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Unit 3 Siobhan Carey Department for International Development

Making cross-national comparisons using micro data. Unit 3 Siobhan Carey Department for International Development. Content. Why it’s important to get it right Preparation Assessing the data Organising your analysis Presentation of results. Does it matter if it’s wrong?.

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Unit 3 Siobhan Carey Department for International Development

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  1. Making cross-national comparisons using micro data Unit 3 Siobhan Carey Department for International Development

  2. Content • Why it’s important to get it right • Preparation • Assessing the data • Organising your analysis • Presentation of results

  3. Does it matter if it’s wrong? • Don’t be famous for the wrong reasons • Importance of reproducibility • Be able to defend your analysis if challenged • Two examples of why it’s important

  4. International Adult Literacy Survey • A survey run by very reputable organisations on literacy skills of adults • How do you explain the differences? • Various theories

  5. ….cont’d • Methodological review found • differences in sampling methods • differences in fieldwork practices • deviation from the survey design • A second review • translation • scoring of tests • …………

  6. Programme for International Student Achievement (PISA) • How do you explain the differences? • didn’t lead to any investigation • results accepted but lead to huge debate • Was IALS worse that other surveys? • Why did PISA not lead to the same questions?

  7. Preparation • Know the data • Read the documentation before you do the analysis • Have clearly defined research questions • Which countries are you going to include • Small number and expand? • Large number and reduce? • Combine countries?

  8. What do you need to consider • Was the survey intended to support cross-national research from the outset? • Is the survey design the same across all countries? • In what areas do the countries differ in design or execution and are these important? e.g. – all school types included?

  9. Understanding the data • Comparability of overall design • What questions were asked – who was asked • Sample design and coverage • Mode • Survey response • Data collection methods • Translation • Data processing and imputation • Missing values • Topic measurement – any special features (anthropometry, biochemical…)

  10. Understanding the variables • Which variables are derived • Which are imputed • How have they been imputed • What other modifications have been made e.g. hours spent watching TV – a categorical variable modified into a continuous variable

  11. Preparation • Creation or transformation of variables • Recording, derived variables, dummy variables, reference group • Reducing dataset to improve speed • Document your analysis - always • Develop good habits – in-flight v programme based - Naming conventions

  12. Weights • Design weights • Population weights • Compound weights • Replicate weights Which you use will depend on what comparisons you are making

  13. Analysis • Rarely unique • Use existing analyses to learn • Check and double check – • Reproduce, check bases, check population, check text to tables, check tables to source…. • Check significance

  14. Analysis - output • Does it pass the common sense test? • Are the bases right? • Are the right weights on? • Is it plausible? • What can you triangulate against? • Is your analysis simply a reflection of distribution? • What could be behind it? – e.g. poverty reduction and population growth

  15. Analysis • Horse race? • Gets media attention but … • Correlates are more interesting • e.g. relationship between sexual behaviour and HIV prevalence • Analysis needs to be set in context • Take into account system factors – e.g. school characteristics, hierarchical features, standardisation

  16. Presenting your analysis • Borrow from others • OECD, World Bank, UN, Lancet … • Try to keep it simple – • what’s the story • and why is it interesting

  17. Rewards? • Help understand social condition • Help unpick complex relationships • Contribute to evidence base so as to improve policy and outcomes • Interesting – only if you’re curious!

  18. Activity • Using ESS – on a topic of interest • formulate a research question - which countries are of interest? • investigate some variables that are relevant • look at difference when weights applied • are there differences in distribution between countries? Why might that be? • what is the unit of analysis and which weights should you use? • Develop an analysis plan

  19. Suggestions • Crime and fear of crime • Religiosity • Attitudes to organs of the state • Values – attitudes to cheating on tax • attitudes to sexual behaviour

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