British Social Attitudes: The 25th Report


Edited by: Alison Park, John Curtice, Katarina Thomson, Miranda Phillips & Elizabeth Clery

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  • Front Matter
  • Back Matter
  • Subject Index
  • The National Centre for Social Research

    The National Centre for Social Research (NatCen) is an independent, non-profit social research organization. It has a large professional staff together with its own interviewing and coding resources. Some of NatCen's work – such as the survey reported in this book – is initiated by NatCen itself and grant-funded by research councils or charitable foundations. Other work is initiated by government departments or quasi-government organisations to provide information on aspects of social or economic policy. NatCen also works frequently with other institutes and academics. Founded in 1969 and now Britain's largest social research organisation, NatCen has a high reputation for the standard of its work in both qualitative and quantitative research.

    The Contributors
    • Simon Anderson
    • Director of the Scottish Centre for Social Research, part of NatCen
    • John Appleby
    • Chief Economist at the King's Fund
    • Rossy Bailey
    • Senior Researcher at the National Centre for Social Research and Co-Director of the British Social Attitudes survey series
    • Julie Brownlie
    • Senior Lecturer in Sociology, Department of Applied Science, Stirling University
    • Sarah Butt
    • Senior Researcher at the National Centre for Social Research and Co-Director of the British Social Attitudes survey series
    • Elizabeth Clery
    • Senior Researcher at the National Centre for Social Research and Co-Director of the British Social Attitudes survey series
    • John Curtice
    • Research Consultant at the Scottish Centre for Social Research, part of NatCen and Professor of Politics at Strathclyde University
    • Geoff Dench
    • Fellow of the Young Foundation and Visiting Professor at Greenwich University
    • Ingrid Esser
    • Assistant Professor of Sociology, Swedish Institute for Social Research (SOFT), Stockholm University
    • Lisa Given
    • Senior Researcher at the Scottish Centre for Social Research, part of NatCen
    • Oliver Heath
    • Lecturer in Politics, Royal Holloway, University of London; formerly Research Fellow in Politics, Strathclyde University
    • Alison Park
    • Research Group Director at theNational Centre for Social Research and Co-Director of the British Social Attitudes survey series
    • Miranda Phillips
    • Research Director at the National Centre for Social Research and Co-Director of theBritish Social Attitudes survey series
    • Andrew Shaw
    • Research Group Director at theNational Centre for Social Research
    • Janet Stockdale
    • Senior Lecturer in Social Psychology at the London School of Economics and Political Science (LSE)
    • Katarina Thomson
    • Formerly Research Director at theNational Centre for Social Research and Co-Director of the British Social Attitudes survey series


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    List of Tables and Figures

