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ANNEX 1: TECHNICAL DETAILS OF THE SURVEY
Background to the survey
1. The Scottish Social Attitudes ( SSA) survey was launched by the Scottish Centre for Social Research9 (part of the National Centre for Social Research) in 1999, following the advent of devolution. Based on annual rounds of interviews with 1,600 people drawn using random probability sampling, its aims are to facilitate the study of public opinion and inform the development of public policy in Scotland. In this it has similar objectives to the British Social Attitudes ( BSA) survey, which was launched by the National Centre in 1983. While BSA interviews people in Scotland, these are usually too few in any one year to permit separate analysis of public opinion in Scotland (see Park et al, 2004 for more details of the BSA survey).
2. SSA is conducted annually and has a modular structure. In any one year it will typically contain four or five modules, each containing 40 questions. Funding for its first two years came from the Economic and Social Research Council while from 2001 onwards different bodies have funded each year's individual modules. These bodies have included the Economic and Social Research Council, the Scottish Executive and various charitable and grant awarding bodies such as the Nuffield Foundation and Leverhulme Trust.
Sample design, fieldwork and response
3.Much of the data in this report is taken from a module of questions asked in the 2004 Scottish Social Attitudes survey. This survey involved a face-to-face interview with respondents and a self-completion questionnaire, completed by over nine in ten of these people (93%). The numbers completing each stage are shown in Table 1. See Bromley et al (2005) for technical details of the 1999-2003 surveys.
Sample design
4. The survey was designed to yield a representative sample of adults aged 18 or over living in Scotland. The sample frame was the Postcode Address File ( PAF), a list of postal delivery points compiled by the Post Office. The sample design involved three stages:
1. 84 postcode sectors were selected from a list of all postal sectors in Scotland, with probability proportional to the number of addresses in each sector. Prior to selection the sectors were stratified by region, population density, and percentage of household heads recorded as employers / managers (taken from the 2001 Census). The list was also stratified using the using the Scottish Household Survey ( SHS) six-fold classification of urban and rural areas (see below for a description of this), and sectors within rural and remote categories were over-sampled.
2. In order to boost the number of respondents from remote and rural areas 31 addresses were selected in each sector located within the first three SHS urban-rural classifications (the four cities, to accessible small towns), while 62 addresses were selected from the sectors within the three most rural categories (remote small towns to remote rural areas). The issued sample size is shown in Table 1.
3. Interviewers called at each selected address, identified its eligibility for the survey. Where more than one household was present at an address, all households were listed systematically and one was selected at random using a computer generated random selection table. In all eligible households with more than one adult aged 18 or over, interviewers also had to carry out a random selection of one adult using a similar procedure.
Weighting
5.Data were weighted to take account of the fact that not all households or individuals had the same probability of selection for the survey. For example, adults living in large households have a lower selection probability than adults who live alone. Weighting was also used to correct the over-sampling of rural addresses. All the percentages presented in this report are based on weighted data, but reported sample sizes are based on the unweighted data.
Fieldwork
6.Fieldwork ran between July and December (with 77% completed by the end of September). An advance letter was sent to all addresses and was followed up by a personal visit from a Scottish Centre for Social Research interviewer. All interviewers attended a one day briefing conference prior to starting work.
7.Interviews were conducted using face-to-face computer-assisted interviewing (a process which involves the use of a laptop computer, with questions appearing on screen and interviewers directly entering respondents' answers into the computer). All respondents were asked to fill in a self-completion questionnaire that was either collected by the interviewer or returned by post. The next table summarises the response rate and the numbers completing the self-completion in 2004.
Table A1.1 2004 Scottish Social Attitudes survey response
| No. | % |
|---|
Addresses issued 1 | 3,007 | |
|---|
Vacant, derelict and other out of scope 2 | 308 | 10.2 |
|---|
In scope | 2,699 | 100.0 |
|---|
Interview achieved | 1,637 | 60.7 |
|---|
Self-completion returned | 1,514 | 56.1 |
|---|
Interview not achieved | 1,062 | 39.3 |
|---|
Refused 3 | 698 | 25.9 |
|---|
Non-contacted 4 | 130 | 4.8 |
|---|
Unknown eligibility 5 | 100 | 3.7 |
|---|
Other non-response | 134 | 5.0 |
|---|
Notes to table
1This includes addresses identified by interviewers during fieldwork.
2This includes empty / derelict addresses, holiday homes, businesses and institutions.
3Refusals include refusals prior to selection of an individual, refusals to the office, refusal by the selected person, 'proxy' refusals made by someone on behalf of the respondent and broken appointments after which a respondent could not be re-contacted.
4Non-contacts comprise households where no one was contacted after at least 4 calls and those where the selected person could not be contacted.
5'Unknown eligibility' includes cases where the address could not be located, where it could not be determined if an address was a residence and where it could not be determined if an address was occupied or not.
Analysis variables
8. A number of standard analysis variables have been used in the tables in this report. Most of the analysis variables are taken directly from the questionnaire and to that extent are self-explanatory. These include age, sex, household income, and highest educational qualification obtained. The analysis groups requiring further definition are set out below.
