Dear all,
I am a PhD student and I am analyzing data with the fsQCA software and I have some difficulty interpreting the results of the analysis. The problem is that in the table of results, I do not have the parsimonious solutions!
But, according to all that I read, there is always PS. So my questions are:
- Does anyone have any idea why this could happen?
- How can I interpret my result without PS? Should I interpret only intermediate solutions?
- Do you know of another study facing the same problem?
Thanks a lot for your help!
Best regards
Benyamin
Dear Benyamin,
I suggest you to see links and attached files on topic.
Chapter 5: Qualitative Comparative Analysis Using Fuzzy Sets (fsQCA ...
www.u.arizona.edu/~cragin/fsQCA/.../Chapter5_CCA.p...
QCA workshop - Women Entrepreneurs
womenentrepreneurs.info/wp.../Pres_Pablo_QCA.pdf
A Fuzzy Set Approach Competitive paper ... - Semantic Scholar
https://pdfs.semanticscholar.org/.../2301637a53496bcab...
Parsimony and Causality
https://people.uib.no/mba110/docs/parsimony.pdf
Best regards
Dear Benyamin,
Most likely, you are not presented with a parsimonious solution because your output function values do not vary. That is, you have only positive minterms in your truth table (rows showing a "1" on the output value). Under such a data situation, nothing can be inferred about potential causes because there are no difference-making situations (identifying a cause always requires some kind of difference-making).
The question why you still get a conservative and an intermediate solution is easy to answer: these two solution types supplement your empirical data with artificial data, i.e. data that do not exist. They do so by creating negative minterms in the truth table (rows showing a "0" on the output value) without you noticing. By doing so, difference-making situations are artifically created, from which inferences are then drawn and presented. Specifically, conservative solutions put a "0" on all minterms that differ in exactly one position from any positive minterm you have. Intermediate solutions put a "0" on all minterms that differ in exactly one position from any positive minterm you have, and which you have designated as so-called "difficult counterfactuals" by using the respective radio buttons for directional expectations in the fs/QCA menu that pops up when conducting so-called "Standard Analyses".
This problem has first been discovered in Baumgartner and Thiem (2017), and has been explained in all necessary technical detail in Thiem (forthcoming).
In conclusion, do not use conservative or intermediate solutions, use parsimonious solutions if you're interested in cause-effect relations. But to do so, you need other/more empirical data. The data you currently have don't allow you to make any inferences about cause-effect relations.
I hope this helps.
Best wishes,
Alrik
References
- Baumgartner, Michael, and Alrik Thiem. 2017. "Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis." Sociological Methods & Research. Advance online publication. DOI: 10.1177/0049124117701487. Available from:
Article Often Trusted but Never (Properly) Tested: Evaluating Qualit...
- Thiem, Alrik. forthcoming. "Beyond the facts: Limited empirical diversity and causal inference in Qualitative Comparative Analysis." Sociological Methods & Research. (you find the preprint version on my ResearchGate website).
Mohamed-Mourad Lafifi Dear Mohamed,
Thank you very much for the articles !
Best regards,
Benyamin
- Badges
- Science topic
- Similar topics
- Philosophy
- Esthetics
- Interpretation