GESIS Zentralarchiv für Empirische Sozialforschung Codebook ZA Study 3910 ISSP 2003 National Identity II Participating Nations: Austria Australia Bulgaria Canada Chile Czech Republic Denmark Finland France Germany Great Britain Hungary Ireland Israel Japan Latvia New Zealand Norway Philippines Poland Portugal Russia Slovakian Republic Slovenia South-Africa South-Korea Spain Sweden Switzerland Taiwan Uruguay United States Venezuela
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Machine readable Codebook - Carleton University...The annual plenary meeting of the ISSP then adopts the final questionnaire. ISSP questions have to be relevant to all countries and
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GESIS Zentralarchiv fürEmpirische Sozialforschung
Codebook ZA Study 3910
I S S P 2 0 0 3
N a t i o n a l I d e n t i t y I I
Participating Nations: Austria Australia Bulgaria Canada Chile Czech Republic Denmark Finland France Germany Great Britain Hungary Ireland Israel Japan Latvia New Zealand Norway Philippines Poland Portugal Russia Slovakian Republic Slovenia South-Africa South-Korea Spain Sweden Switzerland Taiwan Uruguay United States Venezuela
Zentralarchiv fuer Empirische Sozialforschung an der Universitaet zu Koeln Bachemer Str. 40 D-50931 Koeln Tel: (+) 49 221-4 7694 - 0 Fax: (+) 49 221-4 7694 - 44 e-mail: [email protected] URL: http://www.gesis.org/ZA/ First complete edition: October 2005
ZA-No. 3910 I S S P 2003 National Identity II Page I - 1
Table of Contents Preface Acknowledgement of Assistance ............................................................................................... 3
An Introduction to the ISSP ....................................................................................................... 5
The International Social Survey Programme ............................................................... 5
International Occupation Codes: ILO/ISCO 1988 ................................................................. 296
Variable List ........................................................................................................................... 306
JaimeDiez
Note
Accepted set by JaimeDiez
JaimeDiez
Note
MigrationConfirmed set by JaimeDiez
JaimeDiez
Note
MigrationConfirmed set by JaimeDiez
JaimeDiez
Note
Completed set by JaimeDiez
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Acknowledgement of Assistance All manuscripts utilizing data made available through the “Zentralarchiv fuer empirische Sozialforschung“ should acknowledge that fact as well as identify the original collectors of the data. We kindly ask all users to follow some adaptation of the following statement: The data utilized in this (publication) were documented and made available by the
ZENTRALARCHIV FUER EMPIRISCHE SOZIALFORSCHUNG, KOELN. The data for the 'ISSP' were collected by independent institutions in each country (see principal investigators in the study-description-schemes for each participating country). Neither the original data collectors nor the ZENTRALARCHIV bear any responsibility for the analyses or conclusions presented here
In order to provide funding agencies with essential information about the use of archival resources and to facilitate the exchange of information about research activities based on the ZENTRALARCHIV's holdings, each user is expected to send two copies of every completed manuscript to the ZENTRALARCHIV. The production and documentation of the annual ISSP integrated files are done in alternating years by the Zentralarchiv fuer empirische Sozialforschung as the archive of the ISSP, Cologne, Germany, and by ASEP/JDS, Madrid, Spain. The present codebook and data file for the ISSP module 2003 (National Identity II) were produced by ASEP/JDS.
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Please note All marginal frequencies in this documentation are calculated from unweighted data! Please also consider that - especially in the section of the background variables – identical value codes of certain variables may not always have the same meaning for each country. Such country-specific differences are documented in this codebook.
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An Introduction to the ISSP - the International Social Survey Programme The ISSP is a continuing annual programme of cross-national collaboration on surveys covering topics which are important for social science research. It brings together existing social science projects and coordinates research goals. Thereby the ISSP adds a cross-national, cross-cultural perspective to the national studies. As of 2004, thirty-nine countries are members of the ISSP. The founding stones of the ISSP were laid in 1983 when SCPR, London, secured funds from the Nuffield Foundation to hold meetings to further international collaboration between four existing surveys - the General Social Survey, conducted by NORC in the USA, the British Social Attitudes Survey, conducted by SCPR in Great Britain, the Allgemeine Bevölkerungsumfrage der Sozialwissenschaften, conducted by ZUMA in West Germany and the National Social Science Survey, conducted by ANU in Australia. Prior to this, NORC and ZUMA had been collaborating bilaterally on a common set of questions since 1982. The founding members agreed (1) to jointly develop modules addressing important areas of social science, (2) to field the modules as fifteen-minute supplements to the regular national surveys (or a special survey if necessary), (3) to include an extensive common core of background variables and (4) to make the data available to the social science community as quickly as possible. Each research organisation funds its own costs, there are no central funds. Merging and archiving of the data into a cross-national data set is performed by the Zentralarchiv fuer Empirische Sozialforschung, University of Cologne. Since 1997, the Zentralarchiv and the Spanish institute ASEP (Análisis Sociológicos Económicos y Políticos) share the task of merging the national data sets. Since its conception in 1984, 40 nations have participated in the ISSP: the founding four - Australia, Germany, Great Britain and the United States - plus Austria, Bangladesh, Brazil, Bulgaria, Canada, Chile, Cyprus, the Czech Republic, Denmark, Finland, Flanders, France, Hungary, Israel, Ireland, Italy, Japan, Latvia, Mexico, the Netherlands, New Zealand, Norway, the Philippines, Poland, Portugal, Russia, the Slovakian Republic, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Uruguay and Venezuela. The topics for each ISSP survey are developed over several years by a committee and are pre-tested in various countries. The annual plenary meeting of the ISSP then adopts the final questionnaire. ISSP questions have to be relevant to all countries and must be expressed in an equivalent manner in all languages. The questionnaire is originally drafted in British English and then translated to the languages of the member countries. The ISSP marks several new departures in the area of cross-national research: (1), the collaboration between members is organised in a stable framework.(2), the ISSP makes cross-national research an integral part of the research agendas of participating countries. (3), by combining a cross-time perspective with a cross-national perspective, two powerful research designs can be used to study societal processes.
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ISSP Modules 1985 - 2007 Surveys Already Fielded and Archived ISSP 1985 Role of Government I ZA No. 1490 ISSP 1986 Social Networks and Support Systems ZA No. 1620 ISSP 1987 Social Inequality I ZA No. 1680 ISSP 1988 Family and Changing Gender Roles I ZA No. 1700 ISSP 1989 Work Orientations I ZA No. 1840 ISSP 1990 Role of Government II ZA No. 1950 ISSP 1991 Religion I ZA No. 2150 ISSP 1992 Social Inequality II ZA No. 2310 ISSP 1993 Environment I ZA No. 2450 ISSP 1994 Family and Changing Gender Roles II ZA No. 2620 ISSP 1995 National Identity ZA No. 2880 ISSP 1996 Role of Government III ZA No. 2900 ISSP 1997 Work Orientations II ZA No. 3090 ISSP 1998 Religion II ZA No. 3190 ISSP 1999 Social Inequality III ZA No. 3430 ISSP 2000 Environment II ZA No. 3440 ISSP 2001 Social Relations and Support Systems ZA No. 3680 ISSP 2002 Family and Changing Gender Roles III ZA No. 3880 ISSP 2003 National Identity II ZA No. 3910 Currently Conducted or Planned Surveys ISSP 2004 Citizenship ISSP 2005 Work Orientation III ISSP 2006 Role of Government IV ISSP 2007 Leisure Time and Sports
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The ISSP Members Institute Addresses & ISSP Contacts An up to date list of member organisations is always available at the ISSP World Wide Web site: http://www.issp.org Secretariat Knut Kalgraff Skjåk,
Bjørn Henrichsen, Kirstine Kolsrud, NSD (Norwegian Social Science Data Services) Hans Holmboesgate 22 N - 5007 Bergen NORWAY
• Tel: 0047-55-583-246 • Fax: 0047-55-589-650
Archive Markus Quandt,
Wolfgang Jagodzinski Zentralarchiv für Empirische Sozialforschung (ZA) Universitaet zu Koeln Bachemer Str. 40 50931 Koeln GERMANY
• Tel: 0049-221-47694-0 • Fax: 0049-221-47694-44
Australia Rachel Gibson,
ACSPRI Centre for Social Research (ACSR) Research School of Social Sciences Canberra, ACT 0200 The Australian National University AUSTRALIA
Agency for Social Analyses 1 Macedonia Sq. 1040 Sofia BULGARIA
• Tel: 00359-2-917-0455 • Fax: 00359-2-986-1072
Canada Heather Pyman,
Jon Pammett, Carleton University Survey Centre Carleton University 346 St. Patrick's Building Ottawa CANADA KIS 5B6
• Tel: 001-613-520-2600 • Fax: 001-613-520-6690
Chile Carolina Segovia,
Centro de Estudios Publicos Monsenor Sótero Sanz 175 Providencia Santiago CHILE
• Tel: 0056-2-231-5324 • Fax: 0056-2-233-5253
Cyprus Bambos Papageorgiou,
Centre of Applied Research Cyprus College 6 Diogenes Street Engomi P.O. Box 2006 Nicosia CYPRUS
• Tel: 00357-2-713-175 • Fax: 00357-2-664-531
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Czech Republic Petr Mateju, Institute of Sociology Academy of Sciences of the Czech Republic Jilska 1 110 00 Praha 1 CZECH REPUBLIC
• Tel: 0042-2-2222-0678 • Fax: 0042-2-2222-1658
Denmark Jørgen Goul Andersen,
Johannes Andersen, Mette Tobiasen Department of Economics, Politics and Public Administration Aalborg University Fibigerstraede 1 DK-9220 Aalborg DENMARK
• Tel: 0045-9635-8173 • Fax: 0045-9815-5346
Finland Professor Raimo Blom,
Professor Harri Melin, Department of Sociology and Social Psychology FIN-33014 University of Tampere FINLAND
Eero Tanskanen, Survey Research Unit FIN-00022 Statistics Finland FINLAND
• Tel: 00358-9-1734-2549 • Fax: 00358-9-1734-3562
Sami Borg, FSD Finnish Social Science Data Archive FIN-33014 University of Tampere FINLAND
• Tel: 00358-3-215-8519 • Fax: 00358-3-215-8520
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Flanders Ann Carton, Administration of Planning and Statistics – Ministry of Flanders Boudewijnlaan 30 1000 Brussel Belgium
• Tel: 0032-2-553-5687 • Fax: 0032-2-553-5808
Jaak Billiet, Department of Sociology Katholic Universiteit Leuven E.Van Evenstraat 2B B-3000 Leuven BELGIUM
• Tel: 0032-16-323157 • Fax: 0032-16-323365
France Yannick Lemel,
FRANCE-ISSP Association (Centre de Rechere en Économie et Statistique) Laboratorie de Sociologie Quanitative Timbre J350-Bureau E33 bis 92240 Malkoff Cedex FRANCE Pierre Bréchon, Bruno Cautres CIDSP (Centre d'Infomatisation des Données Socio-Politiques) Institut d'Études Politiques de Grenoble Domaine Universitaire BP 45 38402 St. Martin D'Heres Cedex FRANCE
L. Chauvel, M. Forsé OFCE (Observatorie Français des Conjonctures Économiques) 69, Quai d'Orsay 75340 Paris Cedex 07 FRANCE A. Degenne
LASMAS (Laboratoire d'Analyse Secondaire et de Méthodes Appliquées en Sociologie) 59-61, rue Pouchet 75849 Paris Cedex 07 FRANCE
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Germany Janet Harkness, Prof. Dr. Peter Mohler Evi Scholz, Sabine Klein ZUMA (Zentrum für Umfragen, Methoden, und Analysen) P. O. Box 12 21 55 68072 Mannheim GERMANY
• Tel: 0049-621-1246-284 • Fax: 0049-621-1246-100
Great Britain Alison Park,
Roger Jowell, National Centre for Social Research 35 Northampton Square London, EC1V OAX GREAT BRITAIN
• Tel: 0044-171-250-1866 • Fax: 0044-171-250-1524
Hungary Peter Robert,
TÁRKI RT Social Research Institute Budaorsi ut. 45 H-1112 Budapest HUNGARY
• Tel: 0036-1-309-7676 • Fax: 0036-1-309-7666
Ireland Marie Nic Ghiolla Phadraig (Director SSRC)
Conor Ward, Philippa Caithness (Senior Executive Assistant), SSRC (Social Science Research Centre) University College Dublin Dublin 4 IRELAND
• Tel: 00353-1-7167001 • Fax: 00353-1-7161125
Israel Noah Lewin-Epstein,
Eppie Yuchtman-Yaar Dept. of Sociology and Anthropology Tel Aviv University P.O. Box 39040, Ramat Aviv 69978 Tel Aviv ISRAEL
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Italy Luca Diotallevi, CENSIS Piazza di Novella, 2 00199 Roma ITALY
• Tel: 0039-6-860911 • Fax: 0039-6-86211367
Japan Hiroshi Aramaki,
NHK, Broadcasting Culture Research Institute Public Opinion Research Division 2-1-1 Atago, Minato-ku Tokyo 105-0002 JAPAN
• Tel: 0081-3-5400-6800 • Fax: 0081-3-3438-4375
Latvia Aivars Tubuns,
Ausma Tabuna, Institute of Philosophy and Sociology Akademijas 1 LV-1940, Riga LATVIA
• Tel: 00371-7-227110 • Fax: 00371-7-210806
Mexico Federico Curiel Gutierrez,
Institute of Marketing and Opinion Paseo de la Reforma 90, Cuarto piso Mexico, D.F. 06600 MEXICO
• Tel: 0052-55-5535-6239 • Fax: 0052-33-3915-2626
Netherlands Harry Ganzeboom,
SCP (Sociaal en Cultureel Planbureau) Parnassusplein 5 PO Box 16164 2500 BD Den Haag THE NETHERLANDS
• Tel: 0031-70-340-7000 • Fax: 0031-70-340-7044
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New Zealand Philip Gendall, Department of Marketing Massey University Private Bag 11222 Palmerston North NEW ZEALAND
• Tel: 0064-6-350-5582 • Fax: 0064-6-350-2260
Norway Knut Kalgraff Skjåk,
Bjørn Henrichsen, Kirstine Kolsrud, NSD (Norwegian Social Science Data Services) Hans Holmboesgate 22 N - 5007 Bergen NORWAY
• Tel: 0047-55-583-246 • Fax: 0047-55-589-650
Philippines Mahar Mangahas,
52 Malingap Street Sikatuna Village Quezon City 1101 PHILIPPINES
• Tel: 0063-2-926-4308 • Fax: 0063-2-920-2181
Poland Bogdan Cichomski,
ISS (Institute for Social Studies) University of Warsaw Stawki 5/7 00-183 Warsaw POLAND
• Tel: 0048-22-315-153 • Fax: 0048-22-315-153
Portugal Manuel Villaverde Cabral,
Alice Ramos, Instituo de Ciências Sociais University of Lisbon Av. Prof. Anibal Bettencourt, 9 1600-189 Lisbon PORTUGAL
• Tel: 00351-21-780-4700 • Fax: 00351-21-794-0274
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Russia Ludmila Khakhulina, Levada Analytic Center 17, Nikolskaya str. Moscow 103829 RUSSIA
• Tel: 007-95-229-5545 • Fax: 007-95-200-4648
Slovakian Republic Magdalena Piscova,
Institute for Sociology Slovak Academy of Science Klemensova 19 81364 Bratislava SLOVAK REPUBLIC
• Tel: 00421-2-5296-4355 • Fax: 00421-2-5296-2315
Slovenia Mitja Hafner-Fink,
Public Opinion and Mass Communications Research Centre Faculty of Social Sciences University of Ljubljana Kardeljeva ploscad 5 1000 Ljubljana SLOVENIA
• Tel: 00386-1-580-5105 • Fax: 00386-1-580-5106
South Africa Jare Struwig,
Human Science Research Council Private Bag X41 Pretoria 0001 SOUTH AFRICA
• Tel: 0027-12-302-2487 • Fax: 0027-12-302-2525
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Spain Juan Díez-Nicolás, ASEP (Análisis Sociológicos Económicos y Políticos) Po de la Castellana 173, 5o Izquierda 28046 Madrid SPAIN
• Tel: 0034-91-570-5107 • Fax: 0034-91-579-4073
Natalia Garcia Pardo,
CIS (Centro de Investigaciones Sociológicas) Montalbán 8 28014 Madrid SPAIN
• Tel: 0034-91-580-7664 • Fax: 0034-91-580-7619
South Korea Professor Sang-Wook Kim (Principle), Survey Research Center, Sungyunkwan University 53, Myungryun-Dong 3-Ga, Jongno-Gu Seoul, 110-745 Korea
• Tel: 0082-2-760-0412 • Fax: 0082-2-744-6169
Dr. Sook Hee, Choi, Samsung Economic Research Institute 7th Floor, Kukje Center Bldg. 191 hangangro 2-Ga, Yongsan-Gu Seoul, 140-702 Korea
• Tel: 0082-2-3780-8065 • Fax: 0082-2-3780-8008
Sweden Jonas Edlund,
Dept. of Sociology University of Umeå 901 87 Umeå SWEDEN
• Tel: 0046-90-786-7822 • Fax: 0046-90-786-6694
Switzerland Dominique Joye,
SIDOS (Swiss Information and Data Archive for the Social Sciences) Ruelle Vaucher 13 CH-2000 Neuchâtel SWITZERLAND
• Tel: 0041-32-721-2002 • Fax: 0041-32-721-2074
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Taiwan Yang-chih Fu, Institute of Sociology Academica Sinica Nankang, Taipei TAIWAN 11529
Maximo Rossi, Departamento de Economia, Facultad de Ciencias Sociales, Universidad de la Republica J. E. Rodo 1854 Montevideo Uruguay
• Tel: (598-2) 409-2973 (office)
U.S.A. Tom W. Smith, National Opinion Research Center (NORC) 1155 East 60th Street Chicago, IL 60637 U.S.A.
• Tel: 001-773-256-6288 • Fax: 001-773-753-7886
Venezuela Roberto Briceno-Leon,
LACSO (Laboratorio de Ciencias Sociales) Research School of Social Sciences Apartado Postal 47.795 Caracas 1041-A VENEZUELA
• Tel: 0058-212-6931765 0058-212-6619752
• Fax: 0058-414-6931765 0058-414-7534941
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Study Descriptions To differentiate countries in the cross tabulations within this codebook we have decided to use the international ISO codes: Australia AU Austria AT Bulgaria BG Canada CA Chile CL Czech Republic CZ Denmark DK Finland FI France FR Germany (West) DE-W Germany (East) DE-E Great Britain GB Hungary HU Ireland IE Israel IL Japan JP Latvia LV New Zealand NZ Norway NO Philippines PH Poland PL Portugal PT Russia RU Slovakian Republic SK Slovenia SI South Korea KR South Africa ZA Spain ES Sweden SE Switzerland CH Taiwan TW Uruguay UY USA US Venezuela VE
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Study Description: Australia Study title:
ISSP 2003 National Identity
Fieldwork dates:
Start: 27 August 2003 Finish: 24 December 2003
Principal investigators:
Rachel Gibson, Australian National University Shaun Wilson, Australian National University
Sample type:
The sample was stratified by Australian states and territories using population counts from the 2001 Census. The selection of individuals within the stratified sample was random using the Electoral Roll for Australia produced by the Australian Electoral Commission (AEC). The 2001 version of the Electoral Roll was updated for 2002 where state and territory elections had taken place. This frame includes all registered voters who must be citizens of Australia (or a British subject on a Commonwealth electoral roll as at 25 January 1984). Since voting in Australia is compulsory, there is a very high proportion of the adult Australia population covered by this frame— approximately 92 percent in 2003. The sampling method used a random number generator inserted as a field in the electoral roll database; individuals were sorted using the
random number and then selected in order. The sampled units were named individuals.
Fieldwork Institute:
Australian Social Science Data Archive
Fieldwork methods:
Self-completion, paper and pencil, mailed to, mailed back by respondent
Sample size: 2183 Response rates: 5696 A - Total issued 796 B – Not eligible (ill, dead, non-English speaking, not at this address ) 4900 C - Total eligible 2183 D - Total ISSP questionnaires received 2717 E - non-responses (including non-contact; see note above
under “sample type”) 283 F - Refusals (including questionnaires less than half filled in) 2374 G - Non-contact (included in “E”) 60 H - Other non-response (included in “E”) Language:
English
Weighted:
No
Weighting Procedure:
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Known Systematic Properties:
Gross sample Using a sampling frame derived from the AEC Electoral Roll (2001) excludes permanent and temporary residents of Australia (who are not citizens) and any othe r adults in Australia who are non-citizens except for British subjects on a Commonwealth electoral roll as at 25 January 1984. These exclusions amount to approximately 8 percent of the adult population. Net sample The AUSSA 2003 sample demographics were compared with statistics available from the Australian Bureau of Statistics (ABS) including the 2001 Census. The major biases are: age (median age is older than the Census population), education (over-representation of persons with post-secondary school qualifications), and gender (slightover-representation of women).
Deviations from ISSP-questionnaire:
For V4-V6 “Most important group R identifies with”, the response categories were converted from numbers to letters (A, B, C….) after a piloting experiment revealed greater respondent accuracy with the latter. V8 “How close do you feel…”: response category was State or territory (appropriate internal regional breakdown for Australia. V10 “How close do you feel…”: response category was Asia (since Australia is both a continent and country)
All changes were approved by the ISSP prior to the fielding of the Survey.
Publications: Gibson, R. et al. The Australian Survey of Social Attitudes, 2003 [computer file]. Canberra: Australian Social Science Data Archive, The Australian National University, 2004.
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ISSP Characteristics of National Population: Australia The following data are made available to the ISSP from statistics derived from the Australian Bureau of Statistics (ABS) publications including the June 2001 Census. Please note that the ABS does not compile data on years of schooling completed. In its place, we have used educational attainment data (see Table 3) to provide meaningful comparisons between the AuSSA 2003 sample and ABS data. For further information, please contact Shaun Wilson at [email protected] Table 1: Sex distribution of AuSSA sample versus Australian population (18 years and over)
AuSSA 2003 ABS – Census June 2001
difference
Male 46.9 49.1 -2.2Female 53.1 50.9 +2.2
n=4,224 n=14,343,846 Source: Australian Survey of Social Attitudes, 2003; Australian Bureau of Statistics Pub. No. 3105.0.65.001 Australian Historical Population Statistics: TABLE 19 (Population age and sex, Australia, year ended 30
65 and over 22.9 17.0 +5.9 n=4,212 n=14,343,846 Source: Australian Survey of Social Attitudes, 2003; Australian Bureau of Statistics Pub. No. 3105.0.65.001 Australian Historical Population Statistics: TABLE 19 (Population age and sex, Australia, year ended 30 June 2001) * 18-34 year statistic for ABS data calculated using linear interpolation methods
Table 3: Educational attainment *
AuSSA 2003 ABS – Census June 2001
difference
Bachelor degree and higher
25.7 14.6 +11.1
Certificate or diploma
34.5 38.5 -4.0
Year 12 and less 39.8 46.9 -7.1 n=3,267 n=11,578,000 Source: Australian Survey of Social Attitudes, 2003; Australian Bureau of Statistics Pub. No. 1301.0-2004 Year Book Australia: TABLE 10.36 (Level of highest non-school qualification, by age group, May 2002) * 20 – 64 years
Table 4: Labour force participation rate (employment rate)
AuSSA 2003 ABS – Census June 2001
difference
In labour force 60.4 63.6 -3.2Not in labout force 39.6 36.4 +3.2
n=4,207 n/a Source: Australian Survey of Social Attitudes, 2003; Australian Bureau of Statistics Pub. No. 6202.0.55.001 Labour Force, Australia, Spreadsheets: TABLE 01 (Labour force status by Sex - Trend, June 2001)
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Study Description: Austria
Study title:
ISSP-Studie 2004: Nationale Identität; Bürger/innen und Staat (National Identity; Citizenship)
Fieldwork dates:
Nov. and Dec. 2004
Principal investigators:
Dr. Max Haller, Dr. Markus Hadler, Mag. Regina Ressler Institut für Soziologie, Karl-Franzens-Universität Graz, Austria
Sample type:: Stratified Multistage Clustered Random Sampling
Fieldwork institute: Institute for Empirical Social Research (IFES), Vienna
Fieldwork methods: Face-to-face interviews with trained interviewers
Sample size: 1006
Context of ISSP Questionnaire:
Social Survey Austria
Response rates: 2200 A – Total issued (total sample) 76 B – Ineligible (address vacant, wrong ages..)
2124 C – (=A – B) Total eligible (in scope sample) 1669 D – Total ISSP questionnaires received 663 E – (= C – D; = F + G + H) Total no response 278 F – Refusals (refusing to take part) 385 G – Non-contact (never contacted)
- H – Other non-response Language:
German
Weighted: Yes
Weighting procedure: A weighting variable was computed, taking into account sex, age group and province of residence.
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ISSP Characteristics of National Population: Austria SOURCE
Census 2001 Statistics Austria
2004
SEX Male 48,4% Female 51,6% AGE (groups) 0-14 14,4% 15-19 5,2% 20-24 5,0% 25-29 5,7% 30-34 7,1% 35-39 7,5% 40-44 6,7% 45-49 5,6% 50-54 5,5% 55-59 4,8% 60-64 4,8% 65-69 3,5% 70-74 3,5% 75-79 3,1% 80-84 1,6% 85+ 1,5% EDUCATION (15 years and older)
Years of schooling University compl. (Universitaet oä.) 6,8% 15 Secondary compl. (hoehere Schule) 14,6% 12
EMPLOYMENT STATUS (15-59 years) Employed (Erwerbsquote) 48,8% Unemployed 6,9% (annual average 2002) Not in labour force 24,7%
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Study Description: Bulgaria Study title:
Social survey
Fieldwork dates:
April-May 2003
Principal investigators:
Dr. Lilia Dimova, Agency for Social Analyses (ASA)
Sample type:
Two-stage cluster sample, representative for the whole adult population of Bulgaria over 18 year of old.
Context of ISSP questionnaire how it is in the hard copy questionnaire
Qs A1,A2B – A15 = National Identity module (B 1-12; C 1 - 23; E 1 - 5, F 1 - 13, G 1 -6 = Roma issues) D1 – D12, D14 – D16, D17A, D18A, D19 – D20, D23 – D24, D28 - D30, D32, T2 –T4 = The ISSP Standard Background Variables (D13, D21- D22 = Roma issues)
Fieldwork methods:
Face-to-face interview at respondent’s home
Sample size: Achieve sample 1069 cases Response rates: 1200 A - Total issued (total sample) 17 B - Ineligible (address empty) 1183 C - (= A - B) Total eligible (in-scope sample) 1069 D - Total ISSP questionnaires received 114 E - (= C - D; = F + G + H) Total non-response 35 F - Refusals (refusing to take part) 73 G - Non-contact (never contacted, away for long) 6 H - Other (too sick) Language:
Bulgarian
Weighted:
No
Weighting Procedure: Deviations from ISSP-questionnaire:
Missing questions V18, V23, V24 – because of technical reasons.
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ISSP Characteristics of National Population: Bulgaria Table 1. comparisons between population by administrative centres and the sample data
Administrative centres Hard data * Hard data structure (%)
* Source: National Statistical Institute, National Census 2001 – 2% sample distribution ** Source: www.nsi.bg/Population/Population.htm
Table 4. characteristics of national population form
Demographics Hard data March 2001 (%)*
Hard data (%) End of 2002** Sample data (%)
Sex 100 100 100 Male 48,8 48.6 48.7 Female 51,2 51.4 51.3 Urban/Rural distribution 100 100 100 Urban 69,0 69.6 68.8 Rural 31,0 30.4 31.2
Employment status 100 100 100
Employment rate 40.3 40.8 Unemployment rate 16.8 13.1 Not labor force 46.1 * National Statistical Institute, National Census 2001 – 2% sample distribution ** Source: www.nsi.bg/Population/Population.htm , www.nsi.bg/Labour/Labour.htm Table 4. urban distribution
March 2001*(%) Sample data N=1069 (%) Total 100 100 City 68,4 68,8 Village 31,6 31,2 Male 100 100 City 32,5 33,8 Village 15,6 15,0 Female 100 100 City 35,6 35,0 Village 16,3 16,3 * National Statistical Institute, National Census 2001 – 2% sample distribution
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Study Description: Canada Study title:
2003 National Identity 2004 Citizenship
Fieldwork dates:
January 29th, 2004 – March 31st, 2004
Principal investigators:
Carleton University Survey Centre
Sample type:
Stratified Random
Context of ISSP questionnaire how it is in the hard copy questionnaire
National Identity with Citizenship
Fieldwork methods: Self-completion Mail Sample size: Achieve sample 1069 cases Sampling method A stratified random sample (by province by gender) of 3,000
Canadian residents was purchased from Cornerstone List Brokerage, based on residential phone listings. (% of Canadians without phones < 2%) The design consisted of two mailouts and 1 reminder notice. The first mailout of 3,000 packages was mailed on January 29, 2004 and included a questionnaire, explanatory letter printed on the front cover and a postage paid return envelope. On February 10th a reminder postcard was mailed out to the entire sample. On February 26th a second mailout was sent to the respondents who had not returned their survey along with a additional letter explaining the importance of their participation in the project.
Response rates: Process
1st wave -Initial mailout -January 29th 3000 Returned as of Feb.24th 678 2nd wave – reminder cards -February 10th 3000 3rd wave – 2nd full mailout -February 26th 2322
Returned as of March 31st 600 Total Returned Surveys 1278
Composition 40 returned refused 27 incomplete 1211 complete
Sample report
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Sampling Frame 3,000 Returned to sender • address incomplete 60 • moved/not picked up 55 • deceased 11 Total viable sample 3,000-126 = 2,874 Response rate 43%
Language:
French and English
Weighted:
Yes
Weighting Procedure:
Weighted by province and age
Publications: None
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ISSP Characteristics of National Population: Canada Source: Statistics Canada Gender Male 49.52 Female 50.48 Employment Employed 62.4 Unemployed 7.5 Not in labour force 30.10 Total years of schooling (population 18+) 1-9yr. 20.5 10-11yr. 19.0 12-13yr. 30.1 14+yr. 30.1 Population by sex and age group
2003 Canada Male Female Canada Male Female
Age group Persons (thousands) % of total of each group Total 31,629.7 15,661.7 15,967.9 100.0 100.0 100.00–4 1,714.3 877.3 837.0 5.4 5.6 5.25–9 1,949.7 998.6 951.1 6.2 6.4 6.010–14 2,117.6 1,084.8 1,032.9 6.7 6.9 6.515–19 2,120.5 1,088.8 1,031.8 6.7 7.0 6.520–24 2,188.5 1,119.0 1,069.5 6.9 7.1 6.725–29 2,118.1 1,074.0 1,044.2 6.7 6.9 6.530–34 2,228.7 1,124.8 1,103.9 7.0 7.2 6.935–39 2,481.2 1,247.4 1,233.8 7.8 8.0 7.740–44 2,719.3 1,364.3 1,355.0 8.6 8.7 8.545–49 2,515.7 1,251.6 1,264.2 8.0 8.0 7.950–54 2,176.5 1,078.8 1,097.7 6.9 6.9 6.955–59 1,842.5 913.9 928.5 5.8 5.8 5.860–64 1,396.8 684.8 712.0 4.4 4.4 4.565–69 1,147.9 552.2 595.7 3.6 3.5 3.770–74 1,039.1 484.2 554.9 3.3 3.1 3.575–79 839.4 358.8 480.7 2.7 2.3 3.080–84 583.7 221.6 362.0 1.8 1.4 2.385–89 297.0 97.2 199.7 0.9 0.6 1.390 and over 153.1 39.8 113.3 0.5 0.3 0.7Note: Population as of July 1. Source: Statistics Canada, CANSIM II, table 051-0001. Last modified: 2003-11-06.
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Study Description: Chile Study title:
Second National Public Opinion Study 2003
Fieldwork dates:
December 6th to 24th 2003
Principal investigators:
Carolina Segovia, Carla Lehmann and Paulina Valenzuela of Centro de Estudios Públicos
Sample type:
The guiding methodological principle underlying CEP's public opinion surveys is that effective and accurate survey research must be based on a truly representative sample of the universe in question. For CEP’s purpose, this universe is made up of Chilean adults 18 years of age and older. Our studies use a probability multistage cluster sample of 1,505 individuals.
The sample is designed in three stages, such that all adult individuals throughout the country have a calculable probability of being included. The 2002 Census data is consulted to determine the regional population structure of people 18 years of age and older. This makes it possible firstly to establish regional stratification and then each region is stratified by rural and urban zone. Table I shows the regional structure of the Chilean population.
Table I
Regional Breakdown of Population (%)
Region
% Population 18 years of age and older1
Region
% Population 18 years of age and older1
I Tarapacá 2,8 VII Maule 5,9
II Antofagasta 3,2 VIII Bío Bío 12,2
III Atacama 1,6 IX La Araucanía 5,6
IV Coquimbo 3,9 X Los Lagos 7,0
V Valparaíso 10,4 XI Aisén 0,6
VI Libertador Bdo.
O'Higgins 5,1
XII Magallanes and
Antarctic 1,0
XIII Metropolitan
(Santiago)
40,7
1Source: 2002 Census Data National Institute of Statistics.
Sampling Stages
First Stage
The first stage of the sampling process sets the number of completed interviews per cluster at 5; a cluster is defined as a block (manzana) or populated entity (entidad).1 The application of 5 interviews per cluster to the total number of interviews targeted in the sample (1,505) yields
1 Blocks (manzanas) are used in urban areas, while in rural areas the census equivalent is the entity (entidad).
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301 primary sampling units (PSUs) to be identified in the first stage of sample selection.
The PSUs are proportionally distributed throughout the regions of the country, taking into account the region's contribution to population (both urban and rural), as described in Table II.
TABLE 2
Number of Clusters per Region Number of clusters Region Urban Rural Total
I Tarapacá 7 1 8 II Antofagasta 10 0 10 III Atacama 5 0 5 IV Coquimbo 9 3 12 V Valparaíso 28 3 31 VI Libertador Bdo. O'Higgins 10 5 15 VII Maule 12 6 18 VIII Bío Bío 30 7 37 IX La Araucanía 11 6 17 X Los Lagos 14 7 21 XI Aisén 2 0 2 XII Magallanes and Antarctic 3 0 3 XIII Metropolitan (Santiago) 118 4 122
TOTAL 259 42 301
Using the most reliable digital information on hand, i.e. 2002 census data, a cumulative listing of population by province (provincia), borough (comuna), district (distrito), zone (zona) and block (manzana) was prepared in the urban case; and by province, borough, district, locality (localidad) and entity (entidad) in the rural case (geographically arranged).
In both the rural and the urban case a fixed interval is set for each region by dividing the total population for that region by the number of PSU’s assigned to it. Within each region, a purely random selection process is followed, such that each individual (as represented by population statistics) has a calculable probability of being selected as the reference point for a PSU.
This is carried out through a computerised, random, proportionate-to-population process to select blocks in the urban areas and entities in the rural areas. A computer program is developed to select the 259 urban blocks and the 42 rural entities for the sample. All the blocks and entities are identified by number and located on a census map.
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Second Stage
The second stage in the sampling process is to select households (dwellings) within PSUs. Selection rules for households within chosen blocks and entities are provided to interviewers, to enable them to select households randomly within each cluster. After taking a census of each selected block and entity, a random walk or systematic sampling2 procedure is followed, whereby every nth dwelling is included in the sample until a total of 5 households are identified.
Third Stage
The third stage is to select, within each household, a person to be interviewed. Interviewers are instructed to apply a random selection process (random number table) to identify the person to be interviewed.