The Scottish Household Survey six-fold urban-rural classification
9. The six categories used in this classification are: 1) large urban, 2) other urban, 3) small accessible towns, 4) small remote towns, 5) accessible rural, 6) remote rural. For more details see Hope, S. et al (2000).
National Statistics Socio-Economic Classification ( NS- SEC)
10. The most commonly used classification of socio-economic status used on government surveys is the National Statistics Socio-Economic Classification ( NS- SEC). SSA respondents were classified according to their own occupation, rather than that of the 'head of household'. Each respondent was asked about their current or last job, so that all respondents, with the exception of those who had never worked, were classified. The seven NS- SEC categories are:
- Employers in large organisations, higher managerial and professional
- Lower professional and managerial; higher technical and supervisory
- Intermediate occupations
- Small employers and own account workers
- Lower supervisory and technical occupations
- Semi-routine occupations
- Routine occupations
The remaining respondents were grouped as 'never had a job' or 'not classifiable'.
Deprivation
11.The Carstairs (Depcat) scores were originally developed by Vera Carstairs and Russell Morris as a measure which reflects access to those material resources which provide access to 'those goods and services, resources and amenities and of a physical environment which are customary in society'. The scores are seen as a summary measure of relative disadvantage between populations contained within small geographic localities. The scores are derived by creating a composite index from selected Census variables. Note that in contrast to the Scottish Index of Multiple Deprivation this index does not include measures of health and thus has the advantage that it does not include one of the very phenomena whose incidence we are trying to explain. The scores have been applied to postcode sectors covered by the survey (McLoone, 2004). They are divided into seven sectors from 1 (the most affluent postcode sectors) to 7 (the most deprived).
Analysis techniques
Regression
12. A variety of regression models have been used in this report to assess the statistical significance of the relationship between key variables and to control for the effects of other variables.
13. Regression analysis aims to summarise the relationship between a 'dependent' variable and one or more 'independent' explanatory variables. It shows how well we can estimate a respondent's score on the dependent variable from knowledge of their scores on the independent variables. This technique takes into account relationships between the different independent variables (for example, between education and income, or social class and housing tenure). Regression is often undertaken to support a claim that the phenomena measured by the independent variables cause the phenomenon measured by the dependent variable. However, the causal ordering if any, between the variables cannot be verified or falsified by the technique. Causality can only be inferred through special experimental designs or through assumptions made by the analyst.
14. All regression analysis assumes that the relationship between the dependent and each of the independent variables takes a particular form. In linear regression it is assumed that the relationship between the independent and the dependent variables is a straight line and that both are interval level measures. In logistic regression it is assumed that the relationship can be adequately summarised by an S-shaped curve, where the impact on the dependent variable of a one-point increase in an independent variable becomes progressively less the closer the value of the dependent variable approaches 0 or 1. Here the dependent variable has to be a binary variable, though the independent variables can be treated either as categories or as interval level variables. Finally ordinal regression is similar to logistic regression except that it can analysis a dependent variable with more than two values; in so doing it assumes that it is measured at the ordinal level.
15. Multilevel (or hierarchical) regression modelling, be it linear, logistic or ordinal, is used when examining the impact of both individual level and area level variables on a dependent variable (Goldstein, 1995). Because respondents to this survey were nested within postcode sectors and because people with similar individual level characteristics are inclined to live together, there is a danger that the estimated standard errors of the area level variables are underestimated. The standard errors produced by multilevel models take this danger into account.
Factor Analysis
16. Factor analysis is a statistical technique that aims to identify whether there are one or more apparent sources of commonality to the answers given by respondents to a set of questions. It ascertains the smallest number of factors (or dimensions) that can most economically summarise all of the variation found in the set of questions being analysed. Factors are established where respondents who give a particular answer to one question in the set tend to give the same answer as each other to one or more of the other questions in the set. The technique is most useful when a relatively small number of factors is able to account for a relatively large proportion of the variance in all of the questions in the set.
17. The technique produces a factor loading for each question (or variables) on each factor. Where questions have a high loading on the same factor then it will be the case that respondents who give a particular answer to one of these questions tend to give a similar answer to the other questions. The technique is most commonly used in attitudinal research to try to identify the underlying ideological dimensions that apparently structure attitudes towards the subject in question.
References
Bromley, C., Curtice, J., and Given, L. (2005) Public Attitudes to Devolution: the First Four Years, London: The National Centre for Social Research.
Goldstein H. (1995). Multilevel Statistical Models. 2nd.edition London: Edward Arnold.
Hope, S., Braunholtz, S., Playfair, A., Dudleston, A., Ingram, D., Martin, C., Sawyer, B. (2000) Scotland's people: results from the 1999 Scottish Household Survey: Volume 1, Scottish Executive.
Park, A., Curtice, J., Thomson, K., Bromley, C. and Phillips, M., (2004), British Social Attitudes - the 21 st Report, London: Sage.
McLoone, P. (2004 ) Carstairs scores for Scottish Postcode sectors from the 2001 Census, Glasgow: Social and Public Health Science Unit, University of Glasgow.
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