Graphical Retrieval and Analysis of Temporal Information Systems (GRATIS): An Integrative Mixed Methodology and Open-Access Software to Analyze the (Non-)Linear Chronological Evolution of Information Embedded in Textual/Qualitative Data

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Abstract

Like a video that reveals much more than a single photo, the incorporation of time to the analysis of qualitative evidence promotes contextualized understandings and allows research participants and readers to interactively review the processes and rationale that researchers followed to craft their findings and conclusions. However, mixed methods and qualitative methodologies available today forfeit the nuances gained by analyzing the chronological/temporal evolution of processes. We contribute to mixed methods research by introducing graphical retrieval and analysis of temporal information systems (GRATIS), a methodology (and open-access software) designed to visualize and analyze the time-based richness embedded in all qualitative/textual data. GRATIS employs dynamic network visualizations and data science mining/retrieval tools to combat the assumption that longitudinal studies require large timespans. We showcase how all qualitatively- or machine-learning-coded textual data may be analyzed with no extra feature engineering (i.e., data cleaning or preparation), rendering fully integrative/interactive outputs that strengthen the transparency of our findings and conclusions and open the “analytic black box” that characterizes most of mixed methods and qualitative studies to date. GRATIS contributes to democratizing data science by removing financial and computer programming barriers to benefit from data science applications. All data and software to replicate the analyses are provided with this submission.

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González Canché, M. S. (2024). Graphical Retrieval and Analysis of Temporal Information Systems (GRATIS): An Integrative Mixed Methodology and Open-Access Software to Analyze the (Non-)Linear Chronological Evolution of Information Embedded in Textual/Qualitative Data. Journal of Mixed Methods Research, 18(1), 71–103. https://doi.org/10.1177/15586898231166968

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