Synthesizing Indicators of Quality across Traditions of Narrative Research Methods

Kanembe Shanachilubwa, Catherine G.P. Berdanier

Research output: Contribution to journalConference articlepeer-review

Abstract

The purpose of this methods paper is to describe and discuss one of the main indicators of quality in narrative analysis, which is the process of narrative smoothing. Narrative analysis refers to a qualitative and highly interpretive research method that involves the creation of representative stories using data obtained from participants, usually in the form of interviews. Narrative analysis differs from other qualitative research methods in its indicators of quality as it does not seek to produce repeatable claims, because the goal is to capture participants' stories. The strength of a narrative analysis resides in the process of narrative smoothing for which there is limited specific guidance in the literature. Narrative smoothing refers to the process by which a researcher discerns what elements of a participant's experience to use when crafting the narrative. This paper seeks to supplement existing frameworks for assessing quality in educational research by discussing several ways in which research questions, theory, and priorities can influence the process of narrative smoothing. From this methodological discussion, this paper proposes a procedure for future researchers to use when narrative smoothing. Furthermore, and demonstrates how the selection of a smoothing frame should be influenced by research objectives and is essential for adhering to, communicating, and assessing the quality implementation of this interpretive technique.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jun 25 2023
Event2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023 - Baltimore, United States
Duration: Jun 25 2023Jun 28 2023

All Science Journal Classification (ASJC) codes

  • General Engineering

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