A Logistic Regression Extension for the Randomized Response Simple and Crossed Models: Theoretical Results and Empirical Evidence

Autor/inn/en	Hsieh, Shu-Hui; Perri, Pier Francesco
Titel	A Logistic Regression Extension for the Randomized Response Simple and Crossed Models: Theoretical Results and Empirical Evidence
Quelle	In: Sociological Methods & Research, 51 (2022) 3, S.1244-1281 (38 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hsieh, Shu-Hui)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124120914950
Schlagwörter	Surveys; Models; Response Style (Tests); Marijuana; Drug Use; Pregnancy; Bias; Responses; Undocumented Immigrants; Privacy + Suchen Sie Ihr Suchwort? Survey; Umfrage; Befragung; Analogiemodell; Antwortverhalten; Drug consumption; Substance abuse; Drogenkonsum; Schwangerschaft; Illegaler Aufenthalt; Privatsphäre
Abstract	We propose some theoretical and empirical advances by supplying the methodology for analyzing the factors that influence two sensitive variables when data are collected by randomized response (RR) survey modes. First, we provide the framework for obtaining the maximum likelihood estimates of logistic regression coefficients under the RR simple and crossed models, then we carry out a simulation study to assess the performance of the estimation procedure. Finally, logistic regression analysis is illustrated by considering real data about cannabis use and legalization and about abortion and illegal immigration. The empirical results bring out certain considerations about the effect of the RR and direct questioning survey modes on the estimates. The inference about the sign and the significance of the regression coefficients can contribute to the debate on whether the RR approach is an effective survey method to reduce misreporting and improve the validity of analyses. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01