The logistic regression model with response variables subject to randomized response

The univariate and multivariate logistic regression model is discussed where response variables are subject to randomized response (RR). RR is an interview technique that can be used when sensitive questions have to be asked and respondents are reluctant to answer directly. RR variables may be descr...

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Veröffentlicht in:Computational statistics & data analysis Jg. 51; H. 12; S. 6060 - 6069
Hauptverfasser: van den Hout, Ardo, van der Heijden, Peter G.M., Gilchrist, Robert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.08.2007
Elsevier Science
Elsevier
Schriftenreihe:Computational Statistics & Data Analysis
Schlagworte:
Exact sciences and technology
General topics
Linear inference, regression
Mathematics
Misclassification
Multivariate analysis
Multivariate and univariate logistic regression
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Randomized response
Regulatory non-compliance
Sciences and techniques of general use
Sensitive questions
Statistics
Sensitive questions
Misclassification
Randomized response
Multivariate and univariate logistic regression
Regulatory non-compliance
Data analysis
Conditional distribution
Generalized linear model
Response model
Multivariate regression
Probability distribution
Conditional probability
Logistic model
Multivariate analysis
Algorithm
Unemployment
Linear model
Logistic distribution
Statistical regression
Logistic regression
Statistical computation
Regression model
Experimental design
Software
ISSN:0167-9473, 1872-7352
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Beschreibung
Zusammenfassung:The univariate and multivariate logistic regression model is discussed where response variables are subject to randomized response (RR). RR is an interview technique that can be used when sensitive questions have to be asked and respondents are reluctant to answer directly. RR variables may be described as misclassified categorical variables where conditional misclassification probabilities are known. The univariate model is revisited and is presented as a generalized linear model. Standard software can be easily adjusted to take into account the RR design. The multivariate model does not appear to have been considered elsewhere in an RR setting; it is shown how a Fisher scoring algorithm can be used to take the RR aspect into account. The approach is illustrated by analyzing RR data taken from a study in regulatory non-compliance regarding unemployment benefit.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2006.12.002