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Strategies for transnational comparison of secondary aspects: a case study

Strategies for transnational comparison of secondary aspects: a case study. 1 st European Conference on Comparative Electoral Research – Sofia – Friday, 2 nd December 2011. Dr. Frédéric Falkenhagen – Carl-von-Ossietzky University Oldenburg – frederic.falkenhagen@uni-oldenburg.de. Overview.

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Strategies for transnational comparison of secondary aspects: a case study

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  1. Strategies for transnational comparison of secondary aspects: a case study 1st European Conference on Comparative Electoral Research – Sofia – Friday, 2nd December 2011 Dr. Frédéric Falkenhagen – Carl-von-Ossietzky University Oldenburg – frederic.falkenhagen@uni-oldenburg.de

  2. Overview • Research Case • State of authoritarianism research • Data quality and shape • Choosing a method of indicator construction • Results • Lessons from the experience

  3. Research Case I – Context of Research • Research on voters of ethno-regional parties in Western Europe • Quantitative comparison of voters of four parties in three countries • Secondary analysis of 16 post-electoral (or equivalent) surveys over the period 1991-2003

  4. Research Case II – Modelling • National data analysis through GDA and clustering • Comparison of national results • Socio-demographic and ideological models • Baseline models as invalidation devices • Fifth case as contrast case

  5. State of authoritarianism research • Historic research (Adorno et al. 1950) still universally acknowledged and referenced • Compatible theoretical outlooks • Empirical module in dire condition • Bound to initial context • Outdated in content and form • No coordinated development

  6. Data quality and shape • Minor place in questionnaires • Diverse items internationally • Diverse module size internationally • Moderately stable items nationally • Partial absence of single items or modules • Only national indicators can be built meaningfully • Comparison of national indicators

  7. Choosing a method of indicator construction • Factor analysis • Listwise exclusion cuts sample by two thirds • Imputation techniques introduce major artefact risks or demand to much to work out • Hierarchical scaling • Too few items • Very different module size • Specific Multiple Correspondence Analysis • Only relative positions • General indication of data structure

  8. Results of indicator construction • All three national samples close • Scree-test points to bi-dimensional descriptions • Second dimension measures Guttman effect • Irregular intervals (social control) • National differences in polarisation

  9. Lessons from the experience • Data and documentation knowledge is paramount • Common theoretical references essential • Several possible strategies depending on structure and use • Short modules and low data homogeneity favour geometrical data analysis • Do not overstretch results

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