An Analysis of the Properties of a Newly Proposed Non‑Randomised Response Technique

• Authors: Barbara Kowalczyk

Title variants

PL

Analiza własności nowo zaproponowanej techniki nierandomizowanych odpowiedzi

Abstracts

PL

Techniki nierandomizowanych odpowiedzi to nowoczesne i stale rozwijające się metody przeznaczone do radzenia sobie z tematami drażliwymi, takimi jak oszustwa podatkowe, czarny rynek, korupcja itp. W artykule zaproponowano nową technikę nierandomizowanych odpowiedzi, którą można traktować jako uogólnienie znanego modelu krzyżowego. Przedstawiono metodykę nowego uogólnionego modelu krzyżowego oraz podano estymator największej wiarygodności dla nieznanej populacyjnej frakcji cechy drażliwej. Omówiono również problem ochrony prywatności. Przeanalizowano własności nowo zaproponowanego modelu, a następnie porównano go z tradycyjnym modelem krzyżowym. Pokazano, że klasyczny model krzyżowy jest specjalnym przypadkiem zaproponowanego modelu uogólnionego. Wykazano również, że to uogólnienie ma duże znaczenie dla praktyki.

EN

Non‑randomised response (NRR) techniques are modern and constantly evolving methods intended for dealing with sensitive topics in surveys, such as tax evasion, black market, corruption etc. The paper introduces a new NRR technique that can be seen as a generalisation of the well‑known crosswise model (CM). In the paper, methodology of the new generalised crosswise model (GCM) is presented and the maximum likelihood estimator of the unknown population sensitive proportion is obtained. Also, the problem of privacy protection is discussed. The properties of the newly proposed GCM are examined. Then the GCM is compared with the traditional CM. The paper shows that mathematically the CM is a special case of the newly proposed generalised CM and that this generalisation has high practical relevance.

Keywords

PL

ankietowanie pośrednie; pytania drażliwe; techniki nierandomizowanych odpowiedzi; model krzyżowy; estymacja NW; stopień ochrony prywatności

EN

indirect questioning; sensitive questions; non‑randomised response techniques; crosswise model; ML estimation; degree of privacy protection

• Publisher: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
• Journal: Acta Universitatis Lodziensis. Folia Oeconomica
• Year: 2022
• Volume: 1
• Issue: 358
• Pages: 1-13

Dates

published

2022

Contributors

author

Barbara Kowalczyk

• SGH Warsaw School of Economics, Collegium of Economic Analysis Institute of Econometrics, Mathematical Statistics Unit, Warsaw, Poland

• https://orcid.org/0000000254073438

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Identifiers

DOI

10.18778/0208-6018.358.01

Biblioteka Nauki

2154147