Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 23 Dec 2021 English Publisher:Informa UK LimitedJournal:Journal of Criminal Justice Education, volume 33, pages 151-171 (issn: 1051-1253, eissn: 1745-9117,

Copyright policy )

Authors: Luscombe, Alex; Duncan, Jamie; Walby, Kevin;

doi: 10.1080/10511253.2022.2027477 , 10.31235/osf.io/4nhd6

Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

- Summary
- Subjects
- Metrics

Abstract

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in big data and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research – analyses concerned with the particularities of a social context or phenomenon – starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial.

Related Organizations

Keywords

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	1
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average