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Cited by 441

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Recall Bias can be a Threat to Retrospective and Prospective Research Designs

·2006·The Internet Journal of Epidemiology
DOI 10.5580/2732
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Abstract

Recall bias represents a major threat to the internal validity of studies using self-reported data. It arises with the tendency of subjects to report past events in a manner that is different between the two study groups. This pattern of recall errors can lead to differential misclassification of the related variable among study subjects with a subsequent distortion of measure of association in any direction from the null, depending on the magnitude and direction of the bias. Although recall bias has largely been viewed as a common concern in case-control studies, it also has been documented as an issue in some prospective cohort and randomized controlled trial designs. The aim of this paper is to address recall bias in selective studies employing retrospective and prospective designs and present some key methodological strategies to consider in analytic research using reported data in order to avoid or minimize recall bias.

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Metadata

Venue
The Internet Journal of Epidemiology
Publisher
Internet Scientific Publishers, LLC
Published
2006
Volume
3
Issue
2
ISSN
1540-2614 (ISSN-L)
DOI
10.5580/2732
OpenAlex
W2466921826
Cited by
441
References
24
FWCI
2.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Recall Bias can be a Threat to Retrospective and Prospective Research Designs. https://app.cochat.ai/papers/10.5580/2732
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Recall Bias can be a Threat to Retrospective and Prospective Research Designs. (2006). The Internet Journal of Epidemiology. https://doi.org/10.5580/2732

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.