    Chapter 1
    • Table 1.1 Trends in multiple choice national identity, England 1996–2007 3
    • Table 1.2 Trends in forced choice national identity, England 1992–2007 5
    • Table 1.3 Trends in national identity (Moreno scale), England 1997–2007 6
    • Table 1.4 Regional pride, England 2001–2007 6
    • Table 1.5a Attitudes in England towards how Scotland should be governed, 1997–2007 8
    • Table 1.5b Attitudes in England towards how Wales should be governed, 1997–2007 8
    • Table 1.6 Constitutional preferences for England, 1999–2007 10
    • Table 1.7 Attitudes in England towards regional government, 2001–2007 11
    • Table 1.8 Attitudes in England towards the financial relationship between England and Scotland, 2000–2007 13
    • Table 1.9 Attitudes in England towards the ‘West Lothian’ question, 2000–2007 14
    • Table 1.10 Perceived impact of devolution on how Britain is governed, England 2000–2007 15
    • Table 1.11 Constitutional preferences for England by forced choice national identity, England 2003, 2006 and 2007 16
    • Table 1.12 Perceptions of Scotland's share of spending by forced choice national identity, England 2000, 2003 and 2007 17
    • Table 1.13 Perceptions in England of the Houses of Parliament, 1999 and 2007 18
    • Table 1.14 Trust in the UK government to look after long-term interests of England, 2001, 2003 and 2007 19
    Chapter 2
    • Table 2.1 Satisfaction with the NHS, 1983–2007 28
    • Table 2.2 Satisfaction with NHS services, by recent contact with that service 36
    • Table 2.3 Satisfaction with NHS outpatient and inpatient services, by recent contact with that service, 1987–2007 37
    • Table 2.4 Satisfaction with the NHS overall, by age, 1983–2007 38
    • Table 2.5 Satisfaction with the NHS overall, by party identification, 1983–2007 38
    • Table 2.6 Say over hospital, outpatient appointment time and treatment, 2004–2007: England only 40
    • Table 2.7 Satisfaction with NHS overall by perceived say, England only 43
    • Table 2.8 Perceived quality and responsiveness of hospital inpatient care, 1991–2007 44
    • Table 2.9 Satisfaction by perceived quality and responsiveness of hospital inpatient care 45
    • Figure 2.1 Attitudes to tax and spend, 1983–2007 27
    • Figure 2.2a Satisfaction with the NHS, 1983–2007 29
    • Figure 2.2b Net satisfaction with the NHS, 1983–2007 29
    • Figure 2.3 Satisfaction with GPs and NHS dentists, 1983–2007 31
    • Figure 2.4 Satisfaction with hospital services, 1983–2007 32
    • Figure 2.5 Areas of NHS in need of improvement, 1989–2007 34
    Chapter 3
    • Table 3.1 Attitudes towards exercising choice 59
    • Table 3.2 Support for league tables 61
    • Table 3.3 Attitudes towards choice, by class, education and income 63
    • Table 3.4 Attitudes towards choice, by sex and age group 64
    • Table 3.5 Attitudes towards non-state provision of public services 67
    • Table 3.6 Attitudes towards private companies delivering public services, by class, education and income 69
    • Table 3.7 Attitudes towards non-state providers, by attitudes towards choice 71
    • Table 3.8 Attitudes towards choice and private provision, by political attitudes 73
    • Table 3.9 Comparison of attitudes across the UK towards choice 74
    • Table 3.10 Comparison of attitudes across the UK towards private companies providing public services 75
    Chapter 4
    • Table 4.1 Employment commitment in 13 countries, 2005 88
    • Table 4.2 Employment commitment and demographic/socio-economic status in three countries, 2005 91
    • Table 4.3 Demographic and socio-economic status in three countries, 2005 92
    • Table 4.4 Employment commitment among men in four countries, 1989, 1997 and 2005 95
    • Table 4.5 Employment commitment among women in four countries, 1989, 1997 and 2005 96
    • Figure 4.1 Welfare regime generosities in 13 countries, 2000 85
    • Figure 4.2 Employment commitment and welfare regime generosity, 2005 89
    • Figure 4.3 Welfare regime generosity across four countries, 1985, 1995 and 2000 94
    • Figure 4.4 Employment commitment of men and women in paid work, 1997 and 2005 97
    Chapter 5
    • Table 5.1 Parental status, by age, 2007 110
    • Table 5.2 Attitudes towards work and domestic life, by generation, 2006 114
    • Table 5.3 Attitudes towards new and traditional family life, by generation, 2006 115
    • Table 5.4 Attitudes towards work and domestic life, by age/generation and parenthood 117
    • Table 5.5 Attitudes towards work and domestic life, by class and parenting status 119
    • Table 5.6 Attitudes towards new and traditional family life, by class and parenting status 120
    • Table 5.7 Attitudes towards new and traditional family life, by parenting status 122
    • Table 5.8 Attitudes towards work and domestic life, by parenting status 124
    Chapter 6
    • Table 6.1 Consumption of air travel, 2003–2007 134
    • Table 6.2 Characteristics of flyers versus non-flyers 134
    • Table 6.3 Concern about the effect of transport on the environment, 2005–2007 136
    • Table 6.4 Concern about the environmental impact of air travel, by respondent characteristics 137
    • Table 6.5 Attitudes towards air travel, 2003–2007 138
    • Table 6.6 Attitudes towards ticket prices and environmental damage, 2004–2007 139
    • Table 6.7 Attitudes to air travel, by level of environmental concern 140
    • Table 6.8 Attitudes to air travel, by consumption of air travel, 2006/07 142
    • Table 6.9 Agreement that ticket prices should reflect environmental damage, by household income, 2006/07 143
    • Table 6.10 Agreement that ticket prices should reflect environmental damage, by age, 2006/07 144
    • Table 6.11 Agreement that ticket prices should reflect environmental damage, by party identification, 2006/07 145
    Chapter 7
    • Table 7.1 General attitudes towards ‘emotions talk’ 157
    • Table 7.2 General attitudes towards emotions talk, by gender, age, education, well-being and experience of serious mental health problems 158
    • Table 7.3 Attitudes towards therapy and counseling 160
    • Table 7.4 Contact with formal emotional support — ever and in the last year 163
    • Table 7.5 Reasons given for not contacting formal emotional support services 166
    • Figure 7.1 Agree would feel comfortable talking to GP or therapist/counsellor, by age group 161
    • Figure 7.2 Contact with formal emotional support ever, by age group 164
    Chapter 8
    • Table 8.1 Frequency of leisure activities 176
    • Table 8.2 Participation in sport or physical activity (activities selected by four per cent or more) 177
    • Table 8.3 Characteristics associated with frequent participation in three or more different leisure activities 180
    • Table 8.4 Barriers to doing free time activities that would like to do 183
    • Table 8.5 Extent to which “lack of time” is a barrier to doing free time activities that would like to do, by age and working status 184
    • Table 8.6 Enjoyment obtained from participation in different leisure activities 186
    Chapter 9
    • Table 9.1 Levels of social trust, 1959–2007 206
    • Table 9.2 Trust in neighbours, 1998–2007 207
    • Table 9.3 Social trust for different social and demographic groups 209
    • Table 9.4a Perceptions of respect and consideration in public 211
    • Table 9.4b Personal experience of respect and consideration in public 211
    • Table 9.5 Groups identified as treating others with a lack of respect and consideration in public (groups selected by 10 per cent or more) 214
    • Table 9.6 Experiences of inconsiderate behaviour in the last 12 months 216
    • Table 9.7 Experiences of inconsiderate behaviour at least once a fortnight, by levels of social trust and perceived respect 220
    • Figure 9.1 Experiences of inconsiderate behaviour at least once a fortnight (mean number of behaviours), by social and demographic characteristics 218
    Appendix I


    The British Social Attitudes survey series began a quarter of a century ago, in 1983. In his foreword to the report on the first survey, Sir Claus Moser observed:

    What makes the series so important is precisely that it is a series. It is from the monitoring and understanding of trends in attitudes that one can learn most about what is happening in a society … (Moser, 1984)

    We hope that, 25 years on, this report lives up to this prediction. In it, we focus on the results of the 2007 survey and assess what they tell us about the attitudes and behaviour of the British public.