Other Important Aspects in the CEP Sample Design
In the second and third stages, the interviewer has to make a minimum of three attempts on three different days to try to reach the original house or person to be interviewed. In these attempts the interviewer must deliver a letter signed by the CEP director explaining the nature of CEP and the aims of the study.
If the original household or person finally cannot be contacted, they are replaced. The rules for replacement are as follows:
A. Blocks and Entities
The replacement of a block or entity will occur only in the following situations:
1) Vacant lots that could not be detected prior to sample selection
2) Areas which are almost inaccessible
3) Entities and/or blocks intended basically for commercial use
4) Parks or stadiums
5) Areas belonging to the armed forces
Blocks and entities are randomly replaced: the original selection is replaced with the one whose identification number comes immediately before that of the original. If this is not successful, the block/entity with the identification number immediately following the original selection is taken.
2 The total number of numbered dwellings was divided by 5 (the number of interviews per cluster); this gave an interval length, such that if it was 43/5 =
8, starting from the point randomly pre-assigned as the first dwelling, the interviewers went to dwelling No 9, then to No 17, and so on until 5 interviews were completed.
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B. Households
Failed dwellings are accounted for as follows:
1) by outright refusal to receive the interviewer, even having received the letter from the CEP director.
2) by the absence of occupants at a house after three visits on three different days (vacations or other reasons).
3) the house is unoccupied.
4) access denied (condominiums or buildings with security guards)
5) other special cases (foreigners with whom it is impossible to communicate, etc.)
Each failed dwelling is randomly replaced by another one from the same block/entity. The replacement interval is defined as (k-1), where k= the original selection interval. Starting from the last house originally selected, the interval (k-1) is added to select the first replacement house, and so on. The fieldwork treatment of replacement houses as regards the number of repeat visits is the same as for the original houses.
C. Individuals
Individuals are replaced under the following circumstances:
1) when the person selected refuses to answer the questionnaire, even after receiving the letter from the CEP director.
2) when the person selected cannot be located after three attempts on three different days, or will be away for a period longer than the duration of fieldwork.
3) the person offers to respond on a date long after the closing date of the field work.
4) individuals with serious physical or psychological handicaps which prevent them from responding (mentally handicapped, deaf and dumb, etc..)
5) the individual is physically and psychologically fit, but is ill, in bed and does not want, or is unable to respond to the survey.
6) the selected individual is a foreigner with less than 5 years in the country (unable to vote).
7) the person starts to answer but he/she does not want to finish the interview.
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8) other specific cases.
In replacing individuals, the dwelling is replaced according to the rules explained above, and a random selection process chooses an individual from the new household.
It is important to bear in mind that, by using these replacement rules, the sample size achieved is always very close to 1505 (issued sample size).
Context of ISSP questionnaire how it is in the hard copy questionnaire
The National Identity 2003 module of the ISSP was carried out in conjunction with 24 questions relating to Chilean political, economic and social attitudes and tendencies. The questionnaire was structured as follows: firstly, the 24 questions mentioned above, the complete National Identity ISSP module, and, finally, demographic variables.
Fieldwork methods:
The surveys are carried out through personal interview.
Sample size: The sample size finally achieved was 1505 interviews Response rates:
As was explained above in the section “Sample type”, the sampling method used by CEP involves the random replacement of those blocks/entities, dwellings or individuals, which for the reasons described above cannot in the end be contacted. Accordingly, keeping this in mind and using the method for counting the response rate established by the ISSP, we have that: A = 1,505 = Issue sample B = 0 = Ineligible A-B = 1,505 = C= Total eligible D =1,505= Sample achieved E = C-D= 0 Response rate = A/D = 1505/1505 = 100% Non-response rate = 0/1505 = 0.0% However, for the type of sampling used by CEP, these data are not real. CEP uses the following method for calculating the response rate. Let A = Number of original interviews achieved (not replaced) = 1,308 Let B = Number of non-original interviews achieved (replaced) = 197 Let C = Number of questionnaires received = A+B = Total sample = 1,505 B = D1 + E1 + F1 + D2 + E2 + G + F2 = 197 where :
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D1 = Number of interviews replaced due rejection of household = 63 E1 = Number of interviews replaced due to no contact in house (empty house + nobody comes to the door) = 59 F1 = Number of interviews replaced for other household reasons = 11 D2 = Number of interviews replaced due to individual rejection = 28 E2 = Number of interviews replaced due to failure to make contact with selected individual = 24 G = Number of interviews replaced due to physical or psychological impediment of selected individual = 12 F2 = Number of interviews replaced for other individual reasons = 0 No-response rate = Number of non-original interviews achieved (replaced) / total number of interviews (replaced + original) = B / C = 197 / 1,505 =13,1%
Language:
Spanish
Weighted:
No
Weighting procedure: A weighting procedure is applied in order to correct for distortions in the representativeness of the sample as regards three variables of interest: Gender, Age, (grouped in five categories: 18-24 years, 25-34, 35-44, 45-54, 55 or older) and Urbanity (classification of place of residence as urban or rural). This makes it possible to obtain a sample with characteristics similar to those of the population. The weights are constructed by calculating the quotient between the expected distribution and that observed in the cross between Urbanity, Gender and Age. The expected distribution is obtained from the 2002 census data provided by the National Institute of Statistics. The result of the weighting slightly corrects for problems of under- and over-representation among certain specific groups of the population.
Known systematic properties:
The sample design described above has been used in the last 17 surveys and has given good results, so it can be stated that it does not have properties that might be causing some type of bias in the results.
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Deviations from ISSP- questionnaire:
The questionnaire was translated as closely as possible from English to Spanish, maintaining the meaning and significance of each sentence and word. Some general comments: • In questions Q2, Q3, Q4, Q5, Q6, Q7, Q9, Q10, Q11, Q15 and
Q16 the code “can´t choose” was replaced wiyh “don´t know” (8) and “no answer”.(9)
• Due to problems detected in the pre-test, the question Q12 was finally translated as follows “From what country/countries or part(s) of the world did your direct ancestors, loke your parents, grand parents, or great grand parents, come?”.
• Q12 is an open-ended questions, thus the data submitted present a very wide range of codes, so researchers can group them as they want to.
• Q8 was not included in the final version of the questionnaire due to translation problems detected in the pretest.
• Regarding Q1: Due to problems detected in the pre-test this question was asked at the end of the National Identity Module.
DEMOGRAPHICS VARIABLES: • MARITAL : The code 3 is "ANULADO": In Chile we don't have
divorce • UNION : The code 2 was not included. • INCOME and RINCOME : In these questions, incomes were
measured like monthly net - income. • ISCO88 and SPISCO88 : The answers were coded with 4 digit
when it was possible. • RCH_PRTY : The codes are : VALUE LABELS RCH_PRTY 01 Independent National Alliance 02 Communist Party of Chile 03 Christian Democratic Party' 04 'Humanist Party 05 Democracy Party PPD 06 Radical Social Democratic 07 'Chilean Socialist Party 08 National Renewal Party 09 Independent Democratic Union'
10 Other party 11 No party, no preference 98 Don´t know 99 'NA'.
• URBRURAL: It was added new codes, 6 for “Urban, RP total urban” and 7 for “Rural, RP total rural”.
Publications: -
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ISSP Characteristics of National Population: Chile (%) Gender Male 48,5 Female 51,5 Age Group 18 - 24 16,3 25 - 34 22,9 35 - 54 38,6 55 or older 22,2 Years of Schooling - Group 0 - 3 years 11,0 4 - 8 years 26,1 9 -12 years 38,9 13 or more years 24,0 Employment Status Employed (5.675.130) 92,6 Unemployed (453.070) 7,4 Total Labour force (6.128.190) 100,0 Not in Labour Force (5.419.180) 47,0 Sources: For Gender, age group and year of schooling: Census 2002 by National Institute of Statistics. Considers population 18 years of age or older. For employment status: Estimated by National Institute of Statistics. Oct-Dec 2003. Percentage “Not in labour force” is based on population 15 years of age or older.
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Study Description: Czech Republic Study title:
National identity II, ISSP 2003
Fieldwork dates:
26.09.2003 – 19.10.2003
Principal investigators:
Klara Plecita, Institute of Sociology, Academy of Sciences of the Czech Republic
Sample type:
Three stage stratified probability sampling: 1) Stratified probability sampling of election districts 2) Probability sampling of households in selected district 3) Sampling of household members based on a Kish grid
Fieldwork institute:
SC&C
Fieldwork methods:
Face-to-face
Sample size: 1276 Response rates: 2441 A - Total issued (total sample) 52 B - Ineligible (address vacant, wrong ages,...) 2389 C - (= A - B) Total eligible (in scope sample) 1276 D - Total ISSP questionnaires received 1113 E - (= C - D; = F + G + H) Total non-response 617 F - Refusals (refusing to take part) 297 G - Non-contact (never contacted) 199 H – Other non-response Language:
Czech
Weighted: Yes
Weighting procedure: Total weight is constructed from: 1) design weights based on proportion of household sizes 2) post-stratification weights based on region, sex, education, age, economical activity, and size of the community. The weights were derived from data of the Czech Statistical Office. Method: raking based on loglinear modelling
Known systematic properties of the sample:
Due to sampling design the probability of selecting a respondent is 1 / No. of members of his/her household. Due to response differences sex and education significantly differ from the know population characteristics. The biases was corrected by weighting.
Deviations from ISSP questionnaire:
All the core ISSP module and background questions were included. Please see the technical report (cz03info.doc) for specifications of selected country specific backgroun variables.
Publications: not yet ISSP Characteristics of National Population: Czech Republic
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ISSP 2003 data are weighted by the design weight (number of household members - see Weighting). Czech Republic
(December 2003) age 18 +; in %
ISSP 2003 in %
Difference in %
SEX Male 48,1 48,0 -0,1 Female 51,9 52,0 0,1 AGE 18 – 29 23,2 24,2 1,0 30 – 44 25,5 25,5 0,0 45 – 59 27,4 26,6 -0,8 60 and higher 23,9 23,7 -0,2 MARITAL STATUS single 23,3 23,8 0,5 married 56,6 58,0 1,4 divorced 10,8 9,7 -1,1 widowed 9,3 8,5 -0,8 Source of data for the Czech Republic: Statistical Yearbook of the Czech Republic 2003. Czech Republic
(July 2003) age 15 +; in %
ISSP 2003 in %
Difference in %
REGION Prague 11,6 10,6 -1,0 Central Bohemia 11,1 11,1 0,0 South Bohemia 6,1 6,2 0,1 Pilsen 5,4 5,6 0,2 Carlsbad 3 2,9 -0,1 Usti n/L 8 8,2 0,2 Liberec 4,2 4,3 0,1 Hradec Kralove 5,4 5,3 -0,1 Pardubice 4,9 5,1 0,2 Vysocina 5 5,3 0,3 South Moravia 11 11,0 0,0 Olomouc 6,2 6,3 0,1 Zlin 5,8 6,0 0,2 Moravia-Silesia 12,3 12,2 -0,1 Source of data for the Czech Republic: Statistical Yearbook of the Czech Republic 2003 Czech Republic
(December 2002) age 15+; in %
ISSP 2002 in %
Difference in %
EDUCATION Basic 22 18,5 -3,5 Secondary 68,2 70,2 2,0 University 9,5 8,9 -0,6 Without education 0,2 0,9 0,7 Not identified 0,1 1,4 1,3 Source of data for the Czech Republic: Statistical Yearbook of the Czech Republic 2002.
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Study Description: Denmark Study title:
Danish National Identity II 2003 ISSP-module
Fieldwork dates:
The fieldwork was conducted from 10. October 2003 to 31. January 2004.
Principal investigators:
Aalborg University: Department of Economics, Politics and Public Administration. Fibigerstraede 1, 9220 DK-Aalborg Oe: Prof. Jørgen Goul Andersen (Director of the Danish ISSP programme) Associate Prof. Johannes Andersen Associate Prof. Lars Torpe Associate Prof. Henrik Lolle Associate Prof. Mette Tobiasen
Department of Social Studies and Organization Kroghstraede 5, DK-Aalborg Oe: Prof. Jens Christian Tonboe University of Aarhus: Department of Political Science DK-8000 Aarhus C: Prof. Emeritus Ole Borre Prof. Lise Togeby
University of Copenhagen: Associate prof. Hans Jørgen Nielsen Department of Political science Rosenborggade 15, DK-1130 Copenhagen K Associate prof. Bjarne Hjorth Andersen Department of Sociology Linnésgade 22 DK-1361 Copenhagen K. University of Southern Denmark: Assistant prof. Ulrik Kjær Department of Political Science Campusvej 55 DK-5230 Odense M. Senior Researcher & International Consultant Torben Fridberg The Danish National Institute of Social Research Herluf Trolles Grade 11 DK-1052 Copenhagen K
Sample type: Sampling-procedure: A representative sample (simple random
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sample) was drawn from the Central Population Register (CPR) – which is a national register of all Danish citizens – from which respondent’s name and address were identified. Thus, the sampled unit was ’named individuals’. No stratification, clustering etc. was employed. The fieldwork method was postal survey (self-completion). One reminder was send out to respondents who had not returned the questionnaire. After this telephone interviews were attempted. If respondents were not reached, they were contacted at least five times before given up as “not meet”. In total 88,0 percent of the interviews where completed by mail and 12,0 percent were completed as telephone interviews (cf. MODE-variable). No substitutions were permitted at any stage of the selection process or during the fieldwork. The questions in the module were asked in the prescribed order.
Sample size:
Issued: 2000 Achieved: 1322
Response rates: The response rate is calculated to 66,4 percent. Full productive interviews / (Issued names – (respondents moved, no forwarding address + respondents deceased)): 1322/(2000-(6+2)) x 100= 66,4 percent. Description (N) Issued names 2000 Selected respondent moved, no forwarding address 6 Selected respondent too sick/incapacitated to participate 43 Selected respondent deceased 2 Personal refusal by selected respondent 430 Other type of unproductive reaction 197 Full productive interviews 1322
Employed 50,92 % Unemployed 2,22 % Not in labor force 46,86 %
• Source to sex and age: Statistical Yearbook (Statistisk tiårsoversigt 2004 – tema
om arbejdsstyrken). August 2004, 45. årgang, Danmarks statistik. Page 29, table of agedistribution 1. January 2004.
• Source to years of schooling: Statistical Yearbook (Statistisk årbog 2004). June
2004, 108. årgang, Danmarks statistik. Table 98 page 105, highest completed education distributed after age and sex 2003. The years of schooling of the different educations are from the figure 1 on page 89.
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tema om arbejdsstyrken). August 2004, 45. årgang, Danmarks statistik. Page 47, table of the population distributed after sex and affiliation to the labour market 2003. It is based on the population as of 1. January and the labour affiliation the last week of November the year before.
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Study Description: Finland Study title:
Suomi ja suomalaisuus (in Finnish) Finland och att vara finländare (in Swedish)
Fieldwork dates:
September 12th – November 28th, 2003 12th of Sep, 2003 questionnaires mailed 19th of Sep, 2003 thank you/reminder postcard sent to all
respondents 8th of Oct, 2003 dead line of answering the questionnaire 13th of Oct, 2003 replacement questionnaires sent to non-
respondents 5th of Nov, 2003 dead line of answering the replacement
questionnaire 28th of Nov, 2003 last questionnaire received
Principal investigators:
Raimo Blom, University of Tampere Harri Melin, University of Tampere
Sample type:
Target population: household population aged 15 to 74. Sampling design: a systematic random sample of individuals. Sampling frame: population register, sorting order: domicile code and birth date. Stratification: implicit geographic stratification. No clustering.
Fieldwork Institute: Statistics Finland, Helsinki
Fieldwork methods:
Postal (both direction), self-completion, paper and pencil
Sample size:
Issued: 2500
Response rates: A – Total issued 2500 B – Ineligible (address unknown or living abroad 6 C – Total eligible 2494 D – Total ISSP questionnaires received 1379 E – Total non-response 1115 F – Refusals 4 G – Non-contact - H – Other non-response 1111 Language:
Questionnaires in Finnish and Swedish. Information on language from the population register. Finnish (2 362; 94,5 % of total sample) and Swedish (138; 5,5 % of total sample)
Weighted:
Weight variables are included in the data-set. Basic data is unweighted.
Weighting procedure: The design of the survey was systematic sampling. In order to improve the efficiency of estimation and to reduce bias due to non-
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response a calibration method was used for the creation of the weights. The following marginal distributions of the population were used: 1) gender (male, female), 2) age classes (15–24,25–34,…, 65–74), 3) NUTS3 regions with following modifications: the Greater Helsinki Area was dealt as a separate region, 4) type of community (urban - semi-urban - rural). There are two weights available for calculations: 1) a weight that expands the results to the population level (the sum of the weights is the size of the population) and 2) a weight that doesn't have the expansion property (the mean of the weights is 1 and the sum of the weights is the number of accepted responses, i.e. the size of the data). Both of the weights are based on the same calibration process, only the scale differs.
Known systematic properties of the sample:
Sampling frame is updated and covers total population. A cross-sectional sample does not contain attrition by definition. After having used our standard sampling procedures for over 20 years we have not encountered any bias due to using systematic sampling. Design effect of the sampling procedure<=1 by definition.
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Deviations from ISSP-questionnaire:
The questionnaire included ISSP module with necessary background variables. List of deviations and coding specifications compared to ISSP source questionnaire: 1) Age Age is computed from a variable for year of birth in the Finnish questionnaire (FQ). 2) Marital FQ asked separately a category for those living together (married or not married). They were recoded into same category. Added one code, 0 meaning other (FQ). 3) Wrktype Added one code, 8 meaning don’t know (FQ). 4) Spwrktyp Added one code, 8 meaning don’t know (FQ). 5) Hhcycle The FQ asked separately on how many persons in the household, how many of them are 7-17 years old and how many children under 7 years of age there are in the household. Hhcycle includes combined information from these variables. 6) SF_reg Added from register data describing the regions. FQ did not include question for this. 7) SF_size Added from register data describing the population of the municipality. FQ did not include question for this. 8) Weight and weight_2 As described in the study description, -[weight]is a weight that expands the results to the population level (the sum of the weights is the size of the population) and -[weight_2] is a weight that doesn't have the expansion property (the mean of the weights is 1 and the sum of the weights is the number of accepted responses, i.e. the size of the data). 9) SF_ethn Added from register data describing the ethnic identity (language code). FQ did not include question for this.
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ISSP Characteristics of National Population: Finland Source no. 1 Source no. 2 Source no. 3 Source no. 4 31.12.2002 Statistics
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Study Description: France Study title:
National Identity II
Fieldwork dates:
01-09-2003 to 01-12-2003
Principal investigators:
Michel Forse, Yannick Lemel
Sample type:
Equal probability sampling on the whole French European territory amongpersons older than fifteen years living in the household.
Fieldwork Institute: France-ISSP
Fieldwork methods:
Postal (both direction), self-completion, paper and pencil
Sample size:
Issued: 10000
Response rates: A – Total issued 10000 B – Ineligible (address unknown or living abroad 128 C – Total eligible 9872 D – Total ISSP questionnaires received 1669 E – Total non-response 8203 F – Refusals 8148 G – Non-contact - H – Other non-response 55 Language:
French
Weighted:
Yes
Weighting procedure: Post-stratification weighting by age and occupation
Known systematic properties of the sample:
Under representation of youngest people because of responses rates differential
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ISSP Characteristics of National Population: France Data Source: INSEE 2002 Labour force survey Random sample of the population being more than 17 years old; sample size is 136,780
Labour force survey 2001 Gender
Male 47.9% Female 52.1%
Age Group 18-29 years old 19.8% 30-39 years old 18.7% 40-49 years old 18.4% 50-59 years old 16.5% 60-69 years old 11.3%
70 years old and more 15.3% Levels of Education
University or College 18.5% High School completed 11.4% Secondary uncompleted 32.6%
Primary or None 37.5% Employment Status
Employed 52.4% Unemployed 5.1%
Not in labour force 42.5%
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Study Description: Germany Study title:
ISSP 2003 Germany – National Identity II
Fieldwork dates:
2.3.2003-12.7.2003
Principal investigators:
Dr. Janet Harkness, Prof. Dr. Peter Ph. Mohler
Sample type:
Two stage random sample. Names and addresses from registers of inhabitants kept by municipalities. Adults of 18 and older living in private accommodation
Fieldwork institute:
TNS Infratest Sozialforschung (Germany)
Fieldwork methods:
Self-completion questionnaire, interviewer in attendance. Background variables were asked face-to-face (CAPI).
Sample size:
1287 (ISSP 2003 and ISSP 2004 were fielded in split together with ALLBUS, the German General Social Survey; number of ALLBUS 2004 interviews: 2946; number of ISSP 2004 interviews: 1332)
Response Rates:
N=3580 W=2450 E=1130 A – Total issued (total sample)
N=391 W=264 E=127 B – Ineligible (address vacant, wrong ages, etc.) N=3189 W=2186 E=1003 C – (=A−B) Total eligible (in scope sample) N=1287 W=850 E=437 D – Total ISSP interviews received N=1902 W=1336 E=566 E – = C - D; = F + G + H) Total non-response N=1339 W=924 E=415 F – Refusals (refusing to take part) N=216 W=153 E=63 G – Non-contact (never contacted) N=347 W=259 E=88 H – Other non-response Language: German Weighted: No Weighting procedure: Sample for eastern Germany deliberately over-samples the five
eastern federal states. If all of Germany is taken as the unit of analysis (rather than the eastern and western states) weighting is necessary. Weighting factor for West Germany: 1,232524; weighting factor for East Germany: 0,547722; (recoding of the country variable is necessary)
Known systematic properties of the sample:
None for the total sample
Deviations from ISSP questionnaire:
ISSP substantive questionnaire: no deviations; Background variables: no deviations
Publications: ALLBUS Methods Report 2004 (forthcoming); ISSP Methods Report on the German Study (forthcoming)
ZA-No. 3910 I S S P 2003 National Identity II Page I - 51
ISSP Characteristics of National Population: Germany Gender3 Male 40356000Female 42175700Total 82531700 Age group4 Under 6 4519300 6-15 7642800 15-25 9621700 25-45 24461100 45-65 21426800 65 and above 14860000 total 82531700 Education5 Without general school-leaving certificate 1890000Still at school 2992000Secondary general school certificate 29391000Certificate of ten-grad school of general education in the former GDR 4807000Intermediate school-leaving certificate or equivalent 12732000"Fachhochschule" entrance qualification/university entrance qualification
14092000
No data on type of education 669000Respondents providing data on general school education 66574000
"Fachhochschule" degree6 2868000University degree 3758000Doctor's degree 853000Respondents providing data on vocational qualification7 65575000 Employment Status8 Employed 36172000 Unemployed9 4022000 Not in labour force10
42307000
3 Population 31.12.2003, updated 20th December 2004. 4 Population 2003, updated 3. August 2004. 5 Persons who provided data on their general school education; persons aged 51 years and above are not obliged to respond; results from Microcensus May 2003. 6 Degree from specialised college of higher education; including engineering qualification and college of public administration degree. 7 Other types of vocational education not listed here. 8 Results from Microcensus May 2003. 9 Unemployed persons are looking for a job and immediately available.
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10 The inactive population includes persons not engaged in economic activity who are looking for a job but are not immediately available. 11 Population from 31.12.2003. Results of current population statistics.
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Study Description: Great Britain Study title:
British Social Attitudes 2003
Fieldwork dates:
June to September 2003
Principal investigators:
National Centre for Social Research
Sample type:
Clustered random sample: addresses were selected with equal probability in a stratified clustered design. One person aged 18+ was interviewed per address.
Fieldwork institute:
National Centre for Social Research
Fieldwork methods:
The ISSP module is implemented as a self-completion questionnaire, completed by the respondent after the main face-to-face interview and collected by the interviewer.
Sample size:
873
Response rates: 2062 A - Total issued (total sample) 182 B - Ineligible (address vacant, wrong ages,...) 1880 C - (= A - B) Total eligible (in scope sample) 873 D - Total ISSP questionnaires received 1007 E - (= C - D; = F + G + H) Total non-response 569 F - Refusals (refusing to take part) 260 Face-to-face interview but no self-completion 79 G - Non-contact (never contacted) 99 H - Other non-response Language:
English
Weighted: Yes
Weighting procedure: Addresses were selected with equal probability (except in Scotland where allowance was made for MOI – see below) but only one person interviewed at each address. Therefore weights need to be applied to correct for different number of adults at each address. The weights are calculated by the following formula: weight = (number of households at the address * number of adults in selected household) / MOI where MOI is the Multiple Output Indicator (MOI>1 indicates more than one household at the address). Very large weights were capped and the weights were scaled to make the
ZA-No. 3910 I S S P 2003 National Identity II Page I - 54
weighted sample size equal to the unweighted sample size.
Known systematic properties of the sample:
Sample excludes Scotland north of the Great Glen.
Deviations from ISSP questionnaire:
V64 to V74, Ethnic and TopBot are not available. The following variables were collected only if the respondent was not economically active/retired and the spouse partner was economically active/retired : SPISCO88, SpWrkTyp.
Publications: Park, A. et al (eds) (2004 forthcoming) British Social Attitudes: the 21st Report , in particular chpaters by Tilley at al. and by McLaren and Johnson
ZA-No. 3910 I S S P 2003 National Identity II Page I - 55
ISSP Characteristics of National Population: Great Britain Source no. 1 Source no. 2 Source no. 3 Source no. 4 2001 UK
Not available Source no. 1 Source no. 2 Source no. 3 Source no. 4 2001 UK
Census BSA 2003
weightedBSA 2003 un-
weighted
EMPLOYMENT STATUS
Employed
60.2% 56.5% 52.6%
Unemployed
4.5% 4.1% 4.4%
Not in labor force
36.3% 39.4% 43.0%
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Study Description: Hungary Study title:
National Identity II.
Fieldwork dates:
2003.02.28 – 2003.03.12.
Principal investigators:
Antal Örkény, ELTE University Budapest
Sample type:
We used probability sample, which was selected in multiple stages with proportional stratification. In the first stage, localities were chosen. In the second stage, the respondents were chosen from the localities with simple random sampling. Creating the sample of localities: The regions formed the first strata, type of settlements were the second. The localities were chosen from these strata by random sampling. For every county at least 2 towns and 3 villages were selected. The 23 districts of Budapest formed a separate sampling unit. Creating the sample of individuals: After creating the locality sample, and deciding the required number of respondents for every locality, we used ‘pre-selected addresses’ sampling methods for selection of respondents. The number of respondents in the previously chosen localities was defined in accordance with the proportion of the population of the given strata – regions, and different types of localities (towns and villages) within the region. The names and addresses were obtained from the Central Registry and Electoral Office with the help of simple random sampling. To replace dropped-out addresses subsidiary addresses were chosen with ‘random walk’ method, based on the Leslie-Kish—key.
Fieldwork institute:
TÁRKI RT Social Research Centre
Fieldwork methods:
mode of interview: face to face
Sample size:
number of respondents in the final ISSP file:1021
Response rates: 1684 A - Total issued (total sample) 109 B - Ineligible (address vacant, wrong ages,...) 1575 C - (= A - B) Total eligible (in scope sample) 1021 D - Total ISSP questionnaires received 554 E - (= C - D; = F + G + H) Total non-response
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282 F - Refusals (refusing to take part) 96 G - Non-contact (never contacted) 176 H - Other non-response Language:
Hungarian
Weighted: yes or no, whether a weighting factor exists in the data-set: YES
Weighting procedure: Exact description of the weighting procedure / algorithm In order to correct the sampling error we computed a weighting variable taking into account the type of residence (Budapest, other city, village), sex (male, female), age (18- 29, 30- 39, 40- 49, 50- 59, 60- 69, 70- x) and highest level of educational (primary , secondary, tertiary). The weight of each cases was computed as WEIGHT = (n/n’)* (N’/N), where N = respondent above the age of 18 in the sample of 2001 census, N’ = 1000, n = frequency of the population category in the census sub-sample the case belongs to, n’ = parallel frequency in the 2003 National Identity II module of TARKI.
Known systematic properties of the sample:
Description of biases or other deviations of the sample Table. 1. Crosstabulation of year of birth, type of residence, highest educational level and sex in the sample of 1000 in compare with the sample of Population Census Data in Hungary of 2000. Table.1.a. Sex
Sample (unweighted data) Census
Male 44.2 48.8 Female 55.8 53.2 Total 100.0 100.0
Table 1.b. Type of residence Sample (unweighted
data) Census
Budapest 17.5 18.5 Other city 45.7 45.1 Village 36.7 36.4 Total 100.0 100.0
Table 1.c. Age categories Sample (unweighted
data) Census
18- 39 29.4 39.6 40- 59 36.7 35.5 60- x 34.0 24.9 Total 100.0 100.0
Table 1.d. Highest educational level
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Table 1.e. Sex by age by residence by education (see in apendix1)
Deviations from ISSP questionnaire:
None
Publications: Csepeli, Gy. Örkény A. Székelyi M. Poór J. 2004 Nemzeti identitás Magyarországon az ezredfordulón. (National identity in Hungary at the Millenium) In: Kolosi, T. Tóth, I. Gy. Vukovich gy (eds): Társadalmi Riport (Social Report) Budapest: TÁRKI
Appendix 1. Budapest Other city Village
Highest educational level sex age N Sample Census N Sample Census N Sample Census
18-39 43 4,2% 1,5% 30 3,0% 5,6% 4 0,40% 5,2%
40-59 58 5,7% 1,1% 44 4,3% 4,6% 7 0,7% 4,8% male
60-xx 48 4,7% 0,9% 40 3,9% 3,0% 12 1,2% 3,2%
18-39 29 2,8% 1,0% 33 3,2% 4,1% 6 0,6% 3,9%
40-59 44 4,3% 1,1% 37 3,6% 4,4% 7 0,7% 4,3%
Primary level
female
60-xx 64 6,3% 2,1% 69 6,8% 5,6% 23 2,3% 5,4%
18-39 4 0,4% 1,5% 19 1,9% 2,9% 8 0,80% 1,3%
40-59 12 1,2% 0,9% 17 1,7% 1,9% 6 0,6% 1,0% male
60-xx 1 0,0% 0,4% 9 0,9% 0,8% 5 0,5% 0,3%
18-39 16 1,6% 1,8% 33 3,2% 3,9% 15 1,50% 2,0%
40-59 20 1,9% 1,5% 24 2,4% 3,1% 16 1,6% 1,4%
Secondary level
female
60-xx 2 0,2% 0,8% 17 1,7% 1,0% 9 0,9% 0,3%
18-39 4 0,4% 0,6% 12 1,2% 1,0% 12 1,2% 0,3%
40-59 7 0,7% 0,8% 18 1,8% 1,3% 9 0,9% 0,4% male
60-xx 2 0,2% 0,6% 10 1,0% 0,6% 7 0,7% 0,1%
18-39 6 0,6% 0,8% 17 1,7% 1,3% 7 0,7% 0,5%
40-59 9 0,9% 0,8% 23 2,3% 1,3% 12 1,2% 0,4%
Tertiary level
female
60-xx 5 0,5% 0,3% 10 1,0% 0,3% 11 1,10% 0,1%
ZA-No. 3910 I S S P 2003 National Identity II Page I - 59
Tertiary (13 years +) 934,036 934,036 9.2 11.5 EMPLOYMENT STATUS
(aged over 15) 8,479,163
Employed 3,690,269 3,696,928 36.2 43.6 Unemployed 416,210 417,762 4.1 4.9 Not in labor force 6,091,836 4,364,473 59.7 51.5
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Study Description: Ireland Study title:
National Identity II 2003
Fieldwork dates:
1st Oct 2003 to 15th Nov 2003
Fieldwork institute:
Economic and Social Research Institute (ESRI)
Principal investigators:
Nic Ghiolla Phádraig, Máire; Watson, Iarfhlaith Social Science Research Center, University College, Dublin
Sample type:
The sample used in the ISSP survey was selected as a three-stage clustered probability sample of the adult population drawn from the electoral register.
First stage of sample selection
At the first stage of sample selection the national population on the register was grouped into clusters with a minimum population size of 1,000 adults. (These clusters were based on the most geographically disaggregated areal units available in the country – the so-called District Electoral Divisions (DEDs)). These clusters with a minimum population size of 1,000 persons constituted the primary sampling units (PSUs). The PSUs were selected with a probability proportionate to size (PPS). A total of 100 sampling points was selected.
Second stage of sample selection
At the second stage of selection the target household was selected from within each PSU on a systematic basis using a random start.
Third stage of sample selection to identify the respondent
At the third stage we selected the target person for interview within household from within the set of persons aged 18 years or more using a so-called 'next birthday' rule. The next birthday' rule is a simple randomisation procedure which provides a randomly selected respondent in much the same way as a Kish grid etc.
Fieldwork methods:
Face-to-face
Sample size:
1000
Known limitations:
Using the Electoral Register as the population frame means that very recently generated households which are not yet registered on the list have, by definition, a zero probability of selection. This is not a major issue and is, in fact, common to all population lists. The data were reweighted using a minimum information loss program which controls, inter alia, for age; gender; household size (number of adults in the household); marital status; level of educational attainment; region; principal economic status. This reweighting scheme addresses any such small bias as may emerge from frame effects.
ZA-No. 3910 I S S P 2003 National Identity II Page I - 61
Response rates:
Total number of starting or issued names / addresses (gross sample size) 1,648
addresses which could not be traced at all / selected respondents who could not be traced 48
addresses established as empty, demolished or containing no private dwellings 40
selected respondent too sick / incapacitated to participate selected respondent away during survey period selected respondent had inadequate understanding of language of survey no contact at selected address no contact with selected person personal refusal by selected respondent 252proxy refusal (on behalf of selected respondent) other refusal at selected address other type of unproductive reaction (please write in full details in the box below) [Most of these were 'soft' refusals where the respondent was not available for interview throughout the fieldwork period despite repeated call-back]
220
full productive interview (net sample size) 1,065partial productive interview 23
Language:
English
Weighted:
Yes
Weighting procedure: The data were re-weighted using a minimum information loss algorithm called Gross. This is used in almost all surveys carried out by the Economic and Social Research Institute in Dublin. It is similar to the Calmar software prepared by Insee and is based on reconciling distributions in the data to column marginals. The control variables used in reweighting the ISSP were: number of adult sin the household, gender, age cohort (8 categories), marital status, level of educational attainment, principal economic status, and planning region.
ZA-No. 3910 I S S P 2003 National Identity II Page I - 62
ISSP Characteristics of National Population: Ireland
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Study Description: Israel Study title:
National Identity II
Fieldwork dates:
14th Dec 2003 to 29th Feb 2004
Principal investigators:
Noah Lewin-Epstein
Fieldwork Institute: B.I.and Lucille Cohen
Sample type:
First Stage: Division into strata (based of geographic location, community size and socioeconomic characteristics). Second Stage: Sampling of statistical areas (the smallest ecological unit) within strata. Third Stage: Sampling of starting point within statistical areas for the interviewing. Fourth Stage: Interviewing of specified number persons within statistical unit based on kishgrid.
