Understanding Regional Cities: Combining Quantitative and Qualitative Methods in Case Studies of Orange and Goulburn, NSW

Fiona McKenzie

Australasian Journal of Regional Studies2015article
ABDC B
Weight
0.53

Abstract

Decision-makers routinely use statistical data as evidence, however, the picture of 'reality' provided by such data remains incomplete. Measuring the number of small businesses in a town does not reveal the objectives of the owners who may be driven by: profit; lifestyle; prestige or innovation. Such factors may create differences in economic performance irrespective of inherent local competitive advantage. This paper uses a mixed-method approach in order to create an evidence base that goes beyond basic statistical description. The research uses two case study locations - the regional cities of Goulburn and Orange in New South Wales. By combining statistical analysis with in-depth interviews, the study aimed to better understand the factors that contribute to regional economic performance. Findings indicate that social and human capital factors are important in understanding future development pathways for each city, highlighting the importance of qualitative perspectives in regional economic analysis.

4 citations

Cite this paper

@article{fiona2015,
  title        = {{Understanding Regional Cities: Combining Quantitative and Qualitative Methods in Case Studies of Orange and Goulburn, NSW}},
  author       = {Fiona McKenzie},
  journal      = {Australasian Journal of Regional Studies},
  year         = {2015},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Understanding Regional Cities: Combining Quantitative and Qualitative Methods in Case Studies of Orange and Goulburn, NSW

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.53

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.45 × 0.4 = 0.18
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.