    Four chapters demonstrate the value of monitoring trends in attitudes over time. Chapter 1 examines attitudes to devolution in England, as well as feelings of ‘Englishness’, and assesses how these have changed since the advent of devolution elsewhere in the UK. In Chapter 2, we examine how satisfaction with the NHS has changed over the last 25 years and explore the reasons behind satisfaction and dissatisfaction. In Chapter 4 we focus upon employment commitment in different countries across the world, and the extent to which there is any relationship between levels of commitment and welfare state arrangements. In particular, we ask whether there is any evidence that the most generous welfare states contain the laziest and least motivated workers. And in Chapter 6 we examine the increasingly topical subject of air travel, focusing particularly upon how the public are likely to react to any increases in the cost of flying.

    The remaining five chapters consider new topics in the survey series, thus helping ensure that the survey's content remains up to date and relevant. Public services ‘reform’ has been a key priority of the current UK Labour government; Chapter 3 examines the extent to which choice and diversity in the provision of public services is important to the public. Chapter 5 examines whether, and how, parenthood has an impact on people's attitudes and values, focusing particularly on attitudes to family relationships, work and family life. In Chapter 7, we describe the use that people make of emotional support networks, whether these are informal (such as friends and family) or formal (such as counsellors, GPs and therapists). In Chapter 8 we explore leisure patterns, focusing on how these vary between different groups and the extent to which individual leisure choices appear to be linked to happiness, health and social capital. Finally, in Chapter 9, we examine whether Britain is witnessing a decline in levels of social trust, and assess the extent to which people feel that they are the victims (or perpetrators) of inconsiderate or disrespectful behaviour in public.

    Most of the tables in the report are based on British Social Attitudes data from 2007 and earlier years. Conventions for reading the tables are set out in Appendix II of this report. Full details of all the questions included in the 2007 survey can be found at

    Our Thanks

    British Social Attitudes could not take place without its many generous flinders. The Gatsby Charitable Foundation (one of the Sainsbury Family Charitable Trusts) has provided core funding on a continuous basis since the survey's inception and in doing so has ensured the survey's security and independence. A number of government departments have regularly funded modules of interest to them, while respecting the independence of the study. In 2007 we gratefully acknowledge the support of the Departments for Health, Transport, and Work and Pensions. We also thank the Department for Children, Schools and Families, the Department for Business, Enterprise and Regulatory Reform and the Respect Task Force (now the Youth Taskforce). Our thanks are also due to the Hera Trust.

    The Economic and Social Research Council (ESRC), the body primarily responsible for funding academic social science research in Britain, has regularly provided the funds needed to field modules on the survey. In 2007 it funded the module about public services described in Chapter 3 (via the ESRC's Public Services Programme) as well as questions about English devolution (Chapter 1) and emotional support (Chapter 7). The ESRC Public Services Programme also funded work in Scotland, Wales and Northern Ireland, and Chapter 3 includes data from these countries. In Scotland, the module was carried on our sister survey, Scottish Social Attitudes; questions from that survey are also reported on in Chapter 1. Further details about the Scottish Social Attitudes survey can be found in Ormston (2008).

    The ESRC also continued to support the participation of Britain in the International Social Survey Programme (ISSP), a collaboration whereby surveys in over 40 countries field an identical module of questions in order to facilitate comparative research. The British results from 2007 are described in Chapter 8, while Chapter 4 uses ISSP data from 2005 to compare thirteen different countries. The 2007 survey was also part of another international research project about mental health stigma, funded by Indiana University as part of work for the US National Institutes of Health.

    We would also like to thank Professor Richard Topf of London Metropolitan University for all his work in creating and maintaining access to an easy to use website that provides a fully searchable database of all the questions that have ever been carried on a British Social Attitudes survey, together with details of the pattern of responses to every question. This site provides an invaluable resource for those who want to know more than can be found in this report. It is located at

    The British Social Attitudes survey is a team effort. The research group that designs, directs and reports on the study is supported by complementary teams who implement the survey's sampling strategy and carry out data processing. The researchers in turn depend on fieldwork controllers, area managers and field interviewers who are responsible for all the interviewing, and without whose efforts the survey would not happen at all. The survey is heavily dependent too on staff who organise and monitor fieldwork and compile and distribute the survey's extensive documentation, for which we would pay particular thanks to Pauline Burge and her colleagues in NatCen's administrative office in Brentwood. We are also grateful to Sandra Beeson and Sue Corbett in our computing department who expertly translate our questions into a computer assisted questionnaire, and to Roger Stafford who has the unenviable task of editing, checking and documenting the data. Meanwhile the raw data have to be transformed into a workable SPSS system file – a task that has for many years been performed with great care and efficiency by Ann Mair at the Social Statistics Laboratory at the University of Strathclyde. Many thanks are also due to David Mainwaring and Kate Wood at our publishers, Sage.

    Finally, we must praise the people who anonymously gave up their time to take part in our 2007 survey. They are the cornerstone of this enterprise. We hope that some of them might come across this volume and read about themselves and the story they tell of modern Britain with interest.

    The Editors
    Ormston, R. (2008), Scottish Social Attitudes Survey 2007 Core Module — Report 1: Attitudes to Government in Scotland, Scottish Government Social Research, available at
  • Appendix I: Technical Details of the Survey

    In 2007, the sample for the British Social Attitudes survey was split into four sections: versions A, B C and D, each made up a quarter of the sample. Depending on the number of versions in which it was included, each ‘module’ of questions was thus asked either of the full sample (4,124 respondents) or of a random quarter, half or three-quarters of the sample. The structure of the questionnaire can be found at

    Sample Design

    The British Social Attitudes survey is designed to yield a representative sample of adults aged 18 or over. Since 1993, the sampling frame for the survey has been the Postcode Address File (PAF), a list of addresses (or postal delivery points) compiled by the Post Office.1

    For practical reasons, the sample is confined to those living in private households. People living in institutions (though not in private households at such institutions) are excluded, as are households whose addresses were not on PAF.

