Network analysis offers a powerful methodological approach for uncovering patterns and structures within complex relational data. This study argues that qualitatively or quantitatively coded textual information can be conceptualized as a network of relationships between participants and the codes applied to their classified narratives/contributions, thereby forming a natural relational database. The main argument is that data gathered through qualitative methods (e.g., interviews, focus groups) and subsequently coded/classified/labeled contain mathematical structures that can be retrieved, visualized, and statistically analyzed using network methods— even if we are not analyzing social relationships . Furthermore, when participant attributes are incorporated, the resulting structures enable statistical evaluation of the extent to which experiences differ across groups or are similarly distributed across our participants’ attributes. This study introduces the Network Analysis of Qualitative Data (NAQD) framework, which integrates quantitative, mathematical, and qualitative principles to analyze textual data—an approach whose potential for rigorous statistical hypothesis testing remains largely unrealized in common qualitative software. Alongside presenting this analytic framework, and with the goal of democratizing access to data science tools, we introduce a peer-reviewed, free, and no-code software tool that implements NAQD (see https://cutt.ly/2riYDEhH for the published version of the software). However, this software is also freely available for Mac ( https://cutt.ly/unZDUMq ) and Windows ( https://cutt.ly/KnL9frz ) platforms. Unlike commercially available software, NAQD provides community detection, hypothesis testing of group similarities via Quadratic Assignment Procedures (QAP), and fully interactive HTML network renderings. We illustrate all steps and outputs using replication data from a project on teacher training at Minority Serving Institutions.
Manuel S. González Canché (Mon,) studied this question.