29 research outputs found
Advances in estimation by the item sum technique using auxiliary information in complex surveys
To collect sensitive data, survey statisticians have designed many strategies to reduce
nonresponse rates and social desirability response bias. In recent years, the item count
technique (ICT) has gained considerable popularity and credibility as an alternative mode
of indirect questioning survey, and several variants of this technique have been proposed as
new needs and challenges arise. The item sum technique (IST), which was introduced by
Chaudhuri and Christofides (2013) and Trappmann et al. (2014), is one such variant, used
to estimate the mean of a sensitive quantitative variable. In this approach, sampled units are
asked to respond to a two-list of items containing a sensitive question related to the study
variable and various innocuous, nonsensitive, questions. To the best of our knowledge,
very few theoretical and applied papers have addressed the IST. In this article, therefore,
we present certain methodological advances as a contribution to appraising the use of the
IST in real-world surveys. In particular, we employ a generic sampling design to examine
the problem of how to improve the estimates of the sensitive mean when auxiliary information on the population under study is available and is used at the design and estimation
stages. A Horvitz-Thompson type estimator and a calibration type estimator are proposed
and their efficiency is evaluated by means of an extensive simulation study. Using simulation experiments, we show that estimates obtained by the IST are nearly equivalent to those
obtained using “true data” and that in general they outperform the estimates provided by a
competitive randomized response method. Moreover, the variance estimation may be considered satisfactory. These results open up new perspectives for academics, researchers and
survey practitioners, and could justify the use of the IST as a valid alternative to traditional
direct questioning survey modes.Ministerio de EconomĂa y Competitividad of SpainMinisterio de Educacion, Cultura y Deporteproject PRIN-SURWE
Amputation versus imputation of missing values through ratio method in sample surveys
Missing data, Ratio method, Imputation, Amputation,