Small N Analysis In Policy Research && Innovation

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Small N Analysis In Policy Research && Innovation

  1. 1. Large-N && Small-N Analysis in Innovation studies Beyond the quantitative-qualitative divide OPENINNOVA::David Lopez Creative Commons Attribution ShareAlike 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/ )
  2. 2. Two main approaches to social science  analysis: Quantitative Qualitative analysis analysis Quantitative versus Qualitative Analysis in Social Sciences OpenInnova:David López
  3. 3. Quantitative analysis:  ◦ Large-N approach (extensive use of cross- sectional data) Theory Empirical Model Data Multivariate Analysis Large N approach. Provides answers to: what causes revolutions ? OpenInnova:David López
  4. 4. Qualitative analysis:  ◦ Small-N approach (qualitative comparisons of cases) Cases “Soft Qualitative Model” analysis Causal chains Small-N approach. Provides answers to: what caused the French revolution ? OpenInnova:David López
  5. 5.  When it comes to policy analysis, such as innovation regimes and R&D, statistical inference is not enough, holistic approaches are needed in order to: ◦ Explore several combinations and their consequences. ◦ Conduct context-specific assessments.  For instance: What It takes to avoid poverty ? ◦ Does college education make a difference for married white males from families with good incomes ? ◦ And college education for unmarried black females from low-income families ? Moreover, what about scenarios with limited data  such as OECD innovation database for instance ? Small-N analysis in policy research (innovation is about policy after all) OpenInnova:David López
  6. 6.  Small-N approaches consider cases as combinations of causally relevant conditions College High Parent High Poverty Number Educated Parental College AFQT avoidance of cases income educated Score 1 0 0 0 0 0 30 2 0 0 0 1 0 3 3 0 0 1 0 ? 4 …. 16 1 1 1 1 1 23 Causal conditions Outcome Goal of the analysis: Identify different combinations of  case characteristics explicitly linked to poverty avoidance. Small-N Analysis by example: Avoiding poverty (I) López OpenInnova:David
  7. 7. But…. what do we mean by “High parental  income” ???  How strong is the inference:  CollegeAvoiding poverty Charles Ragin suggests Fuzzy-set logic.  High High parental parental income income Low Low parental parental income income Small-N Analysis : Fuzzy-set approach OpenInnova:David López
  8. 8. Y Xi Xi→Y Consistenc y ( X i Yi ) (min( X i , Yi )) (Xi) Small-N Analysis: Sufficiency && Consistency OpenInnova:David López
  9. 9. Xi Y Y →Xi Consistenc y (Y i Xi) (min( X i , Y i )) (Y i ) Small-N Analysis: Necessity && ConsistencyOpenInnova:David López
  10. 10. Y Y Y Xi2 Xi Xi Xi1 Xi→Y Xi→Y X1* X2 →Y Coverage ( X i Yi ) (min( X i , Y i )) (Y i ) Small-N Analysis: Sufficiency && Coverage OpenInnova:David López
