UK Data Archive Study Number British Social Attitudes, British Social Attitudes 2008 User Guide. 2 Data collection methods...

Similar documents
The Efficacy of Focused Enumeration. Patten Smith, IpsosMORI Kevin Pickering, NatCen Joel Williams, TNS-BMRB Rachel Hay, Ipsos-MORI

Trip Generation Model Development for Albany

THE ESTABLISHMENT SURVEY TECHNICAL REPORT

A Note on Methods. ARC Project DP The Demand for Higher Density Housing in Sydney and Melbourne Working Paper 3. City Futures Research Centre

Typical information required from the data collection can be grouped into four categories, enumerated as below.

Understanding Your Community A Guide to Data

What are we like? Population characteristics from UK censuses. Justin Hayes & Richard Wiseman UK Data Service Census Support

Measuring connectivity in London

Data Collection. Lecture Notes in Transportation Systems Engineering. Prof. Tom V. Mathew. 1 Overview 1

GROWING APART: THE CHANGING FIRM-SIZE WAGE PREMIUM AND ITS INEQUALITY CONSEQUENCES ONLINE APPENDIX

British Household Panel Survey, waves 1-17 ( )

Relationships between land use, socioeconomic factors, and travel patterns in Britain

EMPLOYMENT SOCIO- DEMOGRAPHIC PROFILE OF THE DUBLIN DOCKLANDS AREA AND JAMES WILLIAMS THE ECONOMIC AND SOCIAL RESEARCH INSTITUTE AND MUIRIS O CONNOR

Transport Planning in Large Scale Housing Developments. David Knight

Might using the Internet while travelling affect car ownership plans of Millennials? Dr. David McArthur and Dr. Jinhyun Hong

Understanding and accessing 2011 census aggregate data

Advances in Geographic Data Science and Urban Analytics

Population 24/7. David Martin, University of Southampton

Understanding China Census Data with GIS By Shuming Bao and Susan Haynie China Data Center, University of Michigan

Switching to AQA from Edexcel: Draft Geography AS and A-level (teaching from September 2016)

Accessibility analysis of multimodal transport systems using advanced GIS techniques

What is Survey Weighting? Chris Skinner University of Southampton

DATA DISAGGREGATION BY GEOGRAPHIC

Energy Use in Homes. A series of reports on domestic energy use in England. Energy Efficiency

Applying cluster analysis to 2011 Census local authority data

The Church Demographic Specialists

2002 HSC Notes from the Marking Centre Geography

Analysis of travel-to-work patterns and the identification and classification of REDZs

The British Crime Survey Sample: A response to Elliott and Ellingworth

Assessment and management of at risk populations using a novel GIS based UK population database tool

National Statistics 2001 Area Classifications

Economic and Social Council

Data Matrix User Guide

Dwelling Price Ranking vs. Socio-Economic Ranking: Possibility of Imputation

Innovations in Household Surveys. Kathleen Beegle Poverty And Inequality Course Module 1: Multi-Topic Household Surveys January 28, 2010

GALLUP NEWS SERVICE JUNE WAVE 1

Path analysis for discrete variables: The role of education in social mobility

NHS Occupational Health Workforce Survey 2009

Compiled by the Queensland Studies Authority

LEO Catchment Profile (LCP) Key Data for Enterprise Strategy

Measures from the Adult Social Care Outcomes Framework, England

Key elements An open-ended questionnaire can be used (see Quinn 2001).

Mapping the Route Identifying and Consulting with HS2 s Impacted Populations

Afghan Profiles: Finding Structure in Survey Data to Better Understand the Human Terrain. Thomas E. Powell, Ph.D. Philip T. Eles, Ph.D.

Energy Use in Homes 2004

Applying to study geography (or geology) at university. David Richards on behalf of local Universities

School of Geography and Geosciences. Head of School Degree Programmes. Programme Requirements. Modules. Geography and Geosciences 5000 Level Modules

Energy Use in Homes 2007

PUBLIC EMPLOYEE HAZARDOUS CHEMICAL PROTECTION AND RIGHT TO KNOW ACT O.C.G.A

CIMA Professional

Purpose Study conducted to determine the needs of the health care workforce related to GIS use, incorporation and training.

CIMA Professional

The Building Blocks of the City: Points, Lines and Polygons

CS229 Machine Learning Project: Allocating funds to the right people

SCIENCE OCR LEVEL 2 CAMBRIDGE TECHNICAL. Cambridge TECHNICALS CHEMISTRY OF PRODUCTION CERTIFICATE/DIPLOMA IN D/505/3209 LEVEL 2 UNIT 10

Survey nonresponse and the distribution of income

Fundamentals of Applied Sampling

SUPPORTING A THRIVING UK LIFE SCIENCES ECOSYSTEM

Energy Use in Homes 2004

BOOK REVIEW Sampling: Design and Analysis. Sharon L. Lohr. 2nd Edition, International Publication,

BROOKINGS May

CIV3703 Transport Engineering. Module 2 Transport Modelling

Application Issues in GIS: the UCL Centre for Advanced Spatial Analysis. Paul Longley UCL

should inspire in pupils a curiosity and fascination about the world and its people that will remain with them for the rest of their lives.

