title: Non-randomized response model for sensitive survey with noncompliance.
abstract: Collecting representative data on sensitive issues has long been problematic and challenging in public health prevalence investigation (e.g. non-suicidal self-injury), medical research (e.g. drug habits), social issue studies (e.g. history of child abuse), and their interdisciplinary studies (e.g. premarital sexual intercourse). Alternative data collection techniques that can be adopted to study sensitive questions validly become more important and necessary. As an alternative to the famous Warner randomized response model, non-randomized response triangular model has recently been developed to encourage participants to provide truthful responses in surveys involving sensitive questions. Unfortunately, both randomized and non-randomized response models could underestimate the proportion of subjects with the sensitive characteristic as some respondents do not believe that these techniques can protect their anonymity. As a result, some authors hypothesized that lack of trust and noncompliance should be highest among those who have the most to lose and the least to use for the anonymity provided by using these techniques. Some researchers noticed the existence of noncompliance and proposed new models to measure noncompliance in order to get reliable information. However, all proposed methods were based on randomized response models which require randomizing devices, restrict the survey to only face-to-face interview and are lack of reproductivity. Taking the noncompliance into consideration, we introduce new non-randomized response techniques in which no covariate is required. Asymptotic properties of the proposed estimates for sensitive characteristic as well as noncompliance probabilities are developed. Our proposed techniques are empirically shown to yield accurate estimates for both sensitive and noncompliance probabilities. A real example about premarital sex among university students is used to demonstrate our methodologies.
authors.affiliation: Department of Statistics, School of Mathematical Sciences, South China Normal University, Guangzhou, PR China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Department of Mathematics and Statistics, Hang Seng Management College, Shatin, N.T., Hong Kong, PR China mltang@hsmc.edu.hk.
authors.forename: Man-Lai
authors.initials: ML
authors.lastname: Tang
mesh_terms: D000328:Adult; D003299:Cooperative Behavior; D005260:Female; D006801:Humans; D008297:Male; D015995:Prevalence; D018401:Sample Size; D012725:Sexual Behavior / Q000523:psychology / Q000706:statistics & numerical data*; D013334:Students / Q000523:psychology* / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires; D014342:Truth Disclosure; D055815:Young Adult
keywords: non-randomized response technique; noncompliance; sensitive question