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Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain.

Biometrical journal. Biometrische Zeitschrift·2022

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Multiple sensitive estimation and optimal sample size allocation in the item sum technique.

Pier Francesco Perri¹, María Del Mar Rueda García², Beatriz Cobo Rodríguez²

¹Department of Economics, Statistics and Finance, University of Calabria. Via P. Bucci, 87036, Arcavacata di Rende, Italy.

Biometrical Journal. Biometrische Zeitschrift

|September 30, 2017

Summary

The item sum technique (IST) improves sensitive survey data in life sciences by optimizing sample size and allocation. This method enhances response reliability and reduces bias in sensitive research.

Keywords:

Horvitz-Thompson estimator complex sampling indirect questioning methods sensitive research

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Area of Science:

Life Sciences
Survey Methodology
Biostatistics

Background:

Sensitive surveys in life sciences face nonresponse and social desirability bias, compromising data validity.
The item sum technique (IST) offers a novel indirect questioning method to enhance response reliability and anonymity.
Existing IST methods lack guidance on handling multiple sensitive variables and determining optimal sample sizes.

Purpose of the Study:

To address the implementation of IST for multiple sensitive variables, requiring efficient population mean estimation.
To determine optimal sample sizes for IST surveys to achieve minimum variance estimates.
To provide theoretical advancements for survey practitioners in sensitive life science research.

Main Methods:

Developed theoretical results for multiple estimation and optimal allocation under generic sampling designs.
Applied theoretical results to simple random sampling and stratified sampling designs.
Conducted simulation studies using data from real-world surveys, including cannabis consumption among university students.

Main Results:

Established efficient estimation procedures for multiple sensitive variables using IST.
Provided methods for determining optimal sample allocation to minimize variance in IST surveys.
Demonstrated significant efficiency gains through optimal allocation in simulation studies.

Conclusions:

The study offers methodological advances for conducting more reliable and valid sensitive surveys in life sciences using IST.
Optimal allocation in IST surveys is crucial for achieving efficiency gains and reducing estimation variance.
The findings are highly relevant for researchers and practitioners dealing with sensitive data collection in life sciences.