results influence governments and business in ways which are not always obvious to us. However, polling is not always accurate and there have been some really inaccurate polling results which have had major influences on the world going back to the 1930s but also as recently as just the last 3 or 4 years. In this paper we analyse the phenomenon of socially desirable responding (shy voters) which has emerged as one of the reasons for modern day inaccurate polling. We describe how it can be exposed through implicit association tests (IATs) and we demonstrate the shy voter effect in a small survey on opinions in Ireland towards the United Kingdom. We argue for inclusion of IATs in traditional polling and point to the fact that these can be conducted accurately online, which also allows polling to reach a larger and more diverse sample of respondents in the days of Covid-19 restrictions which restricts the opportunities for poll sampling from the general public.