Fieldwork methods:
Face-to-face
Sample size:
1066
Response rates: 1850 A - Total issued (total sample) 125 B - Ineligible (address vacant, wrong ages,...) 1725 C –(A - B) Total eligible 1066 D - Total ISSP questionnaires received 659 E - (=C – D; = F + G + H) Total non-response 452 F – Refusals 193 G - Non-contact (never contacted) 14 H - Other non-response Language:
Hebrew and Arabic
Weighted:
None
Context of ISSP questionnaire:
-
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ISSP Characteristics of National Population: Israel Source no. 1 Source no. 2 Source no. 3 Source no. 4 SEX Total Jews only Arabs only
Male 49.34 49.1 50.8 Female 50.66 50.9 49.2 AGE (groups) Total Jews only Arabs only
0 3.2 2.5 6.2 1-8 11.0 8.6 22.6 9-12 47.2 46.8 49.0 13-15 21.3 23.0 13.2 16+ 17.2 19.0 8.9 EMPLOYMENT STATUS Total Jews only Arabs only
Employed 48.5 51.2 33.8 Unemployed 5.6 5.6 5.2 Not in labor force 45.9 43.2 61.0 Unemployment rate* 10.3 9.8 13.3 *unemployment/ total labor force
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Study Description: Japan Study title:
ISSP National Identity
Fieldwork dates:
November 29 to December 7, 2003
Principal investigators:
ARAMAKI Hiroshi, KOBAYASHI Toshiyuki (NHK Broadcasting Culture Research Institute)
Sample type:
Two-stage stratified random sample of Japanese 16 or older First, urban and rural areas are divided into a number of groups (strata) based on similarities in local features and industrial structures. From each of those groups, streets and village-sections are again grouped together to form sampling units. From among such sampling units, 150 survey spots are selected at random. Then, from the Basic Resident Registers for these spots, 12 sample individuals aged 16 or over are selected according to a fixed random number.
Fieldwork institute:
Central Research Services, Inc.
Fieldwork methods:
Face-to-Face
Sample size: Issued 1.800, Achieved 1.102 Response rates: 1.800 A - Total issued (total sample) 103 B - Ineligible (address vacant, wrong ages,...)
18 respondents were not found 81 respondents moved somewhere else 4 respondents died
1.697 C - (= A - B) Total eligible (in scope sample) 1.102 D - Total ISSP questionnaires received 595 E - (= C - D; = F + G + H) Total non-response 272 F - Refusals (refusing to take part) 311 G - Non-contact (never contacted)
23 respondents had not lived at home for one year or more 46 respondents had not lived at home for 10~364 days 46 respondents had not lived at home for 9 days or less 48 respondents came home at midnight 134 respondents were not at home temporarily 14 respondents were ill in bed at home
12 H - Other non-response Language: Japanese Weighted: No Deviations from ISSP questionnaire:
SPISCO88 is not available.
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Classification of Occupations (country specific) 1. Agricultural, Forestry or Fishery workers
Those who are engaged mainly in agriculture, forestry or fishery and family workers
2. The Self-Employed Those who operate shops, restaurants, plants, etc. each with 9 employees or less, as well as their family workers
3. Sales or Service Workers Employees or sales persons at shops, restaurants, department shores, etc. Employees at barber shops, beauty parlors, places of amusement, etc. Waiters and waitresses, housekeepers, transport conductors, station clerks, travel attendants Sales persons, canvassers, bill collectors
4. Industrial Workers (Skilled Workers) Locomotive-engine and motor-vehicle drivers Those engaged in such work as manufacturing, repairs, assembly, processing, printing, spinning, sewing, tailoring, packing and bailing Carpenters, joiners, plasterers, cabinetmakers (Manual/Unskilled Workers) Laborers in mining, construction, manufacturing and transport Street vendors, deliverymen, garbage collectors
5. Clerical and Technical workers Clerical and technical employees of business firm, public organizations and governmental agencies Section and division chiefs of business firms and organizations with 49 employees or less Those engaged in transportation, such as pilots and navigators Education-related personnel such as college lecturers, teachers and nursery governesses Medical-related persons such as pharmacists and nurses
6. Business operators or Managers Directors and managers with the post of section chief or above of government offices Directors and managers of business firms and organizations with 50 employees or more Those who operate business firms and organizations with 10 to 49 employees Directors of a kindergarten, headmasters, chief teachers, college presidents Captains and crew chief of ocean-going vessels and aircraft and of craft serving major domestic routes
7. Specialists, Free-lancers, etc. Medical doctors, lawyers, judges, certified public accountants, college (assistant) professors Artists (painters, performers, etc.) and masters of various arts Those related to professional sports, religious activities, politics Self-Defense force personnel, policemen
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8. Housewives
Women engaged mainly in housework ×Women who live alone are put in category 10 (Jobless)
9. Students High school students Higher professional school students, junior college students, university students, graduate school students, special school students and miscellaneous school students ×Those who attend night high school or colleges, while working in the daytime, are put in categories 1 to 7
10. Jobless Those who are not now employed and those who live on pension, etc.
97. Refused 99. No Answer *Job-holders Total of those listed in the items numbered 1 to 7 *Employed persons Total of those mentioned in the items numbered 3, 4, 5 and 6
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National Population Characteristics: Japan
Gender Source; the latest National Population Census conducted in 2000
Total population 16 years and more Total 125.386.737 100,0% Total 105.635.374 100,0%Female 64.045.210 51,1% Female 54.408.084 51,5%Male 61.341.527 48,9%
Male 51.227.290 48,5%
Age Group Source; the latest National Population Census conducted in 2000
Total population 16 years and more Total 125.386.737 100,0% Total 105.635.374 100,0% 0- 4 5.849.380 4,7% 16-17 2.978.891 2,8% 5-14 12.469.928 9,9% 18-24 11.292.627 10,7%15-24 15.703.573 12,5% 25-34 18.199.941 17,2%25-34 18.199.941 14,5% 35-44 15.655.391 14,8%35-44 15.655.391 12,5% 45-54 19.202.009 18,2%45-54 19.202.009 15,3% 55-64 16.380.461 15,5%55-64 16.380.461 13,1% 65-74 12.959.760 12,3%65-74 12.959.760 10,3% 75+ 8.966.294 8,5%75+ 8.966.294 7,2%
Schooling Group (15 years old and more, includes 1.157.354 foreigners) Source; the latest National Population Census conducted in 2000 Total 108.224.783 100,0%Compulsory completed (9-11years) 23.807.854 22,0%High school completed (12,13years) 45.024.501 41,6%Junior college completed (14,15years) 11.923.625 11,0%University or graduate school completed (16years and more) 14.651.266 13,5%Type of last school completed not reported 3.813.474 3,5%Student 8.845.172 8,2%None 158.891 0,1%
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Marital Status (16 years old and more) Source; the latest National Population Census conducted in 2000 Total 105.635.374 100,0% Male 51.227.290 48,5%
-Never married 15.743.202 14,9% -Married 32.145.058 30,4%
-Never married 12.330.416 11,7% -Married 32.067.911 30,4%
-Widowed 7.197.680 6,8% -Divorced 2.398.609 2,3%
-Not reported 413.468 0,4%
Employment Status (16age and more, includes 1.146.481 foreigners) Source; the latest National Population Census conducted in 2000 Total 106.781.855 100,0% Employed 62.956.553 59,0%
-Mostly worked 53.316.101 49,9% -Worked besides doing housework 7.845.763 7,3% -Worked besides attending school 971.916 0,9%
-Absent from work 822.773 0,8% Unemployed 3.114.584 2,9% Not in Labor force 38.973.659 36,5%
-Did housework 19.803.786 18,5% -Attending school 6.510.747 6,1%
-Others 12.659.126 11,9% Not reported 1.737.059 1,6%
ZA-No. 3910 I S S P 2003 National Identity II Page I - 70
Study Description: Latvia Study title:
National Identity II
Fieldwork dates:
10.12.2003 – 29.12.2003
Principal investigators:
Aivars Tabuns, University of Latvia; Ilze Koroleva, Institute of Philosophy and Sociology, University of Latvia
Sample type:
Multistage stratified random sample
Fieldwork methods:
Face-to-face interview
Context of ISSP- questionnaire:
ISSP module was fielded together with ISSP module “Family and changing gender roles” (topic A) and it included the 3rd topic of questions (C) on medical care.
Sample size:
N = 1000
Response rates: Real numbers N = 1805 A - Total issued (total sample) N = 96 B - Ineligible (address vacant, wrong ages,...) N = 1709 C –(A - B) Total eligible N = 1000 D - Total ISSP questionnaires received N = 709 E - (=C – D; = F + G) Total non-response N = 285 F – Refusals N = 391 G - Non-contact (never contacted) N = 33 H - Other non-response Language:
Latvian, Russian
Weighted: No
Weighting Procedure:
Known Systematic Properties in Sample:
No
Deviations from ISSP Questionnaire:
No
Publications: No
ZA-No. 3910 I S S P 2003 National Identity II Page I - 71
ISSP Characteristics of National Population: Latvia Source No.1 Central Statistical Bureau of Latvia (2003): Statistical Yearbook of Latvia 2003. Source No.2 Central Statistical Bureau of Latvia (2002): Results of the 2000 Population Census in Latvia. Collection of statistical data. Source No.3 (2003.) Macroeconomics of Latvia in figures 2003. Statistical yearbook. Source No.4 Labour force survey results on the 4th quarter 2002. Central Statistical Bureau of Latvia (http://www.csb.lv) Year 2000 2001 2002 2003
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Study Description: New Zealand Study title:
National Identity II: New Zealand
Fieldwork dates:
11 September 2003 to 28 November 2003
Principal investigators:
Professor Philip Gendall, Department of Marketing, Massey University, Palmerston North, New Zealand
Sample type:
Systematic random sample from electoral rolls
Fieldwork institute:
Mail survey in four waves
Context of ISSP questionnaire:
Dedicated survey, with ISSP questions preceding non-ISSP questions and demographics.
Sample size:
2200
Response rates: 2200 A - Total issued (total sample) 313 B - Ineligible (address vacant, wrong ages,...) 1887 C –(A - B) Total eligible 1038 D - Total ISSP questionnaires received 849 E - (=C – D; = F + G) Total non-response 70 F – Refusals - G - Non-contact (never contacted) 779 H - Other non-response
Language:
English
Weighted: No
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Weighting Procedure: None
Known Systematic Properties in Sample:
Deviations from ISSP Questionnaire:
See notes
Publications: Sampling procedure: The sample was randomly selected from the 2002 New Zealand
electoral roll, which contains the names of all registered voters over the age of 18 years. The achieved sample is generally representative of the New Zealand population over 18 years of age, but people under 30 are underrepresented in the sample, while those over 30 are overrepresented, and the proportion of women is higher than in the population whereas the proportion of men is lower. The data have not been weighted to correct this biases. However, comparisons of unweighted survey results with results obtained from the sample weighted so that its age-sex distribution matched that of the New Zealand population over 18 (taken from the 2001 Census), showed only minor differences between estimates.
Survey administration procedure:
The questionnaire together with a covering letter was sent to the 2200 selected participants on 11 September 2003. A reminder letter and another questionnaire were mailed to participants whose questionnaire had not been returned by 25 September. A second reminder and another questionnaire were sent to those who had not returned their questionnaires by 16 October. A final reminder letter (without an accompanying questionnaire) was sent to all remaining non-respondents on 4 November. The survey was closed off on 28 November, 12 weeks after the initial mailing.
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National Population Characteristics: New Zealand 1. Age and Sex distribution of population
Source: NZ Department of Statistics, 2001 Census, National Summary.
2. Education of population is based on “highest school qualification”. The New Zealand Census does not contain any questions about years of schooling and this information is not available from any government source. Source: NZ Department of Statistics, 2001 Census, National Summary.
3. Employment rate of population Source: NZ Department of Statistics, 2001 Census, National Summary.
Age Group by Sex For the Census Usually Resident Population Count, 1991 1996 and 2001
Census Year
1991 1996 2001 Age Group Male Female Total Male Female Total Male Female Total
Source: NZ Department of Statistics, 2001 Census, National Summary.
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Study Description: Norway Study title:
National Identity
Fieldwork dates:
8 October – 5 December 2003
Principal investigators:
Knut Kalgraff Skjåk, NSD Oddbjørn Knutsen, Department of Political Science, University of Oslo Olav Aagedal, Diaconia College Centre Ole Gunnar Winsnes and Paal Ketil Botvar, Centre for Church Research
Sample type:
The sample was a simple random sample of individuals from the Central Register of Persons, aged 18-79 years.
Fieldwork institute:
TNS Gallup
Fieldwork methods:
The survey was conducted as a mail survey. The fieldwork included one reminder and two follow-ups with questionnaires
Sample size:
1469
Response rates: 2500 A - Total issued (total sample) 50 B - Ineligible (address vacant, wrong ages,...) 2450 C - (= A - B) Total eligible (in scope sample) 1469 D - Total ISSP questionnaires received 981 E - (= C - D; = F + G + H) Total non-response 82 F - Refusals (refusing to take part) 862 G - Non-contact (never contacted) 37 H - Other non-response Language:
Norwegian
Weighted: No
Weighting procedure:
Known systematic properties of the sample:
Net sample: Women in paid work slightly over-represented
Deviations from ISSP questionnaire:
Spouse's work status (SPWRKST) missing
Publications:
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National Population Characteristics: Norway Sex and age, %: Population
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Labour force status,%:13 Labour Force
Surveys 4. quarter 2003, 18-74 years14
Net sample Norwegian Survey
ISSP 2003, 18-74 years
Female Male Female Male Employed (>= 1 hr a week) 69.2 74.5 79.4 78.5 In school (pupil/student) 5.4 4.5 3.3 3.3 Retired 8.0 8.8 8.2 11.0 Social welfare, perm. disabled 8.8 7.0 5.8 4.7 Home working 4.8 0.1 2.0 0.3 Unemployed 2.7 3.6 1.2 1.5 Other 1.1 1.5 0.0 0.6 Missing 0.3 0.2 Education,%: Primary school 16.8 16.1 18.7 13.7 Secondary 52.7 54.7 41.7 46.3 University/college, >= 1 year 30.1 28.9 38.1 35.9 Missing 0.3 0.2 0.8 1.4 N 10 284 10 453 665 754
13 Deviation from WRKST variable in dataset, due to different recodings 14 Source: Statistics Norway
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Study Description: Philippines Study title:
ISSP 2003 Module on National Identity in the Philippines
Fieldwork dates:
March 10 – 25, 2003
Fieldwork:
Location: The ISSP 2003 Module on Identity survey covers the entire Philippines and had four major study areas: National Capital Region (NCR), Balance Luzon (outside NCR), Visayas and Mindanao.
Timetable:
National Capital Region - March 10 - 25, 2003 Balance Luzon - March 14 - 25, 2003 Visayas - March 15 - 23, 2003 Mindanao - March 15 - 24, 2003
Respondents: The gathered data through on voting-age through face-to-face interviews
of voting-age adults (18 years old and above). It asked a host of questions about political, social and economic issues, some undertaken as regular indicators monitored over time and others reflective of current concerns as well as specific personal information. It also obtained information and background characteristics about the household, the household head and family members.
Sampling Method:
Sampling Sizes and Error Margins. An indicator of data quality used is the standard error of the estimate, on which the margin for sampling error is based. As survey statistics are mostly proportions, the key measure of data precision is the standard error of a proportion taken from a sample. It is computed as follows:
Where Z, at 95% confidence level is 1.96; p is the sample proportion estimate and n is the sample size. The overall sample size of 1,200 voting-age adults for each of the questionnaires gives a maximum error margin of ±2.83% at the 95% confidence level, assuming a simple random sampling design. The sampling error is at its highest when the true proportion being estimated is close to 50%. The following approximate 95%-confidence margins for sampling error should be made when aggregating data at various levels:
Sample Size Error margin Philippines 1200 ±3% National Capital Region 300 ±6% Balance Luzon 300 ±6%
± Z * p(1-p)/n
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Visayas 300 ±6% Mindanao 300 ±6%
However, somewhat higher error margins should be expected since multi-stage cluster sampling was used; this design-effect is not readily measurable through established statistical software. Sampling scheme. The Philippines were divided into four study areas: National Capital Region(NCR), Balance Luzon, Visayas, and Mindanao. The sample size for each of the four study areas was 300 voting-age adults. Multi-stage probability sampling was used in the selection of sample spots. The allocation of sample units in each stage was as follows:
Sample
Prov. Sample Mun.
Spots
Probability Respondents
National Capital Region - 17 60 300 Balance. Luzon 10 15 60 300
Visayas 5 15 60 300 Mindanao 5 15 60 300
20 62 240 1200 For the National Capital Region
Stage 1. Selection of sample Precincts For NCR’s first stage, 60 precincts were distributed among the 17 NCR cities and municipalities in such a way that each city/municipality was assigned a number of precincts that was roughly proportional to its population size. An additional provision was that each municipality must receive at least one precinct. Precincts were then selected at random from within each municipality with probability proportional to population size. Stage 2. Selection of Sample Households In each sample precinct, interval sampling was used to draw 5 sample households: A starting street corner in the precinct map was drawn at random. The first sample household was selected randomly from the households nearest to the starting street corner. Subsequent sample households were chosen using a fixed interval of 6 households in between the sample ones; i.e. every 7th household was sampled. Stage 3. Selection of Sample Adult For the third stage, in each selected household, a respondent was randomly chosen among the household members who were 18 years of age and older, using a probability selection table. In selecting the probability respondent in a household, only male family members were
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pre-listed in the probability selection table of odd-numbered questionnaires; only female family members were pre-listed for even-numbered questionnaires; In case where there was no qualified probability respondent of a given gender, the interval sampling of households would continue until five sample respondents were identified. For the rest of the Philippines Stage 1. Allocation and selection of sample provinces Balance Luzon was further divided into 5 regions: CAR + Region II, Region I, Region III, Region IV and Region V; Visayas into 3 regions: Region VI, Region VII and Region VIII; and Mindanao into 5 regions: Region IX, Region X + CARAGA, Region XI, Region XII and ARMM. Using probability proportional to population size (PPS) of the region, the allocation of 10 provinces in Luzon, and 5 each in Visayas and Mindanao were as follows:
Luzon Visayas Mindanao CAR/REG II 1 Region VI 2 Region IX 1 Region I 1 Region VII 1 RegX+CARAGA 1 Region III 2 Region VIII 1 Region XI 1 Region IV 3 Region XII 1 Region V 1 ARMM 1 Non-quota 2 1 0 Total 10 5 5
The non-quota provinces were selected without replacement using probability proportional to their remainders. The remainders are the fractions derived when the proportion of the regions (based on their respective study area) are multiplied by 10 for Luzon, and 5 each for Visayas and Mindanao. For instance, if 1.45 is obtained for Region I, then 1 province is assigned to this region and the remaining fraction of 0.45 is included for the allocation of the non-quota province. Given the quota for each region, sample provinces were then selected by PPS, without replacement. An additional provision is that each region must receive at least one province. Stage 2. Allocation and selection of sample municipalities Within each study area, 15 municipalities were allocated among the sample provinces. 15 were multiplied by the proportion of the provinces. The resulting integers became the number of municipalities in that province. If there were remaining municipalities to be allocated, they were distributed using probability proportional to the remainders. Sample municipalities were then selected from within each sample province with probability proportional to population size, without
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replacement. An additional provision was that each province must receive at least one municipality. Stage 3. Allocation and selection of sample spots Once the sample municipalities were selected, 60 spots for each of the major areas were allocated among the sample municipalities. Using the quota set for spots in each region, the spots were distributed in such a way that each municipality was assigned a number of spots roughly proportional to its population size.
Luzon Visayas Mindanao CAR/REG II 8 Region VI 25 Region IX 10 Region I 8 Region VII 21 RegX+CARAGA 16 Region III 14 Region VIII 14 Region XI 17 Region IV 21 Region XII 9 Region V 9 ARMM 8 Total 60 60 60
If the chosen sample municipality/city is 100% urban based on the 1990 (latest) NSO classification, then sample precincts were systematically drawn from this city/municipality. Otherwise, sample barangays within each sample municipality were selected using simple random sampling without replacement. If based on the National Statistics Office categorization, the chosen sample municipality/city was 100% urban in 1990 (latest), then sample precincts were systematically drawn from this municipality/city. Otherwise, sample barangays within each sample municipality were selected with equal probabilities. In the effort to update the urban-rural classification of barangays, the survey adopted a classification scheme slightly different from the official NSO definition. The interviewers were instructed to ascertain whether the barangay has the following: A. Street patterns, i.e. network of streets in either parallel or right angle orientation B. At least 6 establishments (commercial, manufacturing, recreational and/or personal services) C. At least three of the following:
1. Town/Barangay Hall/Church/Chapel 2. Public Plaza/Park/Cemetery 3. Market Place 4. Public building like school, hospital, health centre or library
If the barangay has all categories as A, B or C, then the barangay were classified as urban. Otherwise, the barangay was classified as rural.
Stage 4. Selection of Sample Households
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For the fourth stage, within each sample spot, five households were established by systematic sampling. In sample (urban) precincts, a random corner was identified; a random start generated; and the interval was seven. In ascertained urban barangays (with no precinct maps), the designated starting point was the same as in rural barangays – it was a school, the barangay captain’s house, a church/chapel or a barangay/municipal hall. The sampling interval for urban barangays was seven, while for rural barangays it was two. Stage 5. Selection of Sample Respondents For the fifth and final stage, as discussed earlier, a respondent was randomly chosen from among the voting-age adults in each selected household using a probability respondent selection table.
Research Methodology:
a. Preparation
1. Questionnaire The definitive language-version of the questionnaire, Tagalog, was translated into English, Cebuano, Ilonggo, Ilocano and Bicolano by language experts. Then the language translation was translated back to Tagalog by another set of experts to make sure that the messages were conveyed accurately.
2. Pre-Testing and finalizing the questionnaire
SWS pre-tested the questionnaire on 10 voting-age adults from different socio-economic classes in order to:
• Determine the time length of the interview • Improve the wording of the questions, if necessary • Eliminate unnecessary questions or add new items, as the
case may be • Test question sequence and identify biases • Correct and improve translation • Change open-ended questions into multiple choice
questions • Find out which items are conceptually vague • Check accuracy and adequacy of the questionnaire
instructions • Determine whether the focus of the question is clear • Identify interviewer’s recording difficulties
3. Training (a) Training was conducted in 3 central locations: Manila, Iloilo city
and Cebu City. The interviewers needed to cover Luzon were trained in Manila. They implemented the Tagalog and Ilocano versions of the questionnaire. Those trained in Iloilo covered Ilonggo-speaking regions, while those in Cebu City covered all of Cebuano-speaking areas (Central & Eastern Visayas and Mindanao).
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(b) Training time – The minimum training time for group supervisors and interviewers was 3 days prior to field implementation.
(c) Training Activities – These mainly consisted of: One or two day’s office training to learn the basics of the project. Mock interviews with co-workers to get accustomed to the flow of interviewing and questionnaire format. Practice interviews with a supervisor until the interviewer could be left on her own.
(d) Evaluation of interviewer’s work – The interviewer was left to interview on her own only after she has conducted 3 successive interviews without committing any error in interviewing and recording.
b. Field Work
1. Supervision Supervisors reporting to the field manager monitored the study full-time. They observed interviewers, (at least 10% of total interviewers were observed by supervisors), followed-up and did surprise checks on the field interviewers. They also ensured that field logistics were received promptly and administered properly.
2. Spot-checking
Spot-checking was done at various stages of fieldwork. The first one took place after about 30% of interviews were completed. The second spot-checking was conducted after 60% completion and the last one, immediately after 90% completion of interviewing. During spot-checking, at least 20% of the unsupervised interviews were reinterviewed/backchecked.
c. Number of calls and Substitution
A respondent not contacted during the first attempt was visited for a second time. If the respondent remained unavailable, a substitute who possessed the same qualities (in terms of gender, age bracket, and socio-economic class) as the original respondent was interviewed. The substitute respondent was taken from another household beyond the covered intervals in the sample precinct/barangay.
d. Field Editing
(a). After each interview, the interviewer was asked to go over her own work and check for consistency.
(b). All accomplished interview sheets were submitted to the assigned group supervisor who, in turn, edited every interview.
(c). Data Processing (1) An office editor conducted a final consistency check on all
interviews prior to coding. (2) Interview sheets were edited/checked twice by office
editors before the information was encoded into diskettes. (3) A data entry computer program verified and checked the
consistency of the encoded data before data tables were generated.
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Weighting procedure: To yield representative figures at the national level, census-based
population weights were applied to the survey data. The weight projection was computed by dividing the projected population in the area by the sample size of the same area. Appropriate projected factors were applied so that original population proportion were reflected in the data tables using this formula: Population Projection factors = ----------------------- (Weight) No. of Interview For questions answered by the sample voting-age adults, the following projection factors were used:
The SPSS version of the data file is already weighted according to the above projection factors. As the data are weighted, the total number of cases that appear is 46,474. This figure is in thousands, i.e., 46,473,919 persons representing NSO’s projected number of adults (18 years old and above) for year 2002 based on the 1995 Census. Researchers who are defining data using the ASCII files should apply these projection factors.
ZA-No. 3910 I S S P 2003 National Identity II Page I - 89
ISSP Characteristics of National Population: Philippines I. Gender and Age DEMOGRAPHY (Source: Census of Population and Housing)
DATA REFERENCE PERIOD Both Sexes Male Female INDICATOR
Year Number Percent Number Percent Number Percent Total
Population 76,504,077 100.00 38,524,267 100.00 37,979,810 100.00
0 – 4 years 9,669,502 12.64 4,951,932 12.85 4,717,570 12.42 5 – 9 years 9,694,781 12.67 4,962,013 12.88 4,732,768 12,46
10 – 14 years 8,949,614 11.70 4,541,197 11.79 4,408,417 11.61 15 – 19 years 8,017,298 10.48 4,017,830 10.43 3,999,468 10.53 20 – 24 years 7,069,403 9.24 3,522,518 9.14 3,546,885 9.34 25 – 29 years 6,071,089 7.94 3,053,616 7.93 3,017,473 7.94 30 – 34 years 5,546,294 7.25 2,804,522 7.28 2,741,772 7.22 35 – 39 years 4,901,023 6.41 2,496,821 6.48 2,404,202 6.33 40 – 44 years 4,163,494 5.44 2,120,314 5.50 2,043,180 5.38 45 – 49 years 3,330,054 4.35 1,696,712 4.40 1,633,342 4.30 50 – 54 years 2,622,316 3.43 1,318,632 3.42 1,303,684 3.43 55 – 59 years 1,903,649 2.49 943,133 2.45 960,516 2.53 60 – 64 years 1,633,150 2.13 786,137 2.04 847,013 2.23 65 – 69 years 1,138,843 1.49 533,469 1.38 605,374 1.59 70 – 74 years 797,970 1.04 361,614 0.94 436,356 1.15 75 – 79 years 505,356 0.66 218,622 0.57 286,734 0.75 80 and over
May 2000
490,241 0.64 195,185 0.51 295,056 0.78 Source: http://www.census.gov.ph/data/quickstat/qsgender.html (as of March 31, 2003)
ZA-No. 3910 I S S P 2003 National Identity II Page I - 90
II. Employment Status LABOR AND EMPLOYMENT (Source: Labour Force Survey)
DATA REFERENCE PERIOD Both Sexes Male Female INDICATOR
Year Number Percent Number Percent Number Percent
Household Population 15 Years Old and Over 50,841 100.00 25,387 100.00 25,454 100.00
Labour Force Employed – Employed persons include all those who, during the reference period are 15 years and
over as of their last birthday are reported
either at work or with a job but not at work.
(In thousands)
30,252 59.50 18,439 72.63 11,812 46.41
Unemployed – it includes all those who, during the reference period are 15 years old and over as of their last birthday who
have no job/business and actively looking for work.
(In thousands)
3,423 6.73 2,076 8.18 1,347 5.29
Not in Labour Force (In thousands)
Oct 2002
17,166 33.76 4,872 19.19 12,295 48.30
Source: http://www.census.gov.ph/data/quickstat/qsgender.html (as of March 31, 2003) III. Educational Attainment
Educational Attainment* Number Percent Household Population 5 Years Old or Over 59,071,714 100.00 No Grade Completed 4,394,719 7.44 Pre-school 1,931,882 3.27 Elementary 25,620,407 43.37
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Study Description: Poland Study title:
Polish General Social Survey/ISSP, 2003
Fieldwork dates:
January, 2005
Principal investigators:
Bogdan Cichomski, Institute for Social Studies, Warsaw University (ISS UW) and Warsaw School of Social Psychology (SWPS)
Sample type:
Multi-stage area probability sample. The sample was drawn from The Common Electronic Population Evidence System - PESEL (a governmental agency). Sampling procedure consists of the three stages: 1. In each of the 16 voivodships in Poland places of living were divided up into three categories 1) capital of the region 2) other cities 3) villages In voivodships with the higher number of people the class “cities” was additionally divided up into 2-6 smaller categories. In Mazowieckie and Slaskie voivodships there was also a division into two strata in the “villages” class. As an effect there was 65 strata taking into consideration the administrative division of the country and type and size of the place of living. Assumed sample size was divided up into the strata taking into consideration different completion rates in classes of places of living in voivodships. It means that from classes of places of living where the completion rates are known to be lower then in other there was an over-representative sub-sample sampled. 2. Then in CBOS (Public Opinion Research Center) cities and villages communes were sampled. After that in PESEL data-base some small areas of the cities and villages communes were sampled. Persons to be interviewed were chosen also from the PESEL data-base from the previously selected areas. In cities, small areas covered some streets and it’s neighborhood and in villages the areas of one village and/or some of it’s streets. 3. In each stratum at least two small areas were sampled. From each small area six adults living in different locations and in different households were sampled.
Fieldwork institute:
Public Opinion Research Center (CBOS), Warsaw
ZA-No. 3910 I S S P 2003 National Identity II Page I - 92
Fieldwork methods:
Self-administered supplement completed after the PGSS and face-to-face interview and after ISSP 2004 Citizenship which was also face-to-face (self-administered/interviewer attending =76,6%) + (face-to-face interview = 23,3%) and in one case the way stays unknown (that is 0.1%) [see MODE-variable in the Polish data file]
Sample size:
1277 = number of respondents achieved on ISSP 2003
Response rates: 2106 A - Total issued (total sample) 204 B - Ineligible (address vacant, wrong ages,...) 1902 C - (= A - B) Total eligible (in scope sample) 1277 D - Total ISSP questionnaires received 625 E - (= C - D; = F + G + H) Total non-response 218 F - Refusals (refusing to take part) 325 G - Non-contact (never contacted) 82 H - Other non-response Language:
Polish
Weighted: YES
Weighting procedure: Weighting procedure assumes that after weighting the sample size is equal to the number of completed interviews.
weight: W1 is = ∑=
n
iis
is
r
r
1
*n
where: i – record ID , i=1,2,.......n n – the number of completed questionnaires N – the number of inhabitants who are 18 or older. r is - completion rate in the class of place of living s where the i interview was conducted. LW s r is ------------- LZR s LW s - sample size drawn in the class of the place of living s, proportional allocation assumed LZR s - number of completed interviews in the class of the place of living s s – class of the place of living (s=1,2,….,6) s=1 - village s=2 – cities up to 19999 inhabitants
ZA-No. 3910 I S S P 2003 National Identity II Page I - 93
s=3 – cities 20000-49999 inhabitants s=4 – cities 50000-99999 inhabitants s=5 – cities 100000-499999 inhabitants s=6 – cities 500000 and more inhabitants After stratification „ex post” for k-categories weights were calculated taking into consideration the structure of polled population on the basis of statistical data coming from the Central Statistical Office (GUS) in division for villages and cities, sex and age category.
W2ki=
XX
W
W
k
kk
isi
n
isi
n
k
k
k
∑
∑
∑
⎛
⎝
⎜⎜⎜
⎞
⎠
⎟⎟⎟
⎛
⎝
⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟
=
=
*
*
100
1
11001
1
n nkk
=⎛⎝⎜
⎞⎠⎟∑
n W isi
n
=⎛⎝⎜
⎞⎠⎟
=∑ 1
1
where: X k - number of people in k-category according to GUS data X= X k
k∑
i k - questionnaires by record ID which belong to the k-category n k - total number of questionnaires which are rated to k-category WX isk = W1 is * W2
ki
WX isk weight for i-respondent belonging to the k-category and drawn in s-class of the place of living.
Known systematic properties of the sample:
No biases or other deviation of the sample (after weighting) (see: National Population Characteristics, 18 years or older; POLAND). You can observe in the National Population Characteristics for Poland some deviations in two highest age categories which is caused by different age grouping for men and women and used for weight calculation.
Deviations from ISSP not asked: V58 (question 14), and ETNIC (background question).
ZA-No. 3910 I S S P 2003 National Identity II Page I - 94
questionnaire: Publications: None
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ISSP Characteristics of National Population: Poland Central
Statistical Office (GUS) December 2002
Polish General Social Survey January 2005 (unweighted)
Polish General Social Survey January 2005 (weighted)
SEX (population 18+ years old)
Male 47.6 47.5 47.6 Female 52.4 52.5 52.4
N(100%) 29554846 1277 1277 AGE GROUPS (population 18+ years old)
N(100%) 29554846 1277 1277 EMPLOYMENT STATUS (population 18+ years old)
Employed ND 43.7 44.7 Unemployed ND 13.3 13.8 Not in labor force ND 43.0 41.5
N(100%) 29554846 1277 1277
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Study Description: Portugal Study title:
Portuguese Social Attitudes 2003/2004
Fieldwork dates:
April to September 2004
Principal investigators:
Manuel Villaverde Cabral, Jorge Vala, Alice Ramos, all at Instituto de Ciências Sociais da Universidade de Lisboa.
Sample type:
Stratified random probability. The sample is designed to be representative of adults aged 18 or over living in private accommodation in Portugal. The sample method involved a multi-stage design: stratification by region and habitat; Selection of sampling units (100); selection or streets: selection of addresses by random root; selection of individuals by the last birthday method.
Stratification factors used:
- NUTES (North, Center, Lisbon and Tagus Valley, Alentejo and Algarve)
- HABITAT (less than 2.000 hab.; 2 - 10 thousand hab; 10 - 30 thousand hab; 30 - 100 thousand hab; more than 100 thousand hab) In order to get a representative sample, near 50% of the interviews were made in habitats with less than 2000hab. Some problems came up, such us, a level of non-responses and refusals higher than expected.
Fieldwork institute:
TNS-Euroteste
Fieldwork methods:
face-to-face interview with visuals
Context of ISSP Questionnaire:
Atitudes Sociais dos Portugueses (Portuguese Social Attitudes)
Response rates: 2907 A - Total issued (total sample)
145 B - Ineligible (address vacant, wrong ages,...)
2762 C - (= A - B) Total eligible (in scope sample)
1602 D - Total ISSP questionnaires received 1160 E - (= C - D; = F + G + H) Total non-response 955 F - Refusals (refusing to take part)
200 G - Non-contact (never contacted)
5 H - Other non-response Language:
Slovak and Hungarian languages
Weighted: Yes
Sampling procedure: Sampling procedure:
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Table 1 - Resident population with 18 or more years old excluding localities with less than 10 dwelings
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Weighting procedure: Data were weighted in order to reproduce the population distribution according to the following variables: NUTSII, size of locality, age groups, gender and educational level. Bellow we present the distribution by gender, age and educational level for the population and for the sample (not weighted and weighted).
