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Sample size determination for interval estimation of the prevalence of a sensitive attribute under non-randomized response models.
Qiu, Shi-Fang; Lei, Jie; Poon, Wai-Yin; Tang, Man-Lai; Wong, Ricky S; Tao, Ji-Ran.
Afiliación
  • Qiu SF; Department of Statistics, Chongqing University of Technology, Chongqing, China.
  • Lei J; Department of Statistics, Chongqing University of Technology, Chongqing, China.
  • Poon WY; Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China.
  • Tang ML; Centre of Data Innovation Research, Department of Physics, Astronomy & Mathematics, School of Physics, Engineering & Computer Science, University of Hertfordshire, College Lane, Hatfield, UK.
  • Wong RS; Business School, University of Hertfordshire, Hatfield, UK.
  • Tao JR; Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
Article en En | MEDLINE | ID: mdl-38409814
ABSTRACT
A sufficient number of participants should be included to adequately address the research interest in the surveys with sensitive questions. In this paper, sample size formulas/iterative algorithms are developed from the perspective of controlling the confidence interval width of the prevalence of a sensitive attribute under four non-randomized response models the crosswise model, parallel model, Poisson item count technique model and negative binomial item count technique model. In contrast to the conventional approach for sample size determination, our sample size formulas/algorithms explicitly incorporate an assurance probability of controlling the width of a confidence interval within the pre-specified range. The performance of the proposed methods is evaluated with respect to the empirical coverage probability, empirical assurance probability and confidence width. Simulation results show that all formulas/algorithms are effective and hence are recommended for practical applications. A real example is used to illustrate the proposed methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Br J Math Stat Psychol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Br J Math Stat Psychol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM