Semi-automated transcription and scoring of autobiographical memory narratives.

Abstract

Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labor. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine, Svoboda, Hay, Winocur, & Moscovitch, Psychology and Aging, 17, 677-89, 2002), but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing, (3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting of scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces the opportunity for error in narrative quantification. Available open access on GitHub ( https://github.com/cMadan/scoreAI ), our pipeline makes narrative methods more accessible for future research.