Center for Survey Research, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.
Department of Economics, Statistics and Finance "Giovanni Anania", University of Calabria, Arcavacata di Rende, CS, Italy.
BMC Public Health. 2024 Sep 17;24(1):2523. doi: 10.1186/s12889-024-19819-6.
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.
医学和健康科学领域的调查研究主要采用传统的直接询问（DQ）格式来收集私人和高度个人化的信息。如果研究的主题是敏感的，甚至是带有污名的，例如与 COVID-19 相关的健康行为以及普遍的非药物干预措施的依从性，由于社会期望偏差（SDB）的影响，DQ 调查可能导致无回应和不真实的回答。这些影响严重威胁到所获得结果的有效性，可能导致对人群中未知流行率的行为的估计值失真。虽然这个问题无法完全避免，但间接询问技术（IQT）提供了一种通过保证个人回答的机密性来减轻 SDB 有害影响的方法。本研究旨在评估最近提出的 IQT，即作弊检测三角模型（CDTRM），在估计与 COVID-19 相关的健康行为的流行率时的有效性，同时考虑到无视指示的作弊者。
在对台湾 1714 名参与者的在线调查中，我们通过期望最大化算法获得了三种不同敏感程度的 COVID-19 相关健康行为的 CDTRM 流行率估计值。将 CDTRM 估计值与 DQ 估计值和台湾疾病控制中心提供的可用官方统计数据进行比较。此外，CDTRM 还允许我们估计无视指示的作弊者的比例，并相应地调整与 COVID-19 相关的健康行为的流行率估计值。
对于敏感性较低的行为，CDTRM 和 DQ 估计值预计是可比的，并且与官方统计数据一致。然而，对于敏感性中等和较高的行为，CDTRM 估计值较高，因此推测比 DQ 估计值更有效。类似地，随着研究中行为的敏感性增加，估计的作弊率也随之增加。
我们的研究结果强烈支持这样一种假设，即 CDTRM 在对三种与 COVID-19 相关的健康行为的调查中成功地控制了 SDB 对有效性的威胁。因此，与传统的 DQ 相比，CDTRM 似乎是一种有前途的技术，可以提高与健康相关的行为以及一般敏感属性的估计有效性，而这些行为的 SDB 影响预计会很强。
