Unrelated question model with two decks of cards

2020 ◽  
Vol 74 (2) ◽  
pp. 192-215
Author(s):  
Stephen A. Sedory ◽  
Sarjinder Singh ◽  
Oluwaseun L. Olanipekun ◽  
Colin Wark
Keyword(s):  
Unrelated Question

The Unrelated-Question Model

2020 ◽  
pp. 15-35
Author(s):  
Arijit Chaudhuri ◽  
Rahul Mukerjee
Keyword(s):  
Unrelated Question

Improved estimation methods for unrelated question randomized response techniques

2016 ◽  
Vol 46 (16) ◽  
pp. 8101-8112 ◽  
Author(s):  
Shen-Ming Lee ◽  
Ter-Chao Peng ◽  
Jean de Dieu Tapsoba ◽  
Shu-Hui Hsieh
Keyword(s):  
Estimation Methods ◽  
Randomized Response ◽  
Randomized Response Techniques ◽  
Unrelated Question

scholarly journals Optional unrelated-question randomized response models

2013 ◽  
Vol 6 (4) ◽  
pp. 483-492 ◽  
Author(s):  
Sat Gupta ◽  
Anna Tuck ◽  
Tracy Gill ◽  
Mary Crowe
Keyword(s):  
Randomized Response ◽  
Response Models ◽  
Randomized Response Models ◽  
Unrelated Question

scholarly journals Language is more abstract than you think, or, why aren’t languages more iconic?

2017 ◽  
Author(s):  
Gary Lupyan ◽  
Bodo Winter
Keyword(s):  
Language Learning ◽  
Abstract Words ◽  
Unrelated Question

How abstract is language? We show that abstractness pervades every corner of language, going far beyond the usual examples of freedom and justice. In light of the ubiquity of abstract words, the need to understand where abstract meanings come from becomes ever more acute. We argue that the best source of knowledge about abstract meanings may be language itself. We then consider a seemingly unrelated question: Why isn’t language more iconic? Iconicity—a resemblance between the form of words and their meanings—can be immensely useful in language learning and communication. Languages could be much more iconic than they currently are. So why aren’t they? We suggest that one reason is that iconicity is inimical to abstraction become iconic forms are too connected to specific contexts and sensory depictions. Form-meaning arbitrariness may allow language to better convey abstract meanings.

scholarly journals Bayesian Analysis of Sparse Counts Obtained From the Unrelated Question Design

2019 ◽  
Vol 8 (5) ◽  
pp. 66
Author(s):  
Balgobin Nandram ◽  
Yuan Yu
Keyword(s):  
Em Algorithm ◽  
Binary Data ◽  
Bayesian Method ◽  
Negative Answer ◽  
Small Sample ◽  
Sample Sizes ◽  
Population Sampling ◽  
The Em Algorithm ◽  
Small Sample Sizes ◽  
Unrelated Question

In sample surveys with sensitive items, sampled units may not respond or they respond untruthfully. Usually a negative answer is given when it is actually positive, thereby leading to an estimate of the population proportion of positives (sensitive proportion) that is too small. In our study, we have binary data obtained from the unrelated-question design, and both the sensitive proportion and the nonsensitive proportion are of interest. A respondent answers the sensitive item with a known probability, and to avoid non-identifiable parameters, at least two (not necessarily exactly two) different random mechanisms are used, but only one for each cluster of respondents. The key point here is that the counts are sparse (very small sample sizes), and we show how to overcome some of the problems associated with the unrelated question design. A standard approach to this problem is to use the expectation-maximization (EM) algorithm. However, because we consider only small sample sizes (sparse counts), the EM algorithm may not converge and asymptotic theory, which can permit normality assumptions for inference, is not appropriate; so we develop a Bayesian method. To compare the EM algorithm and the Bayesian method, we have presented an example with sparse data on college cheating and a simulation study to illustrate the properties of our procedure. Finally, we discuss two extensions to accommodate finite population sampling and optional responses.

A binary unrelated-question RRT model accounting for untruthful responding

2019 ◽  
Vol 12 (7) ◽  
pp. 1163-1173
Author(s):  
Amber Young ◽  
Sat Gupta ◽  
Ryan Parks
Keyword(s):  
Unrelated Question
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