Work

Parsing the Continuum: Manual Segmentation of Monologic Data

Year: 2023

Type: conference-paper

Abstract: Segmentation is a crucial step in analyses of qualitative data where code or code co-occurrence frequencies are of interest. Decisions about how best to segment are inextricably connected to coding decisions, as well as wider analytical goals and research questions. These decisions directly affect resulting models and the interpretations derived from them. However, while there is a wealth of frameworks guiding code development and application, far fewer guidelines exist for segmentation. This paper reports on the development of an initial set of heuristics for the segmentation of monologic data. Using the framework of Epistemic Network Analysis, we demonstrate various approaches to segmentation and show how these segmentation decisions affect models and subsequent interpretations. We argue that segmentation should be aligned with research questions and developed in conjunction with coding, and we offer considerations and techniques for doing so.

Source: Communications in computer and information science

DOI: 10.1007/978-3-031-31726-2_12

Authors: Szilvia Zörgő, Jais Brohinsky

Institutions: Maastricht University, Care and Public Health Research Institute, University of Wisconsin–Madison

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funder: European Commission

Awards: Smart Online Searching To Increase Patient Safety, G434337789