Sage Research Methods Case 2: Using unobtrusive data to study criminal behavior: Understanding malicious contamination incidents
Kilbane, Sarah C. ORCID: 0000-0003-4752-5755 (2018) Sage Research Methods Case 2: Using unobtrusive data to study criminal behavior: Understanding malicious contamination incidents. [Working Paper] (doi:https://doi.org/10.4135/9781526440556)
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Abstract
Malicious contamination is a term used to describe a number of different types of criminal activity, from poisoning and product tampering to extortion and food terrorism. However, little is known about these types of crime as few empirical studies have been conducted to date. In particular, there is a lack of clear definitions in the literature for terms like “product tampering” and “poisoning.” To develop such definitions and to explore crimes of malicious contamination in more depth, a database was constructed consisting of all known incidents of malicious contamination worldwide occurring over a 40-year period. Out of necessity, this database was constructed using unobtrusive, open source data which was then content analyzed for the presence or absence of pertinent behavioral variables. The following case study highlights the advantages and disadvantages of working with unobtrusive data to study criminal behavior, including issues related to validity, reliability, and credibility. Specific examples have been included from the author’s own PhD work and subsequent publication, with an emphasis on how some of the abovementioned challenges were addressed. The importance of unobtrusive data to the study of forensic and investigative psychology is also discussed.
Item Type: | Working Paper |
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Uncontrolled Keywords: | unobtrusive measures, criminal behaviour, malicious contamination |
Subjects: | K Law > K Law (General) |
Faculty / School / Research Centre / Research Group: | Faculty of Liberal Arts & Sciences Faculty of Liberal Arts & Sciences > School of Law & Criminology (LAC) Faculty of Liberal Arts & Sciences > Crime, Law & (In)Security Research Group (CLS) |
Last Modified: | 01 Nov 2021 00:11 |
URI: | http://gala.gre.ac.uk/id/eprint/18576 |
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