Utilizing a Matrix Approach to Analyze Qualitative Longitudinal Research: A Case Example During the COVID-19 Pandemic

Autor: Lauren D. Terzis, Leia Y. Saltzman, Dana A. Logan, Joan M. Blakey, Tonya C. Hansel
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Qualitative Methods, Vol 21 (2022)
Druh dokumentu: article
ISSN: 1609-4069
16094069
DOI: 10.1177/16094069221123723
Popis: Qualitative Longitudinal Research (QLR) is an evolving methodology used in understanding the rich and in-depth experiences of individuals over time. QLR is particularly conducive to pandemic or disaster-related studies, where unique and rapidly changing environments warrant fuller descriptions of the human condition. Despite QLR’s usefulness, there are a limited number of articles that detail the methodology and analysis, especially in the social sciences, and specifically social work literature. As researchers adjust their focus to incorporate the impact of the COVID-19 global pandemic, there is a growing need in understanding the progression and adaptation of the pandemic on individuals’ lives. This article provides a process and strategy for implementing QLR and analyzing data in online diary entries. In the provided case example, we explore a phenomenological QLR conducted with graduate level students during the COVID-19 pandemic ( Saltzman et al., 2021 ) , and outline a matrix framework for QLR analysis. This paper provides an innovative way in which to engage in qualitative data collection and analysis for social science research.
Databáze: Directory of Open Access Journals
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