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Using mixed-methods, a data model and a computational ontology in film audience research

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Publication details

JournalCultural Trends
DateAccepted/In press - 20 Jun 2019
DateE-pub ahead of print - 20 Jun 2019
DatePublished (current) - 2019
Issue number2-3
Number of pages14
Pages (from-to)118-131
Early online date20/06/19
Original languageEnglish


This paper discusses a methodology that seeks to address one of the challenges in working with a range of data in mixed-methods audience research, which is how to sort, order and categorise different data so that they can be systematically combined and interrogated. The methodology was developed as part of the “Beyond the Multiplex: audiences for specialised films in English regions” (BtM) project. This project required a mixed methods approach using surveys, interviews, focus groups and document analysis to explore the richness of audience experiences and trends in the context of regional film policy. This required a mixed methods approach using surveys, interviews, focus groups and document analysis. The project utilised a data model approach that uses the principles of a computational ontology in order to sort, order and categorise data for systematic interrogation. The paper discusses methods, data, coding, and the use of a data model to support data analysis. We argue that this approach enables the cross referencing of data that provides a rich, multi-layered and relational understanding of film audiences but requires time and attention to data management and coding. Although, additionally it also forms the basis of an open access data resource for future research.

Bibliographical note

Funding Information:
This work was supported by the Arts and Humanities Research Council [grant number AH/P005780/1].

Funding Information:
1. Arts and Humanities Research Council (AHRC) funded project: AH/P005780/1 (UKRI, 2017).

Publisher Copyright:
© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

    Research areas

  • audiences, computational ontologies, data model, Film, mixed-methods

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