ASRP06 Quantitative methods of social research for cross-national comparisons

1. ASRP06 Quantitative methods of social research for cross-national comparisons Paul Lambert, 1.2.06

2. Quantitative cross-national social research

3. Introduction: Formats of Quantitative Cross-National research Some parameters: cross-national  between country cross-national  comparative Large-N / Small-N  Quant / Qual.

4. QDA: Analysis of patterns of relationships between variables in the variable-by-case matrix[Low # of vars; stats / graphical summaries]

5. A convenient distinction Both micro- and macro- social data can be Large-N and Small-N

6. a) Macro-Social QnXR Each case represents country, & aggregate statistics are compared

7. b) Micro-social QnXR Cases (eg people) are grouped by country

8. Generic quantitative analysis issues • Data design • Harkness et al 2003: Pre-harmonisation v’s Ex-post harmonisation • Bryman 2001(p53) : 4 data collection models • Data analysis • Selection of alternative techniques • A small number of specific extensions designed for cross-national analysis (eg, ‘mixed models’)

9. Key feature of QnXR:Country as a categorical variable Analyse within countries then compare outcomes(‘country-by-country’) V’s Analyse data pooled between countries, use countries / country level factors as explanations(‘pooled’)

11. Country as a categorical factor Often criticised: • Appears to be overly simplistic However • Same as other QDA factors, eg gender,.. • Critics forget qualified interpretations that good QDA makes:[these patterns] are associated with categories, all other things being equal. • Bad QDA: forget controls for relevant other things

12. Quantitative cross-national social research

13. A popular two-stage story: Early quantitative researchers naively attempted to measure national differences as single variables. They badly misclassified or ignored important national level differences. Much more thoughtful considerations of complex national contexts are needed, & often these are more suited to qualitative research methods. Eg: Hantrais and Mangen 96: moves to interpretive methods; Ragin 87: variable v’s case oriented approaches

14. This inaccurate simplification implies a false Qn/Ql division: • Doesn’t reflect variety of current practice in QnXR (& indeed past practice) • Doesn’t acknowledge multivariate QnXR • Doesn’t do justice to many carefully conducted / reported QnXR projects • Tends to over-estimate QlXR capactity

15. A picture of Quantitative cross-national research under this typology: Crude pooled macro-social analyses (Large-N) C-by-C

16. Multitude of contemporary social research examples don’t fit this • There are a great many quantitative country-by-country outputs • It is unfair to describe all pooled designs as inadequate • ..though to be fair, many pooled projects are genuinely weak!

17. A fairer typology of QnXR Crude pooled analyses Country-by- country Sophisticated pooled analyses

18. Crude pooled analyses Early or recent, micro- and macro- research making claims over country level differences, with: • Insufficient exploration of relevant explanatory factors • Limited or poor quality variable operationalisations & discussions • Relevant national contexts not appreciated • False assumptions of good harmonisation Example: see the illustrated analysis using the ESS

19. Sophisticated pooled analyses Early or recent micro- and macro- research making claims over country level differences, with: • Sufficient exploration of relevant explanatory factors • Good quality variable operationalisations and discussions • Relevant national contexts suitably described • Accurate assumptions of good harmonisation Example: more applications than is often realised…

20. Country-by-country approaches • Qn analyses within countries, then outcomes evaluated between countries by authors / readers • Doesn’t require strong assumptions of data harmonisation • Expertise of report writer covers national context Examples: Edited books; centrally coordinated projects; end user reviews; …

21. Sophisticated pooled analyses • Attractive method: • offers parsimony of XN summary • uses large scale resources • Methodology for good conduct necessary • Reliability, validity, implementation, translation • Sample design • Reporting strategy and claims • Boundary to crude research subjective / contested • Existence often denied by anti-Qn sociologists…

22. Why not be over-cautious? • C-b-C QnXR seems a safe bet? • Doesn’t make claims not justified • But doesn’t make much impact either • Remains need for good pooled research: • Offers a parsimonious summary of national differences • Govt / media with utilise regardless