Using Census data for comparing trends in 74 British City Regions

Environmental Analysis, Chapter 4 Consequences, and Mitigation

KENTUCKY HAZARD MITIGATION PLAN RISK ASSESSMENT

Programme Specification (Undergraduate) Chemistry

ParkServe. The Trust for Public Land s. Emmalee Dolfi Gabriel Patterson-King ESRI User Conference 2017

Demographic Data in ArcGIS. Harry J. Moore IV

Intimate Infrastructures

CHALLENGES IN CONDUCTING SURVEYS/CENSUSES

CIMA Professional 2018

Geospatial Analysis of Job-Housing Mismatch Using ArcGIS and Python

Maps, Graphs and Metrics: How Local Agencies Put Data to Work. Vivien Deparday Manager of the Community Information and Mapping System June 20, 2012

Combining Non-probability and Probability Survey Samples Through Mass Imputation

CIMA Professional 2018

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE. Road Maps for Mainstreaming Ageing

Statistics Division 3 November EXPERT GROUP MEETING on METHODS FOR CONDUCTING TIME-USE SURVEYS

Measuring Perceived Accessibility to Urban Green Space: An Integration of GIS and Participatory Map

The geography of domestic energy consumption

Georgia Kayser, PhD. Module 4 Approaches to Sampling. Hello and Welcome to Monitoring Evaluation and Learning: Approaches to Sampling.

MINISTÉRIO DAS FINANÇAS DIRECÇÃO GERAL DE ESTATÍSTICA DIRECÇÃO NACIONAL DE ESTATÍSTICA ECONOMICAS E SOCIAIS

New Developments in Nonresponse Adjustment Methods

GLA small area population projection methodology

Seymour Centre 2017 Education Program 2071 CURRICULUM LINKS

SPACE-TIME ACCESSIBILITY MEASURES FOR EVALUATING MOBILITY-RELATED SOCIAL EXCLUSION OF THE ELDERLY

Measuring Poverty. Introduction

Accessibility as an Instrument in Planning Practice. Derek Halden DHC 2 Dean Path, Edinburgh EH4 3BA

Protocol between London MAPPA Strategic Management Board (SMB) and (Name of Borough) Local Safeguarding Children Board

Strathprints Institutional Repository

Developing the Transit Demand Index (TDI) Gregory Newmark, Regional Transportation Authority Transport Chicago Presentation July 25, 2012

Source Protection Zones. National Dataset User Guide

emerge Network: CERC Survey Survey Sampling Data Preparation

The SAS System 11:26 Tuesday, May 14,

Geographical Inequalities and Population Change in Britain,

A User s Guide to the Federal Statistical Research Data Centers

Population 24/7 Download: User Guide. Purpose

Poverty and Hazard Linkages

R E SEARCH HIGHLIGHTS

Transcription:

UK Data Archive Study Number 6390 - British Social Attitudes, 2008 o NatCen National Centre for Social Research British Social Attitudes 2008 User Guide Author: Roger Stafford 1 Overview of the survey... 1 2 Data collection methods... 1 3 Sample design... 2 4 Explanation of the dataset... 2 5 Weighting the data...3 6 Socio-economic classifications... 4 7 Related publications...4 8 Contact details... 5

This note provides information in brief about the British Social Attitudes (BSA) survey. It accompanies the final version of the datafile (bsa08.sav). For further details about the surveys, see Stafford, R. et al (forthcoming), British Social Attitudes 2008: Technical Report, London: National Centre for Social Research. 1 Overview of the survey The surveys were conducted by the National Centre for Social Research (NatCen). BSA s core-funding is provided by the Gatsby Charitable Foundation, which is one of the Sainsbury Family Charitable Trusts, and this was supplemented by grants from the Economic and Social Research Council, the Hera Trust, The Nuffield Foundation and The Leverhulme Trust. Various Government departments also supported modules in the 2008 survey: Department for Health, Department for Work and Pensions, Department for Education and Skills (now Department for Children, Schools and Families), Department of Trade and Industry (now Department for Business, Innovation and Skills) and Department of Transport. 2 Data collection methods The fieldwork was conducted by NatCen. Interviews were conducted in the respondent s home, using a laptop computer. In order to increase the number of topics on BSA, four versions of the questionnaire were fielded, and respondents were randomly assigned to one of the versions. All respondents answered a core set of demographic and other classificatory questions and individual modules are then carried on either one, two, three or all four versions. In 2008, the face-to-face interview was designed to last about 63 minutes and was then followed by a self-completion questionnaire.