RESIDENT POPULATION, BY AGE GROUP, LEVEL OF ACADEMIC
QUALIFICATIONS AND SEX (TOTAL)
Geographic area, Level of Academic Qualification Sex 18-24 25-34 35-44 45-54 55-64 65-74 75 or
more N2:Total MF 1027112 1500736 1427556 1274953 1079933 953558 675038 M 519869 750821 701148 612557 506495 424828 257853 F 507243 749915 726408 662396 573438 528730 417185 Without academic qualifications MF 7078 17114 22835 32067 130782 267081 283953 M 3746 9250 11753 13573 42460 86584 80225 F 3332 7864 11082 18494 88322 180497 203728 Primary (incomplete) MF 17508 45263 66479 96268 174709 191052 123412 M 10524 25934 34625 34530 46988 68135 46277 F 6984 19329 31854 61738 127721 122917 77135 Primary (complete) MF 36174 179968 420423 637994 488073 325008 179446 M 20131 89536 201299 308328 259768 180913 87846 F 16043 90432 219124 329666 228305 144095 91600 Secondary (incomplete) MF 560138 690187 548793 264871 156800 89562 48199 M 316242 379321 284243 137170 85238 47015 21802 F 243896 310866 264550 127701 71562 42547 26397 Secondary (complete) MF 110940 219318 164742 93341 46747 24710 14400 M 49450 100957 79438 48522 27479 15149 6996 F 61490 118361 85304 44819 19268 9561 7404 Higher Degree (incomplete) MF 256650 113101 49611 37796 28387 25276 11670 M 107822 40891 26197 21077 15996 11334 5862 F 148828 72210 23414 16719 12391 13942 5808 Higer Degree (complete) MF 38624 235785 154673 112616 54435 26557 13958 M 11954 87080 63593 53457 28566 15698 8845 F 26670 148705 91080 59159 25869 10859 5113 Source: National Institute of Statistics (NIE), Population and Residence General Census - 2001 (Final Results)
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Sex_age_educ.level_sample ISSP PT (not weighted)
Age groups Country specific education: Portugal 18-24 25-34 35-44 45-54 55-64 65-74 75 or more
Group Total
'No formal qualification'
Male 1 1 4 12 8 26
Female 1 1 4 32 37 75 Group Total 2 1 1 8 44 45 101Basic -level 1 Male 3 17 36 44 66 57 40 263 Female 3 18 60 102 92 98 57 430 Group Total 6 35 96 146 158 155 97 693Basic-levels 2 and 3 Male 17 22 40 20 19 6 3 127 Female 9 42 49 27 11 12 6 156 Group Total 26 64 89 47 30 18 9 283Secondary incomplete
Male 16 34 26 6 7 5 1 95
Female 21 33 17 17 4 4 4 100 Group Total 37 67 43 23 11 9 5 195Complete secondary Male 5 14 12 4 2 37 Female 8 17 11 6 4 2 48 Group Total 13 31 23 10 6 2 85'University incomplete
Male 11 13 8 7 2 2 1 44
Female 21 17 6 4 4 2 1 55 Group Total 32 30 14 11 6 4 2 99Complete university Male 1 20 20 15 7 2 65 Female 5 37 16 5 7 2 3 75 Group Total 6 57 36 20 14 4 3 140Table Total 120 289 304 259 233 236 161 1602
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Sex_age_educ.level_sample ISSP PT (weighted)
Age groups Country specific education: Portugal 18-24 25-34 35-44 45-54 55-64 65-74 75 or more
Group Total
'No formal qualification'
Male 4 4 18 44 18 88
Female 9 2 15 61 62 150 Group Total 13 2 4 33 105 80 237Basic -level 1 Male 7 24 40 61 53 35 33 253 Female 6 19 53 89 80 38 21 306 Group Total 13 43 93 150 133 73 54 559Basic-levels 2 and 3 Male 30 25 38 20 22 2 1 137 Female 13 37 36 18 6 4 1 115 Group Total 43 62 74 38 28 6 2 252Secondary incomplete
Male 26 31 24 10 4 2 0 96
Female 34 21 16 9 3 1 1 87 Group Total 60 53 40 19 7 3 2 183Complete secondary Male 18 28 17 9 1 73 Female 13 19 16 5 4 1 59 Group Total 31 47 33 14 5 1 132'University incomplete
Male 22 13 8 7 1 1 0 52
Female 30 14 5 5 2 1 1 58 Group Total 51 27 13 12 3 2 2 110Complete university Male 2 26 14 13 3 1 60 Female 6 31 17 6 3 0 2 66 Group Total 8 57 31 20 6 1 2 126Table Total 207 303 287 257 216 191 141 1602
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National Population Characteristics: Portugal Sex and age Note: The data concerns only the population resident in the Mainland (excluding Azores and Madeira) Total M F n % n n 18-24 years 1027112 12,9 519869 507243 25-34 years 1500736 18,9 750821 749915 35-44 years 1427556 18 701148 726408 45-54 years 1274953 16,1 612557 662396 55-64 years 1079933 13,6 506495 573438 65-74 years 953558 12 424828 528730 75 and more 675038 8,5 257853 417185 Source: Instituto Nacional de Estatística, Censos 2001 Education Note: The data concerns all the population (including Azores and Madeira) Total (>18 years) % None 760910 9,6Basic Primary incomplete 714691 9Basic Primary complete1 2267086 28,6Secondary incomplete2 2358550 29,7Secondary complete3 674198 8,5University incomplete 522491 6,6University complete 636648 8Source: Instituto Nacional de Estatística, Censos 2001 1 4 years 2 10-11 years 3 12 years Note: The data concerns only the population resident in the Mainland (excluding Azores and Madeira)
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Study Description: Russia Study title: ‘ISSP-2003 Module “National Identity II” Fieldwork dates: July,01.-15,2003 Principal investigators:
L.Khakhulina, Levada-Center
Sample type:
Description of the sampling procedure Stratification procedure. Nationwide sample (N=2100) was divided among: a) 10 large economico-geographical macro regions proportionate to
the size of the local population aged 16+ of each macro region b) 5 strata of rural districts and urban settlements* in each of 10
macro regions proportionate to the size of the local population aged 16+ of each stratum.
* 1)less than 10,000; (rural districts & small urban settlements) 2)from 10,000 – to100,000; 3)from 100,000 – to 500,000; 4)from 500,000 – to 750,000; 5)over 750,000 inhabitants Selection primary sampling units (PSUs). All cities over 500, 000 inhabitants were included in the sample as self-representative units. Urban and rural settlements were considered as primary sample units (PSUs). In each stratum (except strata of cities over 500,000 and 2 capital cities) the number of PSUs was calculated on the limitation of 15 interviews per PSU and the PSUs as well were selected with the probability to its sizes (the number of its inhabitants). The total numbers of interviews accounted for a stratum was distributed approximately equally among selected PSUs. Totally 101 PSUs were selected. Selection of secondary sampling points (SSUs). Electoral districts were used as secondary sampling points In the cities over 500,000 inhabitants the number of surveyed SSUs was defined by condition of 7 interviews per SSU.. In the rest of selected PSU two sampling points were randomly selected from the list of all electoral districts of this PSU. Totally 240 sample points were selected.
Selection of households. The households were selected by a random route method. If a household or a respondent refused to participate in the survey or not been achieved for 4 visits an interviewer should visit the next address from the rout in the selected districts. Selection of respondents. Within a household a member with the nearest birthday was selected for interviewing. In order to reach a selected respondent an interviewer visited each address up to 3 times in different days of a week and at different time of a day.
The following categories were excluded from the gross sample: a)persons doing their military service by draft (about 1%) b) persons under imprisonment (about 0,8%) c)population of the areas under the war conflict in North Caucasus (1,9%) d) population of remote or difficult to access regions of Far North (0,9) e) rural localities with less then 50 inhabitants (0,8%)
Fieldwork institute: Levada-Center
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Fieldwork methods: Self-completion Sample size: N=2400( 2100 + 300 extra sample in Moscow) Response rates: 5902 A - Total issued (total sample) 182 B - Ineligible (address vacant, wrong ages,...) 5720 C - (= A - B) Total eligible (in scope sample) 2408 D - Total ISSP questionnaires received 3367 E - (= C - D; = F + G + H) Total non-response 1653 F - Refusals (refusing to take part) 1328 G - Non-contact (never contacted) 331 H - Other non-response Language: Russian Weighted: yes, a weighting factor exists in the data set Weighting procedure: exact description of the weighting procedure / algorithm
a) Main principles of weighting procedure The total expected number N of respondents for a certain region being treated equal N = N0 * P , where N0 denotes the size of total sample, P - the share of the region population in the entire population. As a result of correction, every respondent X[k] has the definite weight W[k], within the limits 0 < W[k] < ~10 , so that the following conditions were valid : 1)the value of sum(W[k]) for the region concerned was equal to N 2)for every controlled group G[i] the value Q[i] being equal to Q[i] = sum( W[k] | X[k].belong to G[i] ) / N, was closed to a proportion P[i] of group G[i] in the region population i.e. Q[i] ~ P[i], i=1,2,...,16. The value of J being equal to J = sum( (Q[i]-P[i])**2 ) + (sum(W[k])/N - 1)**2 , was used as the criterion for minimization on the weights` sets variety. Quality of corrections male fem <25 <40 <55 >54 H S P 1 2 3 4 5 6 7 8 9* Survey: 3785 6214 1590 2490 2549 3370 2553 5371 2075 Weighted : 4578 5421 1719 2709 2761 2810 1541 5444 3014 State Statistics : 4579 5420 1718 2710 2762 2809 1542 5444 3013 * 1-2 –sex 3-6 –age 7-9 – education (higher, secondary, primary) Weights coefficients sum is equal 2107 . Mean values: ZERO 0 -.1 .1- . .2 .2 - .5 .5- 1 1-2 2- 5 5-10 >10 Number: 0 0 291 1021 489 330 266 12 0
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ISSP Characteristics of National Population (Russia) Source: the data of the State Statistics Committee of Russia, 2000 Source no. 1 Source no. 2 Source no. 3 Source no. 4 SEX
Male 45,57 Female 54,43 AGE (groups)
18-24 13,53 25-39 28,12 49-54 28,90 55+ 29,45
YEARS OF SCHOOLING (groups)*
Higher 16,28 Secondary 55,80 Incomplete secondary 27,92 EMPLOYMENT STATUS (1.02.04)**
Employed
58.6
Unemployed
9.5
Not in labor force
31.9
Source – Census , 2002 , Russian State Statistical Committee (Rosstat). *)Data of years of education are not available. **) Social and economic situation in Russia. Rosstat. 2004.
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Study Description: Slovakian Republic Study title:
ISSP Identity 2003
Fieldwork dates:
April 20 – May 17, 2004
Principal investigators:
Comenius university in Bratislava, Department of Sociology (Slovakia)
Sample type:
Three stage stratified probability sampling: 1. Stratified probability sampling of election districts 2. Probability sampling of households in selected districts 3. Sampling of household members based on nearest birth-date
Fieldwork institute:
ASA, s.r.o, Palisády 26, 811 06 Bratislava, Slovakia
Fieldwork methods:
Interview face to face
Sample size:
1152
Response rates: 1475 A - Total issued (total sample)
16 B - Ineligible (address vacant, wrong ages,...)
1459 C - (= A - B) Total eligible (in scope sample)
1152 D - Total ISSP questionnaires received 307 E - (= C - D; = F + G + H) Total non-response 307 F - Refusals (refusing to take part)
G - Non-contact (never contacted)
H - Other non-response Language:
Slovak and Hungarian languages
Weighted: Yes
Weighting procedure: Total weight is constructed from stratification weights based on sex, education and age.
The weights were derived from data of Slovak Statistical Office (Census 2001)
Known systematic properties of the sample:
Due to response differences the basic socio-demographic characteristics – sex and level of education – are significantly different from the known population characteristics. The problem was corrected by the post-stratification weights (see above).
Deviations from ISSP questionnaire:
22 questions were added in the questionnaire – answers are not included in the data-file sent to the archive.
Publications:
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Source: Population and Housing Census 2001, Statistical Office of the Slovak Republic
YEARS OF SCHOOLING (groups)
No formal qualification
0,8 0,4 0,4
Lowest formal qualification
7,7 21,6 26,4
Above lowest qualification
29,4 29,4 30,2
Higher secondary completed
31,7 26,8 32,0
Above higher secondary level
7,8 5,8 1,0
University degree completed
17,0 11,1 9,8
Source: Population and Housing Census 2001, Statistical Office of the Slovak Republic
EMPLOYMENT STATUS
Employed
60,1 52,2 66,8
Unemployed
9,2 10,2 13,9
Not in labor force
30,5 37,4 19,3
Source: Population and Housing Census 2001, Statistical Office of the Slovak Republic
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Study Description: Slovenia Study title:
Slovene Public Opinion Survey 2003/2: International Survey on Family and National Identity and Attitudes on Local Democracy
Fieldwork dates:
February 2003 – March 2003
Principal investigators:
Niko Toš, Public Opinion and Mass Communication Research Centre (CJMMK), Ljubljana.
Sample type:
Universe: The adult residents of SR Slovenia, older then 18 years, living on permanent address. Excluded: Institutionalised people. Central Register of Population (a list of names and addresses constantly undated by public administration) is employed as a sampling frame. Sampling procedure: The sample is two-stage stratified random sample from Central Register of Population , where every population unit has equal probability of selection. First stage PSU selection is made by probability proportional to size of CEA (Clusters of Enumeration Areas). CEA are stratified according to 12 regions*6 type of settlement. At second stage systematic random selection inside CEA brings fixed numbers of persons with name and address. Spilit-halves samples were used for parallel SJM surveys.
Fieldwork methods: Personal interviews with trained interviewers
Fieldwork institute: Public Opinion and Mass Communication Research Centre (CJMMK), Ljubljana
Context of ISSP- questionnaire:
ISSP 2002 questionnaire lie at the beginning of the SJM 2003/2 questionnaire. Then follows the ISSP 2003.
Sample size: 1093 Response rates: 1612 A - Total issued 0 B – Not eligible 1612 C - Total eligible 1093 D - Total SJM2003/2 questionnaires received 519 E – Non-response 204 F – Refusals 254 G – Non-contact 61 H - Other non-response Language:
Slovenian
Weighted:
No
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ISSP Characteristics of National Population: Slovenia
Source no.1 Source no.2 Source no.3 Source no.4 Census 2002, Population 15+ years at years of schooling
Labour Force Survey, 2002 2nd quarter
SEX Male 48,8% Female 51,2% AGE (groups) 0 – 14 15,3% 15 – 29 21,5% 30 – 44 22,7% 45 – 64 25,8% 65+ 14,7% YEARS OF SCHOOLING (groups) 0-7 years of elementary school
7%
Elementary school 26% Completed vocational school
27%
Completed middle school
27%
University degree 12% EMPLOYMENT STATUS (in 1000 of persons) Employed 922 Unemployed 58 Not in labour force 707
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Study Description: South-Africa Study title:
‘official’ title of the study/survey South African Social Attitude Survey
Fieldwork dates:
start and end date of field-work August-September 2003
Principal investigators:
Dr Stephen Rule Human Sciences Research Council
Sample type:
description of the sampling procedure A sample of 3 500 respondents was selected throughout South Africa in groupings of seven and situated in 500 census enumerator areas (EAs) as determined in the 2001 census. Each EA was classified in terms of the dominant lifestyle category by the Human Sciences Research Council (HSRC) in its analysis of the 1996 census data. In order to ensure adequate representation in the sample from each province and from each of the four dominant population groups, the sample was stratified by province and by population group. Disproportionately large samples were selected from areas know to be inhabited by the two smallest components of the population, namely (i) areas with dominantly Indian populations and (ii) the Northern Cape. Within the EAs, the seven households were randomly selected and within the household, the respondent were selected using a Kish grid.
Fieldwork institute:
Human Sciences Research Council
Fieldwork methods:
Face-to-face
Sample size:
number of respondents in the final ISSP file
Response rates: 3500 A - Total issued (total sample) 277 B - Ineligible (address vacant, wrong ages,...) 3223 C - (= A - B) Total eligible (in scope sample) 2483 D - Total ISSP questionnaires received 740 E - (= C - D; = F + G + H) Total non-response 362 F - Refusals (refusing to take part) 334 G - Non-contact (never contacted) 44 H - Other non-response Language:
English, Afrikaans, Tswana, Xhosa, Venda, Zulu
Weighted: Yes
Weighting procedure: Basic sampling weight bas3d on SAS Procedure Survey Select
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compensating for unit non-response by dividing the sampling weights by the response rate per EA.
Known systematic properties of the sample:
Disproportionately large samples were selected from areas know to be inhabited by the two smallest components of the population, namely (i) areas with dominantly Indian populations and (ii) the Northern Cape
Deviations from ISSP questionnaire:
Q 6b ; Q7b o ; Q10 ; Q11 ; Q13 Ommitted due to difficult phrasing and confusion with concept of immigrants
Publications:
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Study Description: South-Korea Study title:
Korean General Social Survey
Fieldwork dates:
July 1, 2003 ~ August 30, 2003
Principal investigators:
Hyunho Seok, Survey Research Center at Sungkyunkwan Univ.
Sample type:
Multi-stage Area Probability Sampling
Fieldwork methods: Interview face to face
Fieldwork institute: Survey Research Center at Sungkyunkwan Univ.
Sample size: 2000 Response rates: 2000 A - Total issued (total sample) 30 B – Ineligible (address vacant, wrong ages, …) 1970 C – (=A – B) Total eligible (in scope sample) 1315 D - Total ISSP questionnaires received 655 E – (=C – D; =F + G + H) Total non-response 591 F – Refusals (refusing to take part) 64 G – Non-contact (never contacted) H - Other non-response Language:
Korean
Weighted: no Weighting procedure: - Known systematic properties of the sample:
-
Deviations from ISSP questionnaire:
In question WRKHRS: Those who are self-employed or helping family member are not asked about their work hour. We modified code:
KR: 95. Those who are self-employed or helping family member. In question WRKTYPE & SPWKRTYP: ‘0’ include those
who are helping family member. In question WRKSUP: Those who have no current job are not
asked about their supervisory experiences. They are coded on ‘3’ (KR: 3).
In question ATTEND: we modified code: KR: 00 Those who have no religion
Publications:
Not yet
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ISSP Characteristics of National Population: South-Korea Source: 2000 Census Gender
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Study Description: Spain Study title:
National Identity II
Fieldwork dates:
From 23 to 30 June, 2003
Principal investigators:
Juan Díez Nicolás
Sample type:
Representative Sample of Adults Aged 18 Years and over Living Private Households in Spain
Fieldwork Institute:
ASEP, S.A.
Fieldwork methods:
Personal interview at R'S home
Context of ISSP questionnaire:
Regular Monthly Omnibus
Sample size:
1212
Response rates: (real numbers)
1230 A - Total issued (total sample)
0 B - Ineligible (address vacant, wrong ages,...) 1230 C - (= A - B) Total eligible (in scope sample) 1212 D - Total ISSP questionnaires received 18 E - (= C - D; = F + G + H) Total non-response 9 F - Refusals (refusing to take part) 7 G - Non-contact (never contacted) 2 H - Other non-response Language:
Spanish
Weighted:
Yes
Weighting procedure: Optional : According to sex and age groups
Known Systematic Properties of the sample:
None
Deviations from ISSP Questionnaire:
Publications: None, yet
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ISSP Characteristics of National Population: Spain
SEX Male 20.021.850 Female 20.825.521 AGE (groups) 18-24 years 4.240.700 25-34 6.875.075 35-44 6.319.966 45-54 5.042.813 55-64 4.063.263 65 years and over 6.964.267 YEARS OF SCHOOLING (groups)
No school 5.211,90 4-10 years 8.775,10
11-14 7.847,00 15-18 5.604,70 19-21 3.976,30
22 years and over 2.273,50 EMPLOYMENT STATUS Employed 15.945,60 Unemployed 1.869,10 Not in labour force 15.834,40
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Study Description: Sweden Study title: National Identity II
Fieldwork dates: February 2003 to April 2003
Principal investigators:
Prof. Stefan Svallfors and Dr.Lonas Edlund, Department of Sociology, Umeå University, Umeå, Sweden
Sample type: Two representative samples of the Swedish population 17-79 years. The samples were subjected to different fieldwork methods, with obvious implications for the response rate.
Fieldwork institute: SIFO
Fieldwork methods: Separate postal survey with several reminders. Sample 1 received an introductory letter and a gift. The gift was free and was not associated with any obligations. See the below table for a detailed description.
The samples can be separated by using the variable: S_MODE Fieldwork details
Sample 1 Sample 2
29/1 Introductory letter about the survey X 3/2 Postal survey X 4/2 Postal survey + lottery ticket X
10/2 Postal reminder X 11/2 Postal reminder + questionnaire X 24/2 Postal reminder + questionnaire X 25/2 Postal reminder + questionnaire X 14/3 Postal reminder (+ questionnaire to those
with unknown telephone numbers) X
17-27/3 Reminder by telephone X 17-31/3 Reminder by telephone (+ questionnaire to
those wishing a questionnaire) X
Context of ISSP questionnaire:
Separate survey
Language: Swedish
Weighted: No
Sample size: 1186 Known bias in data The response patterns for all variables have been subjected to χ2 tests
in order to track significant differences between samples 1 and 2 (not controlling for demographic differences). Differences in response patterns between the two samples are generally small. For all variables: p > 0.10, except v17 (p=0.01) and v39 (p=0.07).
Response rates in different demographic groupings are relatively
ZA-No. 3910 I S S P 2003 National Identity II Page I - 120
evenly distributed. However, the following may be noted: women and the young (higher than average response rate); men, the elderly, those living in Stockholm (lower than average response rate). Special note on background variables referring to occupation 1. All occupational variables refer to the respondent’s present or (if
the respondent is currently not working) last occupation. 2. ISCO. 4-digit. Where information for 4-digit coding is not
available, 3- or 2-digit codings have been applied, using zeros for the missing digits. Examples: isco code 733 is coded as 7330 in the datafile, isco code 73 is coded as 7300 in the datafile.
Response rates:
Sample 1 1000 A – Total issued (total sample)
38 B – Ineligible 962 C – (=A-B) Total eligible (in scope sample) 674 D – Total ISSP questionnaires received (70%) 288 E – (=C-D;=F+G+H) Total non-response 62 F – Refusals (refusing to take part)
185 G – Non-contact (never contacted) 41 H – Other non-response
Sample 2 1000 A – Total issued (total sample)
47 B – Ineligible 953 C – (=A-B) Total eligible (in scope sample) 512 D – Total ISSP questionnaires received (54%) 441 E – (=C-D;=F+G+H) Total non-response 110 F – Refusals (refusing to take part) 266 G – Non-contact (never contacted) 65 H – Other non-response
Samples 1+2 2000 A – Total issued (total sample)
85 B – Ineligible 1915 C – (=A-B) Total eligible (in scope sample) 1186 D – Total ISSP questionnaires received (62%) 729 E – (=C-D;=F+G+H) Total non-response 172 F – Refusals (refusing to take part) 451 G – Non-contact (never contacted) 106 H – Other non-response
Response rates in different groups (percent):
Sample 1 Sample 2 Samples 1+2SEX Men 65 53 59
Women 75 54 65
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STATUS (****) Employed 74Unemployed 4Not in labour force
22
(***) Education register (SUN2000 adjusted to ISCED) age 16-74 (2002). (****) Labour force surveys (AKU) age 16-64 (2003). (*) Region description Codes Administrative provinces (“län” approximative to english “county”) AB Stockholms län C Uppsala län D Södermanlands län E Östergötlands län F Jönköpings län G Kronobergs län H Kalmar län I Gotlands län K Blekinge län M Skåne län N Hallands län O Västra Götalands län S Värmlands län T Örebro län U Västmanlands län W Dalarnas län X Gävleborgs län Y Västernorrlands län Z Jämtlands län AC Västerbottens län BD Norrbottens län REGION 1 North (AC, BD, Y, Z) 2 Mid North (S, W, X) 3 Mid East (AB, C, D, E, T, U, except 4 Stockholm) 4 Stockholm 5 West (N, O, except 6 Göteborg) 6 Göteborg 7 Småland Gotland (F, G, H, I) 8 South (K, L, M, except 9 Malmö) 9 Malmö (**) Urban-rural description
ZA-No. 3910 I S S P 2003 National Identity II Page I - 124
Stockholm (including the suburb municipalities)
Urban 1 (Municipalities (MC) with more than 90 000 inhabitants within an area of 30 kilometres radius from the MC centre)
Urban 2 (MC:s with more than 27 000 inhabitants and less than 90 000 inhabitants within an area of 30 kilometres radius of the MC centre and in the same time with more than 300 000 inhabitants within 100 kilometres radius of the MC centre)
Rural 1 (MC:s with more than 27 000 inhabitants and less than 90 000 inhabitants within an area of 30 kilometres radius of the MC centre and in the same time with less than 300 000 inhabitants within 100 kilometres radius of the MC centre)
Rural 2 (MC:s with less than 27 000 inhabitants within an area of 30 kilometres radius of the centre)
Göteborg region (Göteborg including the suburb municipalities)
Malmö region (Malmö, Lund, Trelleborg including the suburb municipalities)
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The ISSP 2003 survey was combined with the „Eurobarometer in Switzerland“ (EBCH) survey. The questions related to the „Eurobarometer in Switzerland“ were first asked followed by those of the ISSP 2003 using the CAPI method. The sampling procedure was the following:
- Stratification of the households into 6 regions (Espace Mittelland, North of Switzerland, East of Switzerland, Center of Switzerland and Italian-speaking part of Switzerland)
- Random selection of households from the extended phone register
- Sending of an information letter - One half of the sample was contacted by the telephone
central of the fieldwork institute and the other half per telephone or face to face from the interviewer to fix a date for the interview
- Random selection of one person in each household with the KISH method
- EBCH and ISSP Interview (CAPI)
Fieldwork institute:
MIS Trend, Institut pour l’étude des marchés et les sondages d’opinion, Pont-Bessières 3, 1005 Lausanne
Fieldwork methods:
CAPI
Sample size:
1037
Response rates: A - 3640 (total sample) B - 497 (address vacant, wrong ages, sick, language,...) C - (= A - B) 3143 (in scope sample) D - 1037 E - (= C - D; = F + G + H) 2106 F - 1735 (refusing to take part) G - 224 (never contacted) H - 147 Language:
German, French and Italian
Weighted: yes
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Weighting procedure: The weights were defined according to the following criteria: - Size of household - Age - Gender - Language - Employment status The estimation of size of household, age, gender, language is based on the Swiss federal census 2000 and the estimation of the employment status on the Swiss Labour Force Survey 2002. The size of the household was recoded into the following four categories: 1) Household with 1 person 2) Household with 2 persons 3) Household with 3 persons 4) Household with 4 persons and more The age was recoded into the following six categories: 1) 18-24 years 2) 25-34 years 3) 35-44 years 4) 45-54 years 5) 55-64 years 6) 65 and older
The language was recoded into the following five categories: 1) German 2) French 3) Italian 4) Rheto-roman 5) Other
The employment status was recoded into the following two categories: 1) Active 2) Non active
The weights result from the division of the expected through the observed value. This process was repeated until convergence.
Known systematic properties of the sample:
Under representation of young people and over representation of german speaking people.
Deviations from ISSP questionnaire:
None
Publications: -
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ISSP Characteristics of National Population: Switzerland Swiss federal Census
2000 SAKE 2002 (Swiss labour force survey)
SEX
Male 48.4 Female 51.6 AGE (groups)
18-24 Jahre 10.4 25-34 Jahre 18.6 35-44 Jahre 20.6 45-54 Jahre 17.3 55-64 Jahre 13.7 65 Jahre und mehr 19.3
YEARS OF SCHOOLING (groups, approximation, see details next page)
0-8 3.0 9 21.6
12-13 49.4 14 9.5 16 1.1 19 6.8
No indication 8.6
YEARS OF SCHOOLING (total)15
Mean 12.07 12.7 EMPLOYMENT STATUS
Employed 64.8 Unemployed 1.5 Not in labor force 33.7 Note: Indications for population 18 years and older Approximation of years of schooling in Switzerland, population 18 years and older The Swiss federal Census asks for the highest achiedved education, not for the years of schooling. Therefore they make an approximate calculation of the years of schooling.
OFS (Swiss federal statistics office)
Years of schooling (approximation of
ISCED code
ISCED
15 Indications for population 15 years and older
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A. Borkowski, OFS)
Aucune formation achevée 0-8 0 Not completed primary education Scolarité obligatoire 9 2 Lower secondary Ecole de culture générale ou préparant à une formation
12 3 Upper secondary
Apprentissage, école professionnelle à plein temps
12 3
Maturité 13 3 Ecole normale 13 3 Formation professionnelle supérieure 14 4 Post secondary Ecole professionnelle supérieure 14 5 Haute école spécialisée 16 5 First stage of tertiary Université, haute école 19 5-6 First and second stage of tertiary
education Sans indication No indication
ISCED % Approximate years of schooling
Total 100 Not completed primary education 3.0 0-8 Lower secondary or second stage of basic education
21.6 9
Upper secondary education 49.4 12-13 Post secondary, non tertiary education 9.5 14 First stage of tertiary education 1.1 16 First and second stage of tertiary education 6.8 19 No indication 8.6
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Study Description: Taiwan Study title: Taiwan Social Change Survey: the Fourth Survey of the Fourth Cycle Fieldwork dates: 08/10/2003~for about one month Principal investigators:
Chang, Ying-hwa Institute of Sociology, Academia Sinica
Sample type:
Three-stage Stratified PPS Sampling (1) PSU=township, (2) Li (administrative unit under township, (3) respondent
Fieldwork institute: Center for Survey Research, Academia Sinica Fieldwork methods Face-to-face interview Sample size: 2016 Response rates: 4391 A - Total issued (total sample) 138 B - Ineligible (address vacant, wrong ages,...) 4253 C - (= A - B) Total eligible (in scope sample) 2016 D - Total ISSP questionnaires received 2237 E - (= C - D; = F + G + H) Total non-response 594 F - Refusals (refusing to take part) 56 G - Non-contact (never contacted) 1587 H - Other non-response Language: Manderin, Taiwanese, or Hakka Weighted: no Weighting procedure: Known systematic properties of the sample:
A non-response bias comes from the use of household registration data in which some household members in fact do not live in the household.
Deviations from ISSP questionnaire:
Some variables are not included in the data� V65, ETHNIC, V68-V74(optional)
Publications: Report for Taiwan Social Change Survey (2004), Institute of Sociology
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ISSP Characteristics of National Population: Taiwan Source no. 1 Source no. 2 Source no. 3 Source no. 4 SEX
Male 51.0% Female 49.0% AGE (groups)
Less than 14 19.8% 15�64 70.9%
65 and more 9.2%
YEARS OF SCHOOLING (for those more than 15 years old only)
None 4.0% Primary & junior high 36.2% Senior high 33.8% College and more 26.0% EMPLOYMENT STATUS (for those more than 15 years old only)
Employed
75.92%
Unemployed
5.17%
Not in labor force
18.91%
Source no. 1: "Taiwan-Fukien Demographic Fact Book, R.O.C.," by the Ministry of Interior;
" Agricultural Statistics Yearbook," by Council of Agriculture, Executive Yuan.
Source no. 2: "Taiwan-Fukien Demographic Fact Book, R.O.C.," by the Ministry of Interior. Source no. 3: "Yearbook of Manpower Survey Statistics, Taiwan Area, R.O.C.," by
Directorate-General of Budget, Accounting and Statistics, Executive Yuan.
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Study Description: Uruguay Study title: National Identity - (ISSP 2003 Module)
Fieldwork dates: From July 12 to August 16, 2004
Principal investigators:
Zuleika Ferre, Giorgina Piani, Máximo Rossi from Department of Economics and Juan José Goyeneche, Guillermo Zoppolo from Institute of Statistics from University of Uruguay
Sample type:
The sampling frame used was the 1996 Population Census. The Universe population are adults (18 year-old or more) that reside in Urban areas. The design is stratified multistage. Two major regions are represented: Montevideo Metropolitan Area (strata 1-7) and Urban population in the rest of the Country (5000 or more inhabitants cities, strata 11-13). In strata 1 to 7, Census Zones (usually one block) are selected by a systematic proportional to size (pps) scheme (“size” being the number of people on each block), then four households are selected on each block, and one person per household is selected via the “next birthday” rule. In strata 11 to 13, four cities are selected per stratum via systematic pps sampling. Size again being number of people residing on each city. Census Zones, households and persons are selected in the same way as in strata 1 to 7. Sample allocation: Table 1
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Fieldwork institute: Department of Economics – Faculty of Social Sciences, University of
Uruguay Fieldwork methods: Face to face interviews
Context of ISSP questionnaire:
The National Identity 2003 module was carried out in conjunction with de Citizenship 2004 module. The questionnaire was structured as follows: firstly, the Citizenship module, the National Identity module, and finally, demographic variables.
Sample size: 1108
Response rates: CONCEPT Total A - Total issued (total sample) 1389 B - Ineligible (address vacant, wrong ages,...) 0 C - (= A - B) Total eligible (in scope sample) 1389 D – Total ISSP questionnaires received 1108 E - (= C - D; = F + G + H) Total non-response 281 F - Refusals (refusing to take part) 176 G - Non-contact (never contacted) 72 H - Other non-response (incomplete questionnaires) 33 Language:
Spanish
Weighted: The data submitted are not weighted Weighting procedure: The weight is computed as the inverse of the selection probability for
each person. The steps concerning the household selection are done with pps sampling, so the household weights are equal to number of households on stratum divided by the number of households selected on each stratum. The household weight is then expanded by the person weight, which corresponds to the number of adults on the household. On some households the number of adults goes up to 10, as a conservative measure we reduced these weights considering a maximum number of 5 for the person weight. Since we estimated the number of household per stratum, we finally adjusted the weights in order to match the number of people per stratum that appear on the previous table 1.
Known systematic properties of the sample:
None that we are aware.
Deviations from ISSP questionnaire:
The questionnaire was translated as closely as possible from English to Spanish, maintaining the meaning and significance of each sentence and word. Some general comments: • In questions V58: we opened this question in v58_a and v58_b. The
reason for that was: in Uruguay commonly family are formed with people from different countries (mainly from Spain and Italy)
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• DEMOGRAPHICS VARIABLES:
• UNION: The code 2 was not included. • INCOME and RINCOME: In these questions, incomes were
measured in categories and we coded them. • URBRURAL: It was not self assessement. Was coded by the
field supervisor. The value labels for UY_DEGR, INCOME, RINCOME, UY_PRTY, UY_REG AND UY_SIZE are: VALUE LABELS UY_DEGR 01 'None' 02 'Incomplete primary school' 03 'Completed primary school' 04 'Incomplete secondary' 05 'Completed secondary' 06 'Incomplete technical education' 07 'Completed technical education' 08 'Incomplete university' 09 'Completed university' 10 'Incomplete non university high education' 11 'Completed non university high education' 99 'NA' 00 'Not available'. VALUE LABELS INCOME RINCOME 01 'Less than $3.000' 02 '$3.00 to $5.999' 03 '$6.000 to $7.499' 04 '$7.500 to $9.499' 05 '$9.500 to $11.499' 06 '$11.500 to $13.499' 07 '$13.500 to $16.499' 08 '$16.500 to $20.499' 09 '$20.500 to $26.499' 10 '$26.500 to $38.999' 11 'More than $39.000' 999997 'Refused' 999998 'Dont know' 999999 'NA' 000000 'No own income, not in paid'. VALUE LABELS UY_PRTY 01 'Red Party' 02 'National Party' 03 'Wide front Party'
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04 'Independent Party' 05 'Liberal Party' 06 'Other Party' 96 'No party preference' 99 'NA'. VALUE LABELS UY_REG 01 'Metropolitan statistical area of Montevideo' 02 'Rest of urban population'. VALUE LABELS UY_SIZE 01 '1.597.943 urban population' 02 '992.552 urban population'.
