RESUMEN
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Asunto(s)
COVID-19 , Conductas Relacionadas con la Salud , Humanos , COVID-19/epidemiología , Masculino , Femenino , Adulto , Prevalencia , Persona de Mediana Edad , Taiwán/epidemiología , Decepción , Adulto Joven , Encuestas y Cuestionarios , Adolescente , Modelos Estadísticos , AncianoRESUMEN
In social research, investigating sensitive, highly personal or embarrassing issues by means of standard survey techniques based on direct questioning leads to refusals to answer or false responses which, generally, flaw the validity of the analyses and produce incorrect inferences. To correct biases induced by nonresponse or underreporting of sensitive matters, Warner (1965) introduced an indirect questioning approach, known as the randomized response technique, which allows researchers to estimate the proportion of individuals with sensitive attributes or behaviors, while ensuring respondents' privacy protection. In this article, we consider the randomized response model proposed by Christofides (2003) and, through a simulation and an empirical study, compare different estimation methods for the prevalence of a sensitive attribute. Specifically, we discuss how the model has been implemented in a pilot study to collect data and derive maximum likelihood and Bayesian estimates for the proportion of non-heterosexuals aged 20 years or older for the Taiwanese population and for some subgroups of it by sex and age. Our analysis, and in particular the Bayesian approach, seems to meet the expectation of social researchers and experts of sexual behaviors. In fact, the produced estimates are higher than official findings in Taiwan obtained by direct questioning in face-to-face interviews and provide a more reliable picture of sexual identity in the country. Moreover, Bayesian estimates appear more accurate than those produced by the method of moment and the maximum likelihood method.
Asunto(s)
Conducta Sexual , Minorías Sexuales y de Género , Teorema de Bayes , Humanos , Proyectos Piloto , TaiwánRESUMEN
For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved.