Abstract

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in 'big' 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.

摘要

计算方法在刑事司法研究中越来越流行。随着越来越多的“大”和其他数字格式的刑事司法数据变得可用，需要新的方法来拥抱计算转向。在本文中，我们提出了一个使用计算方法进行数据收集和案例抽样的框架，使研究人员能够进行深入的定性研究——关注社会背景或现象的特殊性的分析——从大数据开始，这通常与较薄的定量相关。方法和追求可推广的发现。该方法首先使用开源网络抓取算法从目标网站、在线数据库或类似的在线资源中收集内容。下一个，研究人员使用自然语言处理领域的计算技术来探索更大数据集中的主题和模式。基于这些初步探索，研究人员通过算法生成数据子集以进行深入的定性分析。在这个由计算驱动的数据收集和案例抽样过程中，更大的语料库和子集永远不会完全分离，我们认为这一特征对传统的定性研究技术和原则有影响。为了说明这种方法，我们使用网络抓取、自然语言处理和视觉话语分析的组合来收集、子集和分析来自加拿大皇家骑警网站 (N = 13,637) 的三年新闻发布。为了提高我们干预的教学价值并促进复制和二次分析，