Dataset Information

Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence.

ABSTRACT: Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list experiments are arguably less prone to bias than direct questioning, list experiments are also more susceptible to sampling variability. We show that researchers need not abandon direct questioning altogether in order to gain the advantages of list experimentation. We develop a nonparametric estimator of the prevalence of sensitive behaviors that combines list experimentation and direct questioning. We prove that this estimator is asymptotically more efficient than the standard difference-in-means estimator, and we provide a basis for inference using Wald-type confidence intervals. Additionally, leveraging information from the direct questioning, we derive two nonparametric placebo tests for assessing identifying assumptions underlying list experiments. We demonstrate the effectiveness of our combined estimator and placebo tests with an original survey experiment.

SUBMITTER: Aronow PM

PROVIDER: S-EPMC5589210 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence.

Aronow Peter M PM Coppock Alexander A Crawford Forrest W FW Green Donald P DP

Journal of survey statistics and methodology 20150319 1

Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list experiments are arguably less prone to bias than direct questioning, list experiments are also more susceptible to sampling variability. We show that researchers need not abandon direct questioning altogether in order to gain the advantages of list experimentation ...[more]

PMID: 28890903

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Project description:RationaleSocial desirability bias, which is the tendency to under-report socially, undesirable health behaviours, significantly distorts information on sensitive behaviours, gained from self-reports and prevents accurate estimation of the prevalence of those, behaviours. We contribute to a growing body of literature that seeks to assess the performance of the list experiment method to improve estimation of these sensitive health behaviours.MethodWe use a double-list experiment design in which respondents serve as the treatment group for one list and as the control group for the other list to estimate the prevalence of two sensitive health behaviours in different settings: condom use among 500 female sex workers in urban Senegal and physical intimate partner violence among 1700 partnered women in rural Burkina Faso. First, to assess whether the list experiment improves the accuracy of estimations of the prevalence of sensitive behaviours, we compare the prevalence rates estimated from self-reports with those elicited through the list experiment. Second, we test whether the prevalence rates of the sensitive behaviours obtained using the double-list design are similar, and we estimate the reduction in the standard errors obtained with this design. Finally, we compare the results obtained through another indirect elicitation method, the polling vote method.ResultsWe show that the list experiment method reduces misreporting by 17 percentage points for condom use and 16-20 percentage points for intimate partner violence. Exploiting the double-list experiment design, we also demonstrate that the prevalence estimates obtained through the use of the two lists are identical in the full sample and across sub-groups and that the double-list design reduces the standard errors by approximately 40% compared to the standard errors in the simple list design. Finally, we show that the list experiment method leads to a higher estimation of the prevalence of sensitive behaviours than the polling vote method.ConclusionThe study suggests that list experiments are an effective method to improve estimation of the prevalence of sensitive health behaviours.

Project description:BackgroundAccurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF).MethodsWe assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard.ResultsSelf-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses.ConclusionsOur results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.

Project description:Study objectivesIn order to increase the value of randomized response techniques (RRTs) as tools for studying sensitive issues, the present study investigated whether the prevalence estimate for a sensitive item [Formula: see text] assessed with the unrelated questionnaire method (UQM) is influenced by changing the probability of receiving the sensitive question p.Material and methodsA short paper-and-pencil questionnaire was distributed to 1.243 university students assessing the 12-month prevalence of physical and cognitive doping using two versions of the UQM with different probabilities for receiving the sensitive question (p ≈ 1/3 and p ≈ 2/3). Likelihood ratio tests were used to assess whether the prevalence estimates for physical and cognitive doping differed significantly between p ≈ 1/3 and p ≈ 2/3. The order of questions (physical doping and cognitive doping) as well as the probability of receiving the sensitive question (p ≈ 1/3 or p ≈ 2/3) were counterbalanced across participants. Statistical power analyses were performed to determine sample size.ResultsThe prevalence estimate for physical doping with p ≈ 1/3 was 22.5% (95% CI: 10.8-34.1), and 12.8% (95% CI: 7.6-18.0) with p ≈ 2/3. For cognitive doping with p ≈ 1/3, the estimated prevalence was 22.5% (95% CI: 11.0-34.1), whereas it was 18.0% (95% CI: 12.5-23.5) with p ≈ 2/3. Likelihood-ratio tests revealed that prevalence estimates for both physical and cognitive doping, respectively, did not differ significantly under p ≈ 1/3 and p ≈ 2/3 (physical doping: χ2 = 2.25, df = 1, p = 0.13; cognitive doping: χ2 = 0.49, df = 1, p = 0.48). Bayes factors computed with the Savage-Dickey method favored the null ("the prevalence estimates are identical under p ≈ 1/3 and p ≈ 2/3") over the alternative ("the prevalence estimates differ under p ≈ 1/3 and p ≈ 2/3") hypothesis for both physical doping (BF = 2.3) and cognitive doping (BF = 5.3).ConclusionThe present results suggest that prevalence estimates for physical and cognitive doping assessed by the UQM are largely unaffected by the probability for receiving the sensitive question p.

Project description:Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.

Project description:ObjectivesThis study involved the development of a question prompt list (QPL) booklet designed to facilitate communication and shared decision-making between parents/carers of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and their clinicians; and user-testing of the QPL to assess its usability.DesignBest practice in information writing and design was used to format the QPL content into a 16-page booklet. We then applied user-testing, which uses mixed methods to assess document performance with small cohorts of participants and then improves it in an iterative process. Individual interviews assessed the ability of QPL users to locate and understand key points of information, followed by a semistructured questionnaire, to ascertain their general views about the booklet.Setting and participantsTesting was undertaken with two cohorts of 10 parents/carers of children with ADHD (n=20); matched on age, gender and educational attainment.Tested documentsIn round 1, we tested 15 key points of information related to the QPL. Participant responses and feedback from round 1 informed a revised version of the booklet, tested in a subsequent round.Primary outcome measureThe target was for 8/10 of the participants to be able to find and demonstrate an understanding of all key information points, in accordance with European guidelines for medicine leaflet testing.ResultsAfter round 1, problems related to 4/15 information points were identified (booklet purpose; preparing for appointments; asking about a second medical opinion; selecting which questions to ask). Participants also made suggestions regarding the booklet's layout and design. After round 2, all information points were located and understood by at least 8/10 participants.ConclusionsThis is the first study to have developed a usable ADHD-specific QPL for use by parents/carers of children with ADHD during clinical consultations, and the first demonstration of the utility of user-testing methods in ensuring QPL usability.

Dataset Information

Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence.

Publications

Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence.

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