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Thematic trajectory analysis: A temporal method for analysing dynamic qualitative data
Journal of Occupational and Organizational Psychology ( IF 5.119 ) Pub Date : 2021-06-25 , DOI: 10.1111/joop.12359
Leighann Spencer 1 , Laura Radcliffe 1 , Rachel Spence 1 , Nigel King 2
Affiliation  

This paper introduces a novel, temporally sensitive analytical method for qualitative researchers, which is simultaneously timely and necessary given increasing recognition of the fundamental role that time plays in organizational life and scholarship. As a result of this recognition, research designs considering temporality have substantially increased over the past decade. However, while methods for qualitative data collection using longitudinal and ‘shortitudinal’ designs, in particular qualitative diary methods, have become increasingly common, analytical methods capable of fully exploiting the temporal nature of such data have lagged behind their quantitative counter-parts, where we see marked progression in analytical methods and procedures. In this paper, we argue that this lack of progression in approaches for analysing qualitative diary data hinders our knowledge and theoretical development when it comes to incorporating temporality, particularly in the exploration of phenomena at individual-/micro-levels, arguably most salient to organizational psychology researchers. We respond to these challenges by introducing a novel, step-by-step analytical approach that facilitates rigorous incorporation of temporality into the analysis and theorization of micro-level, qualitative diary data, termed Thematic Trajectory Analysis (TTA).

中文翻译:

专题轨迹分析:一种分析动态定性数据的时间方法

本文为定性研究人员介绍了一种新颖的、对时间敏感的分析方法,鉴于人们越来越认识到时间在组织生活和学术中的基本作用,这种方法既及时又必要。由于这种认识,在过去十年中,考虑时间性的研究设计大幅增加。然而,虽然使用纵向和“短期”设计的定性数据收集方法,特别是定性日记方法,已经变得越来越普遍,但能够充分利用这些数据的时间性质的分析方法已经落后于它们的定量对应部分,我们看到分析方法和程序的显着进步。在本文中,我们认为,分析定性日记数据的方法缺乏进展阻碍了我们在整合时间性方面的知识和理论发展,特别是在个体/微观层面的现象探索中,可以说是组织心理学研究人员最重要的。我们通过引入一种新颖的、循序渐进的分析方法来应对这些挑战,该方法有助于将时间性严格纳入微观层面、定性日记数据的分析和理论化,称为主题轨迹分析 (TTA)。
更新日期:2021-08-04
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