Abstract
Assessing perceived vulnerability to a health threat is essential to understanding how people conceptualize their risk, and to predicting how likely they are to engage in protective behaviors. However, there is limited consensus about which of many measures of perceived vulnerability predict behavior best. We tested whether the ability of different measures to predict protective intentions varies as a function of the type of information people learn about their risk. Online participants (N = 909) read information about a novel respiratory disease before answering measures of perceived vulnerability and vaccination intentions. Type-of-risk information was varied across three between-participant groups. Participants learned either: (1) only information about their comparative standing on the primary risk factors (comparative-only), (2) their comparative standing as well as the base-rate of the disease in the population (+ base-rate), or (3) their comparative standing as well as more specific estimates of their absolute risk (+ absolute-chart). Experiential and affective measures of perceived vulnerability predicted protective intentions well regardless of how participants learned about their risk, while the predictive ability of deliberative numeric and comparative measures varied based on the type of risk information provided. These results broaden the generalizability of key prior findings (i.e., some prior findings about which measures predict best may apply no matter how people learn about their risk), but the results also reveal boundary conditions and critical points of distinction for determining how to best assess perceived vulnerability.
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The data that support the findings of this study are available on the Open Science Framework at https://osf.io/n37r5/?view_only=9a9120571a4f48088e7db3aa26b51dc5.
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Notes
As a secondary issue relevant to this second research question, we also compared how well direct comparative measures, rather than indirect comparative measures, fare as predictors of protective intentions. Past work suggests that direct comparative measures tend to predict behavior better than indirect (e.g., Rose, 2010), but it was unclear if this pattern would hold when participants were given absolute risk information about others (as in the + absolute-chart condition).
Using a fabricated infection ensured that participants would not have pre-existing notions or attitudes about the infection and would not be aware of any potential course of action to prevent it. Additionally, participants were asked to “assume all of the provided information is correct” to allow for a more realistic test of participants’ perceptions of vulnerability. Therefore, despite the fictional nature of this study we felt that the benefits of using a fabricated infection outweighed the potential negatives.
All reported correlations are Pearson’s r, which has been shown to be robust to violations (see Bishara and Hittner, 2012 for a discussion). However, computing Spearman’s rank-order correlations yields the same results across all analyses with two discrepancies noted in text.
The pattern of the inferential results across these correlations was the same when analyzing the single item measure of vaccination intentions rather than the composite.
Participants’ actual standing on these risk factors (calculated as an aggregate) was positively correlated with all measures of perceived vulnerability (all rs were significant and between 0.27 and 0.52), indicating that participants were paying attention and were engaged with the scenario in a meaningful way.
Although the pattern remains the same, when comparing the Spearman correlations, the difference between the absolute-verbal r(280) = 0.50, p < .001, and absolute-numeric correlations r(282) = 0.42, p < .001 is no longer significant, z = 1.90, p = .057.
In a regression on the full sample, in which we dummy coded for conditions (using the comparative-only condition as the reference group) and also included two- and three-way interactions, the two-way interactions were not significant. However, a three-way interaction (verbal measure x numeric measure x + absolute-chart condition dummy) was significant (p = .047), supporting that the relative importance of the two measures in predicting protective intentions was different in the + absolute-chart condition than in the comparative-only condition. The analogous interaction involving the + base-rate condition was not significant (p = .202). As mentioned in the Statistical Analysis section, these regressions test for differences in slopes and are therefore not as pertinent to our main focus on overall differences in predictive utility (see Rohrer and Arslan, 2021).
Recall that we also calculated indirect comparative estimates by subtracting each respondent’s estimate of the average person’s risk from their estimate of self-risk. This was done separately for verbal and numeric question formats. Consistent with previous research (Rose, 2010; see also Ranby et al., 2010), both types of indirect comparative estimates performed worse (i.e., had lower correlations with intentions) than did either the absolute-verbal estimates or the direct comparative estimates (see Table 3 for correlations). Therefore, the rest of this section describes only results relevant to the direct comparative estimates.
Regarding indirect comparative estimates, both the verbal and numeric forms of those estimates performed worse than the absolute-verbal measure across all conditions.
In a regression on the full sample, in which we dummy coded for conditions (using the comparative-only condition as the reference group) and also included two- and three-way interactions, the two-way interaction between the comparative measure and the + absolute-chart condition dummy was significant (p = .007) as well as the three-way interaction between both predictors (comparative and absolute-verbal) and the + absolute-chart condition dummy (p = .044), indicating that the predictive power of the comparative measure was significantly different between the comparative-only condition and + absolute-chart condition. The analogous interactions for the + base-rate condition were not significant (ps = 0.099 and 0.586).
When this comparison is tested using Spearman correlations, the experiential correlation r(305) = 0.55, p < .001, is slightly stronger than the absolute-verbal correlation r(304) = 0.49, p < .001; z = 2.07, p = .039.
Interaction tests from a regression on the full sample were affected by spurious multicolinearily issues (some variance inflation factors were above 10), so are not reported here.
In a regression on the full sample, in which we dummy coded for conditions (using the comparative-only condition as the reference group) and also included two- and three-way interactions, none of the interactions were significant (ps > 0.204), again indicating that the predictive ability of the concern measure is not affected by risk-information type.
For researchers interested in choosing the two or three measures that would maximize the prediction of protective intensions, they might be interested to know that in a regression that includes concern and experiential responses as predictors, adding absolute numeric and/or absolute verbal responses does not significantly improve the R2 value of the model, but adding comparative responses does.
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This research was supported by two research grants, SES 09-61252, awarded to Paul Windschitl and SES-1851738, awarded to Paul Windschitl and Andrew Smith from the National Science Foundation.
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Stuart, J.O., Windschitl, P.D., Bossard, E. et al. Which measures of perceived vulnerability predict protective intentions—and when?. J Behav Med 46, 912–929 (2023). https://doi.org/10.1007/s10865-023-00439-1
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DOI: https://doi.org/10.1007/s10865-023-00439-1