Publications:
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ISSP Characteristics of National Population: Uruguay Source: National Survey of Households 2003 - INE SEX Population >= 18 Total población
Male 45,2 %
46,9 %
Female 54,8 % 53,1 %
N 1.722.921 2.365.492
AGE - Group
18 - 29 23,3 %
30 - 39 15,9 %
40 - 49 17,3 %
50 - 59 14,8 % 60 - 69 12,7 %
70 + 16,0 %
N 1.722.921
YEARS OF SCHOOLING - Group
None 1,5 %
1 - 6 37,7 %
6 -12 37,6 %
12 7,6 %
13 - 15 7,6 %
16 + 8,0 %
N 1.722.921
EMPLOYMENT STATUS Population >= 14
Economically Active Population 62,0 %
58,1 %
Employed (% EAP) 83,9 % 83,1 %
Unemployed (% EAP) 16,1 % 16,9 %
Not in labor force 38,0 % 41,9 %
N 1.722.921 1.877.497 Note: The information is only about urban population in cities greater than 5.000 inhabitants.
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Study Description: USA Study title:
General Social Survey - National Identity
Fieldwork dates:
8/05-12/05
Principal investigators:
Tom W. Smith and James A. Davis, NORC/University of Chicago and Peter V. Marsden, Harvard University
Sample type:
Multi-stage, area probability sample
Fieldwork institute:
NORC
Fieldwork methods:
In-person with self-completion on laptop of ISSP module
Sample size:
1216
Response rates: 2587 A - Total issued (total sample) 792 B - Ineligible (address vacant, wrong ages,...) 1795 C - (= A - B) Total eligible (in scope sample) 1216 D - Total ISSP questionnaires received E - (= C - D; = F + G + H) Total non-response 514 F - Refusals (refusing to take part) 49 G - Non-contact (never contacted) 16 H - Other non-response Language:
English
Weighted: Yes
Weighting procedure: the weight takes into consideration three factors: 1) a sub-sampling of temporray, non-response cases, 2) differential final non-response by area, and 3) number of adults in the household to make representative of individuals rather than households
Known systematic properties of the sample:
Men and resdients of large central cities ar eunderrepresented due to higher nonresponse in the unweighted data
Deviations from ISSP questionnaire:
None
Publications: None
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National Population Characteristics: USA
Table 1 A Detailed Comparison of the 2002 Current Population Survey (CPS) and the Weighted and Unweighted 2002 General Social Survey (GSS) A. AGE
D. Labour Force Participation, 16 years or older**
Employed 63.6 65.0 65.6Unemployed 4.2 4.4 4.4Not in labour force 32.2 30.7 30.0n 2765 2765 * Weighted by number of eligible respondents (i.e. 18+) in the household. ** The GSS figures are based on 18+.
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Study Description: Venezuela Study title:
General Social Survey - National Identity
Fieldwork dates:
22-3-2004 to 08-04-2004
Principal investigators:
Roberto Briceño León
Sample type:
The stratification variable used was the political and administrative division consisting of 23 states. But 2 states ( Amazonas and Delta Amacuro with lesser population) were joined together, so that there was 22 instead of 23 strata. The sampling frame was the list of censal segments used to carry out the XIII Venezuelan National Census. It included demographic and mapping information prepared by the National Institute of Statistics (Instituto Nacional de Estadística) for the 2001 census. The sampling method was carried out in three satges. In the first stage we selected censal segments from each stratum with proportional probability to their number of dwellings In the second stage were selected two "blocks" from each censal segment using a systematic geographical order selection technique with a random start. The selection of blocks was with probability proportional to the number of dwellings in each block. Finally a person was selected according to a quota by sex and age. Five persons were selected from each block with one person selected from each household. The start in each block to get the first dwelling was random. The quota was established using sex and age groups accordingly to the national distribution of people by sex and age revealed in the 2001 National Census.
Fieldwork institute:
Laboratorio de Ciencias Sociales, LACSO
Fieldwork methods:
Face to face personal interview
Sample size:
1199
Response rates: 1337 A - Total issued (total sample) 43 B - Ineligible (address vacant, wrong ages,...) 1294 C - (= A - B) Total eligible (in scope sample) 1199 D - Total ISSP questionnaires received 95 E - (= C - D; = F + G + H) Total non-response 54 F – Refusals (refusing to take part) 29 G - Non-contact (never contacted) 12 H - Other non-response
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Language:
Spanish
Weighted: Yes
Weighting procedure: Post-stratification was carried out by standard of living groups (slg), based on similar previous studies. Final sample weights were adjusted by a factor obtained using the next equation: Fe = Expected % slg “e” / observed % slg “e”
Known systematic properties of the sample:
The isolated indigenous population living in the Amazon (Yanomami, Warao, etc) was excluded, but they represented less than 1% of total population. There were limitations reaching people from High Class areas.
Deviations from ISSP questionnaire:
None
Publications: None
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ISSP Characteristics of National Population: Venezuela
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Codebook Information The example below is a reproduction of information appearing in the machine readable codebook. The numbers in angular brackets < > do not appear in the codebook, but are references to the descriptions which follow the example. Example: <1> <2> <3> Missing Values: 8;9
<4> Q.8 In general, would you say that people should obey the law without exception, or are there exceptional occasions on which people should follow their consciences even if it means breaking the law? (Please tick one box only) <5> <-----> <6> <7> 1. Obey the law without exception 2. Follow own conscience on occasions 8. Can't choose, don't know 9. NA, refused <8>
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Explanations <1> A variable (and reference) number have been assigned to each item in the study. In the present codebook which documents the archived data set, these numbers are identical. Should the data set be subsetted or rearranged the variable numbers might change to reflect the order of the new data set while the reference numbers would remain unchanged to provide a link to the archived data set. <2> Indicates the abbreviated variable label used within OSIRIS or SPSS system files. <3> "Missing Values" indicates the designation of the missing data codes. <4> Indicates the full question text taken from the British questionnaire. Wherever possible the original sequence of questions has been retained, although some changes were necessary to integrate the different national questionnaires. <5> Indicates commentaries and explanations added during the processing of the study. < within question or answer texts may indicate whether the questionnaire in a particular country is deviating from the general format. <6> Indicates the code value for the single answer category. <7> Indicates the textual definition of the codes. Abbreviations commonly used are DK (don't know), NA (no answer), Can't choose, Not applicable and Not available. <8> Indicates percentage frequencies by country. This form is used whenever code categories have the same meaning for all countries. Column percentages are based only on "valid cases". Missing data values were excluded from percentages.
V1 ISSP Study Number
V2 Respondent ID Number Respondent Number This number uniquely identifies each respondent. The first two digits are identical with the country code, the next five digits contain the original identification number.
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V3 Country
Country: (N)
1 Australia (AU) 2183 2 Germany-West (DE-W) 850 3 Germany-East (DE-E) 437 4 Great Britain (GB) 873 6 United States (US) 1216 7 Austria (AT) 1006 8 Hungary (HU) 1021 10 Ireland (IE) 1065 12 Norway (NO) 1469 13 Sweden (SE) 1186 14 Czech Republic (CZ) 1276 15 Slovenia (SI) 1093 16 Poland (PL) 1277 17 Bulgaria (BG) 1069 18 Russia (RU) 2383 19 New Zealand (NZ) 1036 20 Canada (CA) 1211 21 Philippines (PH) 1200 22 Israel Jews (IL-J) 1066 23 Israel Arabs (IL-A) 152 24 Japan (JP) 1102 25 Spain (SP) 1212 26 Latvia (LV) 1000 27 Slovak Republic (SK) 1152 28 France (FR) 1669 30 Portugal (PT) 1602 31 Chile (CL) 1505 32 Denmark (DK) 1322 33 Switzerland (CH) 1037 36 Venezuela (VE) 1199 37 Finland (FI) 1379 40 Southafrica (ZA) 2483 41 Taiwan (TW) 2016 42 South Korea (KR) 1315 43 Uruguay (UY) 1108
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V4 Most important group R identifies with Missing Values: 98; 99 Q. 1. We are all part of different groups. Some are more important to us than others when we think of ourselves. In
general, which in the following list is most important to you in describing who you are? and the second most important? and the third most important?1 Q1a First most important, Q1b Second most important, Q1c Third most important.
1. Your current or previous occupation (or being a homemaker) 2. Your race/ethnic background 3. Your gender (that is, being a man/woman) 4. Your age group (that is, Young, Middle, Age, Old) 5. Your religion (or being agnostic or atheist) 6. Your preferred political party, group, or movement. 7. Your nationality
8. Your family or marital status (that is , son/daughter, mother/father, grandfather/grandmother, husband/wife, widower/widow, nor married, or other similar).
9. Your social class (that is upper, moddle, lower, working, or similar categories) 10. The part of [COUNTRY] that you live in 11. [ES]:None 98. Can´t choose 99. Na, refused Notes: [HU]: Did not include code 7 [KR]: Did not include code 2 (1) In oral interviews, use card with choices
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V5 Second most important group R identifies with Missing Values: 98; 99 Q1.- We are all part of different groups. Some are more important to us than others when we think of ourselves. In general, which in the following list is most important to you in describing who you are? Please tick one box in the first column. And which is the second most important? And the third most important?
Q1a First most important, Q1b Second most important, Q1c Third most important. 1. Your current or previous occupation (or being a homemaker) 2. Your race/ethnic background 3. Your gender (that is, being a man/woman) 4. Your age group (that is, Young, Middle, Age, Old) 5. Your religion (or being agnostic or atheist) 6. Your preferred political party, group, or movement. 7. Your nationality
8. Your family or marital status (that is , son/daughter, mother/father, grandfather/grandmother, husband/wife, widower/widow, nor married, or other similar).
9. Your social class (that is upper, moddle, lower, working, or similar categories) 10. The part of [COUNTRY] that you live in 11. [ES]:None 98. Can´t choose 99. Na, refused, no second response Notes: [KR]: Did not include code 2 [HU]: Did not include code 7
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V6 Third most important group R identifies with Missing Values: 98; 99 Q1.- We are all part of different groups. Some are more important to us than others when we think of ourselves. In general, which in the following list is most important to you in describing who you are? Please tick one box in the first column. And which is the second most important? And the third most important?
Q1a First most important, Q1b Second most important, Q1c Third most important. 1. Your current or previous occupation (or being a homemaker) 2. Your race/ethnic background 3. Your gender (that is, being a man/woman) 4. Your age group (that is, Young, Middle, Age, Old) 5. Your religion (or being agnostic or atheist) 6. Your preferred political party, group, or movement. 7. Your nationality
8. Your family or marital status (that is , son/daughter, mother/father, grandfather/grandmother, husband/wife, widower/widow, nor married, or other similar).
9. Your social class (that is upper, moddle, lower, working, or similar categories) 10. The part of [COUNTRY] that you live in 11. [ES]:None 98. Can´t choose 99. Na, refused, no third response Notes: [KR]: Did not include code 2 [HU]: Did not include code 7
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V7 How close do you feel to: Your town - city Missing Values: 8; 9 Q2.- How close do you feel to1... (Please, tick one box on each line) a) Your town or city 1. Very close. 2. Close. 3. Not very close. 4. Not close at all. 8. Can´t choose. 9. Na, refused.
Notes: (1) Precode: “Feel close to” is to be understood as “emotionally attached to” or “identifying with”.
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V8 How close do you feel to: Your [county] Missing Values: 8; 9 Q2.- How close do you feel to1... (Please, tick one box on each line) b) Your [county]2 1. Very close. 2. Close. 3. Not very close. 4. Not close at all. 8. Can´t choose. 9. Na, refused. Notes: (1) Precode: “Feel close to” is to be understood as “emotionally attached to” or “identifying with”. (2) [county] (or province, state, etc.): to be understood as the most relevant administrative unit smaller than the entire country/nation.
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V9 How close do you feel to: [country] Missing Values: 8; 9 Q2.- How close do you feel to1... (Please, tick one box on each line) c) [COUNTRY] 1. Very close. 2. Close. 3. Not very close. 4. Not close at all. 8. Can´t choose. 9. Na, refused. Notes: (1) Precode: “Feel close to” is to be understood as “emotionally attached to” or “identifying with”.
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V10 How close do you feel to: continent Missing Values: 8; 9 Q2.- How close do you feel to1... (Please, tick one box on each line) d) [Continent; e.g. Europe]2 1. Very close. 2. Close. 3. Not very close. 4. Not close at all. 8. Can´t choose. 9. Na, refused. Notes: (1) Precode: “Feel close to” is to be understood as “emotionally attached to” or “identifying with”. (2) [Europe]: give relevant continent or subcontinent: Europe, North America, East Asia/Southeast Asia]
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V11 Important: to have been born in [Country] Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) a. to have been born in [COUNTRY] 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”.
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V12 Important: To have [Country nationality] citizenship Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) b. to have [COUNTRY NATIONALITY] citizenship 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”.
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V13 Important: To have lived in [Country] for most of one’s life Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) c. to have lived in [COUNTRY] for most of one’s life 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”.
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V14 Important: To be able to speak [Country language] Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) d. to be able to speak [COUNTRY LANGUAGE] 2 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”. 2 [dominant language(s)] If two or more languages are recognized nationwide both are included in the question. However, if there is one national lingua franca (Spanish, Russian) just give this language.
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V15 Important: To be a [religion] Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) e. to be a [religion] 2
1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”. 2 The dominant religion or denomination in your country should be given (eg. Christian in the US and Canada, Catholic in Ireland and Italy, Russian Orthodox in Russia)].
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V16 Important: To respect [Country nationality] political institutions and laws Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) f. to respect [COUNTRY NATIONALITY] political institutions and laws 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”.
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V17 Important: To feel [Country nationality] Missing Values: 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) g. to feel [COUNTRY NATIONALITY] 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”.
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V18 Important: To have [country nationality] ancestry Missing Values: 0; 8; 9 Q3.- Some people say that the following things are important for being truly [NATIONALITY]1. Others say they are not important. How important do you think each of the following is... (Please, tick one box on each line) h. to have [COUNTRY NATIONALITY] ancestry 2 1. Very important. 2. Fairly important. 3. Not very important. 4. Not important at all. 0. Not available (not asked) 8. Can´t choose. 9. Na, refused. Notes: 1 Precode “truly [COUNTRY NATIONALITY]” E.g. “truly British”, American “a true American”. 2 Question not asked in Bulgaria and Latvia
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V19 I would rather be a citizen of [Country] than of any other country in the world Missing Values: 8; 9 Q. 4. How much do you agree or disagree with the following statements? (Please, tick one box on each line) a. I would rather be a citizen of [COUNTRY] than of any other country in the world 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused
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V20 There are some things about [Country] today that make me feel ashamed of [Country] Missing Values: 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) b. There are some things about [COUNTRY] today that make me feel ashamed of [COUNTRY] 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
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V21 The world would be a better place if people from other countries were more like the [Country nationality] Missing Values: 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) c. The world would be a better place if people from other countries were more like the [COUNTRY NATIONALITY] 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
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V22 Generally speaking, [Country] is a better country than most other countries Missing Values: 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) d. Generally speaking, [COUNTRY] is a better country than most other countries 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
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V23 People should support their country even if the country is in the wrong. Missing Values: 0; 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) e. People should support their country even if the country is in the wrong1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (not asked). 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Bulgaria because of technical reasons
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V24 When my country does well in international sports, it makes me proud to be [Country nationality] Missing Values: 0; 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) f. When my country does well in international sports, it makes me proud to be [COUNTRY NATIONALITY]1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Bulgaria because of technical reasons
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V25 I am often less proud of [Country] than I would like to be Missing Values: 8; 9 Q4.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) g. I am often less proud of [COUNTRY] than I would like to be. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
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ZA - No. 3910 I S S P 2003 - National Identity II Page II - 46
V26 How proud: The way democracy works Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). a. the way democracy works 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 47
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 48
V27 How proud: Its political influence in the world Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). b. its political influence in the world 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 49
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 50
V28 How proud: [Country’s] economic achievements Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). c. [COUNTRY’s] economic achievements 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 51
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 52
V29 How proud: Its social security system Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). d. Its social security system 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 53
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 54
V30 How proud: Its scientific and technological achievements Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). e. its scientific and technological achievements 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 55
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 56
V31 How proud: Its achievements in sports Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). f. its achievements in sports 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 57
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 58
V32 How proud: Its achievements in the arts and literature Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). g. its achievements in the arts and literature 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 59
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 60
V33 How proud: [Country’S] armed forces Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). h. [COUNTRY’S] armed forces 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 61
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 62
V34 How proud: Its history Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). i. its history 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 63
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 64
V35 How proud: Its fair and equal treatment of all groups in society Missing Values: 8; 9 Q5.- How proud are you of [COUNTRY] in each of the following? (Please, tick one box on each line). j. Its fair and equal treatment of all groups in society 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 65
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 66
V36 [Country] should limit the import of foreign products in order to protect its national economy. Missing Values: 8; 9 Q6.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) a. [COUNTRY] should limit the import of foreign products in order to protect its national economy. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 67
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 68
V37 For certain problems, like environment pollution, international bodies should have the right to enforce solutions Missing Values: 0; 8; 9 Q6.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) b. For certain problems, like environment pollution, international bodies should have the right to enforce solutions 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 69
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 70
V38 [Country] should follow its own interests, even if this leads to conflicts with other nations Missing Values: 8; 9 Q6.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) c. [COUNTRY] should follow its own interests, even if this leads to conflicts with other nations. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 71
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 72
V39 Foreigners should not be allowed to buy land in [Country] Missing Values: 8; 9 Q6.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) d. Foreigners should not be allowed to buy land in [COUNTRY] 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 73
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 74
V40 [Country’s] television should give preference to [Country] films and programmes Missing Values: 8; 9 Q6.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) e. [COUNTRY’S] television should give preference to [COUNTRY] films and programmes 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 75
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 76
V41 Large international companies are doing more and more damage to local businesses in [Country]. Missing Values: 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) a. Large international companies are doing more and more damage to local businesses in [COUNTRY]. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 77
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 78
V42 Free trade leads to better products becoming available in [Country]. Missing Values: 0; 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) b. Free trade leads to better products becoming available in [COUNTRY]. 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked). 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 79
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 80
V43 In general, [Country] should follow the decisions of international organizations to which it belongs, even if the government does not agree with them. Missing Values: 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) c. In general, [COUNTRY] should follow the decisions of international organizations to which it belongs, even if the government does not agree with them. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 81
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 82
V44 International organizations are taking away too much power from the [Country nationality] government. Missing Values: 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) d. International organizations are taking away too much power from the [COUNTRY NATIONALITY] government. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 83
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 84
V45 Increased exposure to foreign films, music, and books is damaging our national and local cultures. Missing Values: 0; 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) e. Increased exposure to foreign films, music, and books is damaging our national and local cultures. 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in New-Zealand
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 85
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 86
V46 A benefit of the Internet is that it makes information available to more and more people worldwide. Missing Values: 8; 9 Q7.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) f. A benefit of the Internet is that it makes information available to more and more people worldwide. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 87
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 88
V47 It is impossible for people who do not share [Country’s] customs and traditions to become fully [Country’s nationality] Missing Values: 0; 8; 9 Q8.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) a. It is impossible for people who do not share [COUNTRY’s] customs and traditions to become fully [COUNTRY’S NATIONALITY] 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Chile
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 89
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 90
V48 Ethnic minorities should be given government assistance to preserve their customs and traditions Missing Values: 0; 8; 9 Q8.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) b. Ethnic minorities should be given government assistance to preserve their customs and traditions1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Chile
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 91
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 92
V49 Help minorities to preserve traditions Missing Values: 8; 9 Q9.- Some people say that it is better for a country if different racial and ethnic groups maintain their distinct customs and traditions. Others say that it is better if these groups adapt and blend into the larger society. Which of these views comes closer to your own? 1. It is better for society if groups maintain their distinct customs and traditions. 2. It is better if groups adapt and blend into the larger society. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 93
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 94
V50 Immigrants increase crime rates Missing Values: 0; 8; 9 Q10.- There are different opinions about immigrants from other countries living in [COUNTRY]. (By “immigrants” we mean people who come to settle in [COUNTRY])1. How much do you agree or disagree with each of the following statements? (Please, tick one box on each line) a. Immigrants increase crime rates1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
1 The preceding parenthetical comment is part of the question wording
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 95
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 96
V51 Immigrants are generally good for [Country’s] economy Missing Values: 0; 8; 9 Q10.- There are different opinions about immigrants from other countries living in [COUNTRY]. (By “immigrants” we mean people who come to settle in [COUNTRY])2. How much do you agree or disagree with each of the following statements? (Please, tick one box on each line) b. Immigrants are generally good for [COUNTRY’S] economy1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
2 The preceding parenthetical comment is part of the question wording
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 97
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 98
V52 Immigrants take jobs away from people who were born in [Country] Missing Values: 0; 8; 9 Q10.- There are different opinions about immigrants from other countries living in [COUNTRY]. (By “immigrants” we mean people who come to settle in [COUNTRY])3. How much do you agree or disagree with each of the following statements? (Please, tick one box on each line) c. Immigrants take jobs away from people who were born in [COUNTRY] 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
3 The preceding parenthetical comment is part of the question wording
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ZA - No. 3910 I S S P 2003 - National Identity II Page II - 100
V53 Immigrants improve [Country nationality] society by bringing in new ideas and cultures Missing Values: 0; 8; 9 Q10.- There are different opinions about immigrants from other countries living in [COUNTRY]. (By “immigrants” we mean people who come to settle in [COUNTRY])4. How much do you agree or disagree with each of the following statements? (Please, tick one box on each line) d. Immigrants improve [COUNTRY NATIONALITY] society by bringing in new ideas and cultures1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
4 The preceding parenthetical comment is part of the question wording
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 101
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 102
V54 Government spends too much money assisting immigrants. Missing Values: 0; 8; 9 Q10.- There are different opinions about immigrants from other countries living in [COUNTRY]. (By “immigrants” we mean people who come to settle in [COUNTRY])5. How much do you agree or disagree with each of the following statements? (Please, tick one box on each line) e. Government spends too much money assisting immigrants1. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
5 The preceding parenthetical comment is part of the question wording
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 103
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 104
V55 Number of immigrants coming to country Missing Values: 0; 8; 9 Q11.- Do you think the number of immigrants to [COUNTRY] nowadays should be1... 1. Increased a lot. 2. Increased a little. 3. Remain the same. 4. Reduced a little. 5. Reduced a lot. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in South-Africa
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 105
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 106
V56 Are you a citizen of [Country] Missing Values: 8; 9 Q12.- Are you a citizen of [COUNTRY]? 1. Yes. 2. No. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 107
V56 (N) 1 2 9
% % % Australia (AU) 2183 2115 51 17M
97,60% 2,40% Austria (AT) 1006 972 34
96,60% 3,40% Bulgaria (BG) 1069 1061 5 3M
99,50% 0,50% Canada (CA) 1211 1158 26 27M
97,80% 2,20% Chile (CL) 1505 1487 17 1M
98,90% 1,10% Czech Republic (CZ) 1276 7 1234 35M
0,60% 99,40% Denmark (DK) 1322 1316 - 6M
100,00% Finland (FI) 1379 1351 21 7M
98,50% 1,50% France (FR) 1669 1601 32 36M
98,00% 2,00% Germany-West (DE-W) 850 785 65
92,40% 7,60% Germany-East (DE-E) 437 432 5
98,90% 1,10% Great Britain (GB) 873 844 22 7M
97,50% 2,50% Hungary (HU) 1021 1021 -
100,00% Ireland (IE) 1065 1035 27 3M
97,50% 2,50% Israel Jews (IL-J) 1066 1054 11 1M
99,00% 1,00% Israel Arabs (IL-A) 152 152 -
100,00% Japan (JP) 1102 1102 -
100,00% Latvia (LV) 1000 793 207
79,30% 20,70% New Zealand (NZ) 1036 968 55 13M
94,60% 5,40% Norway (NO) 1469 1396 53 20M
96,30% 3,70% Poland (PL) 1277 1277 -
100,00% Portugal (PT) 1602 1514 88
94,50% 5,50% Philippines (PH) 1200 1200 -
100,00% Russia (RU) 2383 2379 4
99,80% 0,20% Slovak Republic (SK) 1152 1149 3
99,70% 0,30% Slovenia (SI) 1093 1088 4 1M
99,60% 0,40% South Africa (ZA) 2483 2389 30 64M
98,80% 1,20% South Korea (KR) 1315 1315 -
100,00% Spain (ES) 1212 1181 23 8M
98,10% 1,90% Sweden (SE) 1186 1135 43 8M
96,30% 3,70% Switzerland (CH) 1037 940 97
90,60% 9,40% Taiwan (TW) 2016 2015 1
100,00% % United States (US) 1216 1168 47 1M
96,10% 3,90% Uruguay (UY) 1108 1086 22
98,00% 2,00% Venezuela (VE) 1199 1138 61
94,90% 5,10% Sum 44170 41624 2288 258M
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 108
V57 Parents citizen of [country] at birth Missing Values: 0; 8; 9 Q13.- At the time of your birth, were both, one or neither of your parents citizens of [COUNTRY]1,2? 1. Both were citizens of. 2. Only father was a citizen of. 3. Only mother was s citizen of. 4. Neither parent was a citizen of. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: 1 If your country was recently part of a larger political union (e.g. Russia, Slovenia, and the Czech and Slovak Republics), parental citizenship should refer to the preceding national state that your country devolved from. 2 Question not asked in Slovenia
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 109
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 110
V58 Racial-ethnic group of R Missing Values: 0; 998; 999 Q14.- (Racial/ethnic (linguistic, religious) group of respondent. No extra question needed if you have the information in your demographics part. 1
35. Danish,Denmark 83. Maori,NZ Maori 131. Turkey,Turkish 36. Danish+English 84. Maranaw/Maranao 132. Ukraine,Ukrainian 37. English + Persian 85. Masbateño 133. Visayan/Cebuano,Boholano,Leyteno 38. English + Tai 86. Metis 134. Waray 39. English,England&Wales,UK,England 87. Montenegro 135. Welsh 40. Esperanto,Latin,Slavonik,Celtic 88. Moravian 136. Whites (all) 41. Estonia 89. Netherlands,Dutch,Flemish 137. Xitsonga 42. Ethiopia 990. No languages at all 138. Zamboangeño 43. European,White/European,Europe 90. Nordic,Scandinavian other 980. Other African language 44. Europeans Mediterranean 91. North Africans 981. Other,East European 45. Finnish,Finland 92. Norwegian,Norway 982. Other,Middle East 46. French,France 93. Occitan, France 983. Other,Mixed origin 47. Gallego 94. One non-Swedish,both non-Swedish 984. Other,Western European 48. Georgian 95. Oriental
0. Not available (Not asked) 998. Don’t know. 999. No answer. NOTES: (1) Not asked in Australia, Chile, Great Britain, Israel, Poland, South-Africa and Venezuela
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 111
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 120
982 983 984 999
V58 (N) % % % %
Australia (AU) 2183 - - - 79
Austria (AT) 1006 - 7,90% - 11
Bulgaria (BG) 1069 - 1,00% - 6M1 27 22
Canada (CA) 1211 0,10% 2,30% 1,90% 30M
Chile (CL) 1505 - - - 1
Czech Republic (CZ) 1276 - 0,10% - 47M2
Denmark (DK) 1322 - 0,20% -
Finland (FI) 1379 - - - 36
France (FR) 1669 - 2,20% - 72
Germany-West (DE-W) 850 - 8,70% - 19M24
Germany-East (DE-E) 437 - 5,60% - 6M
Great Britain (GB) 873 - - - 1
Hungary (HU) 1021 - 0,10% -
Ireland (IE) 1065 - - - 6M
Israel Jews (IL-J) 1066 - - -
Israel Arabs (IL-A) 152 - - -
Japan (JP) 1102 - - - 10M1
Latvia (LV) 1000 - 0,10% - 12M45
New Zealand (NZ) 1036 - 4,30% - 6
Norway (NO) 1469 - 0,40% - 27M
Poland (PL) 1277 - - - 106
Portugal (PT) 1602 - 7,10% - 102M
Philippines (PH) 1200 - - - 2M10
Russia (RU) 2383 - 0,40% - 6M
Slovak Republic (SK) 1152 - - - 2M7
Slovenia (SI) 1093 - 0,60% - 7M
South Africa (ZA) 2483 - - -
South Korea (KR) 1315 - - - 4
Spain (ES) 1212 - 0,30% - 10M
Sweden (SE) 1186 - - - 18M8
Switzerland (CH) 1037 - 0,80% - 6M2
Taiwan (TW) 2016 - 0,10% - 2M4
United States (US) 1216 - 0,30% - 2M
Uruguay (UY) 1108 - - - 330M
Venezuela (VE) 1199 - - - Sum 44170 1 446 22 650M
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 121
V59 Children born in [Country] of parents who are not citizens should have the right to become [Country Nationality] citizens. Missing Values: 8; 9 Q15.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) a. Children born in [COUNTRY] of parents who are not citizens should have the right to become [COUNTRY NATIONALITY] citizens. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 122
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 123
V60 Children born abroad should have the right to become [Country Nationality] citizens if at least one of their parents is a [Country Nationality] citizen. Missing Values: 8; 9 Q15.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) b. Children born abroad should have the right to become [COUNTRY NATIONALITY] citizens if at least one of their parents is a [COUNTRY NATIONALITY] citizen. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 124
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 125
V61 Legal immigrants to [Country] who are not citizens should have the same rights as [Country Nationality] citizens. Missing Values: 8; 9 Q15.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) c. Legal immigrants to [COUNTRY] who are not citizens should have the same rights as [COUNTRY NATIONALITY] citizens. 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused.
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 126
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 127
V62 [Country] should take stronger measures to exclude illegal immigrants? Missing Values: 8; 9 Q15.- How much do you agree or disagree with the following statements? (Please, tick one box on each line) d. [COUNTRY] should take stronger measures to exclude illegal immigrants? 1 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 8. Can´t choose. 9. Na, refused. Notes: (1) Translation note: “Exclude” means “Keep out” and/or “Expel”
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 128
ZA - No. 3910 I S S P 2003 - National Identity II Page II - 129
V63 How proud are you being country national Missing Values: 0; 8; 9 Q16.- How proud are you of being [COUNTRY NATIONALITY]? (Please, tick one box on each line) 0. I am not [COUNTRY NATIONALITY]. 1. Very proud. 2. Somewhat proud. 3. Not very proud. 4. Not proud at all. 8. Can´t choose. 9. Na, refused.
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ZA - No. 3910 I S S P 2003 - National Identity II Page II - 131
V64 What language speak at home 1st mention Missing Values: 00;998;999 Q17.- What languages do you speak most often at home? At home, I speak: [write down up to two choices]
35. Danish,Denmark 83. Maori,NZ Maori 132. Ukraine,Ukrainian 36. Danish+English 84. Maranaw/Maranao 133. Visayan/Cebuano,Boholano,Leyteno 37. English + Persian 85. Masbateño 134. Waray 38. English + Tai 86. Metis 135. Welsh 39. English,England&Wales,UK,England 87. Montenegro 136. Whites (all) 40. Esperanto,Latin,Slavonik,Celtic 88. Moravian 137. Xitsonga 41. Estonia 89. Netherlands,Dutch,Flemish 138. Zamboangeño 42. Ethiopia 90. Nordic,Scandinavian other 980. Other African language 43. European,White/European,Europe 91. North Africans 981. Other,East European 44. Europeans Mediterranean 92. Norwegian,Norway 982. Other,Middle East 45. Finnish,Finland 93. Occitan, France 983. Other,Mixed origin 46. French,France 94. One non-Swedish,both non-
Swedish 984. Other,Western European
47. Gallego 95. Oriental 990. No languages at all 48. Georgian 96. Ozamisnon
0. Not available (Not asked) 998. Don’t know.
999. No answer. NOTES: (1) Not asked in Austria, Bulgaria, Chile, Germany, Great Britain, Hungary, Norway, Portugal, Philippines, Slovenia, Sweden,Uruguay and Venezuela
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V65 What language speak at home 2nd mention Missing Values: 00;99 Q17.- What languages do you speak most often at home? At home, I speak: [write down up to two choices]
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V66 One nation - Separate nation Missing Values: 0; 8; 9 Q18.- Which of these two statements comes closer to your own view?1 1. It is essential that [COUNTRY] remains one [nation/state/country]2
2. Parts of [Country] should be allowed to become fully separate [nations/states/countries] if they choose to 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes (1) Not asked in Austria, Bulgaria, Chile, Finland, Germany, Great Britain, Hungary, Ireland, Israel, Norway, Philippines, Portugal, Slovenia, South Africa, South Korea, Sweden, United States, Uruguay and Venezuela. (2) Whichever word best applies to COUNTRY
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V67 How close do you feel to your ethnic group Missing Values: 0; 8; 9 Q19a.- How close do you feel to your ethnic group? 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes (1) Not asked in Australia, Austria, Bulgaria, Canada, Chile, Finland, Germany, Great Britain, Hungary, Ireland, Norway, Philippines, Portugal, Slovenia, South Korea, Sweden, Switzerland, Uruguay and Venezuela.
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V68 R: More regional or national identity Missing Values: 0; 8; 9 Q19b.- Some people think of themselves first as [COUNTRY NATIONALITY]. Others may think of themselves first as [region] 1. Which if any, of the following best describes how you see yourself? 2 1. Only [regional identity]. 2. More [regional identity] than [COUNTRY NATIONALITY]. 3. As [regional identity] as [COUNTRY NATIONALITY]. 4. More [COUNTRY NATIONALITY] than [regional identity]. 5. Only [COUNTRY NATIONALITY]. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes (1) “Region” should be replaced by the appropriate subnational category in each COUNTRY (e.g. “Scotland” in Britain; “Catalonia” in Spain). If it is a face-to-face interview, insert the name of the appropriate subnational unit where the interview is being conducted; if it is a self-completion questionnaire, all the relevant subnational units should be mentioned (e.g. “see themselves first as Scottish, English, or Welsh”; “see themselves as only Scottish, English, or Welsh”). (2)Not asked in Australia, Austria, Bulgaria, Canada, Chile, Denmark, Finland, Germany, Great Britain, Hungary, Ireland, Israel, Japan, Latvia, Norway, Philippines, Portugal, Slovenia, South Africa, South Korea, Sweden, Taiwan, United States, Uruguay and Venezuela.
