The Effect of Different Graphical and Numerical Likelihood Formats on Perception of Likelihood and Choice

Oudhoff, Jurriaan P.; Timmermans, Daniëlle R. M.

doi:10.1177/0272989x15576487

Cited by 44 publications

(62 citation statements)

References 57 publications

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“…While the effect was smaller for the numerical scales, it was reliable, being directionally replicated across the four experiments featuring a numerical scale, and detected as significant in a meta-analysis. This finding thus replicates and extends the prior research on the "1-in-X" effect (Oudhoff & Timmermans, 2015;Pighin et al, 2011;Sirota et al, 2014b). It is also consistent with the findings that responses provided on numerical scales are less prone to biases than on verbal ones (Windschitl & Weber, 1999).…”

Section: Discussionsupporting

confidence: 90%

“1-in-X” bias: “1-in-X” format causes overestimation of health-related risks.

Sirota

Juanchich

Bonnefon

2018

Journal of Experimental Psychology: Applied

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According to the "1-in-X" effect, "1-in-X" ratios (e.g., 1 in 12) trigger a higher subjective probability than numerically equivalent "N-in-X*N" ratios (e.g., 3 in 36). Here we tested: (i) the effect on objective measures, (ii) its consequences for decision-making, (iii) whether this effect is a form of bias by measuring probability accuracy, and (iv) its amplification in people with lower health literacy and numeracy. In parallel-designed experiments, 975 participants from the general adult population participated in one of five experiments following a 2(format: "1-in-X" or "N-in-X*N") × 4(scenarios) mixed design. Participants assessed the risk of contracting a disease on either a verbal probability scale (Exp. 1), or a numerical probability/frequency scale with immediate (Exp. 2-3) or delayed presentation (Exp. 4-5). Participants also made a health-related decision and completed a health literacy and numeracy scale. The "1-in-X" ratios yielded higher probability perceptions than the "N-in-X*N" ratios and affected relevant decisions. Critically, the "1-in-X" ratios led to a larger objective overestimation of numerical probabilities than the "N-in-X*N" ratios. People with lower levels of health literacy and numeracy were not more sensitive to the bias. Health professionals should use "1-in-X" ratios with great caution when communicating to patients, because they overestimate health risks.

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Section: Discussionsupporting

confidence: 90%

“1-in-X” bias: “1-in-X” format causes overestimation of health-related risks.

Sirota

Juanchich

Bonnefon

2018

Journal of Experimental Psychology: Applied

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“…Precisely how an individual's personalized risk resulting from an online risk calculator should be communicated has become a crucial question . Although some risk formats (eg natural frequencies and some graphical formats in addition to numerical information) in general seem to evoke better risk understanding than other formats (eg percentages only), it remains unclear how the provided risk information supports an individual's understanding of their risk. Previous user tests have shown that risks presented as percentages in risk calculators often have unclear or ambiguous meaning for end‐users, even when accompanied by graphical information .…”

Section: Introductionmentioning

confidence: 99%

“…Other more general problems revealed by such user tests are that the risk message does not necessarily match the individual's existing beliefs and expectations about risk factors, and that, perhaps partly as a result of this, many end‐users with relatively high risks tend to undervalue or normalize their risk . Such problems are particularly urgent as many people, not only those with lower educational levels, have poor health literacy and numeracy skills, thereby placing them at a higher risk of misinterpreting information and making non‐informed decisions . It is therefore important to investigate how end‐users of risk calculators make sense of their risk result and to improve risk communication accordingly.…”

Section: Introductionmentioning

confidence: 99%

How lay people understand and make sense of personalized disease risk information

Damman¹,

Bogaerts²,

Haak³

et al. 2017

Health Expectations

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BackgroundDisease risk calculators are increasingly web‐based, but previous studies have shown that risk information often poses problems for lay users.ObjectiveTo examine how lay people understand the result derived from an online cardiometabolic risk calculator.DesignA qualitative study was performed, using the risk calculator in the Dutch National Prevention Program for cardiometabolic diseases. The study consisted of three parts: (i) attention: completion of the risk calculator while an eye tracker registered eye movements; (ii) recall: completion of a recall task; and (iii) interpretation: participation in a semi‐structured interview.Setting and participantsWe recruited people from the target population through an advertisement in a local newspaper; 16 people participated in the study, which took place in our university laboratory.ResultsEye‐tracking data showed that participants looked most extensively at numerical risk information. Percentages were recalled well, whereas natural frequencies and verbal labels were remembered less well. Five qualitative themes were derived from the interview data: (i) numerical information does not really sink in; (ii) the verbal categorical label made no real impact on people; (iii) people relied heavily on existing knowledge and beliefs; (iv) people zoomed in on risk factors, especially family history of diseases; and (v) people often compared their situation to that of their peers.Discussion and conclusionAlthough people paid attention to and recalled the risk information to a certain extent, they seemed to have difficulty in properly using this information for interpreting their risk.

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“…An accurate understanding of these risks is necessary because the risks typically come with associated benefits, and people need to be able to determine if the risks are worth the potential benefits. To assist people in these tasks, experts often communicate the risk information to relevant decision makers (Fagerlin, Zikmund‐Fisher, & Ubel, ; Feldman‐Stewart, Kocovski, McConnell, Brundage, & Mackillop, ; Fischhoff, ; Lipkus, ; Oudhoff & Timmermans, ; Visschers, Meertens, Passchier, & de Vries, ). Unfortunately, communicating the likelihood that a risky event will occur is challenging.…”

mentioning

confidence: 99%

Salience Versus Proportional Reasoning: Rethinking the Mechanism Behind Graphical Display Effects

Stone

Reeder

Parillo

et al. 2018

Behavioral Decision Making

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Two experiments examined predictions from two separate explanations for previously observed display effects for communicating low-probability risks: foreground:background salience and proportional reasoning. According to foreground:background salience, people's risk perceptions are based on the relative salience of the foreground (number of people harmed) versus the background (number of people at risk), such that calling attention to the background makes the risk seem smaller. Conversely, the proportional reasoning explanation states that what matters is whether the respondent attends to the proportion, which conveys how small the risk is. In Experiment 1, we made the background more salient via color and bolding; in contrast to the foreground:background salience prediction, this manipulation did not influence participants' risk aversion. In Experiment 2, we separately manipulated whether the foreground and the background were displayed graphically or numerically. In keeping with the proportional reasoning hypothesis, there was an interaction whereby participants given formats that displayed the foreground and background in the same modality (graphs or numbers, thereby making the proportion easier to form) saw the probability as smaller and were less risk averse than participants given the information in different modalities. There was also a main effect of displaying the background graphically, providing some support for foreground:background salience. In total, this work suggests that the proportional reasoning account provides a good explanation of many display effects related to communicating low-probability risks, although there is some role for foreground:background salience as well.questions did not influence their response to that question. Next, they filled out a packet containing the rest of the measures listed above and some other items included for exploratory purposes. Figure 4. Experiment 2 risk magnitude information displaying (a) the background information numerically and the foreground information graphically and (b) the background information graphically and the foreground information numerically 480 Journal of Behavioral Decision Making

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The Effect of Different Graphical and Numerical Likelihood Formats on Perception of Likelihood and Choice

Cited by 44 publications

References 57 publications

“1-in-X” bias: “1-in-X” format causes overestimation of health-related risks.

“1-in-X” bias: “1-in-X” format causes overestimation of health-related risks.

How lay people understand and make sense of personalized disease risk information

Salience Versus Proportional Reasoning: Rethinking the Mechanism Behind Graphical Display Effects

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