@misc{10481/52231,
year = {2018},
url = {http://hdl.handle.net/10481/52231},
abstract = {For surveys of sensitive issues in life sciences, statistical procedures can be used to
reduce nonresponse and social desirability response bias. Both of these phenomena
provoke nonsampling errors that are difficult to deal with and can seriously flaw the
validity of the analyses. The item sum technique (IST) is a very recent indirect questioning
method derived from the item count technique that seeks to procure more reliable
responses on quantitative items than direct questioning while preserving respondents'
anonymity. This article addresses two important questions concerning the IST:
(i) its implementation when two or more sensitive variables are investigated and efficient
estimates of their unknown populationmeans are required; (ii) the determination
of the optimal sample size to achieve minimum variance estimates. These aspects are
of great relevance for survey practitioners engaged in sensitive research and, to the best
of our knowledge, were not studied so far. In this article, theoretical results for multiple
estimation and optimal allocation are obtained under a generic sampling design
and then particularized to simple random sampling and stratified sampling designs.
Theoretical considerations are integrated with a number of simulation studies based
on data from two real surveys and conducted to ascertain the efficiency gain derived
from optimal allocation in different situations. One of the surveys concerns cannabis
consumption among university students. Our findings highlight some methodological
advances that can be obtained in life sciences IST surveys when optimal allocation is
achieved.},
organization = {This work is partially supported by Ministerio de Economía y Competitividad (grant MTM2015-63609-R, Spain), Ministerio
de Educación, Cultura y Deporte (grant FPU, Spain), and by the project PRIN-SURWEY (grant 2012F42NS8, Italy).},
publisher = {Wiley},
keywords = {Complex sampling},
keywords = {Horvitz–Thompson estimator},
keywords = {Indirect questioning methods},
keywords = {Sensitive research},
title = {Multiple sensitive estimation and optimal sample size allocation in the item sum technique},
doi = {10.1002/bimj.201700021},
author = {Perri, Pier Francesco and Rueda García, María Del Mar and Cobo Rodríguez, Beatriz},
}