Prevalence estimates for COVID-19-related health behaviors based on the cheating detection triangular model.

Publication date: Sep 17, 2024

Survey studies in medical and health sciences predominantly apply a conventional direct questioning (DQ) format to gather private and highly personal information. If the topic under investigation is sensitive or even stigmatizing, such as COVID-19-related health behaviors and adherence to non-pharmaceutical interventions in general, DQ surveys can lead to nonresponse and untruthful answers due to the influence of social desirability bias (SDB). These effects seriously threaten the validity of the results obtained, potentially leading to distorted prevalence estimates for behaviors for which the prevalence in the population is unknown. While this issue cannot be completely avoided, indirect questioning techniques (IQTs) offer a means to mitigate the harmful influence of SDB by guaranteeing the confidentiality of individual responses. The present study aims at assessing the validity of a recently proposed IQT, the Cheating Detection Triangular Model (CDTRM), in estimating the prevalence of COVID-19-related health behaviors while accounting for cheaters who disregard the instructions. In an online survey of 1,714 participants in Taiwan, we obtained CDTRM prevalence estimates via an Expectation-Maximization algorithm for three COVID-19-related health behaviors with different levels of sensitivity. The CDTRM estimates were compared to DQ estimates and to available official statistics provided by the Taiwan Centers for Disease Control. Additionally, the CDTRM allowed us to estimate the share of cheaters who disregarded the instructions and adjust the prevalence estimates for the COVID-19-related health behaviors accordingly. For a behavior with low sensitivity, CDTRM and DQ estimates were expectedly comparable and in line with official statistics. However, for behaviors with medium and high sensitivity, CDTRM estimates were higher and thus presumably more valid than DQ estimates. Analogously, the estimated cheating rate increased with higher sensitivity of the behavior under study. Our findings strongly support the assumption that the CDTRM successfully controlled for the validity-threatening influence of SDB in a survey on three COVID-19-related health behaviors. Consequently, the CDTRM appears to be a promising technique to increase estimation validity compared to conventional DQ for health-related behaviors, and sensitive attributes in general, for which a strong influence of SDB is to be expected.

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Concepts Keywords
Cheaters Cheating detection
Confidentiality COVID-19
Pharmaceutical Indirect questioning
Sdb Misreporting
Taiwan Nonresponse
Privacy protection
Social desirability bias

Semantics

Type Source Name
disease MESH COVID-19
disease IDO algorithm
disease MESH Long Covid
pathway REACTOME Reproduction
disease MESH Privacy
disease MESH social stigma
disease IDO process
drug DRUGBANK Trestolone
drug DRUGBANK Tropicamide
drug DRUGBANK Coenzyme M
disease IDO quality
drug DRUGBANK Aspartame
drug DRUGBANK Ranitidine
disease IDO susceptibility
drug DRUGBANK S-Arsonocysteine
disease MESH educational achievement
drug DRUGBANK Medical air
drug DRUGBANK Ademetionine
drug DRUGBANK Naproxen
drug DRUGBANK Ilex paraguariensis leaf
disease MESH tics
disease MESH violence
drug DRUGBANK Hexocyclium
drug DRUGBANK Saquinavir
drug DRUGBANK Serine
drug DRUGBANK Alpha-Linolenic Acid

Original Article

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