University of Leicester
Browse

Working at a Remove: Continuous, Collective, and Configurative Research Engagement through Qualitative Secondary Analysis

Download (714.29 kB)
Version 2 2021-09-08, 14:50
Version 1 2020-10-02, 14:03
journal contribution
posted on 2021-09-08, 14:49 authored by Kahryn Hughes, Jason Hughes, Anna Tarrant
In this paper, we define and operationalise three modes of research engagement facilitated by qualitative secondary analysis (QSA). We characterise these forms of engagement as continuous, collective and configurative. Continuous QSA involves modes of engagement that centre on asking new questions of existing datasets to (re)apprehend empirical evidence, and develop continuous samples in ways that principally leverage epistemic distance. Collective QSA characteristically involves generating dialogue between members of different research teams to establish comparisons and linkages across studies, and formulate new analytic directions harnessing relational distance. Configurative QSA refers to how data are brought into conversation with broader sources of theory and evidence, typically in ways which exploit greater temporal distance. In relation to each mode of engagement we discuss how processes of both (re)contextualisation and (re)connection offer opportunities for new analytical engagement through different combinations and degrees of proximity to, and distance from, the formative contexts of data production.

History

Citation

Qual Quant (2021). https://doi.org/10.1007/s11135-021-01105-x

Author affiliation

School of Media, Communication and Sociology.

Version

  • VoR (Version of Record)

Published in

Quality and Quantity: international journal of methodology

Publisher

Springer Verlag

issn

0033-5177

Acceptance date

2020-09-03

Copyright date

2021

Available date

2021-09-08

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC