Thematic trajectory analysis: A temporal method for analysing dynamic qualitative data

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). Practitioner points: Existing qualitative analytical methods have limitations when applied to qualitative diary data and have thereby limited the questions that may be explored, and understood, through qualitative data. Offers an alternative, step-by-step, analytical approach for researchers and practitioners seeking to understand within-person changes and dynamism in organizations. Enables the benefits of qualitative diaries to be better exploited by both researchers and practitioners and thereby lead to better understanding of how organizational processes unfold, and in turn, lead to stronger intervention mechanisms. Demonstrates the utility of combing textual and visualized data outputs in understanding complex and dynamic phenomena in organizations.