I have exchanged some thoughts with Ken Friedman, that I would like to share with you. I also propose some questions about this subject.
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To bridge the gap between design and statistical analysis is not an easy task, partly because of the origins of design studies, largely influenced by qualitative research preference. Let’s be honest: it is much easier to interview people and count words/patterns than to supply statistical models with hundreds of observations data and to run strange mathematical formula. (NOTE: I know several design initiatives already use statistics, such as industrial/product design, but other design fields like graphic design and service design could also be benefited from quantitative statistical tools)
Yes, it is not easy to translate subjective opinion into objective data, ready for analysis. Even similar approaches like Kansei Engineering lacks of substantial body of studies, usually relegated to marginal initiatives. Schutte states that
“Kansei engineering has always been a statically and mathematically advanced methodology. Most types require good expert knowledge and a reasonable amount of experience to carry out the studies sufficiently. This has also been the major obstacle for a widespread application of Kansei engineering.” – Schütte, S. (2007). Towards a common Approach in Kansei Engineering. A proposed model. Proceedings of the Conference: Interfejs użytkownika - Kansei w praktyce, Warszawa 2007 (pp. 8–17). Warsaw: Wydawnictwo PJWSTK.
"We have a lot of studies based on analytical generalization but they have some limitations. "A ... common concern about case studies is that they provide little basis for scientific generalization. "How can you generalize from a single case?" is a frequently heard question. ... In fact, scientific facts are rarely based on single experiments; they are usually based on a multiple set of experiments that have replicated-the same phenomenon under different conditions” – Yin, R. (2010). 'Analytic Generalization.' In Albert J. Mills, G. Durepos, & E. Wiebe (Eds.), Encyclopedia of Case Study Research. (pp. 21-23). Thousand Oaks, CA: SAGE Publications, Inc.
So, it seems to have few initiatives integrating design and statistical generalization. Most of studies offer only ad hoc results with analytical generalization. (Of course, statistical generalization is not the unique benefit of quantitative analysis and mathematical thinking).
But that should not be the unique possibility. We could use statistical tools as Economics already use for decades. Of course, some kinds of causal inference studies wouldn’t be appropriate for some design research (specially behavior related ones) but we have many subjects that would be benefited from inferential tools. For example: color, type, format, size, and so many attributes could be combined into prototypes using a multivariate approach and investigated using statistical analysis. These studies could be replicated in other scenarios to build a body of knowledge about the impact of design choices. Or we could use quantitative analysis to investigate the economic impact of design interventions in a multivariate scenario, bringing the so desired info about ROI and strategic design. All these applications already are commonplace in other fields of knowledge as marketing and economics, but are rare when we talk about serious design studies. Part of the allegation concerning lack of respect that design has on management area is grounded on the absence of replicable studies about the relevance of design on organizational outcomes.
Furthermore, I agree with you (Ken) when you say "Causality research requires massive testing in different programs that ask similar questions, with added testing and replication of promising results”. My point is: will we get any replicable studies without the first ones? We need to start somewhere. And where most of us start? At graduate level. In my opinion the main reason why there are so few studies with statistical approaches is the low amount of design researchers defending their use at schools, post graduation programs, and scientific journals. In some graduate programs, we have almost no mention of quantitative approaches from teachers, reflecting a grounded theory preference. If advisors don’t know this research strategy I think it would be unlikely to have any papers using this kind of statistical tools.
So, in my opinion, we have some hypothesis to be tested:
“Can quantitative analysis and inference studies support design research in the same way they have already been supporting Economics and other social sciences?”
“What is the cause of so few design studies using mathematical support? Lack of support from statisticians? Absence of statistics-oriented advisors?”
“Who have been successfully applying inferential or other statistical tools in design studies?”
“What can they teach us about this approach?”
Any other questions?
Cheers,
M.Sc. Ricardo Martins
Federal University of Parana
+55 41 98855 8007
Skype: ralexm
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