Cookies?
Library Header Image
LSE Research Online LSE Library Services

The item count method for sensitive survey questions: modelling criminal behaviour

Kuha, Jouni ORCID: 0000-0002-1156-8465 and Jackson, Jonathan ORCID: 0000-0003-2426-2219 (2014) The item count method for sensitive survey questions: modelling criminal behaviour. Journal of the Royal Statistical Society. Series C: Applied Statistics, 63 (2). pp. 321-341. ISSN 0035-9254

[img]
Preview
Text - Accepted Version
Download (527kB) | Preview

Identification Number: 10.1111/rssc.12018

Abstract

The item count method is a way of asking sensitive survey questions which protects the anonymity of the respondents by randomization before the interview. It can be used to estimate the probability of sensitive behaviour and to model how it depends on explanatory variables. We analyse item count survey data on the illegal behaviour of buying stolen goods. The analysis of an item count question is best formulated as an instance of modelling incomplete categorical data. We propose an efficient implementation of the estimation which also provides explicit variance estimates for the parameters. We then suggest pecifications for the model for the control items, which is an auxiliary but unavoidable part of the analysis of item count data. These considerations and the results of our analysis of criminal behaviour highlight the fact that careful design of the questions is crucial for the success of the item count method.

Item Type: Article
Official URL: http://onlinelibrary.wiley.com/journal/10.1111/%28...
Additional Information: © 2013 Royal Statistical Society
Divisions: Methodology
Statistics
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HV Social pathology. Social and public welfare. Criminology
Date Deposited: 22 Jan 2013 12:15
Last Modified: 20 Nov 2024 17:03
Projects: 217311
Funders: Seventh framework programme of the European Commission
URI: http://eprints.lse.ac.uk/id/eprint/48069

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics