Transcript Lecture (powerpoint)

ASRP06
Quantitative methods of social
research for cross-national
comparisons
Paul Lambert, 1.2.06
1
Quantitative cross-national social
research
1) Introduction
2) Three traditions in Qn cross-national research
3) Seven themes in Qn cross-national research
Case study 1: Secondary analysis of cross-national
surveys
2
Introduction: Formats of
Quantitative Cross-National
research
Some parameters:
cross-national  between country
cross-national  comparative
Large-N / Small-N  Quant / Qual.
3
QDA: Analysis of patterns of relationships
between variables in the variable-by-case matrix
[Low # of vars; stats / graphical summaries]
Cases 
1
2
3
4
.
N
1
1
2
2
.
17
18
17
18
.
 Variables
1.73 A
.
1.85 B
.
1.60 C
.
1.69 A
.
.
.
.

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4
A convenient distinction
Macro-social data
Micro-social data
Work and/or report Work and/or report
at level of
at level of
aggregated unit
constituent unit (eg
(country)
individuals)
Both micro- and macro- social data can be Large-N
and Small-N
5
a) Macro-Social QnXR
Each case represents country, & aggregate
statistics are compared
Denmark
Ireland
Italy
Portugal
UK
Ideal family size
1979
1989 Religiosity ‘81
2.31
2.13
2.06
3.62
2.79
3.42
2.11
2.20
2.90
2.29
2.23
2.66
2.29
2.14
2.33
(eg from Coleman 1996:39)6
b) Micro-social QnXR
Cases (eg people) are grouped by country
Case id Country Indv. vars
1
1
17 1.73 A
2
3
4
5
6
.
1
1
2
2
3
.
18
17
18
18
19
.
1.85
1.60
1.69
1.65
1.84
.
B
C
A
C
B
.
Natl. var
56.2
56.2
56.2
50.8
50.8
260.3
.
7
Generic quantitative analysis issues
• Data design
– Harkness et al 2003: Pre-harmonisation v’s Expost 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’)
8
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’)
9
Country-byPooled analysis
country analysis
Macrosocial
Large-N Small-N
Large-N Small-N
Micro-social Large-N Small-N Large-N Small-N
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
1) Introduction
2) Three traditions in Qn cross-national research
3) Seven themes in Qn cross-national research
Case study 1: Secondary analysis of cross-national
surveys
12
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
13
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
14
A picture of Quantitative cross-national
research under this typology:
Crude pooled macro-social analyses
(Large-N)
C-by-C
15
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!
16
A fairer typology of QnXR
Crude pooled analyses
Country-bycountry
Sophisticated pooled analyses
17
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
18
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…
19
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; …
20
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…
21
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
22
Quantitative cross-national social
research
1) Introduction
2) Three traditions in Qn cross-national research
3) Seven themes in Qn cross-national research
Case study 1: Secondary analysis of cross-national
surveys
23
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
24
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
25
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
26
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.
27
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...
28
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
29
3.6) Temptation
Cross-national datasets nearly always look
simpler than they really are
 dangerous temptation to rush into
uncritical variable analysis
30
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
31
Quantitative cross-national social
research
1) Introduction
2) Three traditions in Qn cross-national research
3) Seven themes in Qn cross-national research
Case study 1: Secondary analysis of cross-national
surveys
32
Some leading secondary surveys:
(see handout for internet links)
ESS
ISSP
IPUMS
LIS / LES / LWS
Eurobarometer
WVS / EVS
ECHP / CHER / PACO
EU-SILC
Social Stratification:
CASMIN / CCAP / …
Education: PISA / TIMSS
33
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!
34
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
35
Liberal attitudes to homosexuality and
their associations with educational level
(national average and Cramer’s V to educ)
%
Switzerland
Czech Rep
Spain
Finland
UK
Greece
Hungary
Ireland
81
58
70
62
75
51
48
82
CV
10
11
20
14
7
23
5
8
%
Israel
Netherlands
Norway
Poland
Portugal
Sweden
Slovenia
59
88
77
46
71
82
52
CV
20
6
13
16
15
12
18
36
Log-regression prediction of liberalism
to homosexuality for ESS adults
(value & significance of coefficient estimate)
Age-squared
-1.72** Interactions:
Low educ
-0.31** Low educ*NW
0.19**
High educ
0.35** Female*NW
0.35**
Female
0.21** Female*South
-0.15*
North West
1.07** Contrast: medium education
Southern
male from eastern European
0.56** country.
37
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 crossexaminations of data sources and measures
38
..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
39
Summary on Quantitative crossnational 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
40