The Scottish Government

« Previous | Contents | Next »

Listen

ANNEX C: TECHNICAL DETAILS OF THE SURVEY

The Scottish Social Attitudes series

1. The Scottish Social Attitudes ( SSA) survey was launched by the Scottish Centre for Social Research ⁴⁴ (part of the National Centre for Social Research) in 1999, following the advent of devolution. Based on annual rounds of interviews with 1,500-1,600 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 Government and various charitable and grant awarding bodies, such as the Nuffield and Leverhulme Foundations.

The 2006 survey

3. The 2006 survey contained modules of questions on:

• attitudes to government and public services in post-devolution Scotland (funded by the Scottish Executive's Office of Chief Researcher from 2004-2007)
• discrimination in Scotland (funded by the Scottish Executive and Department for Trade and Industry)
• attitudes towards young people and youth crime (funded by the then Scottish Executive)
• views about national identity (in collaboration with David McCrone and Frank Bechhofer at the University of Edinburgh, funded by the Leverhulme Foundation)
• and, attitudes towards homelessness (funded by the Scottish Executive).

4. Findings from the 2006 modules are reported in separate publications produced by ScotCen and their collaborators. This technical annex accompanies ScotCen-authored reports for the Scottish Government. It covers the methodological details of the 2006 survey as well as further discussion of the analysis techniques used in the reports.

Technical details of the survey

5. The Scottish Social Attitudes survey involves a face-to-face interview with respondents and a self-completion questionnaire, completed by nine in ten of these people (90% in 2006). The numbers completing each stage in 2006 are shown in Table 1. See Bromley, Curtice and Given (2005) for technical details of the 1999-2004 surveys and Given and Ormston (2006) for technical details of the 2005 survey.

Table 1: 2006 Scottish Social Attitudes survey response

Notes to table
The table shows a 'lower' and an 'upper' response rate. The former is calculated on the assumption that all addresses whose eligibility to participate was unknown were in fact eligible to take part. The latter is calculated on the assumption that they were all ineligible (because they were empty/derelict, non-residential, etc). The 'true' response is likely to lie somewhere between the two, since some addresses whose eligibility was unknown are likely to have been 'deadwood' while others may have been eligible. See Lynn et al (2001) ⁴⁵ for a discussion of treatment of unknown eligibility in calculating response rates.
1 This includes empty /derelict addresses, holiday homes, businesses and institutions.
2 '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.
3 Refusals 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.
4 Non-contacts comprise households where no one was contacted after at least 4 calls and those where the selected person could not be contacted.
5 'Other non-response' includes people who were ill at home or in hospital during the survey period, people who were physically or mentally unable to participate and people who with insufficient English to participate.

Sample design

6. The survey is designed to yield a representative sample of adults aged 18 or over living in Scotland. The sample frame is the Postcode Address File ( PAF), a list of postal delivery points compiled by the Post Office. The detailed procedure for selecting the 2006 sample was as follows:

1. 88 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 being in non-manual occupations ( SEG 1-6 and 13, taken from the 2001 Census). The list was also stratified 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 and identified its eligibility for the survey. Where more than one dwelling unit was present at an address, all dwelling units were listed systematically and one was selected at random using a computer generated random selection table. In all eligible dwelling units 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

7. The weights applied to the SSA 2006 data are intended to correct for three potential sources of bias in the sample:

1. Differential selection probabilities
2. Deliberate over-sampling of rural areas
3. Non-response.

8. Data were weighted to take account of the fact that not all households or individuals have 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. Differences between responding and non-responding households were taken into account using information from the census about the area of the address as well as interviewer observations about participating and non-participating addresses. Finally, the weights were adjusted to ensure that the weighted data matched the age-sex profile of the Scottish population (based on 2005 mid-year estimates from GROS).

9. Prior to the 2005 dataset, SSA data was only weighted to take account of differential selection probabilities and over-sampling in rural areas. The decision to introduce non-response weighting and 'calibration' weighting to match the sex-age profile of the population was taken following experimentation with the 2004 British Social Attitudes ( BSA) dataset. Both BSA and SSA weights now incorporate these new elements, which are designed to reduce non-response bias.

Fieldwork

10. Fieldwork ran between August 2006 and January 2007 (with 77% completed by the end of October). An advance letter was sent to all addresses and was followed up by a personal visit from a Scottish Centre for Social Research interviewer. Interviewers were required to make a minimum of 4 calls at different times of the day (including at least one evening and one weekend call) in order to try and contact respondents, although in practice interviewers often made many more calls than this. All interviewers attended a one day briefing conference prior to starting work on the study.

11. 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 which was either collected by the interviewer or returned by post. Table 1 summarises the response rate and the numbers completing the self-completion in 2006.

Analysis variables

12. A number of standard analyses have been used in the five reports. 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 main analysis groups requiring further definition are set out below.

The Scottish Government six-fold urban-rural classification

13. 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)

14. 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

15. The remaining respondents were grouped as 'never had a job' or 'not classifiable'.

Scottish Index of Multiple Deprivation ( SIMD)

16. The Scottish Index of Multiple Deprivation ( SIMD) ⁴⁶ 2006 measures the level of deprivation across Scotland - from the least deprived to the most deprived areas. It is based on 37 indicators in seven domains of Current Income, Employment, Health, Education Skills and Training, Geographic Access to Services (including public transport travel times for the first time), Housing and, new for 2006, Crime. SIMD 2006 is presented at data zone level, enabling small pockets of deprivation to be identified. The data zones are ranked from most deprived (1) to least deprived (6,505) on the overall SIMD 2006 and on each of the individual domains. The result is a comprehensive picture of relative area deprivation across Scotland.

