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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

Making cross-national comparisons using micro data

Unit 3

Siobhan Carey

Department for International Development

content
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
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
international adult literacy survey
International Adult Literacy Survey
  • A survey run by very reputable organisations on literacy skills of adults
  • How do you explain the differences?
  • Various theories
cont d
….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
    • …………
programme for international student achievement pisa
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?
preparation
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?
what do you need to consider
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?
understanding the data
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…)
understanding the variables
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

preparation1
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

weights
Weights
  • Design weights
  • Population weights
  • Compound weights
  • Replicate weights

Which you use will depend on what comparisons you are making

analysis
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
analysis output
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
analysis1
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
presenting your analysis
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
rewards
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!
activity
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
suggestions
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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