Publication - Research and analysis

Attitudes Towards Youth Crime and Willingness to Intervene: Findings from the 2006 Scottish Social Attitudes Survey

Published: 4 Feb 2008
Part of:
Education
ISBN:
ISBN97807559

This report presents findings from a module of questions included in the 2006 Scottish Social Attitudes survey and revisits a theme first addressed by survey in 2004, namely public attitudes towards young people and youth crime.

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Contents
Attitudes Towards Youth Crime and Willingness to Intervene: Findings from the 2006 Scottish Social Attitudes Survey
ANNEX A -TECHNICAL DETAILS OF THE SURVEY

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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 7 (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

Lower

Upper

No.

%

No.

%

Addresses issued

3162

3162

Vacant, derelict and other out of scope 1

323

10.2

323

10.2

Unknown eligibility 2

89

3.2

89

3.2

In scope

2839

2750

Interview achieved

1594

56.1

1594

58.0

Self-completion returned

1437

50.6

1437

52.3

Interview not achieved

1245

43.9

1245

42.0

Refused3

916

32.3

916

33.3

Non-contacted4

100

3.5

100

3.6

Other non-response5

140

4.9

140

5.1

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) 8 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:

I. 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.

II. 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.

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 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:

I. Differential selection probabilities

II. Deliberate over-sampling of rural areas

III. 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 reports arising from the survey. 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) 9 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 on 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 belonging to the 'most connected' group using the 'social connectedness scale'. If the respondent belongs to this group, 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 than respondents in the reference category to belong to the 'most connected' group. An odds ratio of below 1 means they were less likely than respondents in the reference category to belong to that group. If we look at sex, we can see that women were more likely than men to belong to the 'most connected' group, since they have an odds ratio of 2.00. However, if we look at perceptions of youth crime problems, we see that those who are most likely to think such problems are common in their area are less likely to belong to the 'most connected' group as they have an odds ratio of less than one.

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).

Regression models

Model 1 'Social connectedness'

Dependent variable coding
1 = belong to 'most connected' group
0 = NOT

Odds ratio

95% confidence interval

P

Sex

(Men)

1.00

Women

2.00

1.59-2.60

0.000

**

Age

(18-24)

1.00

25-34

1.28

0.78-2.10

0.340

NS

35-44

1.19

0.73-1.95

0.487

NS

45-54

0.81

0.49-1.32

0.394

NS

55-64

0.68

0.40-1.15

0.152

NS

65+

1.01

0.61-1.68

0.957

NS

Contact with 11 to 15 year-olds in area

(Know most/all)

1.00

Know some

0.85

0.56-1.25

0.380

NS

Know none

0.61

0.39-0.91

0.016

*

Contact with 16 to 24 year-olds in area

(Know most/all)

1.00

Know some

1.03

0.70-1.51

0.890

NS

Know none

0.47

0.30-0.71

0.001

**

Household income

(Lowest quartile)

1.00

2

1.98

1.33-2.96

0.001

**

3

1.76

1.18-2.62

0.006

**

Highest quartile

2.02

1.32-3.10

0.001

**

Income not known

1.58

1.03-2.39

0.035

*

Perceptions of youth crime problems

(Least common)

1.00

Intermediate

1.03

0.77-1.38

0.828

NS

Most common

0.73

0.53-1.00

0.046

*

Cases included in model = 1,307

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, household income (quartiles), social trust, general attitudes towards young people (tertiles), general perceptions of youth crime problems (tertiles), extent to which directly affected by youth crime (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.

Model 2 Social trust

Dependent variable coding
1 = agreeing that 'you can't be too careful'
0 = NOT agreeing

Odds ratio

95% confidence interval

P

Age

(18-24)

1.00

25-34

0.79

0.51-1.25

0.315

NS

35-44

0.59

0.38-0.92

0.021

*

45-54

0.66

0.42-1.04

0.071

+

55-64

0.43

0.27-0.68

0.000

**

65+

0.34

0.22-0.54

0.000

**

Social connectedness

(Most connected)

1.00

Intermediate

1.02

0.79-1.31

0.896

NS

Least connected

1.44

1.09-1.90

0.010

+

Household income

(Lowest quartile)

1.00

2

0.63

0.44-0.90

0.010

+

3

0.83

0.57-1.22

0.350

NS

Highest quartile

0.65

0.42-1.00

0.050

*

Income unknown

0.94

0.65-1.35

0.726

NS

Tenure

(Owner-occupier)

1.00

Social renter

1.61

1.18-2.18

0.002

**

Private renter

1.20

0.79-1.82

0.391

NS

Scottish index of multiple deprivation

(Most deprived)

1.00

2

0.82

0.58-1.16

0.262

NS

3

1.02

0.71-1.46

0.924

NS

4

1.17

0.82-1.67

0.385

NS

Least deprived

1.43

0.99-2.08

0.058

+

Highest educational qualification

(None)

1.00

Degree

1.61

1.17-2.20

0.003

**

Highers or equivalent

1.35

0.99-1.83

0.060

*

Standard grades or equivalent

1.96

1.36-2.81

0.000

**

Cases included in model = 1,525

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, household income (quartiles), social connectedness (tertiles), general attitudes towards young people (tertiles), general perceptions of youth crime problems (tertiles), extent to which directly affected by youth crime (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.