  11. 11. Data from 18 European countries (Ragin 2008). COUNTRY SURVIVED BREAKDOWN DEVELOPED URBAN LITERATE INDUSTRIAL STABLE Austria 0,01 0,99 0,74 0,14 0,98 0,76 0,35 Belgium 0,98 0,02 0,99 0,89 0,96 0,98 0,96 Czech 0,85 0,015 0,42 0,96 0,97 0,91 0,87 Estonia 0,12 0,88 0,15 0,07 0,96 0,02 0,87 Finland 0,64 0,36 0,43 0,03 0,98 0,09 0,51 France 0,98 0,02 0,97 0,02 0,97 0,83 0,93 Germany 0,01 0,99 0,85 0,83 0,98 0,96 0,23 Greece 0,03 0,97 0,05 0,1 0,11 0,38 0,35 Hungary 0,41 0,59 0,08 0,2 0,81 0,08 0,09 Ireland 0,91 0,09 0,62 0,04 0,96 0,02 0,93 Italy 0,01 0,99 0,25 0,11 0,38 0,49 0,51 Netherland 0,98 0,02 0,97 0,99 0,99 0,94 0,99 Poland 0,12 0,88 0,03 0,22 0,55 0,02 0,02 Portugal 0,01 0,99 0,02 0,01 0,02 0,12 0,02 Romania 0,25 0,75 0,02 0,03 0,15 0,02 0,78 Spain 0,03 0,97 0,04 0,41 0,08 0,22 0,14 Sweden 0,98 0,02 0,93 0,15 0,99 0,7 0,87 UK 0,98 0,02 0,98 0,98 0,99 0,98 0,96 Outcome fzQCA in Action: Democracies in interwar Europe (1918-1936) OpenInnova:David López
  12. 12. DEVELOPED URBAN LITERATE INDUSTRIAL STABLE number survivedconsist 1 1 1 1 1 3 0.884337 1 0 1 0 1 1 0.773810 1 1 0 0 0 0 0.741379 1 1 0 0 1 0 0.736842 1 1 0 1 0 0 0.727273 1 1 0 1 1 0 0.727273 1 0 1 1 1 2 0.725352 0.5 1 1 1 0 1 0 0.712871 1 1 1 0 0 0 0.694737 0 1 1 1 1 1 0.675497 1 0 1 0 0 0 0.674556 0 1 1 0 0 0 0.627907 0 1 1 0 1 0 0.620000 1 0 0 0 0 0 0.594595 1 0 0 0 1 0 0.589041 0 1 0 0 1 0 0.589041 1 0 0 1 1 0 0.577465 1 0 0 1 0 0 0.577465 0 1 0 1 1 0 0.552239 0 1 1 1 0 0 0.528846 0 0 1 0 1 2 0.508197 0 0 1 0 0 2 0.506173 0 1 0 1 0 0 0.493333 0 1 0 0 0 0 0.484615 1 1 1 1 0 1 0.392857 1 0 1 1 0 1 0.379310 0 0 1 1 1 0 0.370629 0 0 1 1 0 0 0.370629 0 0 0 0 1 2 0.306977 0 0 0 1 1 0 0.287879 0 0 0 1 0 0 0.248366 0 0 0 0 0 3 0.225543 fzQCA in Action: Democracies in interwar Europe (1918-1936) OpenInnova:David López
  13. 13. DEVELOPED URBAN LITERATE INDUSTRIAL STABLE number survivedconsist 1 1 1 1 1 1 3 0.884337 0 0 0 0 0 0 3 0.225543 1 1 0 1 0 1 1 0.775352 0 0 0 1 0 1 2 0.508197 0 0 0 1 0 0 2 0.506173 0 0 0 0 0 1 2 0.306977 0 1 0 1 0 1 1 0.773810 0 0 1 1 1 1 1 0.675497 0 1 1 1 1 0 1 0.392857 0 1 0 1 1 0 1 0.379310 Consistent enough 0.75 Non consistent fzQCA in Action: Democracies in interwar Europe (1918-1936) OpenInnova:David López
  14. 14. Least parsimonious solution (remainders not considered)  If all five conditions are present then is sufficient for a democracy to  survive. But NOTurban and NOTindustrial does not really seem to matter  much: DEVELOPED*LITERATE*STABLE  SURVIVAL fzQCA in Action: Democracies in interwar Europe (1918-1936) OpenInnova:David López
  15. 15. Considering Breakdown instead of Survival as the outcome of interest:  Two sufficient conditions emerge:  NOT DEVELOPED * NOT URBAN * NOT INDUSTRIAL  BREAKDOWN  DEVELOPED*LITERATE*INDUSTRIAL*NOT STABLE  BREAKDOWN  fzQCA in Action: Democracies in interwar Europe (1918-1936) OpenInnova:David López
  16. 16. Nested analysis approach (Evan S. Lieberman) The best of both worlds ? Just adding extra workload ? OpenInnova:David López
  17. 17. Triangulation Nested analysis approach (Evan S. Lieberman) The best of both worlds ? Just adding extra workload ? OpenInnova:David López

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