Evidence map›Paper›PMID 35306631›Full record

ArticlePsychometrika2022

Sample Size Determination for Interval Estimation of the Prevalence of a Sensitive Attribute Under Randomized Response Models.

Shi-Fang Qiu, Man-Lai Tang, Ji-Ran Tao, Ricky S Wong

Abstract read
In one paragraph

Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shi-Fang QiuDepartment of Statistics, Chongqing University of Technology, Chongqing, 400054, China.
Man-Lai TangDepartment of Mathematics, Brunel University London, Uxbridge, UB8 3PH, UK. Man.Tang@brunel.ac.uk.
Ji-Ran TaoSchool of Mathematics and Statistics, Beijing Institute of Technology, Beijing, 100081, China.
Ricky S WongBusiness School, University of Hertfordshire, Hatfield, Hertfordshire, AL10 9EU, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies with sensitive questions should include a sufficient number of respondents to adequately address the research interest. While studies with an inadequate number of respondents may not yield significant conclusions, studies with an excess of respondents become wasteful of investigators' budget. Therefore, it is an important step in survey sampling to determine the required number of participants. In this article, we derive sample size formulas based on confidence interval estimation of prevalence for four randomized response models, namely, the Warner's randomized response model, unrelated question model, item count technique model and cheater detection model. Specifically, our sample size formulas control, with a given assurance probability, the width of a confidence interval within the planned range. Simulation results demonstrate that all formulas are accurate in terms of empirical coverage probabilities and empirical assurance probabilities. All formulas are illustrated using a real-life application about the use of unethical tactics in negotiation.

Indexed as

Models, StatisticalComputer SimulationConfidence IntervalsHumansPrevalenceProbabilityPsychometricsSample Sizeassurance probabilityconfidence intervalrandomized response modelssample size determinationsensitive attribute

Identifiers

PMID35306631
PMCPMC9636124

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.