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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 19, 2019
Date Accepted: May 14, 2020

The final, peer-reviewed published version of this preprint can be found here:

Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research

Moreno-Fernández MM, Matute H

Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research

J Med Internet Res 2020;22(7):e17502

DOI: 10.2196/17502

PMID: 32706735

PMCID: 7414405

Biased health information sampling and causal estimations: A laboratory based experimental research

  • María Manuela Moreno-Fernández; 
  • Helena Matute

ABSTRACT

Background:

Nowadays, the Internet has become a relevant provider of health-related information. The huge amount of information available on the Internet forces users to engage in an active process of information selection. Previous research conducted in the field of Experimental Psychology has shown that information selection itself may promote the development of erroneous beliefs even if the information collected along the process does not support them.

Objective:

We conducted a laboratory-based experiment to assess the potential effects of information searching strategies on causal inferences about health while controlling for the effect of additional features of the information.

Methods:

Participants were required to gather information to find out whether a fictitious drug caused an allergic reaction. Although they could select which type of information they wanted to check, they all received similar evidence that eventually pointed toward the absence of a causal link between the drug and the reaction.

Results:

Results showed that participants used different searching information strategies. Some strategies may produce an overrepresentation of certain pieces of evidence in detriment of others modulating the accuracy of causal inferences. In line with this prediction, a significant effect of the way in which information was collected on causal inferences was detected (F(1, 185) = 32.53, P < .001, 2p =.15) suggesting that causal judgments can be affected not only by the information content, but also by the way in which information is gathered.

Conclusions:

Mistaken beliefs about health may arise from pieces of information that do not support them. A key factor for the development of such mistakes is the way in which information is searched. Patients’ or people’s autonomy for gathering health information through the Internet may contribute to the development of false beliefs from actually accurate pieces of information, because searching strategies are often biased.


 Citation

Please cite as:

Moreno-Fernández MM, Matute H

Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research

J Med Internet Res 2020;22(7):e17502

DOI: 10.2196/17502

PMID: 32706735

PMCID: 7414405

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