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Naomi Kamoen & Bregje Holleman

I Get it Using Qualitative and Quantitative Data to Investigate Comprehension Difficulties in Political Attitude Questions. Naomi Kamoen & Bregje Holleman. In election times…. …therefore: Voting Advice Applications!. Taxes on housing should be reduced.

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Naomi Kamoen & Bregje Holleman

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  1. I Get it Using Qualitative and Quantitative Data to Investigate Comprehension Difficulties in Political Attitude Questions Naomi Kamoen & Bregje Holleman

  2. In election times…

  3. …therefore: Voting Advice Applications! Taxes on housing should be reduced

  4. To what extent do VAA users understand the VAA statements that lead to the voting advice? And what do users do when they experience comprehension problems? Research questions

  5. Study 1: Method • Cognitive interviews with 60 users; each user answered 30 Kieskompas Utrecht statements • Coderscodedcomprehensiondifficulties, andscoredthe types of problemsobserved: • semanticproblems, categoriesbased on survey literature (jargon; negation), andcreatedinductively (unkownlocation); • andpragmaticproblems

  6. Study 1: Results

  7. Study 1: Results

  8. Study 1: Results Dog tax should be abolished

  9. Study 1: Results There should be more houses built in the Polder Rijnenburg

  10. Study 1: Results

  11. Study 1: Results The A27 should not be widened

  12. Study 1: Results

  13. Study 1: Results Parking fees in Utrecht may be increased

  14. VAA users hardly ever consult resources foradditional information (N = 26) They make assumptionsaboutwhat a question mightmean “Welfare work…is that voluntary work? It probably is”, “Welfare work…is that some form of care?” “What is welfare work…is that good for one’s well-being?”. Andsupply a neutral or no opinion answer (about 55% of thetimes) Study 1: Results

  15. Both semanticandpragmaticcomprehensionproblemsandpeople do verylittletosolve these problems… …but howecologicallyandexternallyvalid are these findings? Study 1: Conclusion and discussion

  16. Which question characteristicsincreaseneutraland no-opinion responding? Coding of 34 * 30 = 1020 questions Multilevel model predictingtheoccurrence of neutral (M1) and no-opinion (M2) based on 357,858 VAA users Study 2: Method

  17. Study 2: Results 2% 18%

  18. Municipal jargon, tax names, geographical locations, and vague quantifiers complicate the question The choice for ‘neutral’ or ‘no-opinion’ does not seem arbitrary Pragmatic problems -> Neutral Semantic problems -> No Opinion Conclusion

  19. Combining cognitive interviews and large-scale statistical analysis worked well Just quantitative Big Data methods: e.g., we would have missed “location” problems Just qualitative think-aloud methods: e.g., we would have missed the division of labor for neutral vs. no-opinion answers Conclusion

  20. Thank you!

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