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运用矩阵方法分析定性纵向研究:以COVID-19大流行期间的一个案例为例。

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

作者信息

Terzis Lauren D, Saltzman Leia Y, Logan Dana A, Blakey Joan M, Hansel Tonya C

机构信息

School of Social Work, Tulane University, New Orleans, LA, USA.

School of Social Work, University of Minnesota, Minneapolis, MN, USA.

出版信息

Int J Qual Methods. 2022 Sep 3;21:16094069221123723. doi: 10.1177/16094069221123723. eCollection 2022 Jan-Dec.

Abstract

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.

摘要

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。

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本文引用的文献

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Publication Rate and Journal Review Time of COVID-19-Related Research.COVID-19相关研究的发表率和期刊评审时间。
Mayo Clin Proc. 2020 Oct;95(10):2290-2291. doi: 10.1016/j.mayocp.2020.08.017. Epub 2020 Aug 31.

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