    The sampling method involved a multi-stage design, with three separate stages of selection.

    Selection of Sectors

    At the first stage, postcode sectors were selected systematically from a list of all postal sectors in Great Britain. Before selection, any sectors with fewer than 500 addresses were identified and grouped together with an adjacent sector; in Scotland all sectors north of the Caledonian Canal were excluded (because of the prohibitive costs of interviewing there). Sectors were then stratified on the basis of:

    • 37 sub-regions;
    • population density with variable banding used, in order to create three equal-sized strata per sub-region; and
    • ranking by percentage of homes that were owner-occupied.

    Four hundred and four postcode sectors were selected, with probability proportional to the number of addresses in each sector.

    Selection of Addresses

    Twenty-two addresses were selected in each of the 404 sectors. The issued sample was therefore 404 × 22 = 8,888 addresses, selected by starting from a random point on the list of addresses for each sector, and choosing each address at a fixed interval. The fixed interval was calculated for each sector in order to generate the correct number of addresses.

    The Multiple-Occupancy Indicator (MOI) available through PAF was used when selecting addresses in Scotland. The MOI shows the number of accommodation spaces sharing one address. Thus, if the MOI indicates more than one accommodation space at a given address, the chances of the given address being selected from the list of addresses would increase so that it matched the total number of accommodation spaces. The MOI is largely irrelevant in England and Wales, as separate dwelling units generally appear as separate entries on PAF. In Scotland, tenements with many flats tend to appear as one entry on PAF. However, even in Scotland, the vast majority of MOIs had a value of one. The remainder were incorporated into the weighting procedures (described below).

    Selection of Individuals

    Interviewers called at each address selected from PAF and listed all those eligible for inclusion in the British Social Attitudes sample — that is, all persons currently aged 18 or over and resident at the selected address. The interviewer then selected one respondent using a computer-generated random selection procedure. Where there were two or more ‘dwelling units’ at the selected address, interviewers first had to select one dwelling unit using the same random procedure. They then followed the same procedure to select a person for interview within the selected dwelling unit.


    The weights for the British Social Attitudes survey correct for the unequal selection of addresses, dwelling units (DU) and individuals and for biases caused by differential non-response. The different stages of the weighting scheme are outlined in detail below.

    Selection Weights

    Selection weights are required because not all the units covered in the survey had the same probability of selection. The weighting reflects the relative selection probabilities of the individual at the three main stages of selection: address, DU and individual. First, because addresses in Scotland were selected using the MOI, weights were needed to compensate for the greater probability of an address with an MOI of more than one being selected, compared to an address with an MOI of one. (This stage was omitted for the English and Welsh data.) Secondly, data were weighted to compensate for the fact that a DU at an address that contained a large number of DUs was less likely to be selected for inclusion in the survey than a DU at an address that contained fewer DUs. (We use this procedure because in most cases where the MOI is greater than one, the two stages will cancel each other out, resulting in more efficient weights.) Thirdly, data were weighted to compensate for the lower selection probabilities of adults living in large households, compared with those in small households.

    At each stage the selection weights were trimmed to avoid a small number of very high or very low weights in the sample; such weights would inflate standard errors, reducing the precision of the survey estimates and causing the weighted sample to be less efficient. Less than one per cent of the sample was trimmed at each stage.

    Non-Response Model

    It is known that certain subgroups in the population are more likely to respond to surveys than others. These groups can end up over-represented in the sample, which can bias the survey estimates. Where information is available about non-responding households, the response behaviour of the sample members can be modelled and the results used to generate a non-response weight. This non-response weight is intended to reduce bias in the sample resulting from differential response to the survey.

    The data was modelled using logistic regression, with the dependent variable indicating whether or not the selected individual responded to the survey. Ineligible households2 were not included in the non-response modelling. A number of area-level and interviewer observation variables were used to model response. Not all the variables examined were retained for the final model: variables not strongly related to a household's propensity to respond were dropped from the analysis.

    The variables found to be related to response were; Government Office Region (GOR), population density (population in private households according to the Census 2001 divided by the area in hectares), condition of the local area and relative condition of the address and whether there were entry barriers to the selected address. The full model is given in Table A.1.

    Table A.1 The final non-response model

    The model shows non-response increases if there are barriers to entry (for instance, if there are locked gates around the address or an entry phone) and if the general condition of the address and area is good. Response is lower in areas where there is a higher population density and lower if the address is in the West Midlands, London, the South East or South West.

    The non-response weight is calculated as the inverse of the predicted response probabilities saved from the logistic regression model. The non-response weight was then combined with the selection weights to create the final non-response weight. The top and bottom one per cent of the weight were trimmed before the weight was scaled to the achieved sample size (resulting in the weight being standardised around an average of one).

    Calibration Weighting

    The final stage of the weighting was to adjust the final non-response weight so that the weighted respondent sample matched the population in terms of age, sex and region.

    Only adults aged 18 and over are eligible to take part in the survey; therefore the data have been weighted to the British population aged 18+ based on the 2006 mid-year population estimates from the Office for National Statistics/General Register Office for Scotland.

    The survey data were weighted to the marginal age/sex and GOR distributions using raking-ratio (or rim) weighting. As a result, the weighted data should exactly match the population across these three dimensions. This is shown in Table A.2.

