1. Using Exploratory and Confirmatory Factor Analysis to Understand Social Attitudes
Paula Surridge
Dept. of Sociology
University of Bristol

2. The project The making of social values
Examine relationship between education, social class and social attitudes
Framework based around idea of underlying values which structure social attitudes

3. Core values?
Underlying values that determine how specific issues are viewed
Not directly observable
Stable and durable over time

4. Measuring Core Values ‘Socialist Laissez-faire’ (Left-right)
‘Liberal-Authoritarian’
Evans et al 1996
Heath et al 1994
Use a combination of attitudinal items to measure core values

5. Project research questions
Are the ‘left-right’ and ‘liberal-authoritarian’ values of British public changing?
How is this related to increases in educational levels, especially higher education?

6. Assumptions There are two basic values underpinning social attitudes
Invariant in structure over time
Invariant in structure over groups
New issues do not disrupt basic structure
Need to assess if these assumptions reasonable

7. Exploratory Factor Analysis
Key question:
How are ‘new’ issues related to the two core values as measured by ‘left-right’ and ‘liberal-authoritarian’ scales
Exploratory analysis no preconceived ideas of how issues might be related

8. The data British social attitudes survey, 2004 & 2005
Sample size ~2500
Analysis conducted for 2004, 2005 used for validation

9. Exploratory Factor Analysis
Issues:
Suitability of the data
Sample size
Number of measures per factor
Technical aspects of technique
Factor extraction
Factor rotation
Number of factors

10. Left-right scale
Ordinary working people do not get their fair share of the nation’s wealth
Big business benefits owners at the expense of workers
Government should redistribute income from the better-off to those who are less well off
There is one law for the rich and one for the poor
Management will always try to get the better of employees if it gets the chance

11. Liberal-Authoritarian scale
Censorship of films and magazines is necessary to uphold moral standards
Schools should teach children to obey authority
Young people today don’t have enough respect for traditional British values
People who break the law should be given stiffer sentences
For some crimes, the death penalty is the most appropriate sentence
The law should always be obeyed even if a particular law is wrong

12. Initial Analysis Two factor structure confirmed
Not sensitive to technical issues
Extraction method
Rotation procedure
But what about ‘new’ issues?

13. ‘New’ issue Additional item
‘Refugees who are in danger because of their political beliefs should always be welcome in Britain’
How does this item relate to the other two scales?

14. Redistribution
0.498
-0.048
0.284
Big business
0.793
0.056
0.158
Wealth
0.817
0.017
0.008
One law for rich
0.745
-0.033
-0.163
Management
0.694
0.012
-0.189
Trad values
0.105
0.536
-0.100
Stiffer sentences
0.054
0.634
-0.202
Death Penalty
0.084
0.278
-0.492
Schools teach obey
0.005
0.611
-0.051
Law always obeyed
-0.090
0.428
0.047
Censorship
0.019
0.470
0.067
Refugees
0.024
-0.011
0.593

15. Additional item Three factor solution
Third factor suggests ‘liberal-authoritarian’ values may be multi-faceted
Are ‘left-right’ values also multi-faceted?

16. Additional measures
It’s only right that taxes paid by the majority help support those in need
If we want to live in a healthy, well-educated society we have to be willing to pay the taxes to find it.
It’s not fair that some people pay a lot of money in tax and hardly use the services their taxes pay for
The best reason for paying taxes now is that you never know when you might need benefits and services yourself
It’s not right that people benefit from services they haven’t helped to pay for
Inequality continues to exist because it benefits the rich and powerful

17. Redistribution
0.479
-0.196
0.255
Big business
0.783
-0.044
0.101
Wealth
0.824
-0.016
-0.046
One law for rich
0.749
0.031
-0.088
Management
0.697
0.095
-0.166
Trad values
0.094
0.549
-0.073
Stiffer sentences
0.036
0.687
Death Penalty
0.103
0.532
-0.208
Schools teach obey
-0.027
0.669
0.116
Law always obeyed
-0.117
0.388
0.040
Censorship
-0.022
0.460

18. Political Refugees
-0.032
-0.311
0.395
Support needy
0.007
-0.025
0.647
Pay taxes for society
-0.028
0.001
0.573
Pay and not use
-0.036
0.274
-0.354
Never know
0.126
0.187
0.362
Benefit and not pay
0.106
0.479
-0.301
Inequality
0.603
-0.019

19. EFA: Summary Technical issues have relatively small impact
Data issues very important for secondary analysis
Interpretation of factors requires caution!

20. Confirmatory Factor Analysis
Posits a structure and assesses goodness of fit of structure to data
Formal goodness of fit statistics allow for comparison between groups (years)

21. Learning CFA Very different approach than EFA
Despite similarities in underlying methods
Requires specialist software
Availability
Training
May be little support within institutions

22. Model structure

23. Model fit
0.06
0.95
RMSEA
CFI

24. Modification
Period all years acceptable fit between model and data
Period less acceptable fit, in 1998, 2001, 2002 and 2005 fit is not acceptable. Why?
Modification indexes => cross-loading between redistribution and liberal-authoritarian scale

25. Modified model Acceptable fit in each year Measurement invariance
‘Configural invariance’
‘Weak measurement invariance’
1986 used as base-line
Compared each year to 1986

26. Measurement Invariance
For each year both configural and weak measurement invariance models fit data
Suggests that the structure of attitudes is not significantly different between 1986 and 2005
Good news! Expect core values to be stable but not the whole story.

27. Redistribution loading on left-right scale

28. Death penalty loading on Lib-Auth scale

29. Redistribution cross-loading

30. Scale correlation

31. Conclusions
Undoubtedly Factor Analysis the right approach to the initial research questions
EFA – helped to understand the structure but very sensitive to the available measures
Be wary of SPSS ‘defaults’
CFA – may be difficult to interpret model fit data, especially with large sample sizes and/or many groups for comparison.
Can be daunting to learn, especially new software