Event Model Analysis

Abstract

Event model analysis (EMA) is a narrative analysis technique that utilizes research on event cognition to reconstruct how people explain and interpret events. Event cognition research is premised on the idea that humans process the flow of information from their bodies and environment by simultaneously segmenting it into events and subevents (event segmentation) and generating working models of what is happening based on prior experience (event schemas) that are recalled only if stored in long-term memory as event models. Because we rely on event segmentation, models, and schemas – whether we are remembering past events, planning future events, telling a fictional story, or understanding a story told to us by others – historians can use research on event cognition to structure the way they extract and analyze data from narrative accounts. EMA can take two basic forms depending on the nature of the sources: attribution analysis and schema analysis. Attribution analysis, which is best suited to first- and third-person narratives, reveals the implicit attributions embedded in the narrative of an event and how attributions shift over time or between narrators and contexts. Schema analysis, which can be applied to second-person accounts, reveals how event schemas operate in models of specific events. We can use codes and charts to analyze relevant aspects of event models systematically.