    Table A.2 Weighted and unweighted sample distribution, by GOR, age and sex

    The calibration weight is the final non-response weight to be used in the analysis of the 2007 survey; this weight has been scaled to the responding sample size. The range of the weights is given in Table A.3.

    Table A.3 Range of weights
    Effective Sample Size

    The effect of the sample design on the precision of survey estimates is indicated by the effective sample size (neff). The effective sample size measures the size of an (unweighted) simple random sample that would achieve the same precision (standard error) as the design being implemented. If the effective sample size is close to the actual sample size, then we have an efficient design with a good level of precision. The lower the effective sample size is, the lower the level of precision. The efficiency of a sample is given by the ratio of the effective sample size to the actual sample size. Samples that select one person per household tend to have lower efficiency than samples that select all household members. The final calibrated non-response weights have an effective sample size (neff) of 3,347 and efficiency of 81 per cent.

    All the percentages presented in this report are based on weighted data.

    Questionnaire Versions

    Each address in each sector (sampling point) was allocated to either the A, B, C or D portion of the sample. If one serial number was version A, the next was version B, the third version C and the fourth version D. Thus, each interviewer was allocated seven or eight cases from each of versions A, B, C and D. There were 2,222 issued addresses for each version.


    Interviewing was mainly carried out between June and September 2007, with a small number of interviews taking place in October and November.

    Fieldwork was conducted by interviewers drawn from the National Centre for Social Research'?, regular panel and conducted using face-to-face computerassisted interviewing.3 Interviewers attended a one-day briefing conference to familiarise them with the selection procedures and questionnaires.

    The mean interview length was 68 minutes for version A of the questionnaire, 75 minutes for version B and 66 minutes for versions C and D.4 Interviewers achieved an overall response rate of between 51.5 and 53.1 per cent. Details are shown in Table A.4.

    Table A.4 Response rate1 on British Social Attitudes, 2007

    As in earlier rounds of the series, the respondent was asked to fill in a self-completion questionnaire which, whenever possible, was collected by the interviewer. Otherwise, the respondent was asked to post it to the National Centre for Social Research. If necessary, up to three postal reminders were sent to obtain the self-completion supplement.

    A total of 546 respondents (13 per cent of those interviewed) did not return their self-completion questionnaire. Version A of the self-completion questionnaire was returned by 88 per cent of respondents to the face-to-face interview, version B by 85 per cent, version C by 86 per cent and version D by 87 per cent. As in previous rounds, we judged that it was not necessary to apply additional weights to correct for non-response to the self-completion questionnaire.

    Advance Letter

    Interviewers were supplied with letters describing the purpose of the survey and the coverage of the questionnaire, which they posted to sampled addresses before making any calls.5

    Analysis Variables

    A number of standard analyses have been used in the tables that appear in this report. The analysis groups requiring further definition are set out below. For further details see Stafford and Thomson (2006). Where there are references to specific question numbers, the full question text, including frequencies, can be found at


    The dataset is classified by the 12 Government Office Regions.

    Standard Occupational Classification

    Respondents are classified according to their own occupation, not that of the ‘head of household’. Each respondent was asked about their current or last job, so that all respondents except those who had never worked were coded. Additionally, all job details were collected for all spouses and partners in work.

    With the 2001 survey, we began coding occupation to the new Standard Occupational Classification 2000 (SOC 2000) instead of the Standard Occupational Classification 1990 (SOC 90). The main socio-economic grouping based on SOC 2000 is the National Statistics Socio-Economic Classification (NS-SEC). However, to maintain time-series, some analysis has continued to use the older schemes based on SOC 90 — Registrar General's Social Class, Socio-Economic Group and the Goldthorpe schema.

    National Statistics Socio-Economic Classification (NS-SEC)

    The combination of SOC 2000 and employment status for current or last job generates the following NS-SEC analytic classes:

    • 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 are grouped as “never had a job” or “not classifiable”. For some analyses, it may be more appropriate to classify respondents according to their current socio-economic status, which takes into account only their present economic position. In this case, in addition to the seven classes listed above, the remaining respondents not currently in paid work fall into one of the following categories: “not classifiable”, “retired”, “looking after the home”, “unemployed” or “others not in paid occupations”.

    Registrar General's Social Class

    As with NS-SEC, each respondent's Social Class is based on his or her current or last occupation. The combination of SOC 90 with employment status for current or last job generates the following six Social Classes:

    They are usually collapsed into four groups: I & II, III Non-manual, III Manual, and IV & V.

    Socio-Economic Group

    As with NS-SEC, each respondent's Socio-Economic Group (SEG) is based on his or her current or last occupation. SEG aims to bring together people with jobs of similar social and economic status, and is derived from a combination of employment status and occupation. The full SEG classification identifies 18 categories, but these are usually condensed into six groups:

    • Professionals, employers and managers
    • Intermediate non-manual workers
    • Junior non-manual workers
    • Skilled manual workers
    • Semi-skilled manual workers
    • Unskilled manual workers

    As with NS-SEC, the remaining respondents are grouped as “never had a job” or “not classifiable”.

    Goldthorpe Schema

    The Goldthorpe schema classifies occupations by their ‘general comparability’, considering such factors as sources and levels of income, economic security, promotion prospects, and level of job autonomy and authority. The Goldthorpe schema was derived from the SOC 90 codes combined with employment status. Two versions of the schema are coded: the full schema has 11 categories; the ‘compressed schema’ combines these into the five classes shown below.