17. The SSA analysis used three variables created from SIMD data indicating the level of deprivation of the data zone in which the respondent lived. The first variable (nsimd06s) indicates which SIMD quintile the respondent lives in (with 1 being the least deprived and 5 being the most deprived); the second ( SNIMD15) indicates whether or not the respondent lives in the most deprived 15% of data zones as measured on the SIMD; the third indicates which tertile the respondent lives in (with 1 being the least deprived and 3 being the most deprived. All three variables are based the SIMD scores for all datazones - not simply those included in the SSA sample.

Analysis techniques

Regression

18. For the more complex analysis in the reports, logistic regression models have been used to assess whether there is reliable evidence that particular variables are associated with each other.

19. 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. All regression analysis assumes that the relationship between the dependent and each of the independent variables takes a particular form.

20. The Scottish Social Attitudes 2006 reports use logistic regression - a method that summarises the relationship between a binary 'dependent' variable (one that takes the values '0' or '1') and one or more 'independent' explanatory variables. The tables in this report show how the odds ratios for each category in significant explanatory variables compares to the odds ratio for the reference category (always taken to be 1.00).

21. Taking Model 1 (below) as an example, the dependent variable is based on disagreeing with the statement 'most homeless people have just been unlucky in their lives'. If the respondent disagrees with this statement, the dependent variable takes a value of 1. If not, it takes a value of 0. An odds ratio of above 1 means respondents in that category were more likely to disagree with this statement than respondents in the reference category. An odds ratio of below 1 means they were less likely to disagree with the statement than respondents in the reference category. If we look at sex, we can see that women were less likely than men to disagree with the statement 'most homeless people have just been unlucky in their lives', since they have an odds ratio of 0.59. However, if we look at education, we see that the odds of someone with a degree disagreeing with this statement are 1.68 times greater among those with a degree compared with those with no qualifications.

22. The significance of differences between the reference category and other categories are indicated by 'P'. A p-value of 0.05 or less indicates that there is less than a 5% chance we would have found such a difference just by chance if in fact no such difference exists, while a p-value of 0.01 or less indicates that there is a less than 1% chance. P-values of 0.05 or less are generally considered to indicate that the difference is highly statistically significant, while a p-value of 0.06 to 0.10 may be considered marginally significant. As shorthand to aid interpretation, we have used symbols to summarise statistically significant differences:

• '+' denotes results that are significantly different from 0 at the 10% level (p = 0.06-0.10)
• '*' denotes results that are significant from 0 at the 5% level (p = 0.015 - 0.05) and
• '**' denotes results that are significantly different from 0 at the 1% level (p = 0.01 or below).

23. It should be noted that the final regression models reported below were produced following a 2-step process. First, forward stepwise regression analysis was conducted in SPSS 12.0. The variables entered into these initial models are noted below each final model, below. Second, those variables found to be significantly associated with the dependent variable by these forward stepwise models were entered into a final regression model run through STATA. Unlike SPSS 12.0, STATA can account for complex sample designs (in particular, the effects of clustering and associated weighting) when calculating odds ratios and determining significance. The final models shown below include only those variables found to be significant after the regression models were run in STATA.

Regression models

Model 1 Most homeless people have just been unlucky in their lives

Cases included in model = 1,539

Independent variables included in initial forward stepwise model: Age, sex, area deprivation ( SIMD quintiles), socio-economic class ( NS- SEC), SHS urban-rural classification (6-fold), tenure, highest educational qualification, newspaper readership, self-assessed hardship, household income (quartiles), position on libertarian-authoritarian scale (tertiles), position on left-right scale (tertiles), how often come across someone you think is homeless, self/someone know experienced homelessness.

Model 2 Most homeless people could find somewhere to live if they really tried

Cases included in model = 1,414

Independent variables included in initial forward stepwise model: Age, sex, area deprivation ( SIMD quintiles), socio-economic class ( NS- SEC), SHS urban-rural classification (6-fold), tenure, highest educational qualification, newspaper readership, self-assessed hardship, household income (quartiles), position on libertarian-authoritarian scale (tertiles), position on left-right scale (tertiles), how often come across someone you think is homeless, self/someone know experienced homelessness.

Model 3 Many people say they are homeless just to get a house from the council

Cases included in model = 1,369

Independent variables included in initial forward stepwise model: Age, sex, area deprivation ( SIMD quintiles), socio-economic class ( NS- SEC), SHS urban-rural classification (6-fold), tenure, highest educational qualification, newspaper readership, self-assessed hardship, household income (quartiles), position on libertarian-authoritarian scale (tertiles), position on left-right scale (tertiles), how often come across someone you think is homeless, self/someone know experienced homelessness.

Model 4 Some who become homeless for different reasons more deserving of help than others

Cases included in model = 1,383

Independent variables included in initial forward stepwise model: Age, sex, area deprivation ( SIMD quintiles), socio-economic class ( NS- SEC), SHS urban-rural classification (6-fold), tenure, highest educational qualification, newspaper readership, self-assessed hardship, household income (quartiles), position on libertarian-authoritarian scale (tertiles), position on left-right scale (tertiles), how often come across someone you think is homeless, self/someone know experienced homelessness.

Model 5 Some kinds of people should get more help finding a new home than others

Cases included in model = 1,550

Independent variables included in initial forward stepwise model: Age, sex, area deprivation ( SIMD quintiles), socio-economic class ( NS- SEC), SHS urban-rural classification (6-fold), tenure, highest educational qualification, newspaper readership, self-assessed hardship, household income (quartiles), position on libertarian-authoritarian scale (tertiles), position on left-right scale (tertiles), how often come across someone you think is homeless, self/someone know experienced homelessness.

« Previous | Contents | Next »