Qualitative Research in Digital Era: Innovations, Methodologies and Collaborations

Abstract

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.

Qualitative and Mixed Methods Research in Cardiovascular Care: Opportunities in the Coming Decades

“…In addition, qualitative coding software programs such as MAXQDA, Atlas.ti, and others are rapidly evolving and incorporating AI to allow for new forms of analysis of text, audio, images, videos, and social media, as well as novel data visualization tools to convey complex data effectively. 10 While the excitement around emerging technologies is understandable, we agree with those who urge caution not to lose the unique and valuable ability of qualitative methods to capture nuance and to describe phenomenon in rich depth. Finally, we see the growth of authentic team science that includes qualitative and mixed methods researchers as appropriate given the focal topic as a potentially high-impact innovation.…”

Labor Market Transformation: Implications of Technological Change in Society 5.0

“…Integrating these two methods will allow researchers to draw on a deep understanding of existing literature while using historical data to make predictions or projections about how the labor market might change due to Society 5.0. As such, this research is expected to provide a more structured and in-depth understanding of the impact of Society 5.0 on the labor market and a solid basis for formulating appropriate solutions and strategies in the face of these changes (Bryda & Costa, 2023).…”

Asking questions that are “close to the bone”: integrating thematic analysis and natural language processing to explore the experiences of people with traumatic brain injuries engaging with patient-reported outcome measures

“…Although these methods of ensuring quality are not entirely exempt from criticism ( 102 – 105 ), it is undeniable that similar debates on the rigour, trustworthiness and replicability of data analysis performed via NLP are still in their infancy ( 106 , 107 ). In this regard, emerging debates highlight that not all NLP tools and methods that are consensually used have gone through systematic and comprehensive standardisation processes to ensure their validation and integrity ( 108 , 109 ). In light of this, one of the salient challenges of the growing application of NLP methods in the research context will be to ensure a rigorous evaluation and optimisation of data analysis processes and transparency in the way results are obtained and explained ( 110 ).…”