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V69 Heard-read about [the European Union] Missing Values: 0; 8; 9 Q20.- How much have you heard or read about [the European Union] 1,2? 1. A lot. 2. Quite a bit. 3. Not much. 4. Nothing at all. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes (1) Not asked in Australia, Bulgaria, Chile, Denmark, Germany, Great Britain, Hungary, Ireland, Israel, Japan, New Zealand, Philippines, Portugal, Slovenia, South Africa, South Korea, Sweden, Taiwan, Venezuela. (2) Precode: [the European Union]: Take the appropriate association for your continent/subcontinent—EU, NAFTA, etc... Uruguay: refers to MERCOSUR Canada, United States: refers to NAFTA
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V70 Benefits from being member of [the European Union] Missing Values: 0; 8; 9 Q21.- Generally speaking, would you say that [COUNTRY] benefits or does not benefit from being a member of [the European Union]? (Non-members “would benefit” or “would not benefit”)1,2 1. Greatly benefits. 2. Largely benefits. 3. Somewhat benefits. 4. Only a little. 5. Not at all benefit. 0. Not available (Not asked); not applicable, never heard 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Australia, Bulgaria, Chile, Denmark, Germany, Great Britain, Ireland, Israel, Japan, Latvia, New Zealand, Norway, Philippines, Portugal, Slovenia, South Africa, South Korea, Sweden, Taiwan, Venezuela. (2) Precode: take the appropriate association, as in Q20. Scale for non-members of whatever association is used: Would benefit/Would not benefit/Don’t know/Have never heard of [the European Union] Uruguay: refers to MERCOSUR Canada, United States: refers to NAFTA
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V70b Would benefit from being member of [the European Union] Missing Values: 0; 8; 9 Q21.- Generally speaking, would you say that [COUNTRY] would benefit or would not benefit from being a member of [the European Union]? 1,2 1. Would benefit 2. Would not benefit 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Asked only in Latvia and Norway
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V71 [Country] should follow decisions of EU Missing Values: 0; 8; 9 Q22.- How strongly do you agree or disagree with the following statement? (Please, tick one box) 1. Agree strongly. 2. Agree. 3. Neither agree nor disagree. 4. Disagree. 5. Disagree strongly. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Australia, Bulgaria, Canada, Chile, Denmark, Germany, Great Britain, Hungary, Ireland, Israel, Japan, New Zealand, Philippines, Portugal, Russia, Slovenia, South Africa, South Korea, Switzerland, Taiwan and Venezuela. (2) Precode: take the appropriate association, as in 20 Uruguay: refers to MERCOSUR United States: refers to NAFTA
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V72 EU should have more power than national government Missing Values: 0; 8; 9 Q23.- Generally, do you think that the European Union should have... Much more, more, as much, less, or much less power than the national governments of its member states?1,2 1. Much more. 2. More. 3. As much. 4. Less. 5. Much less. 0. Not available (Not asked) ; Not applicable 8. Can´t choose. 9. Na, refused. Notes: (1) Not asked in Australia, Bulgaria, Canada, Chile, Germany, Great Britain, Ireland, Israel, Japan, New Zealand, Philippines, Russia, Slovenia, South Africa, South Korea, Taiwan, United States, Uruguay and Venezuela. (2) Precode: take the appropriate association, as in 20
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V73 Vote if referendum to become new member Missing Values: 0; 8; 9 Q24.- [For prospective EU members only]. If there were a referendum1 today to decide whether [COUNTRY] does or does not become a member of the European Union, would you vote in favor or would you vote against? 2,3
1. Vote in favor 2. Vote against
0. Not available (Not asked); Not applicable 8. Can´t choose. 9. Na, refused. Notes: (1) If Referenda are not possible in COUNTRY, use the word “vote” (2) Not asked in Australia, Austria, Bulgaria, Canada, Chile, Denmark, Finland, France, Germany, Great Britain, Ireland, Japan, New Zealand, Poland, Portugal, Philippines, Russia, Slovenia, South Africa, South Korea, Sweden, Switzerland, Taiwan, United States, Uruguay and Venezuela. (3) Precode: take the appropriate association, as in 20
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0 1 2 8 9
V73 (N) % % % % %
Australia (AU) 2183 2183M - -
Austria (AT) 1006 1006M - -
Bulgaria (BG) 1069 1069M - -
Canada (CA) 1211 1211M - -
Chile (CL) 1505 1505M - - 655 291
Czech Republic (CZ) 1276 69,20% 30,80% 164M 166M
Denmark (DK) 1322 1322M - -
Finland (FI) 1379 1379M - -
France (FR) 1669 1669M - -
Germany-West (DE-W) 850 850M - -
Germany-East (DE-E) 437 437M - -
Great Britain (GB) 873 873M - - 626 165
Hungary (HU) 1021 79,10% 20,90% 56M 174M
Ireland (IE) 1065 1065M - - 768 187
Israel Jews (IL-J) 1066 80,40% 19,60% 91M 20M121 11
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V74 EU members: Referendum to remain member Missing Values: 0; 8; 9 Q24b.- [For current EU members only]. If there were a referendum1 today to decide whether [COUNTRY] does or does not remain a member of the European Union, would you vote in favor or would you vote against? 2,3 1. Vote in favour. 2. Vote against. 0. Not available (Not asked) 8. Can´t choose. 9. Na, refused. Notes: (1) If Referenda are not possible in COUNTRY, use the word “vote” (2) Not asked in Australia, Austria, Bulgaria, Canada, Chile, Denmark, Finland, France, Germany, Great Britain, Ireland, Japan, New Zealand, Poland, Portugal, Philippines, Russia, Slovenia, South Africa, South Korea, Sweden, Switzerland, Taiwan, United States, Uruguay and Venezuela. (3) Precode: take the appropriate association, as in 20 Uruguay: refers to MERCOSUR
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MARITAL R's marital status Missing Values: 9 Marital. Marital status of respondent (legal status) 1. Married. 2. Widowed. 3. Divorced. 4. Separated, but married1 5. Single, never married. 9. Na, refused. NOTES: (1)Category 4 not asked in Sweden
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COHAB: R: Steady life-partner Missing Values: 0;9 Cohab:. (If 'not married and living together with spouse'). Do you have/live together with a partner? 1 1. Yes. 2. No. 0. Not available (Not asked) 9. Na, refused. NOTES: (1) Not asked in South Korea
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EDUCYRS: R's Education I: years in school Missing Values: 00;98;99 Educyrs. Education I - years (of full time) schooling including university but not vocational training. 00. Not available. nn. Number of years 95. Still at school,N:+uni. 96. Still at college,uni. 97. No form school. 98. Don't know 99. No answer, refused
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XX_DEGR: Country Specific Education Degree Missing Values: Education Country Specific - highest education level / degree. 0. Not available (Not asked) xx. Country specific code 97. None, No formal education, 98. Don’t know 99. No answer Australia
AU_DEGR n % 1. Did not complete High School to Year 10 337 15,4 2. Completed High School to Year 10 342 15,7 3. Completed High School to Year 12 255 11,7 4. Trade qualification or apprenticeship 263 12,0 5. Certificate or Diploma (TAFE or business college) 465 21,3 6. Bachelor Degree (including Honors) 279 12,8 7. Postgraduate Degree or Postgraduate Diploma 194 8,9 99. NA 48 2,2 (N) 2183 100% Austria (Note: Labels not available) AT_DEGR n % 1. Compulsory school without apprenticeship 202 20,1 2. Compulsory school with apprenticeship 358 35,6 3. Middle school vocational 148 14,7 4. Secondary general completed, Matura 88 8,7 5. Higher vocational, technical school 102 10,1 6. University 106 10,5 99. NA 2 0,2 (N) 1006 100% Bulgaria BG_DEGR n % 2. Primary 99 9,3 3. Lower secondary 265 24,8 4. Upper secondary 187 17,5 5. Secondary technical 300 28,1 6. College 50 4,7 7. Higher 130 12,2 8. University and higher 13 1,2 97. No formal schooling 20 1,9 99. NA 5 0,5 (N) 1069 100%
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Canada CA_DEGR n % 2. Some grade school 25 2,1 3. Finshed grade school 52 4,3 4. Some high school 136 11,2 5. Finished high school 299 24,7 6. College/CEGEP/some university 357 29,5 7. Completed university 205 16,9 8. Graduate studies 110 9,1 97. None, still at school, university 7 0,6 99. NA,refused 20 1,7 (N) 1211 100% Chile CL_DEGR n % 2. Incomplete primary 331 22,0 3. Primary completed 149 9,9 4. Incomplete secondary 194 12,9 5. Secondary completed 382 25,4 6. University incompleted 87 5,8 7. University completed 136 9,0 8. Incompl non-university higher 43 2,9 9. Completed non-university higher 125 8,3 97. None 50 3,3 99. NA 8 0,5 (N) 1505 100%
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Denmark DK_DEGR n % 1. 7 years primary school or shorter 197 14,9 2. 8 years primary school 64 4,8 3. 9 years primary school or similar 138 10,4 4. 10 years primary school or similar 308 23,3 5. Gymnasium, general 350 26,5 6. Gymnasium, technical & commercial 151 11,4 7. Other 85 6,4 99. No Answer 29 2,2 (N) 1322 100% Finland FI_DEGR n % 2. Primary school 175 12,7 3. Comprehensive (primary + lower secondary) school 98 7,1 4. Post-comprehensive vocational school or course 297 21,5 5. Post-comprehensive upper secondary school 135 9,8 6. Post-comprehensive vocational college 305 22,1 7. Polytechnic 66 4,8 8. University, lower academic degree (BA) 60 4,4 9. University, higher academic degree (MA) 147 10,7 97. None; still at school (comprehensive,upper second., vocat.) 68 4,9 99. Na 28 2,0 (N) 1379 100% France FR_DEGR n % 2. Primary incomplete 44 2,6 3. Primary completed 161 9,6 4. Gen. sec. lev 1 111 6,7 5. Vocat.sec. lev 1 300 18,0 6. Voc. sec. lev 2 68 4,1 7. Incpl gen.sec. lev 2 125 7,5 8. Gen.sec.lev 2 115 6,9 9. College 241 14,4 10. University 451 27,0 97. None 32 1,9 99. NA 21 1,3 (N) 1669 100%
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Germany - West DE_DEGR n % 2. School left without qualification 27 3,23. Lower secondary (Hauptschule) 378 44,54. Middle school (Mittlere Reife) 216 25,45. Secondary technical (Fachhochschulreife) 41 4,86. Higher secondary (Abitur) 69 8,17. Special university qualification (Fachhochschulabschluss) 44 5,28. University 68 8,09. Other qualification 1 0,197. Still at school 5 0,699. Na, refused 1 0,1(N) 850 100%
Germany - East DE_DEGR n % 2. School left without qualification 4 0,93. Lower secondary (Hauptschule) 126 28,84. Middle school (Mittlere Reife) 199 45,55. Secondary technical (Fachhochschulreife) 8 1,86. Higher secondary (Abitur) 26 5,97. Special university qualification (Fachhochschulabschluss) 20 4,68. University 43 9,89. Other qualification 2 0,597. Still at school 8 1,899. Na, refused 1 0,9(N) 437 100%
Great Britain GB_DEGR n % 2. CSE or equilvalent 104 11,9 3. O-level or equivalent 163 18,7 4. A-level or equivalent 122 14,0 5. Higher below degree level 115 13,2 6. Degree, university o CNAA o diploma 129 14,8 7. Foreign or other 11 1,3 97. No sec. qualifications 228 26,1 99. DK, NA 1 0,1 (N) 873 100% Hungary HU_DEGR n % 2. Less than primary 99 9,7 3. 8 years elementary 248 24,3 4. Vocational 252 24,7 6. Sec. technical+matura 137 13,4 7. Gymnasium, matura 100 9,8 8. College 132 12,9 9. University 50 4,9 99. NA 3 0,3 (N) 1021 100%
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Ireland IE_DEGR n % 2. Incomplete primary 31 2,9 3. Primary complete 167 15,7 4. Incomplete first cycle secondary 63 5,9 5. Complete first cycle secondary 177 16,6 6. Secondary complete 301 28,3 7. Incomplete third level 53 5,0 8. Cert or Diploma 103 9,7 9. University first degree 98 9,2 10. University degree Masters or higher 65 6,1 97. None 7 0,7 (N) 1065 100%
Israel - Jews IL_DEGR n % 1. primary 52 4,92. Incpl vocational 41 3,83. Vocational compl 75 7,04. Vocational with matriculation 74 6,95. Incpl academic 44 4,16. academic compl 60 5,67. academic with matriculation 142 13,38. Yeshiva 10 0,99. Yeshiva with matriculation 3 0,310. post secondary 182 17,111. Incpl university 62 5,812. university compl (BA or more) 295 27,797. No formal education 19 1,899. NA 7 0,7(N) 1066 100% Israel - Arabs IL_DEGR n % 1. primary 10 6,62. Incpl vocational 3 2,03. Vocational compl 8 5,34. Vocational with matriculation 13 8,65. Incpl academic 13 8,66. academic compl 4 2,67. academic with matriculation 30 19,78. Yeshiva 1 0,79. Yeshiva with matriculation 10. post secondary 17 6,611. Incpl university 28 18,412. university compl (BA or more) 22 14,597. No formal education 1 0,799. NA 2 1,3(N) 152 100%
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Japan JP_DEGR n % 1. Secondary completed 248 22,5 2. High school completed 483 43,8 3. Junior college compl 135 12,3 4. Finished university 148 13,4 5. Still at High school 38 3,4 6. Still at Junior college 28 2,5 7. Others 1 0,1 99. NA 21 1,9 (N) 1102 100% Latvia LV_DEGR n % (N) 0 100% New Zealand NZ_DEGR n % 2. School qualifications only 265 25,6 3. Trade or professional cert 228 22,0 4. Diploma below Bachelors 101 9,7 5. Bachelors degree 97 9,4 6. Post-grad or higher 62 6,0 97. No formal qualification 250 24,1 99. NA 33 3,2 (N) 1036 100% Norway NO_DEGR n % 1. Primary 145 9,9 2. Sec. vocational, inc 91 6,2 3. Sec. academic, inc 148 10,1 4. Sec. voc., compl 225 15,3 5. Sec. acad., compl 256 17,4 6. Univ.,coll. 43 2,9 7. Univ., coll. 1-2 yrs 118 8,0 8. Univ., coll. 3-4 yrs 274 18,7 9. Univ., coll. >= 5 yrs 147 10,0 99. NA 22 1,5 (N) 1469 100%
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Poland PL_DEGR n % 2. Incomplete elementary 42 3,3 3. Elementary 279 21,8 4. Basic vocational 353 27,6 5. Incomplete secondary 51 4,0 6. Secondary general 95 7,4 7. Secondary vocational 232 18,2 8. Post secondary 44 3,4 9. Incomplete higher 25 2,0 10. Completed higher 151 11,8 97. No formal schooling 5 0,4 (N) 1277 100% Portugal PT_DEGR n % 1. Basic -level 1 693 43,3 2. Basic-levels 2 and 3 283 17,7 3. Secondary incomplete 195 12,2 4. Complete secondary 85 5,3 5. University incomplete 99 6,2 6. Complete university 140 8,7 97. No formal qualification 101 6,3 98. DK 1 0,1 99. NA 5 0,3 (N) 1602 100% Philippines PH_DEGR n % 2. Incomplete primary 121 10,1 3. Primary completed 148 12,3 4. Incomplete secondary 160 13,3 5. Secondary completed 314 26,2 6. Some Vocational 26 2,2 7. Completed vocational 59 4,9 8. Some college 170 14,2 9. Completed college 169 14,1 10. Post college 9 0,8 97. None 24 2,0 (N) 1200 100% Russia RU_DEGR n % 2. Incompleted primary 2 0,1 3. Primary completed 103 4,3 4. Incompleted secondary 355 14,9 5. Secondary completed 1148 48,2 6. Semi-higher, incompleted university 57 2,4 7. University comleted 490 20,6 97. None, still at school, university 228 9,6 (N) 2383 100%
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Slovakia SK_DEGR n % 1. Incomplete primary 89 7,7 2. Primary completed 347 30,1 3. General sec.,no diploma 365 31,7 4. General secondary 91 7,9 5. Tertiary,master degree 196 17,0 97. No formal education 9 0,8 99. No answer 55 4,8 (N) 1152 100% Slovenia SI_DEGR n % 1. Incomplete primary 58 5,3 2. Primary completed 221 20,2 3. Incomplete vocational 27 2,5 4. 2-3 yrs vocational 276 25,3 5. 4 yrs middle school 322 29,5 6. Incomplete university 27 2,5 7. Higher degree compl 51 4,7 8. University compl 106 9,7 99. NA 5 0,5 (N) 1093 100% South Africa ZA_DEGR n % 1. Grade 0 5 0,2 2. Sub A/Grade 1 29 1,2 3. Sub B/Grade 2 25 1,0 4. Grade 3/Standard 1 54 2,2 5. Grade 4/Standard 2 65 2,6 6. Grade 5/Standard 3 102 4,1 7. Grade 6/Standard 4 122 4,9 8. Grade 7/Standard 5 168 6,8 9. Grade 8/Standard 6/Form 1 230 9,3 10. Grade 9/Standard 7/Form 2 168 6,8 11. Grade 10/Standard 8/Form 3 273 11,0 12. Grade 11/Standard 9/Form 4 188 7,6 13. Grade 12/Standard 10/Form 5/Matric 534 21,5 14. NTC I 5 0,2 15. NTC II 7 0,3 16. NTC III 9 0,4 17. Diploma/certificate with 35 1,4 18. Diploma/certificate with Grade 12/Std 10 80 3,2 19. Degree 71 2,9 20. Postgraduate degree or diploma 65 2,6 21. Other, specify 26 1,0 97. No schooling 200 8,1 98. Do not know 1 99. NA, NAP, NAV 21 0,8 (N) 2483 100%
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South Korea KR_DEGR n % 1. Elementary incompleted 22 1,7 2. Elementary completed 85 6,8 3. Middle school incompleted 16 1,3 4. Middle school completed 107 8,5 5. High school incompleted 33 2,6 6. High school completed 405 32,2 7. Junior college incompleted 42 3,3 8. Junior college completed 125 9,9 9. University incompleted 117 9,3 10. University completed 242 19,2 11. Graduate school incompleted 10 0,8 12. Graduate school completed 40 3,2 99. NA 15 1,2 (N) 1259 100% Spain ES_DEGR n % 1. Incpl primary 162 13,4 2. Primary compl 224 18,5 3. Incpl secondary 319 26,3 4. Vocational school 116 9,6 5. Secondary compl 88 7,3 6. COU - PREU 91 7,5 7. Incpl university 93 7,7 8. University compl 99 8,2 97. None 18 1,5 99. NA 2 0,2 (N) 1212 100% Sweden SE_DEGR n % 1. Primary or comprehensive 255 21,5 2. Vocational school (1972-92) 137 11,6 3. Vocational school (post 1992) 50 4,2 4. Vocational school (pre 1972) 86 7,3 5. Alternative secondary school 24 2,0 6. Lower secondary school 48 4,0 7. 3 o 4 yr gymnasium 130 11,0 8. Gymnasium (academic) 28 2,4 9. Higher sec. school 32 2,7 10. University studies without degree 115 9,7 11. University degree 262 22,1 99. NA 19 1,6 (N) 1186 100%
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Switzerland CH_DEGR n % 1. Incomplete compulsory education 7 0,7 2. Compulsory education 110 10,6 3. Elementary vocational training (enterprise + school) 36 3,5 4. Secondary education (maturity, teacher training college) 72 6,9 5. Graduation diploma school (vocational maturity) 19 1,8 6. 1 year: trade school,adm educ,domestic science/ling course 33 3,2 7. Apprenticeship 361 34,8 8. 2 to 3 years: general training school, administrative edu 23 2,2 9. 2 to 3 years: full time vocational education 53 5,1 10. Higher vocational education, federal diploma 90 8,7 11. Tech or vocational college (2 yrs full time/3 yrs part time) 24 2,3 12. Advanced tech college (3 yrs full time/4 yrs part time) 72 6,9 13. University (3 years, short bachelor´s degree) 12 1,2 14. University, tech uni (4 yrs and more, bachelor´s degree) 71 6,8 15. University, tech higher spec uni (masters, post-grade) 46 4,4 97. Other education 6 0,6 98. Don´t know 1 0,1 99. NA 1 0,1 (N) 1037 100% Taiwan TW_DEGR n % 2. Self-study 21 1,0 3. Elementary school 363 18,0 4. Junior high school¡BVocational junior high school 281 13,9 5. High school(general subject)¡BHigh school(vocational subject 540 26,8 6. Five-year vocational school(after junior high) 72 3,6 7. two-year vocation school(after vocational high school) 208 10,3 8. Three-year vocational school(after high school of general su 24 1,2 9. Cadet school 4 0,2 10. Military/Police training course 5 0,2 11. Military/Police specialist course¡BMilitary/Police officers 24 1,2 12. College 252 12,5 13. Graduate school(M.A. degree) 54 2,7 14. Graduate school(Ph.D degree) 9 0,4 15. other 1 97. None 158 7,8 (N) 2016 100% United States US_DEGR n % 1. LT high school 135 11,1 2. High school 608 50,0 3. Junior college 104 8,6 4. Bachelor 249 20,5 5. Graduate 120 9,9 (N) 1216 100%
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Uruguay UY_DEGR n % 2. Incomplete primary school 139 12,5 3. Completed primary school 209 18,9 4. Incomplete secondary 289 26,1 5. Completed secondary 112 10,1 6. Incomplete technical education 57 5,1 7. Completed technical education 74 6,7 8. Incomplete university 88 7,9 9. Completed university 69 6,2 10. Incomplete non university high education 14 1,3 11. Completed non university high education 39 3,5 97. None 18 1,6 (N) 1108 100% Venezuela VE_DEGR n % 1. Before primary school 4 0,3 2. Basic school (1-9) / Primary (1-6) 608 50,7 3. Special education 1 0,1 4. High school (1-2) 209 17,4 5. Technical school (1-3) 34 2,8 6. Secondary school (1-5) 46 3,8 7. University Technician 92 7,7 8. University 157 13,1 97. None 45 3,8 98. Dont know 2 0,2 99. Na 1 0,1 (N) 1199 100%
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WRKST: R's current employment status Missing Values: 97;98;99 Wrkst. Current employment status, current economic position, main source of living. . 01. Employed-full time. 02. Employed-part time. 03. Employe less than part-time. 04. Helping family member. 05. Unemployed. 06. Student,school,vocational training 07. Retired. 08. Housewife,home duties. 09. Permanently disabled. 10. Other,not in labour force. 97. Refused. 98. Don't know. 99- No answer.
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SPWRKST: R's Spouse-Partner Current employment status Missing Values: 00;97;98;99 Spwrkst. Spouse / partner: current employment status, current economic position, main source of living. (1) 01. Employed-full time. 02. Employed-part time. 03. Employe less than part-time. 04. Helping family member. 05. Unemployed. 06. Student,school,vocational training 07. Retired. 08. Housewife,home duties. 09. Permanently disabled. 10. Other,not in labour force. 0. NAV;Not married;no spouse/partner, Not asked 97. Refused. 98. Don't know. 99. No asnwer. Notes: (1) Not asked in Norway and Venezuela
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WRKHRS: R's hours worked weekly Missing Values: 00;97;98;99 Wrkhrs. Working hours - number of hours (usually) worked weekly. . xx. Number of hours 00. Not applicable, not available. 97. Refused. 98. DK,cant say,varies too much. 99. No answer. Notes: (1) Not asked in South Africa
WRKHRS Count Minimum Maximum Mean Std Deviation Valid N
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ISCO88: R: Occupation ILO,ISCO 1988 4-digit Missing Values: 0;9997;9998;9999 ISCO88. Respondent occupation, using ISCO 1998 4-digit notation 1 xxxx ISCO code 110. Armed Forces 1000. Legislators, Senior Officials and Managers 2000. Professionals 3000. Technicians and Associate Professionals 4000. Clerks 5000. Service Workers and Shop Market Sales workers 6000. Skilled Agricultural and Fishery workers 7000. Craft and Related Trades workers 8000. Plant and Machine Operators and Assemblers 9000. Elementary Occupations 9996. Not otherwise specified 0. NAP, NAV, Not asked; Never had a job 9997. Refused. 9998. Dont know. 9999. NA. Notes: (1) Venezuela used a different notation. France used 3-digit ISCO. South-Africa used 1-digit ISCO. Table shows 1 digit ISCO distribution for simplicity
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SPISCO88: SP: Occupation ILO,ISCO 1988 4-digit Missing Values: 0;9997;9998;9999 SPISCO88. Spouse/partner occupation, using ISCO 1998 4-digit notation 1,2 xxxx ISCO code 110. Armed Forces 1000. Legislators, Senior Officials and Managers 2000. Professionals 3000. Technicians and Associate Professionals 4000. Clerks 5000. Service Workers and Shop Market Sales workers 6000. Skilled Agricultural and Fishery workers 7000. Craft and Related Trades workers 8000. Plant and Machine Operators and Assemblers 9000. Elementary Occupations 9996. Not otherwise specified 0. NAP, NAV, Not asked; Never had a job; No spouse 9997. Refused. 9998. Dont know. 9999. NA. Notes: (1) Venezuela used a different notation. France used 3-digit ISCO. South-Africa used 1-digit ISCO. (2) Not asked in Japan Table shows 1 digit ISCO distribution for simplicity
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WRKTYPE: R: Working for private,public sector, selfemployed Missing Values: 0;9 Wrktype. Private vs. public - Working for private versus public sector 1 1. Work for government. 2. Public owned firm,national industry 3. Private. 4. Self employed. 5. BG: Cooperative firm. 6. GB: Other. 0. Not applicable, not available; Not asked 9. No answer, don't know. Notes: (1) Not asked in South Africa
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SPWRKTYP: Rs Spouse-Partner working for private,public sector, selfemployed Missing Values: 0;9 Spwrktyp. Spouse-Partner: Private vs. public - Working for private versus public sector1. 1. Work for government. 2. Public owned firm,national industry 3. Private. 4. Self employed. 5. BG: Cooperative firm. 6. GB: Other. 0. NAV;Not married;no spouse/partner, Not asked 9. No answer, don't know. Notes: (1) Not asked in Bulgaria, Canada, Israel, Norway, South Africa and United States
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NEMPLOY: Self-employment II - how many employees Missing Values: 0;9997;9998;9999 Nemploy. (If self employed) Do you have any employees, If so, how many? (exact number of employees). 1 . xxxx number of employees. 0. NAP, NAV, Not asked 9995. No employee. 9997. Refused. 9998. Dont know. 9999. NA. Notes: (1) Not asked in Austria, South Africa NEMPLOY Count Minimum Maximum Mean Std Deviation Valid N Australia (AU) 2183 1 1000 19,7 96,58 N=135 Austria (AT) 1006 , , , , N=0 Bulgaria (BG) 1069 1 20 3,7 3,87 N=31 Canada (CA) 1211 1 425 19,5 60,46 N=83 Czech Republic (CZ) 1276 1 30 5,6 6,59 N=24 Chile (CL) 1505 1 300 15,0 41,11 N=109 Denmark (DK) 1322 1 120 9,3 16,89 N=73 Finland (FI) 1379 1 50 5,6 8,42 N=58 France (FR) 1669 1 60 5,6 10,04 N=34 Germany-East (DE-E) 437 9 50 16,2 15,51 N=23 Germany-West (DE-W) 850 9 49 12,3 9,78 N=47 Great Britain (GB) 873 1 100 11,4 22,32 N=28 Hungary (HU) 1021 1 160 19,8 33,61 N=86 Ireland (IE) 1065 1 30 2,8 4,67 N=109 Israel Arabs (IL-A) 152 1 4 2,3 1,04 N=8 Israel Jews (IL-J) 1066 1 30 4,8 5,67 N=38 Japan (JP) 1102 1 449 11,9 53,26 N=136 Latvia (LV) 1000 1 40 5,9 9,46 N=50 New Zealand (NZ) 1036 1 170 6,4 18,45 N=87 Norway (NO) 1469 1 250 13,0 34,18 N=59 Philippines (PH) 1200 1 20 4,3 5,01 N=32 Poland (PL) 1277 1 85 5,5 11,74 N=106 Portugal (PT) 1602 1 400 13,0 52,54 N=61 Russia (RU) 2383 2 300 38,0 98,31 N=9 Slovak Republic (SK) 1152 1 120 11,5 23,89 N=35 Slovenia (SI) 1093 1 988 41,7 161,41 N=61 South Africa (ZA) 2483 , , , , N=0 South Korea (KR) 1315 1 20 5,2 5,02 N=58 Spain (ES) 1212 1 50 6,3 9,41 N=35 Sweden (SE) 1186 1 22 3,4 4,95 N=31 Switzerland (CH) 1037 1 60 3,8 7,76 N=126 Taiwan (TW) 2016 1 300 10,4 34,10 N=165 United States (US) 1216 1 30 7,7 8,10 N=55 Uruguay (UY) 1108 1 50 7,0 11,07 N=47 Venezuela (VE) 1199 1 658 14,2 49,43 N=219
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WRKSUP: R Supervises Missing Values: 0;7;8;9 Wrksup. R Supervises – Do you supervise others at work?1. . 1. Yes. 2. No. 0. Not applicable, unemployed, not in labour force; Not asked 7. Refused. 8. Don't know. 9. No answer. Notes: (1) Not asked in South Africa
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UNION: R's syndical affiliation Missing Values: 0;8;9 Union. Is respondent member of a trade union? 1. 1. Currently member. 2. Once member, not now. 3. Never member. 0. NAP;Unemployed, etc; Not asked 8. Don't know. 9. No answer, refused. Notes: (1) Not asked in South Africa
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0 1 2 3 8 9
UNION (N) % % % % % % 382 769 855
Australia (AU) 2183 19,00% 38,30% 42,60% 177M366 143 478
Austria (AT) 1006 37,10% 14,50% 48,40% 19M 93 472 486
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HOMPOP: R's size of household Missing Values: 00;98;99 Hompop. Number of people in household1. xx. xx persons2. 00. Not applicable, not available; Not asked 98. Don't know. 99. No answer, refused. Notes: (1) Not asked in South Africa (2) Venezuela: 2= 2 or more persons Spain: 9= 9 or more persons Sweden: 8= 8 or more persons
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INCOME: Family income Missing values 0;999997;999998;999999 INCOME. Family income in [Country] currency 1..999996 Income 0. Not available; No income; Not asked 999997. Refused 999998. Don’t know 999999. No answer Notes: This variable is country specific, even it if is integrated in the same variable. Some countries have coded the income as mid points of intervals that were asked to respondents. Unless specified otherwise, values are monthly incomes. Each country provided values in their own currency. An approximate exchange rate to the Euro at the field time is given for conversion purposes only (no conversion is provided within data itself). AUSTRALIA Respondent was asked for the Annual Income Mid points (Amounts in Australian $; 1 Australian Dollar = 0.62033 Euro) 1040 3120 5200 7280 9360 13000 18200 23400 25600 33800 39000 46800 65000 91000 104000 AUSTRIA Mid points (Amounts in Euros) 160 385 552 675 825 975 1125 1275 1425 1575 1725 1875 2025 2175 2325 2550 2850 3150 3450 3750 5000 BULGARIA Absolute values (Amounts in Bulgarian Levs; 1 Bulgarian Lev = 0.50826 Euro) 0: No income or No answer
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CANADA Respondent was asked for the Annual Income before taxes Mid-points (Amounts in Canadian $; 1 Canadian Dollar = 0.68516 Euro) Under $15.000 7500 $15.000-$24.999 20000 $25.000-$34.999 30000 $35.000-$44.999 40000 $45.000-$54.999 50000 $55.000-$64.999 60000 $65.000-$74.999 70000 $75.000+ 80000 CZECH REPUBLIC Mid-points (Amounts in Czech Crowns; 1 Czech Koruna = 0.03319 Euro) 4000,00 9000,00 11000,00 13000,00 15000,00 17000,00 19000,00 21500,00 24500,00 28000,00 32500,00 37500,00 45000,00 55000,00 67500,00 82500,00 97500,00 CHILE Mid points (Amounts in Chilean Pesos; 1 Chilean Peso = 0.001428 Euro) 17500 Less than $35.000 45000 $35.001 to $56.000 58000 $56.001 to $78.000 89500 $78.001 to $101.000 127500 $101.001 to $134.000 156500 $134.001 to $179.000 201500 $179.001 to $224.000 257500 $224.001 to $291.000 324500 $291.001 to $358.000 403000 $358.001 to $448.000 724000 $448.001 to $1.000.000 1500000 $1.000.001 to $2.000.000 2500000 $2.000.001 to $3.000.000 3500000 More than $3.000.000 DENMARK Respondent were asked total annual income Mid points (Amounts in Danish Crowns; 1 Danish Krone = 0.13412 Euro) 50000 'below dkr 100.000 125000 'dkr 100.000 to 149.999 175000 'dkr 150.000 to 199.999 225000 'dkr 200.000 to 249.999 275000 'dkr 250.000 to 299.999 350000 'dkr 300.000 to 399.999 450000 'dkr 400.000 to 499.999 550000 'dkr 500.000 to 599.999 650000 'dkr 600.000 to 699.999 750000 'dkr 700.000 to 799.999
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850000 'dkr 800.000 to 899.999 950000 'dkr 900.000 to 999.999 1050000 'dkr 1 million or more FINLAND Continuous values (Amounts in Finnish Markka; 1 Finnish Markka = 0.16819 Euro) FRANCE Mid points (Amounts in Euros) 305 euros 610 euros 915 euros 1296 euros 1906 euros 2668 euros 3430 euros 4192 euros 5335 euros 6860 euros 9909 euros GERMANY Continuous values (Amounts in Euros) GREAT BRITAIN Respondent were asked total annual income Mid points (Amounts in English Pounds; 1 British Pound = 1.45298 Euro) 2000 < 3 999 5000 4 000 - 5 999 7000 6 000 - 7 999 9000 8 000 - 9 999 11000 10 000 - 11 999 13500 12 000 - 14 999 16500 15 000 - 17 999 19000 18 000 - 19 999 21500 20 000 - 22 999 24500 23 000 - 25 999 27500 26 000 - 28 999 30500 29 000 - 31 999 35000 32 000 - 37 999 41000 38 000 - 43 999 47000 44 000 - 49 999 53000 50 000 - 55 999 59000 56 000 or more HUNGARY Continuous values (Amounts in Hungarian Forints; 1 Hungarian Forint = 0.004051 Euro) IRELAND Respondent were asked total annual income Mid points (Amounts in Euros) 6900 9025 13575 18700 24450 31000 38325 47500 61175 75000
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ISRAEL Mid points (Amounts in Israeli Shekel; 1 Israeli New Shekel = 0.18203 Euro) 2250,00 5000,00 6000,00 7000,00 8000,00 9500,00 12000,00 14500,00 17750,00 22000,00 JAPAN Mid points (Amounts in Yens; 1 Japanese Yen = 0.007445 Euro) 1000 2500 3500 4500 5500 6500 7500 8500 9500 11000 13500 20000 LATVIA Continuous values (Amounts in Lats; 1 Latvian Lats = 1.43748 Euro) NEW ZEALAND Respondent were asked total annual income Mid points (Amounts in 1 New Zealand Dollar = 0.56331 Euro) 7500 17500 25000 35000 45000 55000 65000 75000 90000 120000 NORWAY Continuous values (Amounts in thousands Norwegian Kroner; 1 Norwegian Kroner = 0.12702 Euro) PHILIPPINES Continuous values (Amounts in Philippines Pesos ; 1 Philippine Peso = 0.01491 Euro) POLAND Continuous values (Amounts in Polish Zlotys; 1 Polish Zloty = 0.24569 Euro) PORTUGAL Mid points (Amounts in Euros) 175 < or=350 425 351-500 euros 650 501-800 euros 1150 801-1500 euros 2000 1501-2500 euros 5000 >2500 euros
