Terzis Lauren D, Saltzman Leia Y, Logan Dana A, Blakey Joan M, Hansel Tonya C
School of Social Work, Tulane University, New Orleans, LA, USA.
School of Social Work, University of Minnesota, Minneapolis, MN, USA.
Int J Qual Methods. 2022 Sep 3;21:16094069221123723. doi: 10.1177/16094069221123723. eCollection 2022 Jan-Dec.
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.
质性纵向研究（QLR）是一种不断发展的方法，用于理解个体随时间推移丰富而深入的经历。QLR特别适用于与大流行或灾难相关的研究，在这些独特且迅速变化的环境中，需要对人类状况进行更全面的描述。尽管QLR很有用，但详细介绍该方法和分析的文章数量有限，尤其是在社会科学领域，特别是社会工作文献中。随着研究人员调整重点以纳入新冠疫情全球大流行的影响，了解大流行在个人生活中的发展和适应情况的需求日益增长。本文提供了一种在在线日记条目中实施QLR和分析数据的过程与策略。在提供的案例中，我们探讨了在新冠疫情期间对研究生进行的现象学质性纵向研究（Saltzman等人，2021），并概述了质性纵向研究分析的矩阵框架。本文提供了一种创新方法，用于社会科学研究中的质性数据收集和分析。
