title: Estimating the prevalence of socially sensitive behaviors: attributing guilty and innocent noncompliance with the single sample count method.
abstract: Prevalence estimation models, using randomized or fuzzy responses, provide protection against exposure to respondents beyond anonymity and represent a useful research tool in socially sensitive situations. However, both guilty and innocent noncompliance can have a profound impact on prevalence estimations derived from these models. In this article, we introduce the maximum-likelihood extension of the single sample count (SSC-MLE) estimation model to detect and attribute noncompliance through testing 5 competing hypotheses on possible ways of noncompliance. We demonstrate the ability of the SSC-MLE to estimate and attribute noncompliance with a single sample using the observed distribution of affirmative answers on recent recreational drug use from a sample of university students (N = 1,441). Based on the survey answers, the drug use prevalence was estimated at 17.62% (± 6.75%), which is in line with relevant drug use statistics. Only 2.51% (± 1.54%) were noncompliant, of which 0.55% (± 0.44%) was attributed to guilty noncompliance (i.e., have used drugs but did not admit) and 2.17% (± 1.44%) to innocent noncompliers with no drug use in the past 3 months to hide. The SSC-MLE indirect estimation method represents an important tool for estimating the prevalence of a broad range of socially sensitive behaviors. Subsequent applications of the SSC-MLE to a range of transgressive behaviors with varying sensitivity will contribute to establishing the SSC-MLE's performance properties, along with obtaining empirical evidence to test the underlying assumption of independence of noncompliance from involvement. Freely downloadable, user-friendly software to facilitate applications of the SSC-MLE model is provided.
authors.affiliation: Faculty of Science, Engineering and Computing.
authors.forename: Tamás
authors.initials: T
authors.lastname: Nepusz
authors.affiliation: Faculty of Science, Engineering and Computing.
authors.forename: Andrea
authors.initials: A
authors.lastname: Petróczi
authors.affiliation: Faculty of Science, Engineering and Computing.
authors.forename: Declan P
authors.initials: DP
authors.lastname: Naughton
authors.affiliation: Department of Psychology, University of Sheffield.
authors.forename: Tracy
authors.initials: T
authors.lastname: Epton
authors.affiliation: Department of Psychology, University of Sheffield.
authors.forename: Paul
authors.initials: P
authors.lastname: Norman
mesh_terms: D000293:Adolescent; D008192:Deception; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D015995:Prevalence; D057566:Self Report; D013223:Statistics as Topic; D019966:Substance-Related Disorders / Q000453:epidemiology*; D006113:United Kingdom / Q000453:epidemiology; D055815:Young Adult