Analysing narrative data using McCormack’s Lenses

Dibley, Lesley (2011) Analysing narrative data using McCormack’s Lenses. Nurse Researcher, 18 (3). pp. 13-19. ISSN 1351-5578

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Abstract

Aim: In health and social care research, pure naturalistic enquiry often seeks to understand personal experiences, and adopts data collection methods that can generate vast amounts of rich, thick text. This article demonstrates the way in which the author used a particular approach to analyse the complex narrative data arising from her MPhil research into experiences of lesbian parents in the healthcare system. Background: Narrative data is often cumbersome, prolific and chaotic, and it can be difficult to manage such data effectively while remaining sympathetic to the original meaning of the storyteller. Guidance exists on what narrative analysis should achieve, but finding ways of interpreting and demonstrating meaning in different types of stories can be challenging. Data sources: The data discussed here arose from six unstructured interviews with ten lesbian parents from across the UK, collected in 2003-04 during the author’s MPhil studies. Review methods: The use of McCormack’s Lenses to analyse narrative data supported the philosophical underpinnings of Heideggerian phenomenology which provided the framework for the research endeavour. Discussion: Locating the analysis method that best suits the methodology, purpose and data is not always easy. Naturalistic or qualitative researchers may need to make extra effort to explain the philosophical and practical aspects of their work, and while it can be tempting to select an adequate analysis approach, seeking out the framework which best fits the data will enhance the credibility of the findings Conclusion: McCormack’s Lenses provides a flexible framework for the analysis of complex narrative data. It enables the researcher to take core themes and stories of experience in the original story and reveal these to the reader with openness. As well as remaining true to the original story, the framework enables the researcher to demonstrate that the reported findings are situated in the original data.

Item Type: Article
Divisions: ?? BucksNewUniversity ??
Depositing User: ULCC Admin
Date Deposited: 03 May 2012 17:12
Last Modified: 11 Dec 2017 19:18
URI: http://bucks.repository.guildhe.ac.uk/id/eprint/9634

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