English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Design and quality criteria for archetype analysis

Authors

Eisenack,  K.
External Organizations;

Villamayor-Tomas,  S.
External Organizations;

Epstein,  G.
External Organizations;

Kimmich,  C.
External Organizations;

Magliocca,  N.
External Organizations;

Manuel-Navarrete,  D.
External Organizations;

Oberlack,  C.
External Organizations;

Roggero,  M.
External Organizations;

/persons/resource/Diana.Sietz

Sietz,  Diana
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)

8481oa.pdf
(Publisher version), 245KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Eisenack, K., Villamayor-Tomas, S., Epstein, G., Kimmich, C., Magliocca, N., Manuel-Navarrete, D., Oberlack, C., Roggero, M., Sietz, D. (2019): Design and quality criteria for archetype analysis. - Ecology and Society, 24, 3, 6.
https://doi.org/10.5751/ES-10855-240306


Cite as: https://publications.pik-potsdam.de/pubman/item/item_23133
Abstract
A key challenge in addressing the global degradation of natural resources and the environment is to effectively transfer successful strategies across heterogeneous contexts. Archetype analysis is a particularly salient approach in this regard that helps researchers to understand and compare patterns of (un)sustainability in heterogeneous cases. Archetype analysis avoids traps of overgeneralization and ideography by identifying reappearing but nonuniversal patterns that hold for well-defined subsets of cases. It can be applied by researchers working in inter- or transdisciplinary settings to study sustainability issues from a broad range of theoretical and methodological standpoints. However, there is still an urgent need for quality standards to guide the design of theoretically rigorous and practically useful archetype analyses. To this end, we propose four quality criteria and corresponding research strategies to address them: (1) specify the domain of validity for each archetype, (2) ensure that archetypes can be combined to characterize single cases, (3) explicitly navigate levels of abstraction, and (4) obtain a fit between attribute configurations, theories, and empirical domains of validity. These criteria are based on a stocktaking of current methodological challenges in archetypes research, including: to demonstrate the validity of the analysis, delineate boundaries of archetypes, and select appropriate attributes to define them. We thus contribute to a better common understanding of the approach and to the improvement of the research design of future archetype analyses.