    • Salariat (professional and managerial)
    • Routine non-manual workers (office and sales)
    • Petty bourgeoisie (the self-employed, including farmers, with and without employees)
    • Manual foremen and supervisors
    • Working class (skilled, semi-skilled and unskilled manual workers, personal service and agricultural workers)

    There is a residual category comprising those who have never had a job or who gave insufficient information for classification purposes.


    All respondents whose occupation could be coded were allocated a Standard Industrial Classification 2003 (SIC 03). Two-digit class codes are used. As with Social Class, SIC may be generated on the basis of the respondent's current occupation only, or on his or her most recently classifiable occupation.

    Party Identification

    Respondents can be classified as identifying with a particular political party on one of three counts: if they consider themselves supporters of that party, as closer to it than to others, or as more likely to support it in the event of a general election. The three groups are generally described respectively as partisans, sympathizers and residual identifiers. In combination, the three groups are referred to as ‘identifiers’. Responses are derived from the following questions:

    Generally speaking, do you think of yourself as a supporter of any one political party? [Yes/No]

    [If “No” “Don't know”]

    Do you think of yourself as a little closer to one political party than to the others? [Yes/No]

    [If “Yes” at either question or “No”/“Don't know” at 2nd question][Which one?/If there were a general election tomorrow, which political party do you think you would be most likely to support?]

    [Conservative; Labour; Liberal Democrat; Scottish National Party; Plaid Cymru; Green Party; UK Independence Party (UKIP)/Veritas; British National Party (BNP)/National Front; RESPECT/Scottish Socialist Party (SSP)/Socialist Party; Other party; Other answer; None; Refused to say]

    Attitude Scales

    Since 1986, the British Social Attitudes surveys have included two attitude scales which aim to measure where respondents stand on certain underlying value dimensions — left-right and libertarian-authoritarian.6 Since 1987 (except 1990), a similar scale on ‘welfarism’ has been asked. Some of the items in the welfarism scale were changed in 2000–2001. The current version of the scale is listed below.

    A useful way of summarising the information from a number of questions of this sort is to construct an additive index (Spector, 1992; DeVellis, 2003). This approach rests on the assumption that there is an underlying — ‘latent’ — attitudinal dimension which characterises the answers to all the questions within each scale. If so, scores on the index are likely to be a more reliable indication of the underlying attitude than the answers to any one question.

    Each of these scales consists of a number of statements to which the respondent is invited to “agree strongly”, “agree”, “neither agree nor disagree”, “disagree” or “disagree strongly”.

    The items are:

    Left-Right Scale

    Government should redistribute income from the better off to those who are less well off. [Redistrb]

    Big business benefits owners at the expense of workers. [BigBusnN]

    Ordinary working people do not get their fair share of the nation's wealth. [Wealth]7

    There is one law for the rich and one for the poor. [RichLaw]

    Management will always try to get the better of employees if it gets the chance. [Indust4]

    Libertarian-Authoritarian Scale

    Young people today don't have enough respect for traditional British values. [TradVals]

    People who break the law should be given stiffer sentences. [StifSent]

    For some crimes, the death penalty is the most appropriate sentence. [DeathApp]

    Schools should teach children to obey authority. [Obey]

    The law should always be obeyed, even if a particular law is wrong. [WrongLaw]

    Censorship of films and magazines is necessary to uphold moral standards. [Censor]

    Welfarism Scale

    The welfare state encourages people to stop helping each other. [WelfHelp)

    The government should spend more money on welfare benefits for the poor, even if it leads to higher taxes. [MoreWelf]

    Around here, most unemployed people could find a job if they really wanted one. [UnempJob]

    Many people who get social security don't really deserve any help. [SocHelp]

    Most people on the dole are fiddling in one way or another. [DoleFidl]

    If welfare benefits weren't so generous, people would learn to stand on their own two feet. [WelfFeet]

    Cutting welfare benefits would damage too many people's lives. [DamLives]

    The creation of the welfare state is one of Britain's proudest achievements. [ProudWlf]

    The indices for the three scales are formed by scoring the leftmost, most libertarian or most pro-welfare position as 1 and the rightmost, most authoritarian or most anti-welfarist position as 5. The “neither agree nor disagree” option is scored as 3. The scores to all the questions in each scale are added and then divided by the number of items in the scale, giving indices ranging from 1 (leftmost, most libertarian, most pro-welfare) to 5 (rightmost, most authoritarian, most anti-welfare). The scores on the three indices have been placed on the dataset.8

    The scales have been tested for reliability (as measured by Cronbach's alpha). The Cronbach's alpha (unstandardised items) for the scales in 2007 are 0.80 for the left-right scale, 0.81 for the ‘welfarism’ scale and 0.74 for the libertarian-authoritarian scale. This level of reliability can be considered “very good” for the left-right and welfarism scales and “respectable” for the libertarian-authoritarian scale (DeVellis, 2003: 95–96).

    Other Analysis Variables

    These are taken directly from the questionnaire and to that extent are self-explanatory (see The principal ones are:

    Sex (Q. 44)

    Age (Q. 45)

    Household income (Q. 1443)

    Economic position (Q. 817)

    Religion (Q. 1166)

    Highest educational qualification obtained (Qs. 1299–1300)

    Marital status (Qs. 138–144)

    Benefits received (Qs. 1363–1436)

    Sampling Errors

    No sample precisely reflects the characteristics of the population it represents, because of both sampling and non-sampling errors. If a sample were designed as a random sample (if every adult had an equal and independent chance of inclusion in the sample), then we could calculate the sampling error of any percentage, p, using the formula:

    where n is the number of respondents on which the percentage is based. Once the sampling error had been calculated, it would be a straightforward exercise to calculate a confidence interval for the true population percentage. For example, a 95 per cent confidence interval would be given by the formula:

    Clearly, for a simple random sample (srs), the sampling error depends only on the values ofp and n. However, simple random sampling is almost never used in practice because of its inefficiency in terms of time and cost.

