Scottish Government

ANNEX A -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 (ScotCen) in 1999, following the advent of devolution. Based on annual rounds of interviews with around 1,500 people drawn using probability sampling (based on a stratified, clustered sample) ²⁴, 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 ScotCen's parent organisation, the National Centre for Social Research (NatCen) 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., 2010 for more details of the BSA survey).

2. SSA has been conducted annually each year since 1999, with the exception of 2008. The survey has a modular structure. In any one year it typically contains 4 or 5 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 individual modules each year. These bodies have included the Economic and Social Research Council, the Scottish Government and various charitable and grant awarding bodies, such as the Nuffield Foundation and Leverhulme Trust.

The 2009 survey

3. The 2009 survey contained modules of questions on:

• Government and public services in Scotland (funded by the Scottish Government Office of the Chief Researcher from 2004-2007 and again in 2009)
• Antisocial behaviour (funded by the Scottish Government)
• What makes somewhere a good place to live, with a particular focus on the importance of greenspace (funded by the Scottish Government)
• Drugs and recovery from problem drug use (funded by the Scottish Government)
• National identity, in collaboration with David McCrone and Frank Bechhofer of the University of Edinburgh (funded by the Leverhulme Trust)
• Escape places and violence (funded by NHS Health Scotland), and
• Constitutional change (self-funded by ScotCen).

4. Findings from the modules funded by the Scottish Government will be available in reports published on their website ( www.scotland.gov.uk ), while separate programmes of dissemination are planned for each of the other modules. This technical annex is designed to accompany Scottish Government reports based on SSA 2009. It covers the methodological details of the survey as well as further discussion of the analysis techniques used in the reports.

Sample design

5. 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 2009 sample was as follows:

I. 102 postcode sectors were selected from a list of all postal sectors in Scotland, with probability proportional to the number of addresses in each sector for addresses in urban areas and a probability of twice the address count for sectors in rural areas ( i.e. the last 3 categories in the Scottish Government's 6 fold urban-rural classification). Prior to selection the sectors were stratified by Scottish Government urban-rural classification ²⁵, region and percentage of household heads recorded as being in non-manual occupations ( SEG 1-6 and 13, taken from the 2001 Census).

II. 30 addresses were selected at random from each of these 102 postcode sectors

III. 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 had to carry out a random selection of one adult using a similar procedure.

Response rates

6. The Scottish Social Attitudes survey involves a face-to-face interview with respondents and a self-completion questionnaire, completed by around nine in ten of these people (89% in 2009). The numbers completing each stage in 2009 are shown in Table 1. See Bromley, Curtice and Given (2005) for technical details of the 1999-2004 surveys, Given and Ormston (2006) for details of the 2005 survey, Cleghorn, Ormston and Sharp (2007) for the 2006 survey and Ormston (2008) for the 2007 survey.

Table 1: 2009 Scottish Social Attitudes survey response

Notes to table
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 residential 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 6 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 with insufficient English to participate.

Weighting

7. All percentages cited in this report are based on weighted data. The weights applied to the SSA 2009 data are intended to correct for three potential sources of bias in the sample:

• Differential selection probabilities
• Deliberate over-sampling of rural areas
• 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 2008 mid-year estimates from the General Register Office for Scotland).

Fieldwork

9. Fieldwork for the 2009 survey ran between late April and early September 2009. An advance letter was sent to all addresses and was followed up by a personal visit from a ScotCen interviewer. Interviewers were required to make a minimum of 6 calls at different times of the day (including at least one evening and one weekend call) in order to try and contact respondents. All interviewers attended a one day briefing conference prior to starting work on the study.

10. 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 (above) summarises the response rate and the numbers completing the self-completion in 2009.

Analysis variables

11. 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 variables requiring further definition are set out below.

National Statistics Socio-Economic Classification ( NS- SEC)

12. 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.

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

Scottish Index of Multiple Deprivation ( SIMD)

14. The Scottish Index of Multiple Deprivation ( SIMD) ²⁶ 2009 measures the level of deprivation across Scotland - from the least deprived to the most deprived areas. It is based on 38 indicators in seven domains of: income, employment, health, education skills and training, housing, geographic access and crime. SIMD 2009 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 2009 and on each of the individual domains. The result is a comprehensive picture of relative area deprivation across Scotland.

15. The analysis in this report used a variable created from SIMD data indicating the level of deprivation of the data zone in which the respondent lived in quintiles, from most to least deprived. ²⁷

Analysis techniques

16. 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.

17. All regression analysis assumes that the relationship between the dependent and each of the independent variables takes a particular form. This report was informed by logistic regression analysis - 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 annex 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).

18. Taking Model 2.1 (below) as an example, the dependent variable is based on whether or not the respondent has ever tried cannabis. If they answered yes, the dependent variable takes a value of 1. If not, it takes a value of 0. An odds ratio of above 1 means that, compared with respondents in the reference category, respondents in that category have higher odds of saying they have ever tried cannabis. Conversely, an odds ratio of below 1 means they have lower odds of saying this than respondents in the reference category. The 95% confidence intervals for these odds ratios are also important. Where the confidence interval does not include 1, this category is significantly different from the reference category. For example, if we look at age in Model 2.1, we can see that people aged 60+ have an odds ratio of 0.16. This indicates that they have a lower odds of reporting that they have ever tried cannabis compared with those in the reference category (18-24 year-olds). Moreover, the 95% confidence interval for this odds ratio is 0.08-0.31, indicating that the difference in the odds is significantly significant at the 95% level. Meanwhile, the p-value for age as a whole is 0.000 - indicating that age is highly significantly related to self-reported cannabis use (there is a less than 1% chance that we would have found the differences reported here by age by chance if differences by age did not in fact exist in the population as a whole).