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RINCOME: Respondent’s earnings RINCOME. Respondent earnings in [Country] currency 1..999996 R’s Income 0. Not available; No income; Not asked 999997. Refused 999998. Don’t know 999999. No answer Notes: This variable is country specific, even it if is integrated in the same variable. Some countries have coded the income as mid points of intervals that were asked to respondents. Unless specified otherwise, values are monthly incomes. Each country provided values in their own currency. An approximate exchange rate to the Euro at the field time is given for conversion purposes only (no conversion is provided within data itself). AUSTRALIA Respondent was asked for the Annual Income Mid points (Amounts in Australian $; 1 Australian Dollar = 0.62033 Euro) 1040 3120 5200 7280 9360 13000 18200 23400 25600 33800 39000 46800 65000 91000 104000 AUSTRIA Mid points (Amounts in Euros) 160 385 552 675 825 975 1125 1275 1425 1575 1725 1875 2025 2175 2325 2550 2850 3150 3450 3750 5000
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BULGARIA Absolute values (Amounts in Bulgarian Levs; 1 Bulgarian Lev = 0.50826 Euro) 0: No income or No answer CANADA Respondent was asked for the Annual Income before taxes Mid-points (Amounts in Canadian $; 1 Canadian Dollar = 0.68516 Euro) Under $15.000 7500 $15.000-$24.999 20000 $25.000-$34.999 30000 $35.000-$44.999 40000 $45.000-$54.999 50000 $55.000-$64.999 60000 $65.000-$74.999 70000 $75.000+ 80000 CZECH REPUBLIC Mid-points (Amounts in Czech Crowns; 1 Czech Koruna = 0.03319 Euro) 3000,00 6750,00 8250,00 9500,00 10500,00 11500,00 12750,00 14250,00 16000,00 18500,00 22500,00 27500,00 32500,00 37500,00 45000,00 55000,00 CHILE Mid points (Amounts in Chilean Pesos; 1 Chilean Peso = 0.001428 Euro) 17500 Less than $35.000 45000 $35.001 to $56.000 58000 $56.001 to $78.000 89500 $78.001 to $101.000 127500 $101.001 to $134.000 156500 $134.001 to $179.000 201500 $179.001 to $224.000 257500 $224.001 to $291.000 324500 $291.001 to $358.000 403000 $358.001 to $448.000 724000 $448.001 to $1.000.000 1500000 $1.000.001 to $2.000.000 2500000 $2.000.001 to $3.000.000 3500000 More than $3.000.000
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DENMARK Respondent were asked total annual income Mid points (Amounts in Danish Crowns; 1 Danish Krone = 0.13412 Euro) 50000 'below dkr 100.000 125000 'dkr 100.000 to 149.999 175000 'dkr 150.000 to 199.999 225000 'dkr 200.000 to 249.999 275000 'dkr 250.000 to 299.999 350000 'dkr 300.000 to 399.999 450000 'dkr 400.000 to 499.999 550000 'dkr 500.000 to 599.999 650000 'dkr 600.000 or more FINLAND Continuous values (Amounts in Finnish Markka; 1 Finnish Markka = 0.16819 Euro) FRANCE Mid points (Amounts in Euros) 305 euros 610 euros 915 euros 1296 euros 1906 euros 2668 euros 3430 euros 4192 euros 5335 euros 6860 euros 9909 euros GERMANY Continuous values (Amounts in Euros) GREAT BRITAIN Respondent were asked total annual income Mid points (Amounts in English Pounds; 1 British Pound = 1.45298 Euro) 2000 < 3 999 5000 4 000 - 5 999 7000 6 000 - 7 999 9000 8 000 - 9 999 11000 10 000 - 11 999 13500 12 000 - 14 999 16500 15 000 - 17 999 19000 18 000 - 19 999 21500 20 000 - 22 999 24500 23 000 - 25 999 27500 26 000 - 28 999 30500 29 000 - 31 999 35000 32 000 - 37 999 41000 38 000 - 43 999 47000 44 000 - 49 999 53000 50 000 - 55 999 59000 56 000 or more HUNGARY Continuous values (Amounts in Hungarian Forints; 1 Hungarian Forint = 0.004051 Euro)
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IRELAND Respondent were asked total annual income Mid points (Amounts in Euros) 6900 9025 13575 18700 24450 31000 38325 47500 61175 75000 ISRAEL Mid points (Amounts in Israeli Shekel; 1 Israeli New Shekel = 0.18203 Euro) 1000,00 3000,00 5000,00 7000,00 9000,00 13000,00 15500,00 JAPAN Mid points (Amounts in Yens; 1 Japanese Yen = 0.007445 Euro) 500 1500 2500 3500 4500 5500 6500 7500 8500 9500 11000 13500 20000 LATVIA Continuous values (Amounts in Lats; 1 Latvian Lats = 1.43748 Euro) NEW ZEALAND Respondent were asked total annual income Mid points (Amounts in 1 New Zealand Dollar = 0.56331 Euro) 5000 12500 17500 22500 27500 35000 45000 60000 85000 120000 NORWAY Continuous values (Amounts in thousands Norwegian Kroner; 1 Norwegian Kroner = 0.12702 Euro) PHILIPPINES Continuous values (Amounts in Philippines Pesos ; 1 Philippine Peso = 0.01491 Euro)
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PARTY_LR: R's scale of political ideology Missing Values: 0;8;9 Party_LR. Party preference or vote intention, coded to left-right position 1. 1. Far left. 2. Left,center left. 3. Center,liberal. 4. Right,conservative. 5. Far right. 6. Other. 0. Not applicable, not available; variable from which is derived not asked 7. No party. 8. Don't know, DK: Don't remember. 9. No answer, refused. Notes: (1) Not asked in Chile, Israel, South Africa and Taiwan
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XX_PRTY: Country Specific Vote Intention Missing Values: 00;97;98;99 R's voting intention in national elections (Country specific) xx. Party 00. Not applicable, not available.; Not asked 95. Other Party 96. Would not vote 97. Refuse. 98. Don't know. 99. No answer. Notes: (1) Not asked in Bulgaria and South Africa Australia AU_PRTY n % 1. Liberal Party 691 31,7 2. Labour Party 750 34,4 3. National Party 60 2,7 4. Democrats 53 2,4 5. Greens 107 4,9 6. One nation 41 1,9 95. Other Party 4 0,2 96. None 434 19,9 99. NA, refused 43 2,0 (N) 2183 100% Austria AT_PRTY n % 1. SPOE (Soc-Democr) 238 25,3 2. OEVP (Cons) 216 23,0 3. FPOE (Liberal) 62 6,6 4. GRUENE (Green) 80 8,5 95. Other party 5 0,5 96. Would not vote 93 9,9 97. Refused 29 3,1 99. NA 218 23,2 (N) 941 100% Bulgaria BG_PRTY n % (N) 0 100%
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Canada CA_PRTY n % 1. New Democratic Party 101 8,3 2. Liberal 446 36,8 3. Conservative 202 16,7 4. Bloc Quebecois 71 5,9 95. Other Party 105 8,7 96. Would not vote;no party preference 225 18,6 99. NA 61 5,0 (N) 1211 100% Chile CL_PRTY n % 1. Independent National Alliance 35 2,3 2. Communist Party of Chile 37 2,5 3. Christian Democratic Party 166 11,0 4. Humanist Party 29 1,9 5. Democracy Party PPD 135 9,0 6. Radical Social Democratic 12 0,8 7. Chilean Socialist Party 122 8,1 8. National Renewal Party 100 6,6 9. Independent Democratic Union 141 9,4 95. Other Party 3 0,2 96. No party, no preference 625 41,5 98. Dont know 47 3,1 99. NA 53 3,5 (N) 1505 100% Czech Republic CZ_PRTY n % 1. CSSD Social Democrats 125 9,8 2. ODA Civil Democratic Alliance 8 0,6 3. Nadeje Hope 5 0,4 4. RMS Republicans of Miroslav Sladek 6 0,5 5. CSNS Czech National Social Praty 2 0,2 6. SV-SOS party of the Countryside-United Civil Powers 3 0,2 7. Association of Independens 25 2,0 8. ODS Civic Democratic 251 19,7 9. KSCM Communist Party 107 8,4 10. KDU-CSL Chirtian Dem Party-Czechoslovak Peoples P 67 5,3 11. US-DEU Freedom Union - Democartic Union 13 1,0 12. SZJ Party for Life Securities 11 0,9 13. PB Right Block 8 0,6 14. SZ Party of Greens 13 1,0 95. Other Party 8 0,6 97. Refused 214 16,8 98. Dont know 265 20,8 99. NA 145 11,4 (N) 1276 100%
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Denmark DK_PRTY n % 1. Social Democratic P 328 26,5 2. Radical Liberal P 83 6,7 3. Conservative P 88 7,1 4. Centre Democratic 13 1,1 5. Socialist Peoples P 91 7,4 6. Danish Peoples Prty 85 6,9 7. Christian Peoples P 18 1,5 9. Liberal 387 31,3 10. Progressive 4 0,3 11. Leftwing Alliance 29 2,3 95. Other Party 3 0,2 96. No preference,no vote 9 0,7 98. Dont know 77 6,2 99. NA 23 1,9 (N) 1238 100%
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Finland FI_PRTY n % 1. Social Democratic Party of Finland 245 17,8 2. Center Party of Finland 174 12,6 3. National Coalition Party 196 14,2 4. Left Alliance 53 3,8 5. Swedish People´s Party in Finland 46 3,3 6. Green League 127 9,2 7. Christian Democrats 49 3,6 8. True Finns 16 1,2 9. Reform Group 3 0,2 95. Other party 9 0,7 96. Would not vote 80 5,8 97. Refused 114 8,3 98. Dont know 252 18,3 99. No answer 15 1,1 (N) 1379 100% France FR_PRTY n % 1. Far left 38 2,3 2. Communist party 49 2,9 3. Socialist party 332 19,9 4. Green 150 9,0 5. UDF-Center Right 125 7,5 6. UMP-Conservative 356 21,3 7. National Front 68 4,1 95. Other Party 17 1,0 96. No preference 367 22,0 97. Refused 73 4,4 99. NA 94 5,6 (N) 1669 100% Germany DE_PRTY n % 1. CDU/ CSU 347 28,5 2. SPD 188 15,4 3. FDP 68 5,6 4. Buendnis 90/Gruene 83 6,8 7. Republikaner 13 1,1 8. PDS/Linke Liste 58 4,8 95. Other Party 11 0,9 96. Would not vote;not eligible 197 16,2 97. Refused 48 3,9 98. Dont know 204 16,8 (N) 1217 100%
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Great Britain GB_PRTY n % 1. Conservative 227 26,0 2. Labour 321 36,8 3. Liberal Democrats,SLD 104 11,9 6. SNP (Scot National) 12 1,4 7. Plaid Cymru 1 0,1 93. Other answer 7 0,8 95. Other Party 24 2,7 96. Would not vote;No party preference 133 15,2 98. Dont know 31 3,6 99. NA 13 1,5 (N) 873 100% Hungary HU_PRTY n % 1. HDF-Hungarian Democratic Forum 16 1,6 2. FFD-Free Democrats 33 3,2 3. ISHP-Independent Small Holders 2 0,2 4. HSP-Hungarian Socialist Party 423 41,4 5. FYD-Federation of Young Democrats 255 25,0 7. HSPW-Socialist workers 6 0,6 8. HIJP-Hungarina Truth 13 1,3 95. Other Party 9 0,9 96. Would not vote;no party preference 174 17,0 97. Refused 77 7,5 99. NA 13 1,3 (N) 1021 100% Ireland IE_PRTY n % 1. Fianna Fail 218 55,3 2. Fine Gael 83 21,1 3. Labour 39 9,9 4. Progressive Democrats 9 2,3 5. Green Party 10 2,5 6. Socialist Party 7 1,8 7. Sinn Fein 20 5,1 95. Other Party 4 1,0 99. NA 4 1,0 (N) 394 100%
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Israel IL_PRTY n % 1. Israel ahat 123 10,1 2. Likud 321 26,4 3. Shase 51 4,2 4. Meretz 71 5,8 5. Mafdal 50 4,1 6. Yahadut-hatora, Agudat-Isarel, Degel-hatora 56 4,6 7. Am ehad 6 0,5 8. Shinuy 97 8,0 9. Haehud haleumi 17 1,4 10. The center party 3 0,2 11. Israel baliya 4 0,3 12. Israel byteno 21 1,7 15. Tzomet 2 0,2 16. Gimlaim 3 0,2 21. Hadash 41 3,4 22. Balad 34 2,8 23. Tnua Haravit Leshinui 8 0,7 24. Hatnua Harabit Hameauhedet 22 1,8 26. ale yarok 4 0,3 97. Refusal 74 6,1 98. Don´t know 89 7,3 99. No answer 121 9,9 (N) 1218 100% Japan JP_PRTY n % 1. Liberal Democratic Party 348 31,6 2. The Democratic Party 140 12,7 4. New Komeito 42 3,8 5. Japanese Communist Party 19 1,7 6. Social Democratic Party 19 1,7 95. Other Party 1 0,1 96. No party preference 472 42,8 99. NA 61 5,5 (N) 1102 100%
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Latvia LV_PRTY n % 1. Alliance "Tçvzemei un Brîvîbai"/LNNK 18 1,8 2. The political organisation -For Human Rights in United Latvi 63 6,3 3. Social-democratic wellfare party 2 0,2 5. Latvian Social Democratic Workers’ Party (LSDSP) 11 1,1 7. Jaunais laiks 123 12,3 8. The People´s party 45 4,5 9. Latvia´s First Party 17 1,7 13. Social Democratic Union + SDS 2 0,2 15. Union -Latvia´s Way 17 1,7 18. Latgales Gaisma 1 0,1 20. Political organisation "Green and Farmer union" 27 2,7 96. No vote 445 44,5 97. Refused 46 4,6 98. DK 183 18,3 (N) 1000 100% New Zealand NZ_PRTY n % 1. ACT 40 3,9 2. Alliance 9 0,9 3. Green 45 4,3 4. Labour 283 27,3 5. National 147 14,2 6. New Zealand First 67 6,5 7. Progressive Coalition 2 0,2 8. United Future 41 4,0 9. Undocumented 17 1,6 96. Didnt vote/not eligible 65 6,3 98. Dont know/Cant remember 47 4,5 99. NA 273 26,4 (N) 1036 100% Norway NO_PRTY n % 1. Red Electoral All 21 1,4 2. Labour Party 317 21,6 3. Progress Party 258 17,6 4. Conserv Party 198 13,5 5. Christ Democr Party 60 4,1 6. Centre party 78 5,3 7. Socialist Left 213 14,5 8. Liberal 35 2,4 95. Other Party 15 1,0 96. No party preference;wouldnt vote 65 4,4 98. Dont know 186 12,7 99. NA 23 1,6 (N) 1469 100%
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Poland PL_PRTY n % 1. SLD-UP: Democratic left alliance/labour union 182 22,2 2. AWS: Solidarity election action 44 5,4 3. UW: Freedom union 57 7,0 4. Lepper´s self-defense trade union 62 7,6 5. PiS: Law and justice party 63 7,7 6. PSL: Polish peasants party 46 5,6 7. PO: Civic platform of the rep of Poland 85 10,4 9. PWN: Polish national fellowship 1 0,1 10. LPR: League of polish families 49 6,0 11. German minority 3 0,4 13. PUG: Polish economic union 3 0,4 95. Other list 11 1,3 98. Don´t know 210 25,7 99. No answer 2 0,2 (N) 818 100% Portugal PT_PRTY n % 1. Bloco de Esquerda 62 3,9 2. CDS/PP 18 1,1 3. CDU-PCP 91 5,7 5. PPD/PSD 290 18,1 6. PS 419 26,2 7. PSR 2 0,1 8. UDP 1 0,1 95. Other 4 0,2 96. None, no preference 518 32,3 99. NA 197 12,3 (N) 1602 100%
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Philippines PH_PRTY n %
1. Lakas NUCD-UMDP(Lakas ng tao-National Union of Christian de) 33 2,8 2. LDP(Laban ng Demokratikong Pilipino) 7 0,6 3. NPC(Nationalists Peoples Coalition) 7 0,6 5. LP(Liberal Party) 17 1,4 6. NP(Nacionalista Party) 4 0,3 8. PDSP(Partido Demokratiko sosyalista ng pilipinas) 1 0,1 10. PMP(Partido ng Masang Pilipino) 8 0,7 16. Reforma 3 0,2 18. Estrada Party 14 1,2 19. Raul Roco Party 1 0,1 21. Miriam defensor party 1 0,1 22. Arroyo Party 9 0,8 25. OSME¥A PARTY 5 0,4 26. Mayor romanate in tubod Party 1 0,1 33. Ted Failon Party 1 0,1 34. Ibanez Party 1 0,1 35. Promdi 1 0,1 36. FPJ(Colonia Brigade) 1 0,1 38. BOPK(Bando Osme¥a Pondok Kaliswagan) 2 0,2 41. Robert Barbers Party 1 0,1 42. PPC 3 0,2 43. Balani 2 0,2 44. Mayor Parohinog Party 1 0,1 56. Lammp 1 0,1 57. Mayor of placer party 1 0,1 60. Professional Criminilogist Association Phil. 1 0,1 61. Drilon Party 1 0,1 62. Danding Party 1 0,1 63. Cory Party 1 0,1 64. Enrile Party 1 0,1 96. None 1069 89,1 (N) 1200 100% Russia RU_PRTY n % 1. Agrarian Party 11 0,5 2. Women of Russia 71 3,0 3. Yabloko 84 3,5 4. Unated Russia 305 12,8 5. LDPR 86 3,6 6. KPRF 219 9,2 7. Union of right forces 64 2,7 95. Other Party 119 5,0 96. Would not vote;no party preference 186 7,8 98. Dont know 1238 52,0 (N) 2383 100%
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Slovakia SK_PRTY n % 1. S. Bernat (KSS) 7 0,6 2. M. Butora (Civic org.) 66 5,7 3. I. Gasparovic (HZD) 244 21,2 4. J. Kalman (Ind.) 5 0,4 5. J. Kralik (SDL) 9 0,8 6. J. Kubik (Ind.) 3 0,3 7. E. Kukan (SDKU) 175 15,2 8. V. Meciar (HZDS-LS) 183 15,9 9. F. Miklosko (KDH) 49 4,3 10. R. Schuster (Former President) 43 3,7 11. J. Sestak (Ind.) 3 0,3 96. Abstain, no party preference 338 29,3 99. NA 27 2,3 (N) 1152 100% Slovenia SI_PRTY n % 1. Democratic ret DESUS 35 3,2 2. Liberal Democrat LDS 319 29,2 3. Peoples party SLS 64 5,9 4. Slovenian Nation SNS 26 2,4 5. Social Democratic SDS 94 8,6 6. NSI - New Slovenia 55 5,0 7. Combined list ZLSD 87 8,0 8. Youth party SMS 36 3,3 95. Other Party 31 2,8 96. Would not vote;No party preference 45 4,1 97. Refused 202 18,5 98. Dont know 99 9,1 (N) 1093 100% South Africa ZA_PRTY n % (N) 0 100% South Korea KR_PRTY n % 1. United Liberal Democrats 12 0,9 2. Grand National Party 226 17,2 3. National Alliance 21 22 1,7 4. Millennium Democratic Party 249 18,9 5. People´s party for reform 18 1,4 6. Democratic Labor Party 65 4,9 7. No party affiliation 635 48,3 8. The Uri Party 5 0,4 95. Other party 6 0,5 98. DK 77 5,9 (N) 1315 100%
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Spain ES_PRTY n % 1. Popular Party (Partido Popular - PP) 332 27,4 2. Socialist Party (Partido Socialista Obrero Español - PSOE) 369 30,4 3. Communist Party (Izquierda Unida - IU) 65 5,4 4. Regional Party of Center (Part Nac de Centro y Derecha) 52 4,3 5. Regional Party of Left (Partidos Nacionalistas de Izquierda) 33 2,7 6. Ecologist Party (Ecologistas - Verdes) 19 1,6 94. White (En Blanco) 27 2,2 95. Other Party (Otros) 2 0,2 96. No vote (No votará) 133 11,0 98. Don´t Know (NS) 105 8,7 99. No answer (NC) 75 6,2 (N) 1212 100% Sweden SE_PRTY n % 1. C (Centre Party) 71 6,0 2. FP (Liberals) 144 12,1 3. KD (Christ Democr) 91 7,7 4. MP (Ecologists) 56 4,7 5. M (Liberal Conserv) 158 13,3 6. S (Social Democrats) 431 36,3 7. V (Socialists) 106 8,9 95. Other Party 64 5,4 99. NA 65 5,5 (N) 1186 100% Switzerland CH_PRTY n % 1. Christian Democratic Party 46 4,4 2. Evangelical Peoples Party 6 0,6 3. Radical Party 93 9,0 4. Social Democratic Party 147 14,2 5. Swiss Peoples Party 69 6,7 6. Independent Party 2 0,2 7. Liberal Party 9 0,9 8. Labour Party 5 0,5 9. Swiss Democrats 7 0,7 10. Green Party 62 6,0 95. Other Party 8 0,8 96. No party preference 552 53,2 98. Not interested so much in politics 11 1,1 99. NA 20 1,9 (N) 1037 100%
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Taiwan TW_PRTY n % 1. KuoMinTang 170 8,4 2. Democratic Progressive Party 38 1,9 3. People Frist Party 12 0,6 95. Other Party 6 0,3 96. None 1775 88,0 97. Refuse to answer which party 13 0,6 98. Refused 2 0,1 (N) 2016 100% United States US_PRTY n % 1. Strong democrat 212 17,4 2. N very strong democrat 203 16,7 3. Indep,close democrat 108 8,9 4. Independent 204 16,8 5. Indep,close republic 115 9,5 6. N very strong republic 178 14,6 7. Strong republican 182 15,0 95. Other party 9 0,7 99. No answer 5 0,4 (N) 1216 100% Uruguay UY_PRTY n % 1. Red Party 129 11,6 2. National Party 230 20,8 3. Wide front Party 454 41,0 4. Independent Party 6 0,5 5. Liberal Party 1 0,1 95. Other Party 2 0,2 96. No party preference 275 24,8 99. NA 11 1,0 (N) 1108 100% Venezuela VE_PRTY n % 1. AD (Soc-Democr) 126 10,5 2. Copei (Cons) 42 3,5 3. MAS (Center left) 17 1,4 4. MVR (Left) 350 29,2 5. Proyecto Venezuela (Liberal) 69 5,8 6. Primero Justicia (Liberal) 69 5,8 7. PPT (Center left) 13 1,1 8. Causa R (Left) 5 0,4 9. Convergencia (Cons) 8 0,7 10. Chavista (Left) 18 1,5 95. Others 8 0,7 96. None 456 38,0 98. Dont know 13 1,1 99. Na 5 0,4 (N) 1199 100%
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VOTE_LE: R: Vote last election: yes, no Missing Values: 0;8;9 Vote_LE. Did you vote in last election? 1. 1. Yes. 2. No. 0. Not applicable, not available. 8. Don't know. 9. No answer. NOTES: (1) Not asked in South Africa
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RELIG: Religious denomination Missing Values: 000;997;998;999 Relig. Religious denomination. 000. No religion. 423. Filipinista. 100. Roman Catholic. 424. Iglesia ng Dios. 110. Greek Catholic. 429. Kristohanon. 200. Protestant (evangelische) free church. 430. Apostle Twelve. 210. Anglican,Ch Engl,Episcopal. 431. African Evangelical. 220. Baptists. 432. Duthc Reform. 230. Congregationalists. 433. Full Gospel Church of God. 240. Mennonite. 434. Faith Mission. 250. Lutheran, evangelical church. 435. St Johns Apostolic. 260. Methodist. 436. Nazareth. 270. Pentecostal, ZA: Pentecostal Holiness Church. 437. Zionist Christian Church. 280. Presbyterian,Ch of Scot. 444. Wesleyan. 289. Universal Church of God. 448. Camacop Alliance. 290. Other Protestants (no spec.rel). 457. Faith Tabernacle. 291. Brethren. 490. Unspecified Christian Groups. 292. Mormon. 491. Jehova´s Witness. 293. Salvation Army. 500. Jewish. 294. Assemblies of God. 530. Reform. 295. Seventh Day Adventists. 590. Jewish Religion general. 296. Hussites. 600. Islam. 297. Unitarians,AUS:Uniting church. 670. Druse. 298. United Church CDN. 690. Muslim,Mohammedan,Islam. 299. Church of God and Saint of Christ. 700. Buddhists. 300. Orthodox; Eastern Orthodox. 790. Buddhism general. 310. Greek Orthodox. 800. Hinduism. 320. Russian Orthodox. 820. Sikhism. 390. Orthodox (no spec. mentioned). 890. Hinduism general. 400. Other Christian Groups. 900. Other Asian Religion. 401. Aglipayan. 901. Shintoism. 402. Born Again. 902. Taoism. 405. United Church of Christ in the Phils. 950. Other East Asian Religion. 407. Dating Daan. 960. Other Religions. 412. Christians. 961. Ratana/Ringatu. 413. Espiritista. 997. Refused. 414. Iglesia ni Cristo. 998. Don't know. 415. Church of Christ. 999. No answer. 418. Evangelical Christian.
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997 998 999
RELIG (N) % % %
Australia (AU) 2183 - 129M
Austria (AT) 1006 - 3M 22M
Bulgaria (BG) 1069 - 10M
Canada (CA) 1211 - 230M
Chile (CL) 1505 - 5M 17M
Czech Republic (CZ) 1276 - 17M 136M
Denmark (DK) 1322 - 9M 9M
Finland (FI) 1379 - 6M
France (FR) 1669 - 3M 93M
Germany-West (DE-W) 850 - 4M
Germany-East (DE-E) 437 - 1M
Great Britain (GB) 873 - 1M 1M
Hungary (HU) 1021 - 4M 82M
Ireland (IE) 1065 - 1M 35M
Israel Jews (IL-J) 1066 -
Israel Arabs (IL-A) 152 -
Japan (JP) 1102 - 8M 21M
Latvia (LV) 1000 - 6M
New Zealand (NZ) 1036 - 33M
Norway (NO) 1469 - 24M
Poland (PL) 1277 - 1M
Portugal (PT) 1602 - 1M 10M 4
Philippines (PH) 1200 0,30%
Russia (RU) 2383 - 57M
Slovak Republic (SK) 1152 - 16M 68M
Slovenia (SI) 1093 - 4M 40M 5
South Africa (ZA) 2483 0,20% 5M 69M
South Korea (KR) 1315 - 1M
Spain (ES) 1212 - 5M
Sweden (SE) 1186 - 3M 19M
Switzerland (CH) 1037 - 1M 1M
Taiwan (TW) 2016 -
United States (US) 1216 - 1M
Uruguay (UY) 1108 - 1M
Venezuela (VE) 1199 - Sum 44170 9 138M 1075M
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RELIGGRP: R: Religious main groups (derived) Missing Values: 97;98;99 Religgrp. Religious denomination. 01. No religion. 02. Roman Catholic. 03. Protestant. 04. Christian Orthodox. 05. Jewish. 06. Islam. 07. Buddhism. 08. Hinduism. 09. Other Christian Religions. 10. Other Eastern Religions. 11. Other Religions. 97. Refused. 98. Don't know. 99. No answer.
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ATTEND: Religious services - how often Missing Values: 00;97;98;99 Attend. Attendance of religious services. 01. Several times a week. 02. Once a week. 03. 2 or 3 times a month. 04. Once a month. 05. Sev times a year. 06. Once a year. 07. Less frequently. 08. Never. 00. NAP; No religion; Not asked 97. Refused. 98. DK, varies too much. 99. No answer.
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TOPBOT: R:Top Bottom self-placement 10 point scale Missing Values: 00;97;98;99 Topbot. Top Bottom self-placement of respondent in a 10 point scale 1,2. 01. Lowest. 02. 03. 04. 05. 06. 07. 08. 09. 10. Highest. 0. Not applicable, Not available; Not asked 97. Refused. 98. DK, varies too much. 99. No answer. NOTES: (1) Not asked in Great Britain and South Africa (2) In Canada, the variable was derived from CLASS, with these categories:
1 The lower class 2 The working class 3 Upper working class/lower middle class 4 Middle class 5 Upper middle class 6 Upper class
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XX_REG: Country specific region Missing Values: 00;(98;99) or (8;9) depending on variable XX_REG. Country specific region 1 xx. Region code 00. Not available; Not asked 8/98. Don’t know 9/99. No answer NOTES: (1) Not asked in Taiwan and Venezuela Australia AU_REG n % 1. New South Wales 739 33,9 2. Victoria 569 26,1 3. Queensland 366 16,8 4. South Australia 189 8,7 5. Western Australia 186 8,5 6. Tasmania 62 2,8 7. Australian Capital Territory 42 1,9 8. Northern Territory 10 0,5 99. NA 20 0,9 (N) 2183 100% Austria AT_REG n % 1. Vorarlberg 40 4,0 2. Tirol 75 7,5 3. Salzburg 71 7,1 4. Oberoesterreich 169 16,8 5. Kaernten 76 7,6 6. Steiermark 152 15,1 7. Burgenland 37 3,7 8. Niederoesterreich 203 20,2 9. Wien 183 18,2 (N) 1006 100%
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Norway NO_REG n % 1. Central East 317 21,6 2. East 420 28,6 3. South 78 5,3 4. West 382 26,0 5. Middle 139 9,5 6. North 133 9,1 (N) 1469 100% Poland PL_REG n % 1. Dolnoslaskie 98 7,7 2. Kujawsko-Pomorskie 64 5,0 3. Lubelskie 70 5,5 4. Lubuskie 38 3,0 5. Lodzkie 93 7,3 6. Maloposlkie 103 8,1 7. Mazowieckie 170 13,3 8. Opolskie 41 3,2 9. Podkarpackie 65 5,1 10. Podlaskie 38 3,0 11. Pomorskie 56 4,4 12. Slaskie 179 14,0 13. Swietokrzyskie 47 3,7 14. Warminsko-Mazurskie 47 3,7 15. Wielkopolskie 115 9,0 16. Zachodniopomorskie 53 4,2 (N) 1277 100% Portugal PT_REG n % 1. North 422 26,3 2. Centre 222 13,9 3. Lisbon a Tagus Val 808 50,4 4. Alentejo 99 6,2 5. Algarve 51 3,2 (N) 1602 100% Philippines PH_REG n % 1. National Capital Region (NCR) 300 25,0 2. Balance Luzon 300 25,0 3. Visayas 300 25,0 4. Mindanao 300 25,0 (N) 1200 100%
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Russia RU_REG n % 1. North 139 5,8 2. North-West 82 3,4 3. Central 714 30,0 4. Volga-Vyatka 119 5,0 5. Black Earth 110 4,6 6. Volga Region 236 9,9 7. North Caucasus 237 9,9 8. Urals 289 12,1 9. West Siberia 212 8,9 10. East Siberia 131 5,5 11. Far East 114 4,8 (N) 2383 100% Slovakia SK_REG n % 1. Bratislava county 131 11,4 2. Trnava county 117 10,2 3. Trencin county 128 11,1 4. Nitra county 153 13,3 5. Zilina county 148 12,8 6. Ban. Bystrica county 141 12,2 7. Presov county 169 14,7 8. Kosice county 164 14,2 99. NA 1 0,1 (N) 1152 100% Slovenia SI_REG n % 1. Pomurska 80 7,3 2. Podravska 181 16,6 3. Koroska 51 4,7 4. Savinjska 158 14,5 5. Gorenjska 122 11,2 6. Zasavska 20 1,8 7. Osrednja 244 22,3 8. Spodnjeposavska 37 3,4 9. Dolenjska 62 5,7 10. Goriska 65 5,9 11. Obalnokraska 49 4,5 12. Kraska 24 2,2 (N) 1093 100%
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South Africa ZA_REG n % 1. WC 270 10,9 2. EC 286 11,5 3. NC 243 9,8 4. FS 210 8,5 5. KZN 399 16,1 6. NW 203 8,2 7. GT 301 12,1 8. MP 276 11,1 9. LP 295 11,9 (N) 2483 100% South Korea KR_REG n % 1. Seoul Metropolitan City 291 22,1 2. Incheon Metropolitan city 74 5,6 3. Daejeon Metropolitan city 34 2,6 4. Busan Metropolitan city 107 8,1 5. Ulsan Metropolitan City 24 1,8 6. Deagu Metropolitan City 65 4,9 7. Gwangju Metropolitan city 43 3,3 8. Gyeonggi Province 249 18,9 9. Gangwon Province 44 3,3 10. Chungcheong Province 112 8,5 11. Gyeongsang Province 145 11,0 12. Jeolla Province 112 8,5 13. Jeju-do 15 1,1 (N) 1315 100% Spain ES_REG n % 1. Andalucia 210 17,3 2. Aragon 38 3,1 3. Asturias 34 2,8 4. Baleares 24 2,0 5. Cataluña 191 15,8 6. Canarias 49 4,0 7. Cantabria 17 1,4 8. Castilla-Leon 78 6,4 9. Castilla-La Mancha 52 4,3 10. Extremadura 31 2,6 11. Galicia 87 7,2 12. La Rioja 7 0,6 13. Madrid 152 12,5 14. Murcia 33 2,7 15. Navarra 17 1,4 16. Pais Vasco 68 5,6 17. Comunidad Valenciana 124 10,2 (N) 1212 100%
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Sweden SE_REG n % 1. North 144 12,1 2. Mid North 126 10,6 3. Mid East 188 15,9 4. Stockholm 214 18,0 5. West 111 9,4 6. Goeteborg 124 10,5 7. Smaaland Gotland 102 8,6 8. South 103 8,7 9. Malmoe 70 5,9 99. NA 4 0,3 (N) 1186 100% Switzerland CH_REG n % 1. Zuerich 231 22,3 2. Bern 187 18,0 3. Luzern 41 4,0 8. Glarus 19 1,8 9. Zug 12 1,2 10. Fribourg 40 3,9 11. Solothurn 40 3,9 12. Baselstadt 7 0,7 17. St. Gallen 86 8,3 18. Graubuenden 27 2,6 19. Aargau 71 6,8 20. Thurgau 58 5,6 21. Ticino 42 4,1 22. Vaud 80 7,7 23. Wallis 41 4,0 25. Geneve 31 3,0 26. Jura 24 2,3 (N) 1037 100% Taiwan TW_REG n % 1. North 969 48,12. Central 323 16,03. South 624 31,04. East 100 5,0(N) 2016 100%
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United States US_REG n % 1. New England 33 2,7 2. Middle Atlantic 152 12,5 3. East North Central 234 19,2 4. West North Central 90 7,4 5. South Atlantic 259 21,3 6. East South Central 67 5,5 7. West South Central 110 9,0 8. Mountain 94 7,7 9. Pacific 177 14,6 (N) 1216 100%
Uruguay UY_REG n % 1. Metropolitan statistical area of Montevideo 680 61,4 2. Rest of urban population 428 38,6 (N) 1108 100% Venezuela (Not asked) VE_REG n % (N) 0 100%
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XX_SIZE: Country specific town size Missing Values: 00; (98;99) or (8;9) depending on variable XX_SIZE. Country specific town size 1 xx. Town size 00. Not available; Not asked 8/98. Don’t know 9/99. No answer NOTES: (1) Not asked in Chile, New Zealand and Venezuela (2) All except Finland and Philippines use categories that have been ordered so that 1 represents large cities and (nn) small cities. In Finland and Philippines, the town size in habitants has been used. Australia AU_SIZE n % 1. Inner metropolitan (over 100,000 people) 600 27,5 2. Outer metropolitan (over 100,000 people) 770 35,3 3. A large town (over 25,000 people) 269 12,3 4. A larger country town (over 10,000 people) 125 5,7 5. A small country town (under 10,000 people) 249 11,4 6. A rural area or on a farm 140 6,4 99. NA 30 1,4 (N) 2183 100% Austria AT_SIZE n % 1. >1 mill+Vienna 183 18,2 2. 50 001-1 million 142 14,1 3. 20 001-50 000 256 25,4 8. Less than 2 000 425 42,2 (N) 1006 100% Bulgaria BG_SIZE n % 1. Sofia (1.2Mill) 120 11,2 2. 100001-500000 183 17,1 3. 20001+100000 223 20,9 4. 2001-20000 293 27,4 5. up to 2000 250 23,4 (N) 1069 100%
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Russia RU_SIZE n % 1. > 1 million 672 28,2 2. 500 001-1 000 000 209 8,8 3. 250 001- 500 000 229 9,6 4. 100 001- 250 000 199 8,4 5. 50 001- 100 000 205 8,6 6. 20 001- 50 000 169 7,1 7. Urban < 20 000 152 6,4 8. Rural 548 23,0 (N) 2383 100% Slovakia SK_SIZE n % 1. More than 100 000 144 12,5 2. 50 000 - 100 000 163 14,1 3. 10 000 - 50 000 315 27,3 4. 2 000 - 10 000 207 18,0 5. Up to 2 000 323 28,0 (N) 1152 100% Slovenia SI_SIZE n % 1. > 50 000 (Lblj,Mar) 168 15,4 2. 10 000-50 000 146 13,4 3. 4 000-10 000 65 5,9 4. 2 000-4 000 74 6,8 5. 500-2 000 285 26,1 6. < 500 347 31,7 99. NA 8 0,7 (N) 1093 100% South Africa ZA_SIZE n % 1. Tribal 490 19,7 2. Farms 287 11,6 3. Smallholdings 15 0,6 4. Urban formal 1377 55,5 5. Urban informal 296 11,9 9. Hostels 18 0,7 (N) 2483 100% South Korea KR_SIZE n % 1. 1 000 001 and more; metropolitan 638 48,5 2. 100 001-1 000 000; large city 510 38,8 3. 50 001- 100 000; middle city 129 9,8 4. Less than 50 000; rular county 38 2,9 (N) 1315 100%
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Spain ES_SIZE n % 1. Madrid and Barcelona 143 11,8 2. > Than 250.000 179 14,8 3. 100.001 - 250.000 165 13,6 4. 50.001 - 100.000 129 10,6 5. 10.001 - 50.000 285 23,5 6. 5.001 - 10.000 132 10,9 7. 2.001 - 5.000 95 7,8 8. < Than 2.000 84 6,9 (N) 1212 100% Sweden SE_SIZE n % 1. More than 300 000 408 34,4 2. 90 000 - 300 000 415 35,0 3. 27 000 - 90 000 293 24,7 4. Less than 27 000 66 5,6 99. NA 4 0,3 (N) 1186 100% Switzerland CH_SIZE n % 7367900. Switzerland 1037 100,0 (N) 1037 100% Taiwan TW_SIZE n % 1. More than 9000 21 1,0 2. 8000-8999 25 1,2 3. 7000-7999 116 5,8 4. 6000-6999 143 7,1 5. 5000-5999 160 7,9 6. 4000-4999 290 14,4 7. 3000-3999 390 19,3 8. 2000-2999 328 16,3 9. 1000-1999 436 21,6 10. Less than 999 107 5,3 (N) 2016 100% United States US_SIZE n % 1. 1-9 millions 75 6,2 2. 500 000-999 999 48 3,9 3. 100 000-499 999 164 13,5 4. 50 000-99 999 137 11,3 5. 10 000-49 999 408 33,6 6. 1 000-9 999 360 29,6 7. - 999 24 2,0 (N) 1216 100%
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Uruguay UY_SIZE n % 1. 1.597.943 urban population 680 61,4 2. 992.552 urban population 428 38,6 (N) 1108 100% Venezuela VE_SIZE n % (N) 0 100%
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URBRURAL: Place of residence: urban-rural Missing Values: 0;9 Urbrural. Type of community: urban / rural 1,2. 0. Not available. 1. Urban,RP:total urban. 2. Suburb,city,town. 3. Town or small city. 4. Country village. 5. Farm or home in the country. 9. No answer. NOTES: (1) Not asked in Venezuela (2) Austria, Canada, France, Hungary, Israel, Poland, Portugal, Russia, Slovenia, Spain, US and Uruguay used the
old three categories classification Chile used a two category classification (total urban/total rural)
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35. Danish,Denmark 83. Maori,NZ Maori 131. Turkey,Turkish 36. Danish+English 84. Maranaw/Maranao 132. Ukraine,Ukrainian 37. English + Persian 85. Masbateño 133. Visayan/Cebuano,Boholano,Leyteno 38. English + Tai 86. Metis 134. Waray 39. English,England&Wales,UK,England 87. Montenegro 135. Welsh 40. Esperanto,Latin,Slavonik,Celtic 88. Moravian 136. Whites (all) 41. Estonia 89. Netherlands,Dutch,Flemish 137. Xitsonga 42. Ethiopia 990. No languages at all 138. Zamboangeño 43. European,White/European,Europe 90. Nordic,Scandinavian other 980. Other African language 44. Europeans Mediterranean 91. North Africans 981. Other,East European 45. Finnish,Finland 92. Norwegian,Norway 982. Other,Middle East 46. French,France 93. Occitan, France 983. Other,Mixed origin 47. Gallego 94. One non-Swedish,both non-Swe 984. Other,Western European 48. Georgian 95. Oriental 0. Nap; Nav; Not asked
0. Not available (Not asked) 998. Don’t know. 999. No answer.
NOTES: (1) Not asked in Australia, Austria, Chile, Denmark, France, Great Britain, Hungary, Israel, Japan, New Zealand, Norway, Poland, Portugal, Spain, Switzerland, Taiwan and Uruguay.
