Open AccessDismissing “Don’t Know” Responses to Perceived Risk Survey Items Threatens the Validity of Theoretical and Empirical Behavior-Change Research
Author(s) -
Erika A. Waters,
Marc T. Kiviniemi,
Jennifer L. Hay,
Heather Orom
Publication year - 2021
Publication title -
perspectives on psychological science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.234
H-Index - 140
eISSN - 1745-6924
pISSN - 1745-6916
DOI - 10.1177/17456916211017860
Subject(s) - generalizability theory , perception , psychology , risk perception , social psychology , external validity , intervention (counseling) , empirical research , population , hazard , developmental psychology , medicine , epistemology , environmental health , philosophy , chemistry , organic chemistry , neuroscience , psychiatry
Since the middle of the 20th century, perceptions of risk have been critical to understanding engagement in volitional behavior change. However, theoretical and empirical risk perception research seldom considers the possibility that risk perceptions do not simply exist: They must be formed. Thus, some people may not have formulated a perception of risk for a hazard at the time a researcher asks them, or they may not be confident in the extent to which their perception matches reality. We describe a decade-long research program that investigates the possibility that some people may genuinely not know their risk of even well-publicized hazards. We demonstrate that indications of not knowing (i.e., "don't know" responses) are prevalent in the U.S. population, are systematically more likely to occur among marginalized sociodemographic groups, and are associated with less engagement in protective health behaviors. "Don't know" responses are likely indications of genuinely limited knowledge and therefore may indicate populations in need of targeted intervention. This body of research suggests that not allowing participants to indicate their uncertainty may threaten the validity and generalizability of behavior-change research. We provide concrete recommendations for scientists to allow participants to express uncertainty and to analyze the resulting data.