Model 3 General attitudes towards young people

Dependent variable coding
1 = belonging to 'most positive' group
0 = NOT

Odds ratio

95% confidence interval

P

Age

(18-24)

25-34

1.32

0.76-2.30

0.322

NS

35-44

1.75

1.03-2.98

0.039

*

45-54

2.49

1.45-4.25

0.001

**

55-64

1.92

1.09-3.38

0.024

*

65+

1.98

1.12-3.51

0.019

*

Contact with 16 to 24 year-olds in area

(No contact)

Some contact

1.68

1.22-2.32

0.002

**

Social trust

(Most people can be trusted)

0.40

0.31-0.52

0.000

**

You can't be too careful

0.34

0.17-0.69

0.003

**

Highest educational qualification

(None)

Degree

0.81

0.57-1.14

0.222

NS

Highers or equivalent

0.57

0.41-0.78

0.001

**

Standard grades or equivalent

0.36

0.24-0.54

0.000

**

Perceptions of youth crime problems

(Least common)

Intermediate

0.66

0.50-0.87

0.004

**

Most common

0.27

0.20-0.38

0.000

**

Cases included in model = 1,328

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, household income (quartiles), social connectedness (tertiles), social trust, general perceptions of youth crime problems (tertiles), extent to which directly affected by youth crime (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.

Model 4 General perceptions of prevalence of youth crime problems

Dependent variable coding
1 = belonging to 'most common'
roup
0 = NOT

Odds ratio

95% confidence interval

P

Contact with 16 to 24 year-olds in area

(Know most/all)

1.00

Know some

0.62

0.41-0.92

0.017

*

Know none

0.46

0.31-0.70

0.000

**

General attitudes towards young people

(Most positive)

1.00

Intermediate

1.89

1.30-2.75

0.001

**

Least positive

3.46

2.41-4.95

0.000

**

Directly affected by youth crime problems

(Most affected)

1.00

Intermediate

2.55

1.70-3.80

0.000

**

Least affected

14.55

9.85-21.50

0.000

**

Scottish index of multiple deprivation

(Most deprived)

2

2.57

1.53-4.33

0.000

**

3

2.45

1.44-4.16

0.001

**

4

2.84

1.72-4.69

0.000

**

Least deprived

5.25

3.15-8.74

0.000

**

Tenure

(Owner occupier)

1.00

Social rented

1.52

1.06-2.18

0.022

*

Private rented

1.68

1.01-2.78

0.045

*

Cases included in model = 1,306

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, household income (quartiles), social connectedness (tertiles), social trust, general attitudes towards young people (tertiles), extent to which directly affected by youth crime (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.

Model 5 Extent to which directly affected by youth crime problems

Dependent variable coding
1 = belonging to 'most affected' group
0 = NOT

Odds ratio

95% confidence interval

P

General attitudes towards young people

(Most positive)

1.00

Intermediate

1.07

0.73-1.55

0.738

NS

Least positive

1.55

1.08-2.23

0.016

*

Perceptions of youth crime problems

(Least common)

1.00

Intermediate

24.45

11.29-52.94

0.000

**

Most common

112.10

51.45-244.25

0.000

**

Age

(18-24)

1.00

25-34

2.06

1.21-3.49

0.007

**

35-44

2.21

1.30-3.75

0.003

**

45-54

2.52

1.47-4.31

0.001

**

55-64

1.78

1.03-3.10

0.039

*

65+

1.08

0.63-1.87

0.774

NS

SHS urban-rural classification

(Large urban)

1.00

Other urban

0.80

0.57-1.12

0.190

NS

Accessible small towns

1.13

0.69-1.84

0.633

NS

Remote small towns

0.32

0.13-0.78

0.012

*

Accessible rural

0.68

0.43-1.09

0.111

NS

Remote rural

0.39

0.19-0.80

0.010

+

Cases included in model = 1,313

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, household income (quartiles), social connectedness (tertiles), social trust, general perceptions of youth crime problems (tertiles), general attitudes towards young people (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.

Model 6 Avoidance behaviour when faced with group of teenagers outside shop

Dependent variable coding
1 = would feel 'not bothered at all'
0 = NOT

Odds ratio

95% confidence interval

P

Sex

(Male)

Female

0.44

0.34-0.56

0.000

**

Age

(18-24)

25-34

1.18

0.71-1.96

0.518

NS

35-44

0.95

0.58-1.54

0.827

NS

45-54

0.91

0.55-1.50

0.697

NS

55-64

0.77

0.46-1.30

0.337

NS

65+

0.51

0.30-0.84

0.009

**

General attitudes towards young people

(Most positive)

Intermediate

0.54

0.40-0.72

0.000

**

Least positive

0.47

0.35-0.65

0.000

**

Directly affected by youth crime problems

(Most affected)

Intermediate

0.48

0.35-0.64

0.000

**

Least affected

0.32

0.24-0.45

0.000

**

SHS urban-rural classification

Large urban

Other urban

1.40

1.04-1.89

0.027

*

Accessible small towns

1.77

1.15-2.74

0.010

**

Remote small towns

1.86

0.98-3.56

0.059

+

Accessible rural

1.82

1.23-2.69

0.003

**

Remote rural

3.58

2.07-6.19

0.000

**

Tenure

(Owner-occupier)

Social renter

1.61

1.18-2.20

0.003

**

Private renter

0.90

0.57-1.44

0.669

NS

Contact with 16 to 24 year-olds in area

(Know most/all)

Know some

0.44

0.31-0.63

0.000

**

Know none

0.44

0.30-0.65

0.000

**

Social trust

(Most can be trusted)

You can't be too careful

0.64

0.50-0.83

0.001

**

Cases included in model = 1,306

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, household income (quartiles), social connectedness (tertiles), social trust, general perceptions of youth crime problems (tertiles), general attitudes towards young people (tertiles), extent to which directly affected by youth crime (tertiles), whether household contains children, contact with 11 to 15 year-olds in area, contact with 16 to 24 year-olds in area.