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A living journals approach for the remote study of young children’s digital practices in Azerbaijan

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/03/2023
<mark>Journal</mark>Global Studies of Childhood
Issue number1
Volume13
Number of pages15
Pages (from-to)64-78
Publication StatusPublished
Early online date27/07/21
<mark>Original language</mark>English

Abstract

This article proposes the living journals method for remotely studying participants, elevating participant agency in the data generation process and minimising or completely removing the need for a researcher to be physically present in the field. Employing this method, the paper describes how the method was used to explore 5-year-old children’s digital practices in five families in Azerbaijan. Mothers were assigned as ‘proxy’ researchers to generate the data following prompts sent through a smartphone application. Mothers’ answers were used to create journals, and subsequently, fathers separately, and mothers and children together were requested to interpret their own journals and those of other participant children. Allowing other families to comment on one another’s journals further revealed their attitudes towards using digital technologies and enriched the data, emphasising its multivocality and metatextuality. The article describes the living journals method in detail, highlighting its affordances for researchers to generate data from a distance in other contexts. The article also discusses the methodological and empirical contribution of the method to this study about young children’s engagements with digital media at home. By decentring the researcher in the data generation process, the method allows researchers to generate both visually and textually complex and rich data. The visual and personal nature of the method goes beyond text-based research accounts to bring the data to life, allowing the researcher to generate multimodal, multivocal, metatextual and multifunctional data.