Fieldwork was carried out between June and September 2008, with a small number of interviews taking place in October and November. A summary of the response is as follows: Number Lower limit of response (%) Upper limit of response (%) Addresses issued 9060 Out of scope 870 Upper limit of eligible cases 8190 100.0 Uncertain eligibility 166 2.0 Lower limit of eligible cases 8024 100.0 Interview achieved 4486 54.8 55.9 Interview not achieved 3538 43.2 44.1 Refused 2 2763 33.7 34.4 Non-contacted 3 357 4.4 4.4 Other non-response 418 5.1 5.2 1 Response is calculated as a range from a lower limit where all unknown eligibility cases (for example, address inaccessible, or unknown whether address is residential) are assumed to be eligible and therefore included in the unproductive outcomes, to an upper limit where all these cases are assumed to be ineligible (and are therefore excluded from the response calculation). 2 Refused comprises refusals before selection of an individual at the address, refusals to the office, refusal by the selected person, proxy refusals (on behalf of the selected respondent) and broken appointments after which the selected person could not be recontacted. 3 Non-contacted comprises households where no one was contacted and those where the selected person could not be contacted. The data file should be used in conjunction with the following documentation: Outline of the BSA questionnaire Documentation of the BSA questionnaire program (final version dated Jan 2010) BSA showcards BSA self-completion questionnaire (one per questionnaire version) Address Record Form 3 Sample design The BSA survey was designed to yield a representative sample of the population in Britain aged 18+. The sample of addresses was drawn from the Postcode Address File. At each address, the interviewer established how many occupied dwelling units it contained. If there were several, one was selected at random for interview (using a Kish grid and random numbers). The interviewer then established how many adults aged 18+ lived in the (selected) dwelling unit. If there were several, one adult was selected (using a similar procedure as that used for dwelling units). The unequal selection probabilities arising from these procedures are taken into account by the weighting. 4 Explanation of the dataset The BSA questionnaire covered: Attitudes to health, social welfare, transport, religion and faith, education, food technology and national identity. Versions A and B of the self-completion questionnaire included a module of questions about religion, which were fielded as part of the International Social Survey Programme, of which the BSA series is a member.

5 Weighting the data The datasets (in common with all surveys based on samples from the Postcode Address File) must be weighted to take account of differing selection probabilities. Simplifying slightly: households are selected with equal probability, but only one person in each household is interviewed for BSA. People in small households therefore have a higher probability of selection than people in large households and the weighting corrects for this. Following some experimentation with the BSA 2004 dataset, we have decided to implement a more sophisticated weighting approach from BSA 2005 onwards. In addition to the selection weighting, the weights for BSA 2008 incorporate two components: Non-response weighting: Where information is available about both responding and nonresponding addresses, this can be used in the weighting to reduce non-response bias. Information about non-responding addresses is available from two sources: census information about the area of the address and interviewer observation. Calibration weighting: this is designed to adjust the sample to the regional sex and age profiles of the population. Obviously any change in procedures on a survey series like BSA risks disrupting the time-series. We have therefore carried out some comparisons of the frequencies produced using the old and the new weighting schema on the 2005 data. The results are as follows: A random sample of variables: A random of sample of 53 variables were compared. Although there were small differences between the distributions produced by the old weights and the new weights, none of them were statistically significant at the 95% level. A selection of time-series variables: 42 variables with particular time-series interest were selected and compared. Although there were small differences between the distributions produced by the old weights and the new weights, none of them were statistically significant at the 95% level. NatCen advice on the use of weights on BSA 2008 The data must be weighted in all analysis. The new weighting scheme is superior to the old weighting scheme in that it reduces non-response bias. The new weights should therefore be used in all reported analysis. The datafile is not pre-weighted and the new weights must be applied using the SPSS command: WEIGHT BY WtFactor. However, when reporting time-series analysis, there is a small possibility that the change of weighting scheme could disrupt the time-series. Our tests show that there is unlikely to be many statistically significant differences in the frequencies produced by the old and new weights on any substantive variables. Some socio-demographic variables, however, do show small, but statistically significant, differences. This is particularly important in the case of age, as age is often related to attitudes. (Region, on the other hand, is rarely related to attitudes). In most cases where age is an important factor, people will have already taken this into account in analysis e.g. by cross-tabulating variables by age or by including age as an independent variable in regressions. Hence the changes to the age structure of the sample is unlikely to change the conclusions to substantive analyses conducted in the past. We do not propose to go back and reweight past datasets.