    As noted above, the British Social Attitudes sample, like that drawn for most large-scale surveys, was clustered according to a stratified multi-stage design into 286 postcode sectors (or combinations of sectors). With a complex design like this, the sampling error of a percentage giving a particular response is not simply a function of the number of respondents in the sample and the size of the percentage; it also depends on how that percentage response is spread within and between sample points.

    The complex design may be assessed relative to simple random sampling by calculating a range of design factors (DEFTs) associated with it, where:

    and represents the multiplying factor to be applied to the simple random sampling error to produce its complex equivalent. A design factor of one means that the complex sample has achieved the same precision as a simple random sample of the same size. A design factor greater than one means the complex sample is less precise than its simple random sample equivalent. If the DEFT for a particular characteristic is known, a 95 per cent confidence interval for a percentage may be calculated using the formula:

    Calculations of sampling errors and design effects were made using the statistical analysis package STATA.

    Table A.5 gives examples of the confidence intervals and DEFTs calculated for a range of different questions. Most background variables were fielded on the whole sample, whereas many attitudinal variables were asked only of a half or quarter of the sample; some were asked on the interview questionnaire and some on the self-completion supplement.

    Table A.5 Complex standard errors and confidence intervals of selected variables

    The table shows that most of the questions asked of all sample members have a confidence interval of around plus or minus two to three per cent of the survey percentage. This means that we can be 95 per cent certain that the true population percentage is within two to three per cent (in either direction) of the percentage we report.

    Variables with much larger variation are, as might be expected, those closely related to the geographic location of the respondent (for example, whether they live in a big city, a small town or a village). Here, the variation may be as large as five or six per cent either way around the percentage found on the survey. Consequently, the design effects calculated for these variables in a clustered sample will be greater than the design effects calculated for variables less strongly associated with area. Also, sampling errors for percentages based only on respondents to just one of the versions of the questionnaire, or on subgroups within the sample, are larger than they would have been had the questions been asked of everyone.

    Analysis Techniques

    Regression analysis aims to summarise the relationship between a ‘dependent’ variable and one or more ‘independent’ 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. It 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. In linear regression, it is assumed that the relationship can be adequately summarised by a straight line. This means that a one percentage point increase in the value of an independent variable is assumed to have the same impact on the value of the dependent variable on average, irrespective of the previous values of those variables.

    Strictly speaking the technique assumes that both the dependent and the independent variables are measured on an interval-level scale, although it may sometimes still be applied even where this is not the case. For example, one can use an ordinal variable (e.g. a Likert scale) as a dependent variable if one is willing to assume that there is an underlying interval-level scale and the difference between the observed ordinal scale and the underlying interval scale is due to random measurement error. Often the answers to a number of Likert-type questions are averaged to give a dependent variable that is more like a continuous variable. Categorical or nominal data can be used as independent variables by converting them into dummy or binary variables; these are variables where the only valid scores are 0 and 1, with 1 signifying membership of a particular category and 0 otherwise.

    The assumptions of linear regression cause particular difficulties where the dependent variable is binary. The assumption that the relationship between the dependent and the independent variables is a straight line means that it can produce estimated values for the dependent variable of less than 0 or greater than 1. In this case it may be more appropriate to assume that the relationship between the dependent and the independent variables takes the form of an S-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. Logistic regression is an alternative form of regression which fits such an S-curve rather than a straight line. The technique can also be adapted to analyse multinomial non-interval-level dependent variables, that is, variables which classify respondents into more than two categories.

    The two statistical scores most commonly reported from the results of regression analyses are:

    A measure of variance explained: This summarises how well all the independent variables combined can account for the variation in respondent's scores in the dependent variable. The higher the measure, the more accurately we are able in general to estimate the correct value of each respondent's score on the dependent variable from knowledge of their scores on the independent variables.

    A parameter estimate: This shows how much the dependent variable will change on average, given a one-unit change in the independent variable (while holding all other independent variables in the model constant). The parameter estimate has a positive sign if an increase in the value of the independent variable results in an increase in the value of the dependent variable. It has a negative sign if an increase in the value of the independent variable results in a decrease in the value of the dependent variable. If the parameter estimates are standardised, it is possible to compare the relative impact of different independent variables; those variables with the largest standardised estimates can be said to have the biggest impact on the value of the dependent variable.

    Regression also tests for the statistical significance of parameter estimates. A parameter estimate is said to be significant at the five per cent level if the range of the values encompassed by its 95 per cent confidence interval (see also section on sampling errors) are either all positive or all negative. This means that there is less than a five per cent chance that the association we have found between the dependent variable and the independent variable is simply the result of sampling error and does not reflect a relationship that actually exists in the general population.

    Factor Analysis

    Factor analysis is a statistical technique which 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) which 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 are able to account for a relatively large proportion of the variance in all of the questions in the set.

    The technique produces a factor loading for each question (or variable) 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 which apparently structure attitudes towards the subject in question.

