Randomized response estimation in multiple frame surveys
Identificadores
URI: http://hdl.handle.net/10481/68503Metadatos
Mostrar el registro completo del ítemMateria
Auxiliary information Monte Carlo simulation Multiple frame sampling Privacy protection Randomized response theory Sensitive issues
Fecha
2018-05-31Referencia bibliográfica
M. Rueda, B. Cobo & P. F. Perri (2020) Randomized response estimation in multiple frame surveys, International Journal of Computer Mathematics, 97:1-2, 189-206, DOI: 10.1080/00207160.2018.1476856
Patrocinador
Ministerio de Economía y Competitividad; FPU grant program; Consejería de Empleo, Empresa y Comercio, Junta de AndalucíaResumen
Large scale surveys are increasingly delving into sensitive topics such as gambling,
alcoholism, drug use, sexual behavior, domestic violence. Sensitive, stigmatizing
or even incriminating themes are difficult to investigate by using standard datacollection techniques since respondents are generally reluctant to release information which concern their personal sphere. Further, such topics usually pertain elusive
population (e.g., irregular immigrants and homeless, alcoholics, drug users, rape and
sexual assault victims) which are difficult to sample since not adequately covered
in a single sampling frame. On the other hand, researchers often utilize more than
one data-collection mode (i.e., mixed-mode surveys) in order to increase response
rates and/or improve coverage of the population of interest. Surveying sensitive and
elusive populations and mixed-mode researches are strictly connected with multiple
frame surveys which are becoming widely used to decrease bias due to undercoverage
of the target population. In this work, we combine sensitive research and multiple
frame surveys. In particular, we consider statistical techniques for handling sensitive
data coming from multiple frame surveys using complex sampling designs. Our aim
is to estimate the mean of a sensitive variable connected to undesirable behaviors
when data are collected by using the randomized response theory. Some estimators
are constructed and their properties theoretically investigated. Variance estimation
is also discussed by means of the jackknife technique. Finally, a Monte Carlo simulation study is conducted to evaluate the performance of the proposed estimators
and the accuracy of variance estimation..