However, as a precaution, our recommendation is that when reporting time-series analysis the 2008 figures should be rerun using the old weighting structure (OLDWT) to check that this does not present a radically different picture. The figures produced using the new weights (WTFACTOR) should still be the ones used in reporting, but any substantial differences should be mentioned in a note. Please note that the data must be weighted in all analysis. The file is not preweighted. Before running any analysis, please use the following SPSS command: weight by wtfactor. 6 Socio-economic classifications With the 2001 census, the Office for National Statistics have switched from SOC90 to SOC2000 for the coding of occupations. At the same time, they switched from the Social Class and Socio- Economic Group classifications to the new National Statistics Socio-Economic Classification (NS- SEC). The BSA data file contains the following variables based on the new classification: Respondent Spouse/partner NS-SEC operational categories ROpCat S2OpCat NS-SEC analytic classes RClass S2Class NS-SEC analytic classes (grouped) RClassGp S2ClassG Further information about these new classifications is available on the ONS web site: http://www.statistics.gov.uk/methods_quality/ns_sec/default.asp It is our advice that the new classifications should be used whenever possible. However, there are some time-series analysis where the old classifications may be needed, for example, analysis of changes in the role of class over time. For this purpose, best estimates of the older classifications have also been included on the BSA datafile: Respondent Spouse/partner Socio-Economic Group RNSEG S2NSEG Socio-Economic Group compressed RNSEGGrp S2NSEGGp Registrar General s Social Class RNSocCl S2NSocCl Goldthorpe scale RNGH S2NGH Goldthorpe scale compressed RNGHGrp S2NGHGrp 7 Related publications The results of the BSA survey are published in: Park, A., Curtice, J., Thomson, K., Phillips, M., Clery, E and Butt, S (eds.) (2010) British Social Attitudes: the 26 th Report, London: Sage.

8 Contact details Roger Stafford National Centre for Social Research 35 Northampton Square London EC1V 0AX tel: 020 7549 7123 e-mail: roger.stafford@natcen.ac.uk User Guide 5

British Social Attitudes: 2008 Survey Version A Version B Version C Version D (Quarter of sample) (Quarter of sample) (Quarter of sample) (Quarter of sample) CAPI questionnaire Household grid, media exposure and party identification - Social security and welfare Transport - Transport - Health Education - Education Food - - ISSP Religion extension Faith Matters National Identity - Job details Employment relations - Employment relations Classification Self-completion questionnaire ISSP (religion) - - - Social security and welfare Transport - Transport - Health Education - Education Food - - Employment Relations - Employment Relations - - Faith matters Standard scales

Self Completion question module assignment A B C D module 1 1 - - ISSP 2 2 - - ISSP 3 3 - - ISSP 4 4 - - ISSP 5 5 - - ISSP 6 6 - - ISSP 7 7 - - ISSP 8 8 - - ISSP 9 9 - - ISSP 10 10 - - ISSP 11 11 - - ISSP 12 12 - - ISSP 13 13 - - ISSP 14 14 - - ISSP 15 15 - - ISSP 16 16 - - ISSP 17 17 - - ISSP 18 18 - - ISSP 19 19 - - ISSP 20 20 - - ISSP 21 21 - - ISSP 22 22 - - ISSP 23 23 - - ISSP 24 24 - - ISSP 25 25 - - ISSP 26 26 - - ISSP 27 27 - - ISSP 28 28 - - ISSP 29 29 - - ISSP 30 30 - - ISSP 31 31 - - ISSP 32 32 - - ISSP 33 33 - - ISSP 34 34 - - ISSP - 35 1 1 SocSec - 36 2 2 SocSec 35-3 3 Transport 36-4 4 Transport 37-5 5 Transport 38 - - - Transport 39 - - - Transport - - 6 - Transport - - 7 - Transport - - - 6 Transport - - - 7 Transport A B C D module - 37 8 8 Health - 38 9 9 Health - 39 10 10 Health 40-11 11 Education 41-12 12 Education 42-13 13 Education 43 40 - - Food 44 41 - - Food 45 42 - - Food 46 43-14 EmpRel 47 44-15 EmpRel 48 45-16 EmpRel 49 46-17 EmpRel 50 47-18 EmpRel 51 48-19 EmpRel 52 49-20 EmpRel 53 50-21 EmpRel 54 51-22 EmpRel 55 52-23 EmpRel 56 53-24 EmpRel - - 14 Faith matters - - - 25 Faith matters - - 15 26 Faith matters - - 16 27 Faith matters - - 17 28 Faith matters - - 18 29 Faith matters - - 19 30 Faith matters - - 20 31 Faith matters - - 21 32 Faith matters - - 22 33 Faith matters - - 23 34 Faith matters - - 24 - Faith matters - - 25 35 Faith matters - - 26 36 Faith matters - - 27 37 Faith matters - - 28 38 Faith matters - - 29 39 Faith matters - - 30 40 Faith matters - - 31 41 Faith matters - - 32 42 Faith matters - - 33 43 Faith matters - - 34 44 Faith matters