    International Social Survey Programme

    The International Social Survey Programme (ISSP) is run by a group of research organisations, each of which undertakes to field annually an agreed module of questions on a chosen topic area. Since 1985, an International Social Survey Programme module has been included in one of the British Social Attitudes self-completion questionnaires. Each module is chosen for repetition at intervals to allow comparisons both between countries (membership is currently standing at over 40) and over time. In 2007, the chosen subject was Leisure Time and Sports, and the module was carried on the A version of the self-completion questionnaire (Qs. 1.1–1.18).9


    1. Until 1991 all British Social Attitudes samples were drawn from the Electoral Register (ER). However, following concern that this sampling frame might be deficient in its coverage of certain population subgroups, a ‘splicing’ experiment was conducted in 1991. We are grateful to the Market Research Development Fund for contributing towards the costs of this experiment. Its purpose was to investigate whether a switch to PAF would disrupt the time-series — for instance, by lowering response rates or affecting the distribution of responses to particular questions. In the event, it was concluded that the change from ER to PAF was unlikely to affect time trends in any noticeable ways, and that no adjustment factors were necessary. Since significant differences in efficiency exist between PAF and ER, and because we considered it untenable to continue to use a frame that is known to be biased, we decided to adopt PAF as the sampling frame for future British Social Attitudes surveys. For details of the PAF/ER ‘splicing’ experiment, see Lynn and Taylor (1995).

    2. This includes households not containing any adults aged 18 and over, vacant dwelling units, derelict dwelling units, non-resident addresses and other deadwood.

    3. In 1993 it was decided to mount a split-sample experiment designed to test the applicability of Computer-Assisted Personal Interviewing (CAPI) to the British Social Attitudes survey series. CAPI has been used increasingly over the past decade as an alternative to traditional interviewing techniques. As the name implies, CAPI involves the use of lap-top computers during the interview, with interviewers entering responses directly into the computer. One of the advantages of CAPI is that it significantly reduces both the amount of time spent on data processing and the number of coding and editing errors. There was, however, concern that a different interviewing technique might alter the distribution of responses and so affect the year-on-year consistency of British Social Attitudes data. Following the experiment, it was decided to change over to CAPI completely in 1994 (the self-completion questionnaire still being administered in the conventional way). The results of the experiment are discussed in The 11th Report (Lynn and Purdon, 1994).

    4. Interview times recorded as less than 14 minutes were excluded, as these timings were likely to be errors.

    5. An experiment was conducted on the 1991 British Social Attitudes survey (Jowell et ah, 1992) which showed that sending advance letters to sampled addresses before fieldwork begins has very little impact on response rates. However, interviewers do find that an advance letter helps them to introduce the survey on the doorstep, and a majority of respondents have said that they preferred some advance notice. For these reasons, advance letters have been used on the British Social Attitudes surveys since 1991.

    6. Because of methodological experiments on scale development, the exact items detailed in this section have not been asked on all versions of the questionnaire each year.

    7. In 1994 only, this item was replaced by: Ordinary people get their fair share of the nation's wealth. [Wealthl]

    8. In constructing the scale, a decision had to be taken on how to treat missing values (‘Don't knows,’ ‘Refused’ and ‘Not answered’). Respondents who had more than two missing values on the left-right scale and more than three missing values on the libertarian-authoritarian and welfarism scales were excluded from that scale. For respondents with just a few missing values, ‘Don't knows’ were recoded to the midpoint of the scale and ‘Refused’ or ‘Not answered’ were recoded to the scale mean for that respondent on their valid items.

    9. See

    DeVellis, R.F. (2003), Scale Development: Theory and Applications (
    2nd Edition
    ), Applied Social Research Methods Series, 26, Thousand Oaks, CA: Sage.
    Jowell, R., Brook, L., Prior, G. and Taylor, B. (1992), British Social Attitudes: the 9th Report, Aldershot: Dartmouth
    Lynn, P. and Purdon, S. (1994), ‘Time-series and lap-tops: the change to computer-assisted interviewing’, in Jowell, R., Curtice, J., Brook, L. and Ahrendt, D. (eds.), British Social Attitudes: the 11th Report, Aldershot: Dartmouth
    Lynn, P. and Taylor, B. (1995), ‘On the bias and variance of samples of individuals: a comparison of the Electoral Registers and Postcode Address File as sampling frames’, The Statistician, 44: 173–194.
    Spector, P.E. (1992), Summated Rating Scale Construction: An Introduction, Quantitative Applications in the Social Sciences, 82, Newbury Park, CA: Sage.
    Stafford, R. and Thomson, K. (2006), British Social Attitudes and Young People's Social Attitudes surveys 2003: Technical Report, London: National Centre for Social Research.

    Appendix II: Notes on the Tabulations in Chapters

    1. Figures in the tables are from the 2007 British Social Attitudes survey unless otherwise indicated.

    2. Tables are percentaged as indicated by the percentage signs.

    3. In tables, ‘*’ indicates less than 0.5 per cent but greater than zero, and ‘—’ indicates zero.

    4. When findings based on the responses of fewer than 100 respondents are reported in the text, reference is made to the small base size.

    5. Percentages equal to or greater than 0.5 have been rounded up (e.g. 0.5 per cent = one per cent; 36.5 per cent = 37 per cent).

    6. In many tables the proportions of respondents answering “Don't know” or not giving an answer are not shown. This, together with the effects of rounding and weighting, means that percentages will not always add to 100 per cent.

    7. The self-completion questionnaire was not completed by all respondents to the main questionnaire (see Appendix I). Percentage responses to the self-completion questionnaire are based on all those who completed it.

    8. The bases shown in the tables (the number of respondents who answered the question) are printed in small italics. The bases are unweighted, unless otherwise stated.

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