Optimizing Grounded Theory for Policy Research
Grounded theory (GT) has become an influential methodology for research inquiry which involves the texts of human communication and language use as its primary empirical focus of data collection and analysis. However, influential models of GT have been most typically applied to the process of interp...
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doaj-d5b78f9aaeae4cadb3f054160d1b260e2020-11-25T03:18:22ZengSAGE PublishingInternational Journal of Qualitative Methods1609-40692016-02-011510.1177/160940691562138010.1177_1609406915621380Optimizing Grounded Theory for Policy ResearchCameron Richards0Farrokh Farrokhnia1 Faculty of Education, Southern Cross University, Gold Coast, Australia Perdana School of Policy Studies, University of Technology Malaysia, Kuala Lumpur, MalaysiaGrounded theory (GT) has become an influential methodology for research inquiry which involves the texts of human communication and language use as its primary empirical focus of data collection and analysis. However, influential models of GT have been most typically applied to the process of interpreting nonformal and especially “unstructured” texts such as conversations, interviews, and online communications. By contrast, policy researchers have the rather different challenge of interpreting the innate contradictions, diverse imperatives, and often conflicting perspectives which inform the meanings of policy positions as well as documents across different contexts. Building upon the interdisciplinary implications of recent constructivist models of the methodology, this article investigates how an optimizing “knowledge-building” approach represents an exemplary model for using GT for policy document research purposes. It does so in relation to the case study of a project focused on emergent as well as contextual dilemmas of analyzing World Trade Organization e-commerce policies.https://doi.org/10.1177/1609406915621380 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cameron Richards Farrokh Farrokhnia |
spellingShingle |
Cameron Richards Farrokh Farrokhnia Optimizing Grounded Theory for Policy Research International Journal of Qualitative Methods |
author_facet |
Cameron Richards Farrokh Farrokhnia |
author_sort |
Cameron Richards |
title |
Optimizing Grounded Theory for Policy Research |
title_short |
Optimizing Grounded Theory for Policy Research |
title_full |
Optimizing Grounded Theory for Policy Research |
title_fullStr |
Optimizing Grounded Theory for Policy Research |
title_full_unstemmed |
Optimizing Grounded Theory for Policy Research |
title_sort |
optimizing grounded theory for policy research |
publisher |
SAGE Publishing |
series |
International Journal of Qualitative Methods |
issn |
1609-4069 |
publishDate |
2016-02-01 |
description |
Grounded theory (GT) has become an influential methodology for research inquiry which involves the texts of human communication and language use as its primary empirical focus of data collection and analysis. However, influential models of GT have been most typically applied to the process of interpreting nonformal and especially “unstructured” texts such as conversations, interviews, and online communications. By contrast, policy researchers have the rather different challenge of interpreting the innate contradictions, diverse imperatives, and often conflicting perspectives which inform the meanings of policy positions as well as documents across different contexts. Building upon the interdisciplinary implications of recent constructivist models of the methodology, this article investigates how an optimizing “knowledge-building” approach represents an exemplary model for using GT for policy document research purposes. It does so in relation to the case study of a project focused on emergent as well as contextual dilemmas of analyzing World Trade Organization e-commerce policies. |
url |
https://doi.org/10.1177/1609406915621380 |
work_keys_str_mv |
AT cameronrichards optimizinggroundedtheoryforpolicyresearch AT farrokhfarrokhnia optimizinggroundedtheoryforpolicyresearch |
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