MSc ASR, SR06 Session 9 Quantitative methods of social research for cross-national comparisons

1. MSc ASR, SR06 Session 9 Quantitative methods of social research for cross-national comparisons Paul Lambert, 5.2.02 http://staff.stir.ac.uk/paul.lambert/teaching.htm

2. Quantitative cross-national social research

3. Introduction: Formats of Quantitative Cross-National research Aside: cross-national  between country cross-national  comparative • But in quantitative methods, ‘XN’ & ‘comparative’ often used interchangeably

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 Micro-macro distinction isn’t always important (& can be confusing). But is widely used, & tends to be associated with different research fields.

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. Data Analytical techniques Same core data analysis techniques as for other social science applications, eg : • Critical issue is ‘level of measurement’ • Univariate, bivariate, multivariate • Description v’s inference • Survey methodology issues • A few advanced extensions, eg ‘mixed models’ to cater for hierarchical effects.

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

10. 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

11. Quantitative cross-national social research

12. A typology of quantitative cross-national research designs? • Bryman 2001(p53): 4 types of cross-cultural research • Ragin 1987: 2 analytical orientations, one mainly Qn, the other mainly Ql; proposed resolution with Qn-style summaries of Ql research • No typology is perfect – there is much overlap and ambiguity in methods – but it can be useful to classify patterns of modern 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 QnXR under this typology: Crude variable oriented Case oriented

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

17. A fairer typology of QnXR Crude variable-oriented Case oriented Sophisticated variable oriented

18. Crude variable oriented 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 variable oriented 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. Case oriented • 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 variable oriented • 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? • Case oriented QnXR seems a safe bet? • Doesn’t make claims not justified • But doesn’t make much impact either • Remains need for good variable oriented: • 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 • New annual attitudes / values / social circumstances cross-sections, 2002 • Equivalence of design and survey implementation between countries • Extensive methodological resources • Free access to data

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’ variable oriented • 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 variable oriented • 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 ‘variable’ & ‘case’ oriented comparisons • Crude variable oriented widely criticised, and many bad examples persist • Sophisticated variable oriented research can be found, and represents most attractive format of QnXR