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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

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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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