The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher’s identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.

This study’s objective was to examine attitudes toward the environment, and to determine the level of environmental knowledge among pre-school children and their parents. This study covered 686 parents and 674 six-year-olds from 30 pre-schools from the Malopolskie, Mazowieckie and Warmiésko-Mazurskie Provinces. The questionnaire used in the children’s survey was made up of 10 pairs of drawings; that for the adults’ survey consisted of 27 questions. The results indicated that the six-year-olds were familiar with basic notions and could identify improper behaviour with regard to the environment; in the case of more detailed environmental issues, their knowledge and practical application of environmental protection principles were worse. Pre-school children’s attitudes to the environment depended on their place of residence (town/ village). Generally, parents showed favourable environmental attitudes, but were not always willing to change their habits or make sacrifices for environmental conservation. Their ...

The integration of artificial intelligence (AI) into qualitative research is shifting the boundaries of knowledge production. Instead of mere tools, AI is increasingly conceptualized as a co-researcher or research partner—an epistemic agent that helps in the analysis, interpretation, and reflexivity of data. This article proposes an abductive model, called AbductivAI, for analyzing qualitative data, based on collaboration between humans and AI agents. It explores how AI functions as an analytical partner, reflexive collaborator, and participant in distributed cognition within the framework of the human-AI qualitative research workflow and data analysis approach. To build the model, we used advanced prompting techniques (Chain-of-Prompting) and used the 323 abstracts submitted to the World Conference on Qualitative Research as data. Although this data was not intended for research, its exploration allowed us to test the AbductivAI model, showing that interactions between humans and AI agents can benefit qualitative data analysis if the human recognizes and defines their role as a reflexive agent responsible for the entire process.

The nature of qualitative research practices is multiparadigmaticity which creates coexistence of different research and analytical approaches to the study of human experience in the living world. This diversity is particularly observed in the contemporary field of narrative research and data analysis. The purpose of this article is a methodological reflection on the process of developing typology and a proposition of new data-driven and practice-based typology of narrative analyses used by qualitative researchers in the lived experience research. I merge the CAQDAS, Corpus Linguistics, and Text Mining procedures to examine the analytical strategies inherited in a vivid language of English-language research articles, published in five influential qualitative methodological journals between 2002-2016. Using the dictionary-based content analysis in the coding process, hierarchical clustering, and topic modeling – a text-mining tool for discovering hidden semantic structures in a textual body – I confront Catherine Kohler Riessman’s heuristic typology with the data-driven approach in order to contribute the more coherent image of narrative analysis in the contemporary field of qualitative research. Finally, I propose a new model of thinking about the typology of narrative analyses based upon research practices.