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MODE: Administrative mode of data-collection Missing Values: 00;98;99 Mode. Interview method. 10. f2f,paper and pencil,no visuals. 11. Visuals. 12. R reading questionnaire. 13. Interpreter, no visuals. 14. Interpreter, visuals. 20. f2f,computer-assisted,no visuals. 21. Visuals. 22. R reading questionnaire. 23. Interpreter,no visuals. 24. Interpreter,visuals. 30. Self-completion,paper a pencil,interviewer attending. 31. Drop-off,pick up later. 32. Drop-off,mailed back by R. 33. Mailed to,hold for pick up. 34. Mailed back by R. 40. Self-completion, computer-assisted 50. Telephone, paper and pencil, no visuals 51. Telephone, paper and pencil, visuals
52. Telephone, paper and pencil, respondent reading questionnaire (DK: Telephone interview after mailing, where the interviewer read out the questionnaire and noted the respondents’ answers)
00. NAP/NAV.
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International Occupation Codes: ILO/ISCO 1988 This Note refers to ISCO88 and SPISCO88 and contains the International Occupation Codes of 1988. 1, 2 and 3 digit ISCO codes are also included with trailing zeros to complete four digits. In this way, some countries providing 3-digit ISCO codes only could also be included. Country specific codes were recoded to the 3 digit category to which they belong or, if it didn’t exist, to the 2 or 1 digit category (for example country specific German code 7243 was recoded to 7240). Source: ILO/ ISCO 1988 International Standard Classification of Occupations: International Labour Office, Geneva 1991 Armed forces 110 'Armed forces' Legislators, senior officials and managers 1000 'Legislators, senior officials and managers' 1100 'Legislators and senior officials' 1110 'Legislators and senior government officials' 1120 'Senior government official' 1130 'Traditional chiefs+heads of villages' 1140 'Senior officials of special-interest organisations' 1141 'Senior officials of political party organisations' 1142 'Senior officials of employers´, workers´ and other economic-interest organisations' 1143 'Senior officials of humanitarian and other special-interest organisations' 1200 'Corporate managers' 1210 'Directors and chief executives' 1220 'Production and operations managers' 1221 'Production and operations managers in agriculture, hunting, forestry and fishing' 1222 'Production and operations managers in manufacturing' 1223 'Production and operations managers in construction' 1224 'Production and operations managers in wholesale and retail trade' 1225 'Production and operations managers in restaurants and hotels' 1226 'Production and operations managers in transport, storage and communications' 1227 'Production and operations managers in business services enterprises' 1228 'Production and operations managers in personal care, cleaning and related services' 1229 'Production and operations managers not elsewhere classified' 1230 'Other specialists managers' 1231 'Finance and administration managers' 1232 'Personnel and industrial relations managers' 1233 'Sales and marketing managers' 1234 'Advertising and public relations managers' 1235 'Supply and distribution managers' 1236 'Computing services managers' 1237 'Research and development managers' 1238 'Other department managers nec'
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1239 'Other specialist managers not elsewhere classified' 1240 'USA:Miscelaneous office supervisors' 1250 'H:Miscelaneous officers' 1251 'H:High-grade military officer' 1252 'H:Low-grade commissioned officer' 1300 'Managers of small enterprises' 1310 'Managers of small enterprises' 1311 'Managers of small enterprises in agriculture, hunting, forestry and fishing' 1312 'Managers of small enterprises in manufacturing' 1313 'Managers of small enterprises in construction' 1314 'Managers of small enterprises in wholesale and retail trade' 1315 'Managers of small enterprises of restaurants and hotels' 1316 'Managers of small enterprises in transport, storage and communications' 1317 'Managers of small enterprises in business services enterprises' 1318 'Managers of small enterprises in personal care, cleaning and related services' 1319 'Managers of small enterprises not elsewhere classified' Professionals 2000 'Professionals' 2100 'Physical, mathematical and engineering science professionals' 2110 'Physicists, chemists and related professionals' 2111 'Physicists and astronomers' 2112 'Meteorologists' 2113 'Chemists' 2114 'Geologists and geophysicists' 2120 'Mathematicians, statisticians and related professionals' 2121 'Mathematicians and related professionals' 2122 'Statisticians' 2130 'Computing professionals' 2131 'Computer systems designers, analysts and programmers' 2139 'Computing professionals not elsewhere classified' 2140 'Architects, engineers and related professionals' 2141 'Architects, town and traffic planners' 2142 'Civil engineers' 2143 'Electrical engineers' 2144 'Electronics and telecommunications engineers' 2145 'Mechanical engineers' 2146 'Chemical engineers' 2147 'Mining engineers, metallurgists and related professionals' 2148 'Cartographers and surveyors' 2149 'Architects, engineers and related professionals not elsewhere classified' 2200 'Life science and health professionals' 2210 'Life science professionals' 2211 'Biologists, botanists, zoologists and related professionals' 2212 'Pharmacologists, pathologists and related professionals' 2213 'Agronomists and related professionals' 2220 'Health professionals (except nursing) ' 2221 'Medical doctors'
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2222 'Dentists' 2223 'Veterinarians' 2224 'Pharmacists' 2229 'Health professionals (except nursing) not elsewhere classified' 2230 'Nursing and midwifery professionals' 2300 'Teaching professionals' 2310 'College, university and higher education teaching professionals' 2320 'Secondary education teaching professionals' 2330 'Primary and pre-primary education teaching professionals' 2331 'Primary education teaching professionals' 2332 'Pre-primary education teaching professionals' 2340 'Special education teaching professionals' 2350 'Other teaching professionals' 2351 'Education methods specialists' 2352 'School inspectors' 2359 'Other teaching professionals not elsewhere classified' 2400 'Other professionals' 2410 'Business professionals' 2411 'Accountants' 2412 'Personnel and careers professionals' 2419 'Business professionals not elsewhere classified' 2420 'Legal professionals' 2421 'Lawyers' 2422 'Judges' 2429 'Legal professionals not elswhere classified' 2430 'Archivists, librarians and related information professionals' 2431 'Archivists and curators' 2432 'Librarians and related information professionals' 2440 'Social science and related professionals' 2441 'Economists' 2442 'Sociologists, anthropologists and related professionals' 2443 'Philosophers, historians and political scientists' 2444 'Philologists, translators and interpreters' 2445 'Psychologists' 2446 'Social work professionals' 2450 'Writers and creative or performing artists' 2451 'Authors, journalists and other writers' 2452 'Sculptors, painters and related artists' 2453 'Composers, musicians and singers' 2454 'Choreographers and dancers' 2455 'Film, stage and related actors and directors' 2460 'Religious professionals' 2470 'Public service administrative professionals' Technicians and associate professionals 3000 'Technicians and associate professionals' 3100 'Physical and engineering science associate professionals' 3110 'Physical and engineering science technicians'
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3111 'Chemical and physical science technicians' 3112 'Civil engineering technicians' 3113 'Electrical engineering technicians' 3114 'Electronics and telecommunications engineering technicians' 3115 'Mechanical engineering technicians' 3116 'Chemical engineering technicians' 3117 'Mining and metallurgical technicians' 3118 'Draughtspersons' 3119 'Physical and engineering science technicians not elsewhere classified' 3120 'Computer associate professionals' 3121 'Computer assistants' 3122 'Computer equipment operators' 3123 'Industrial robot controllers' 3130 'Optical and electronic equipment operators' 3131 'Photographers and image and sound recording equipment operators' 3132 'Broadcasting and telecommunications equipment operators' 3133 'Medical equipment operators' 3139 'Optical and electronic equipment operators not elsewhere classified' 3140 'Ship and aircraft controllers and technicians' 3141 'Ships´ engineers' 3142 'Ships´ deck officers and pilots' 3143 'Aircraft pilots and related associate professionals' 3144 'Air traffic controllers' 3145 'Air traffic safety technicians' 3150 'Safety and quality inspectors' 3151 'Building and fire inspectors' 3152 'Safety, health and quality inspectors' 3200 'Life science and health associate professionals' 3210 'Life science technicians and related associate professional' 3211 'Life science technicians' 3212 'Agronomy and forestry technicians' 3213 'Farming and forestry advisers' 3220 'Health associate professionals (except nursing)' 3221 'Medical assistants' 3222 'Hygienists, health and environmental officers' 3223 'Dieticians and nutritionists' 3224 'Optometrists and opticians' 3225 'Dental assistants' 3226 'Physiotherapists and related associate professionals' 3227 'Veterinary assistants' 3228 'Pharmaceutical assistants' 3229 'Health associate professionals (except nursing) not elsewhere classified' 3230 'Nursing and midwifery associate professionals' 3231 'Nursing associate professionals' 3232 'Midwifery associate professionals' 3300 'Teaching associate professionals' 3310 'Primary education teaching associate professionals' 3320 'Pre-primary education teaching associate professionals'
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3330 'Special education teaching associate professionals' 3340 'Other teaching associate professionals' 3400 'Other associate professionals' 3410 'Finance and sales associate professionals' 3411 'Securities and finance dealers and brokers' 3412 'Insurance representatives' 3413 'Estate agents' 3414 'Travel consultants and organisers' 3415 'Technical and commercial sales representatives' 3416 'Buyers' 3417 'Appraisers, valuers and auctioneers' 3419 'Finance and sales associate professionals not elsewhere classified' 3420 'Business services agents and trade brokers' 3421 'Trade brokers' 3422 'Clearing and forwarding agents' 3423 'Employment agents and labour contractors' 3429 'Business services agents and trade brokers not elsewhere classified' 3430 'Administrative associate professionals' 3431 'Administrative secretaries and related associate professionals' 3432 'Legal and related business associate professionals' 3433 'Bookkeepers' 3434 'Statistical, mathematical and related associate professionals' 3440 'Customs, tax and related government associate professionals' 3441 'Customs and border inspectors' 3442 'Government tax and excise officials' 3443 'Government social benefits officials' 3444 'Government licensing officials' 3449 'Customs, tax and related government associate professionals not elsewhere classified' 3450 'Police inspectors and detectives' 3460 'Social work associate professionals' 3470 'Artistic, entertainment and sports associate professionals' 3471 'Decorators and commercial designers' 3472 'Radio, television and other announcers' 3473 'Street, night-club and related musicians, singers and dancers' 3474 'Clowns, magicians, acrobats and related associate professionals' 3475 'Athletes, sports persons and related associate professionals' 3480 'Religious associate professionals' Clerks 4000 'Clerks' 4100 'Office clerks' 4110 'Secretaries and keyboard-operating clerks' 4111 'Stenographers and typists' 4112 'Word-processor and related operators' 4113 'Data entry operators' 4114 'Calculating-machine operators' 4115 'Secretaries' 4120 'Numerical clerks'
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4121 'Accounting and book-keeping clerks' 4122 'Statistical and finance clerks' 4130 'Material-recording and transport clerks' 4131 'Stock clerks' 4132 'Production clerks' 4133 'Transport clerks' 4140 'Library, mail and related clerks' 4141 'Library and filing clerks' 4142 'Mail carriers and sorting clerks' 4143 'Coding, proof-reading and related clerks' 4144 'Scribes and related workers' 4190 'Other office clerks' 4200 'Customer services clerks' 4210 'Cashiers, tellers and related clerks' 4211 'Cashiers and ticket clerks' 4212 'Tellers and other counter clerks' 4213 'Bookmakers and croupiers' 4214 'Pawnbrokers and money-lenders' 4215 'Debt-collectors and related workers' 4220 'Client information clerks' 4221 'Travel agency and related clerks' 4222 'Receptionists and information clerks' 4223 'Telephone switchboard operators' Service workers and shop and market sales workers 5000 'Service workers and shop and market sales workers' 5100 'Personal and protective services workers' 5110 'Travel attendants and related workers' 5111 'Travel attendants and travel stewards' 5112 'Transport conductors' 5113 'Travel guides' 5120 'Housekeeping and restaurant services workers' 5121 'Housekeepers and related workers' 5122 'Cooks' 5123 'Waiters, waitresses and bartenders' 5130 'Personal care and related workers' 5131 'Child-care workers' 5132 'Institution-based personal care workers' 5133 'Home-based personal care workers' 5139 'Personal care and related workers not elsewhere classified' 5140 'Other personal services workers' 5141 'Hairdressers, barbers, beauticians and related workers' 5142 'Companions and valets' 5143 'Undertakers and embalmers' 5149 'Other personal services workers not elsewhere classified' 5160 'Protective services workers' 5161 'Fire-fighters' 5162 'Police officers'
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5163 'Prison guards' 5169 'Protective services workers not elsewhere classified' 5200 'Models, salespersons and demonstrators' 5210 'Fashion and other models' 5220 'Shop, stall and market salespersons and demonstrators' Skilled agricultural and fishery workers 6000 'Skilled agricultural and fishery workers' 6100 'Skilled agricultural and fishery workers' 6110 'Market gardeners and crop growers' 6111 'Field crop and vegetable growers' 6112 'Gardeners, horticultural and nursery growers' 6120 'Animal producers and related workers' 6121 'Dairy and livestock producers' 6122 'Poultry producers ' 6129 'Animal producers and related workers not elsewhere classified' 6130 'Crop and animal producers' 6140 'Forestry and related workers' 6141 'Forestry workers and loggers' 6142 'Charcoal burners and related workers' 6150 'Fishery workers, hunters and trappers' 6151 'Aquatic life cultivation workers' 6152 'Inland and coastal waters fishery workers' 6153 'Deep-sea fishery workers' 6154 'Hunters and trappers' Craft and related trades workers 7000 'Craft and related trades workers' 7100 'Extraction and building trades workers' 7110 'Miners, shotfirers, stone cutters and carvers' 7111 'Miners and quarry workers' 7112 'Shotfirers and blasters' 7113 'Stone splitters, cutters and carvers' 7120 'Building frame and related trades workers' 7121 'Builders' 7122 'Bricklayers and stonemasons' 7123 'Concrete placers, concrete finishers and related workers' 7124 'Carpenters and joiners' 7129 'Building frame and related trades workers not elsewhere classified' 7130 'Building finishers and related trades workers' 7131 'Roofers' 7132 'Floor layers and tile setters' 7133 'Plasterers' 7134 'Insulation workers' 7135 'Glaziers' 7136 'Plumbers and pipe fitters' 7137 'Building and related electricians' 7139 'Building finishers and related trade workers not elsewhere classified'
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7140 'Painters, building structure cleaners and related trades workers' 7141 'Painters and related workers' 7143 'Building structure cleaners' 7200 'Metal, machinery and related trades workers' 7210 'Metal moulders, welders, sheet-metal workers, structural-metal preparers, and related trades workers' 7211 'Metal moulders and coremakers' 7212 'Welders and flame cutters' 7213 'Sheet-metal workers' 7214 'Structural-metal preparers and erectors' 7215 'Riggers and cable splicers' 7216 'Underwater workers' 7220 'Blacksmiths, tool-makers and related trades workers' 7221 'Blacksmiths, hammer-smiths and forging-press workers' 7222 'Tool-makers and related workers' 7223 'Machine-tool setters and setter-operators' 7224 'Metal wheel-grinders, polishers and tool sharpeners' 7230 'Machinery mechanics and fitters' 7231 'Motor vehicle mechanics and fitters' 7232 'Aircraft engine mechanics and fitters' 7233 'Agricultural- or industrial-machinery mechanics and fitters' 7240 'Electrical and electronic equipment mechanics and fitters' 7241 'Electrical mechanics fitters and services' 7242 'Electronics mechanics, fitters and servicers' 7244 'Telegraph and telephone installers and servicers' 7245 'Electrical line installers, repairers and cable jointers' 7300 'Precision, handicraft, craft printing and related trades workers' 7310 'Precision workers in metal and related materials' 7311 'Precision-instrument makers and repairers' 7312 'Musical-instrument makers and tuners' 7313 'Jewellery and precious-metal workers' 7320 'Potters, glass-makers and related trades workers' 7321 'Abrasive wheel formers, potters and related workers' 7322 'Glass-makers, cutters, grinders and finishers' 7323 'Glass engravers and etchers' 7324 'Glass, ceramics and related decorative painters' 7330 'Handicraft workers in wood, textile, leather and related materials' 7331 'Handicraft workers in wood and related materials' 7332 'Handicraft workers in textile, leather and related materials' 7340 'Craft printing and related trades workers' 7341 'Compositors, typesetters and related workers' 7342 'Stereotypers and electrotypers' 7343 'Printing engravers and etchers' 7344 'Photographic and related workers' 7345 'Bookbinders and related workers' 7346 'Silk-screen, block and craft textile printers' 7400 'Other craft and related trades workers' 7410 'Food processing and related trades workers'
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7411 'Butchers, fishmongers and related food preparers' 7412 'Bakers, pastry-cooks and confectionery makers' 7413 'Dairy-products workers' 7414 'Fruit, vegetable and related preservers' 7415 'Food and beverage tasters and graders' 7416 'Tobacco preparers and tobacco products makers' 7420 'Wood treaters, cabinet-makers and related trades workers' 7421 'Wood treaters' 7422 'Cabinetmakers and related workers' 7423 'Woodworking machine setters and setter-operators' 7424 'Basketry weavers, brush makers and related workers' 7430 'Textile, garment and related trades workers' 7431 'Fibre preparers' 7432 'Weavers, knitters and related workers' 7433 'Tailors, dressmakers and hatters' 7434 'Furriers and related workers' 7435 'Textile, leather and related pattern-makers and cutters' 7436 'Sewers, embroiderers and related workers' 7437 'Upholsterers and related workers' 7440 'Pelt, leather and shoemaking trades workers' 7441 'Pelt dressers, tanners and fellmongers' 7442 'Shoe-makers and related workers' Plant and machine operators and assemblers 8000 'Plant and machine operators and assemblers' 8100 'Stationary plant and related operators' 8110 'Mining and mineral-processing-plant operators' 8111 'Mining plant operators' 8112 'Mineral-ore and stone-processing-plant operators' 8113 'Well drillers and borers and related workers' 8120 'Metal-processing plant operators' 8121 'Ore and metal furnace operators' 8122 'Metal melters, casters and rolling-mill operators' 8123 'Metal heat-treating-plant operators' 8124 'Metal drawers and extruders' 8130 'Glass, ceramics and related plant operators' 8131 'Glass and ceramics kiln and related machine operators' 8139 'Glass, ceramics and related plant operators not elsewhere classified' 8140 'Wood-processing- and papermaking-plant operators' 8141 'Wood-processing-plant operators' 8142 'Paper-pulp plant operators' 8143 'Papermaking-plant operators' 8150 'Chemical-processing-plant operators' 8151 'Crushing-, grinding- and chemical-mixing-machinery operators' 8152 'Chemical-heat-treating-plant operators' 8153 'Chemical-filtering- and separating-equipment operators' 8154 'Chemical-still and reactor operators (except petroleum and natural gas)' 8155 'Petroleum- and natural-gas-refining-plant operators'
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8159 'Chemical-processing-plant operators not elsewhere classified' 8160 'Power-production and related plant operators' 8161 'Power-production plant operators' 8162 'Steam-engine and boiler operators' 8163 'Incinerator, water-treatment and related plant operators' 8170 'Industrial robot operators' 8200 'Machine operators and assemblers' 8210 'Metal- and mineral-products machine operators' 8211 'Machine-tool operators' 8212 'Cement and other mineral products machine operators' 8220 'Chemical-products machine operators' 8221 'Pharmaceutical-and toiletry-products machine operators' 8222 'Ammunition- and explosive-products machine operators' 8223 'Metal finishing-, plating- and coating-machine operators' 8224 'Photographic-products machine operators' 8229 'Chemical-products machine operators not elsewhere classified' 8230 'Rubber- and plastic-products machine operators' 8231 'Rubber-products machine operators' 8232 'Plastic-products machine operators' 8240 'Wood-products machine operators' 8250 'Printing-, binding- and paper-products machine operators' 8251 'Printing-machine operators' 8252 'Book-binding-machine operators' 8253 'Paper-products machine operators' 8260 'Textile-, fur- and leather-products machine operators' 8261 'Fibre-preparing-, spinning- and winding-machine operators' 8262 'Weaving- and knitting-machine operators' 8263 'Sewing-machine operators' 8264 'Bleaching-, dyeing- and cleaning-machine operators' 8265 'Fur- and leather-preparing-machine operators' 8266 'Shoemaking- and related machine operators' 8269 'Textile-, fur- and leather-products machine operators not elsewhere classified' 8270 'Food and related products machine operators' 8271 'Meat- and fish-processing-machine operators' 8272 'Dairy-products machine operators' 8273 'Grain- and spice-milling-machine operators' 8274 'Baked-goods, cereal- and chocolate-products machine operators' 8275 'Fruit-, vegetable- and nut-processing-machine operators' 8276 'Sugar production machine operators' 8277 'Tea-, coffee- and cocoa-processing-machine operators' 8278 'Brewers, wine and other beverage machine operators' 8279 'Tobacco production machine operators' 8280 'Assemblers' 8281 'Mechanical-machinery assemblers' 8282 'Electrical-equipment assemblers' 8283 'Electronic-equipment assemblers' 8284 'Metal-, rubber- and plastic-products assemblers' 8285 'Wood and related products assemblers'
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8286 'Paperboard, textile and related products assemblers' 8287 'Composite products assemblers' 8290 'Other machine operators not elsewhere classified' 8300 'Drivers and mobile plant operators' 8310 'Locomotive engine drivers and related workers' 8311 'Locomotive engine drivers' 8312 'Railway brakers, signallers and shunters' 8320 'Motor vehicle drivers' 8321 'Motorcycle drivers' 8322 'Car, taxi and van drivers' 8323 'Bus and tram drivers' 8324 'Heavy truck and lorry drivers' 8330 'Agricultural and other mobile plant operators' 8331 'Motorised farm and forestry plant operators' 8332 'Earth-moving and related plant operators' 8333 'Crane, hoist and related plant operators' 8334 'Lifting-truck operators' 8340 'Ships´ deck crews and related workers' Elementary occupations 9000 'Elementary occupations' 9100 'Sales and services elementary occupations' 9110 'Street vendors and related workers' 9111 'Street vendors' 9113 'Door-to-door and telephone salespersons' 9120 'Shoe cleaning and other street services elementary occupations' 9130 'Domestic and related helpers, cleaners and launderers' 9131 'Domestic helpers and cleaners' 9132 'Helpers and cleaners in offices, hotels and other establishments' 9133 'Hand-launderers and pressers' 9140 'Building caretakers, window and related cleaners' 9141 'Building caretakers' 9142 'Vehicle, window and related cleaners' 9150 'Messengers, porters, doorkeepers and related workers' 9151 'Messengers, package and luggage porters and deliverers' 9152 'Doorkeepers, watchpersons and related workers' 9153 'Vending-machine money collectors, meter readers and related workers' 9160 'Garbage collectors and related labourers' 9161 'Garbage collectors' 9162 'Sweepers and related labourers' 9200 'Agricultural, fishery and related labourers' 9210 'Agricultural, fishery and related labourers' 9211 'Farm-hands and labourers' 9212 'Forestry labourers' 9213 'Fishery, hunting and trapping labourers' 9300 'Labourers in mining, construction, manufacturing and transport' 9310 'Mining and construction labourers' 9311 'Mining and quarrying labourers'
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9312 'Construction and maintenance labourers: roads, dams and similar constructions' 9313 'Building construction labourers' 9320 'Manufacturing labourers' 9330 'Transport labourers and freight handlers' 9996 'Not classified;inadequately described' 9997 'Refused' 9998 'Dont know' 9999 'Na' 0 'NAP,NAV, No spouse, never had a job'.
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Variable List V1 ISSP Study Number........................................................................................................................................................ 1 V2 Respondent ID Number.................................................................................................................................................. 1 V3 Country............................................................................................................................................................................ 2 V4 Most important group R identifies with........................................................................................................................ 3 V5 Second most important group R identifies with........................................................................................................... 5 V6 Third most important group R identifies with.............................................................................................................. 7 V7 How close do you feel to: Your town - city ................................................................................................................. 9 V8 How close do you feel to: Your [county] ..................................................................................................................... 11 V9 How close do you feel to: [country] ............................................................................................................................. 13 V10 How close do you feel to: continent ........................................................................................................................... 15 V11 Important: to have been born in [Country]................................................................................................................. 17 V12 Important: To have [Country nationality] citizenship................................................................................................ 19 V13 Important: To have lived in [Country] for most of one’s life.................................................................................... 21 V14 Important: To be able to speak [Country language] .................................................................................................. 23 V15 Important: To be a [religion]....................................................................................................................................... 25 V16 Important: To respect [Country nationality] political institutions and laws ............................................................. 27 V17 Important: To feel [Country nationality] .................................................................................................................... 29 V18 Important: To have [country nationality] ancestry..................................................................................................... 31 V19 I would rather be a citizen of [Country] than of any other country in the world...................................................... 33 V20 There are some things about [Country] today that make me feel ashamed of [Country] ....................................... 35 V21 The world would be a better place if people from other countries were more like the [Country nationality] ........ 37 V22 Generally speaking, [Country] is a better country than most other countries........................................................... 39 V23 People should support their country even if the country is in the wrong.................................................................. 41 V25 I am often less proud of [Country] than I would like to be......................................................................................... 45 V26 How proud: The way democracy works .................................................................................................................... 47 V27 How proud: Its political influence in the world ......................................................................................................... 49 V28 How proud: [Country’s] economic achievements ..................................................................................................... 51 V29 How proud: Its social security system........................................................................................................................ 53 V30 How proud: Its scientific and technological achievements ....................................................................................... 55 V31 How proud: Its achievements in sports....................................................................................................................... 57 V32 How proud: Its achievements in the arts and literature.............................................................................................. 59 V33 How proud: [Country’S] armed forces....................................................................................................................... 61 V34 How proud: Its history................................................................................................................................................. 63 V35 How proud: Its fair and equal treatment of all groups in society .............................................................................. 65 V36 [Country] should limit the import of foreign products in order to protect its national economy............................. 67 V37 For certain problems, like environment pollution, international bodies should have the right to enforce
solutions......................................................................................................................................................................... 69 V38 [Country] should follow its own interests, even if this leads to conflicts with other nations................................... 71 V39 Foreigners should not be allowed to buy land in [Country] ...................................................................................... 73 V40 [Country’s] television should give preference to [Country] films and programmes................................................ 75 V41 Large international companies are doing more and more damage to local businesses in [Country]. ..................... 77 V42 Free trade leads to better products becoming available in [Country]........................................................................ 79 V43 In general, [Country] should follow the decisions of international organizations to which it belongs, even if the government does not agree with them. ................................................................................................................................ 81 V44 International organizations are taking away too much power from the [Country nationality] government. .......... 83 V45 Increased exposure to foreign films, music, and books is damaging our national and local cultures. .................... 85 V46 A benefit of the Internet is that it makes information available to more and more people worldwide. .................. 87 V47 It is impossible for people who do not share [Country’s] customs and traditions to become fully [Country’s nationality] ............................................................................................................................................................................ 89 V48 Ethnic minorities should be given government assistance to preserve their customs and traditions....................... 91 V49 Help minorities to preserve traditions......................................................................................................................... 93 V50 Immigrants increase crime rates ................................................................................................................................. 95 V51 Immigrants are generally good for [Country’s] economy ......................................................................................... 97 V52 Immigrants take jobs away from people who were born in [Country] ..................................................................... 99 V53 Immigrants improve [Country nationality] society by bringing in new ideas and cultures..................................... 101 V54 Government spends too much money assisting immigrants. .................................................................................... 103
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V55 Number of immigrants coming to country................................................................................................................. 105 V56 Are you a citizen of [Country] .................................................................................................................................... 107 V57 Parents citizen of [country] at birth............................................................................................................................. 109 V58 Racial-ethnic group of R ............................................................................................................................................. 111 V59 Children born in [Country] of parents who are not citizens should have the right to become [Country
Nationality] citizens. ..................................................................................................................................................... 122 V60 Children born abroad should have the right to become [Country Nationality] citizens if at least one of their
parents is a [Country Nationality] citizen. ................................................................................................................... 124 V61 Legal immigrants to [Country] who are not citizens should have the same rights as [Country Nationality]
citizens. .......................................................................................................................................................................... 126 V62 [Country] should take stronger measures to exclude illegal immigrants? ................................................................ 128 V63 How proud are you being country national ................................................................................................................ 130 V64 What language speak at home 1st mention ................................................................................................................ 132 V65 What language speak at home 2nd mention............................................................................................................... 140 V66 One nation - Separate nation....................................................................................................................................... 147 V67 How close do you feel to your ethnic group .............................................................................................................. 149 V68 R: More regional or national identity ......................................................................................................................... 151 V69 Heard-read about [the European Union] .................................................................................................................... 153 V70 Benefits from being member of [the European Union] ............................................................................................. 155 V70b Would benefit from being member of [the European Union]................................................................................. 157 V71 [Country] should follow decisions of EU................................................................................................................... 159 V72 EU should have more power than national government............................................................................................ 161 V73 Vote if referendum to become new member.............................................................................................................. 163 V74 EU members: Referendum to remain member .......................................................................................................... 165 SEX R: Sex .......................................................................................................................................................................... 167 AGE R's age......................................................................................................................................................................... 169 MARITAL R's marital status ............................................................................................................................................. 170 COHAB: R: Steady life-partner........................................................................................................................................... 172 EDUCYRS: R's Education I: years in school...................................................................................................................... 174 DEGREE: R's Education II: categories ............................................................................................................................... 180 XX_DEGR: Country Specific Education Degree............................................................................................................... 182 WRKST: R's current employment status............................................................................................................................. 193 SPWRKST: R's Spouse-Partner Current employment status............................................................................................. 195 WRKHRS: R's hours worked weekly.................................................................................................................................. 197 ISCO88: R: Occupation ILO,ISCO 1988 4-digit................................................................................................................ 198 SPISCO88: SP: Occupation ILO,ISCO 1988 4-digit ......................................................................................................... 200 WRKTYPE: R: Working for private,public sector, selfemployed..................................................................................... 202 SPWRKTYP: Rs Spouse-Partner working for private,public sector, selfemployed......................................................... 204 NEMPLOY: Self-employment II - how many employees ................................................................................................. 206 WRKSUP: R Supervises ...................................................................................................................................................... 207 UNION: R's syndical affiliation........................................................................................................................................... 209 HOMPOP: R's size of household......................................................................................................................................... 211 INCOME: Family income.................................................................................................................................................... 214 RINCOME: Respondent’s earnings .................................................................................................................................... 222 HHCYCLE: Household composition.................................................................................................................................. 230 PARTY_LR: R's scale of political ideology ....................................................................................................................... 233 XX_PRTY: Country Specific Vote Intention ..................................................................................................................... 235 VOTE_LE: R: Vote last election: yes, no ........................................................................................................................... 247 RELIG: Religious denomination ......................................................................................................................................... 249 RELIGGRP: R: Religious main groups (derived) .............................................................................................................. 257 ATTEND: Religious services - how often .......................................................................................................................... 259 TOPBOT: R:Top Bottom self-placement 10 point scale.................................................................................................... 261 XX_REG: Country specific region...................................................................................................................................... 263 XX_SIZE: Country specific town size ................................................................................................................................ 276 URBRURAL: Place of residence: urban-rural.................................................................................................................... 285 ETHNIC: R's Ethnicity or nationality (Country specific)................................................................................................... 287 MODE: Administrative mode of data-collection................................................................................................................ 294
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