23. Quantitative cross-national social research

24. 3.1) Data availability • Massive increases in data resources accessible to social researchers • Secondary survey datasets • Official statistics resources • Internet provision / communications • Many data resources under-exploited • Most data originates from survey sources - but some exceptions

25. 3.2) Dataset complexity • Secondary surveys tend to feature • Many variables and cases • Complex variable operationalisation choices • Complex structuring (eg multiple hierarchies) • Complex weighting / sampling information • Data analysis & management software needs • Aggregate statistics’ features • Difficulty understanding source derivation

26. 3.3) Variable operationalisation • Single biggest issue in most QnXR conduct • Survey design • Dataset analysis • Result reporting • Models of comparability • Exact equivalence of measures • Relativistic equivalence of meanings • Wide literature on ‘reliability’, ‘validity’ of X-N variable measures and aggregate statistics

27. Variable harmonisation ctd • Choices over key variables allow use of previous literatures (eg H-Z & Wolf 2003). Eg measures of income; occupation; ethnic group; education; region; crime; health; .. • Choices over specific analytical variables require new efforts Eg, attitude harmonisations of Inglehart.

28. 3.4) Survey design Harkness et al 2003: Ex post facto harmonisation (more widespread, eg Eurostat, IPUMS, LIS) v’s Coordinated design, sampling, & implementation (big money projects, eg ESS, ISSP) Latter as preferable – but whilst many projects attempt this model, far fewer succeed...

29. 3.5) Conduct and logistics • High costs of coordinated surveys • Considerable efforts, and many errors, in ex post facto harmonisation • Issues of cooperating with colleagues / diverging academic traditions, eg • different views data access / confidentiality • Technical / software compatibility • different organisations involved in survey production  QnXR can be very slow process

30. 3.6) Temptation Cross-national datasets nearly always look simpler than they really are  dangerous temptation to rush into uncritical variable analysis

31. 3.7) Prejudice • Prejudices against quantitative methods pronounced in European sociology, especially wrt cross-national comparisons • QnXR evidence often ignored • QnXR researchers portrayed as simplistic • Prejudices favouring quantitative methods often seen in governmental and media organisations • Mainly: uncritical acceptance of harmonisations

32. Quantitative cross-national social research

33. Some leading secondary surveys:(see handout for internet links)

34. European Social Survey • Annual attitudes / values / social circumstances cross-sections, 2002 • Equivalence of design and survey implementation between countries • Extensive methodological resources • Free access to data • Won the European Union’s 2005 Descartes Prize for outstanding research!

35. Analysis (see SPSS syntax eg) • Opens harmonised files from 15 countries in 2002 • Select variables measuring attitudes, age, gender and educational levels • Generate tables of patterns split by countries • Use regression models to evaluate contribution of mulitiple explanatory factors: • Country specific ‘structural breaks’ • Country effects as variables / interactions

36. Liberal attitudes to homosexuality and their associations with educational level (national average and Cramer’s V to educ)

37. Log-regression prediction of liberalism to homosexuality for ESS adults (value & significance of coefficient estimate)

38. This is ‘crude’ pooled analysis • Didn’t try out sufficient relevant explanatory factors • Didn’t check variable choices extensively • Merged variable categories for convenience • Didn’t use survey weights • Didn’t contextualise reporting with sufficient substantive national background and cross-examinations of data sources and measures

39. ..but it could have been sophisticated pooled analysis • Could have evaluated variable meanings • Could have studied backgrounds • Could have added more explanatory factors • Could have reported more carefully • .. Research consumption = understanding how well the results were prepared

40. Summary on Quantitative cross-national research • Quant methods contribute to both ‘pooled’ & ‘country-by-country’ comparisons • Crude pooled analyses widely criticised, and many bad examples persist • Sophisticated pooled research can be found, and represents most attractive format of QnXR