Phenomena-centered Text Analysis (PTA): a new approach to foster the qualitative paradigm in text analysis

Abstract

Content or text analysis is one of the most common evaluation methods employed in qualitative research. Despite its wide application, however, a clear structure of how such evaluation should be conducted is often lacking due to the complexity of qualitative data. As a consequence, highly differentiated category systems with small-step subdivisions of categories and sub-categories are often used, leading to a loss of context both among categories and for the content as a whole. The aim of this paper is to describe the Phenomena-centered Text Analysis (PTA) as a novel form of qualitative text analysis, which takes these shortcomings into account by focusing on text-inherent phenomena. These phenomena are identified in two preceding quantitative analysis steps that identifying overlapping coding for subsequently qualitative analysis. We explain the structured code- and context-based approach of this new method and demonstrate its application with an empirical example. The PTA contributes to an increasing demand of qualitative methods especially for small-scale projects that need a structured kind of qualitative data analysis.

Poetic inquiry to explore the relational values of a transforming peat landscape

“…those that represented at least >50% of all the verbatims related to one subcategory [Appendix S2]). To identify potential phrases, we complemented qualitative content analysis with phenomena‐centred text analysis, a novel method that is useful for uncovering and quantifying overlap among code segments belonging to different categories and subcategories (Krikser & Jahnke, 2022). We used the code matrix browser and code relation browser in MAXQDA 2022 for this purpose.…”

Wearing face masks impairs dyadic micro-activities in nonverbal social encounter: A mixed-methods first-person study on the sense of I and Thou

“…Aggregation of frequencies of overlapping codes in a cross table with selected categories (“Code Relations Browser” in MAXQDA) allows one to find and evaluate the most pronounced code relations. Following the approach of Krikser and Jahnke (2021) , the analysis was limited to an interval from the maximum of category overlap to its half. At Level 2, the maximum of seven overlaps occurred between “Attention Regulation” and “Intention,” five between “Thought” and “Affective Feelings,” and four between “Eye Gaze” and “Affective Feelings” (see Table 1 ).…”