19. It should be noted that the final regression models reported below were produced after several stages, with initial models using forward stepwise analysis to identify significant factors from a longer list of possible variables. The models below show the final models, produced using the Complex Survey command ( CS Logistic) in SPSS 15.0. Unlike forward stepwise models, CS Logistic models can account for complex sample designs (in particular, the effects of clustering and associated weighting) when calculating odds ratios and determining significance. The models shown below include only those variables found to be significant (at at least the 90% level and in most cases at the 95% level) after the regression models were run using CS logistic.

Regression models

Model 2.1: Ever tried cannabis?

Nagelkerke R2 = 43%

Other factors included in the initial stepwise model but which were not significant in the final model are: income, education, social class, presence of children in the household, area deprivation, urban-rural, position on liberal-authoritarian scale, newspaper readership and whether discarded needles are a problem in local area.

Model 2.2: Friends or family ever tried drugs?

Nagelkerke R2 = 38%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, marital status, presence of children in the household, area deprivation, urban-rural, newspaper readership, position on liberal-authoritarian scale, and whether discarded needles are a problem in local area.

Model 2.3: Agree smoking cannabis should be legalised

Nagelkerke R2 = 25%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, age, education, social class, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership and whether discarded needles are a problem in local area.

Model 2.4: Agree should NOT prosecute people for possessing small amounts of cannabis for their own use

Nagelkerke R2 = 21%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, education, income, social class, marital status, presence of children in household, area deprivation, newspaper readership, and whether discarded needles are a problem in local area.

Model 2.5: Taking cocaine occasionally is very seriously wrong

Nagelkerke R2 = 14%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, age, education, income, social class, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, whether friends/family used drugs, and whether discarded needles are a problem in local area.

Model 2.6: Agree 'We need to accept that taking drugs is a normal part of some people's lives'

Nagelkerke R2 = 17%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, education, income, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, and whether discarded needles are a problem in local area.

Model 3.1: Agree that most heroin users come from difficult backgrounds

Nagelkerke R2 = 6%

Other factors included in the initial stepwise model but which were not significant in the final model are: education, income, marital status, area deprivation, urban-rural, newspaper readership, whether friends/family used drugs, whether ever tried cannabis (self), position on liberal-authoritarian scale, and whether discarded needles are a problem in local area.

Model 3.2: Agree that most people who end up addicted to heroin have only themselves to blame

Nagelkerke R2 = 18%

Other factors included in the initial stepwise model but which were not significant in the final model are: age, marital status, presence of children in household, area deprivation, urban-rural, whether ever tried cannabis (self), and whether discarded needles are a problem in local area.

Model 3.3: Comfortable with someone getting help to stop using heroin moving near you

Nagelkerke R2 = 5%

Other factors included in the initial stepwise model but which were not significant in the final model are: age, gender, education, socio-economic class, marital status, presence of children in household, area deprivation, newspaper readership, position on liberal-authoritarian scale, whether friends/family ever used drugs, whether ever tried cannabis (self), social connectedness.

Model 3.4: Comfortable working with someone who had used heroin in past?

Nagelkerke R2 = 15%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, socio-economic class, income, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, and whether discarded needles are a problem in local area.

Model 4.1: Agree 'People should NOT be prosecuted for possessing small amounts of heroin for their own use'

Nagelkerke R2 = 13%

Other factors included in the initial stepwise model but which were not significant in the final model are: education, socio-economic class, income, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, whether friends/family ever used drugs, and whether discarded needles are a problem in local area.

Model 4.2: Agree 'Drugs users should be given clean needles to stop them getting diseases'

Nagelkerke R2 = 10%

Other factors included in the initial stepwise model but which were not significant in the final model are: education, socio-economic class, income, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, and whether discarded needles are a problem in local area.

Model 4.3: Agree 'Young people should be given information about how to use drugs more safely'

Nagelkerke R2 = 7%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, age, education, income, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, whether ever tried cannabis (self), and whether discarded needles are a problem in local area.

Model 4.4: Agree strongly 'Only real way of helping drug addicts is to get them to stop using drugs altogether'

Nagelkerke R2 = 6%

Other factors included in the initial stepwise model but which were not significant in the final model are: age, income, education, socio-economic class, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, whether friends/family ever used drugs, and whether discarded needles are a problem in local area.

Model 4.5: Agree 'Most heroin users can never stop using drugs completely'

Nagelkerke R2 = 9%

Other factors included in the initial stepwise model but which were not significant in the final model are: gender, age, income, socio-economic class, marital status, presence of children in household, area deprivation, urban-rural, newspaper readership, whether ever tried cannabis (self), whether friends/family ever used drugs.

Model 4.6: Agree 'Someone who had been a heroin addict can never make a good parent, even if their drug problems are in the past'

Nagelkerke R2 = 15%

Other factors included in the initial stepwise model but which were not significant in the final model are: age, income, socio-economic class, presence of children in household, area deprivation, urban-rural, newspaper readership, whether ever tried cannabis (self), whether friends/family ever used drugs, and whether discarded needles are a problem in local area.