Contextual Text Coding: A Mixed-methods Approach for Large-scale Textual Data
Contextual Text Coding: A Mixed-methods Approach for Large-scale Textual Data
复制标题
上下文文本编码：大规模文本数据的混合方法
DOI：
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发表时间：
2021
影响因子：
6.3
通讯作者：
Zawadi Rucks
中科院分区：
文献类型：
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作者：
Matty Lichtenstein;Zawadi Rucks
随着越来越多的大规模文本数据集的可用性，有越来越多的需要一个访问和系统的方法来分析定性文本。本文介绍并详细介绍了上下文文本编码（CTC）方法作为一种混合方法的大规模定性数据分析。该方法特别适用于复杂的文本，文本数据的特点是上下文特定的含义和缺乏一致的术语。CTC提供了一种替代目前的方法来分析大型文本数据集，特别是计算文本分析和手工编码，这两种方法都没有捕获大规模文本数据集的定性和定量分析潜力。基于手工编码技术和系统抽样方法，CTC提供了一个清晰的六步流程，用于对大规模复杂文本数据源进行定量和定性分析。本文包括两个例子，使用项目侧重于期刊和访谈数据，分别说明该方法的通用性。
With the growing availability of large-scale text-based data sets, there is an increasing need for an accessible and systematic way to analyze qualitative texts. This article introduces and details the contextual text coding (CTC) method as a mixed-methods approach to large-scale qualitative data analysis. The method is particularly useful for complex text, textual data characterized by context-specific meanings and a lack of consistent terminology. CTC provides an alternative to current approaches to analyzing large textual data sets, specifically computational text analysis and hand coding, neither of which capture both the qualitative and quantitative analytical potential of large-scale textual data sets. Building on hand coding techniques and systematic sampling methods, CTC provides a clear six-step process to produce both quantitative and qualitative analyses of large-scale complex textual data sources. This article includes two examples, using projects focusing on journal and interview data, respectively, to illustrate the method’s versatility.