Census Population Coverage Error: Results and Methods
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Geographical Links Between Paid and Unpaid Work for Transnational Care Workers in Toronto’S Suburbs
FINDING HOME: GEOGRAPHICAL LINKS BETWEEN PAID AND UNPAID WORK FOR TRANSNATIONAL CARE WORKERS IN TORONTO’S SUBURBS SILVIA D’ADDARIO A DISSERTATION SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN GEOGRAPHY YORK UNIVERSITY, TORONTO, ONTARIO SEPTEMBER, 2012 © SILVIA D’ADDARIO, 2012 Library and Archives Bibliotheque et Canada Archives Canada Published Heritage Direction du 1+1 Branch Patrimoine de I'edition 395 Wellington Street 395, rue Wellington Ottawa ON K1A0N4 Ottawa ON K1A 0N4 Canada Canada Your file Votre reference ISBN: 978-0-494-92812-7 Our file Notre reference ISBN: 978-0-494-92812-7 NOTICE: AVIS: The author has granted a non L'auteur a accorde une licence non exclusive exclusive license allowing Library and permettant a la Bibliotheque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par telecommunication ou par I'lnternet, preter, telecommunication or on the Internet, distribuer et vendre des theses partout dans le loan, distrbute and sell theses monde, a des fins commerciales ou autres, sur worldwide, for commercial or non support microforme, papier, electronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette these. Ni thesis. Neither the thesis nor la these ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent etre imprimes ou autrement printed or otherwise reproduced reproduits sans son autorisation. -
1988: Coverage Error in Establishment Surveys
COVERAGE ERROR IN ESTABLISHMENT SURVEYS Carl A. Konschnik U.S. Bureau of the CensusI I. Definition of Coverage Error in the survey planning stage results in a sam- pled population which is too far removed from Coverage error which includes both under- the target population. Since estimates based coverage and overcoverage, is defined as "the on data drawn from the sampled population apply error in an estimate that results from (I) fail- properly only to the sampled population, inter- ure to include all units belonging to the est in the target population dictates that the defined population or failure to include speci- sampled population be as close as practicable fied units in the conduct of the survey to the target population. Nevertheless, in the (undercoverage), and (2) inclusion of some units following discussion of the sources, measure- erroneously either because of a defective frame ment, and control of coverage error, only or because of inclusion of unspecified units or deficiencies relative to the sampled population inclusion of specified units more than once in are included. Thus, when speaking of defective the actual survey (overcoverage)" (Office of frames, only those deficiencies are discussed Federal Statistical Policy and Standards, 1978). which arise when the population which is sampled Coverage errors are closely related to but differs from the population intended to be clearly distinct from content errors, which are sampled (the sampled population). defined as the "errors of observation or objec- tive measurement, of recording, of imputation, Coverage Error Source Categories or of other processing which results in associ- ating a wrong value of the characteristic with a We will now look briefly at the two cate- specified unit" (Office of Federal Statistical gories of coverage error--defective frames and Policy and Standards, 1978). -
American Community Survey Design and Methodology (January 2014) Chapter 15: Improving Data Quality by Reducing Non-Sampling Erro
American Community Survey Design and Methodology (January 2014) Chapter 15: Improving Data Quality by Reducing Non-Sampling Error Version 2.0 January 30, 2014 ACS Design and Methodology (January 2014) – Improving Data Quality Page ii [This page intentionally left blank] Version 2.0 January 30, 2014 ACS Design and Methodology (January 2014) – Improving Data Quality Page iii Table of Contents Chapter 15: Improving Data Quality by Reducing Non-Sampling Error ............................... 1 15.1 Overview .......................................................................................................................... 1 15.2 Coverage Error ................................................................................................................. 2 15.3 Nonresponse Error............................................................................................................ 3 15.4 Measurement Error ........................................................................................................... 6 15.5 Processing Error ............................................................................................................... 7 15.6 Census Bureau Statistical Quality Standards ................................................................... 8 15.7 References ........................................................................................................................ 9 Version 2.0 January 30, 2014 ACS Design and Methodology (January 2014) – Improving Data Quality Page iv [This page intentionally left -
R(Y NONRESPONSE in SURVEY RESEARCH Proceedings of the Eighth International Workshop on Household Survey Nonresponse 24-26 September 1997
ZUMA Zentrum für Umfragen, Melhoden und Analysen No. 4 r(y NONRESPONSE IN SURVEY RESEARCH Proceedings of the Eighth International Workshop on Household Survey Nonresponse 24-26 September 1997 Edited by Achim Koch and Rolf Porst Copyright O 1998 by ZUMA, Mannheini, Gerinany All rights reserved. No part of tliis book rnay be reproduced or utilized in any form or by aiiy means, electronic or mechanical, including photocopying, recording, or by any inforniation Storage and retrieval System, without permission in writing froni the publisher. Editors: Achim Koch and Rolf Porst Publisher: Zentrum für Umfragen, Methoden und Analysen (ZUMA) ZUMA is a member of the Gesellschaft Sozialwissenschaftlicher Infrastruktureinrichtungen e.V. (GESIS) ZUMA Board Chair: Prof. Dr. Max Kaase Dii-ector: Prof. Dr. Peter Ph. Mohlcr P.O. Box 12 21 55 D - 68072.-Mannheim Germany Phone: +49-62 1- 1246-0 Fax: +49-62 1- 1246- 100 Internet: http://www.social-science-gesis.de/ Printed by Druck & Kopie hanel, Mannheim ISBN 3-924220-15-8 Contents Preface and Acknowledgements Current Issues in Household Survey Nonresponse at Statistics Canada Larry Swin und David Dolson Assessment of Efforts to Reduce Nonresponse Bias: 1996 Survey of Income and Program Participation (SIPP) Preston. Jay Waite, Vicki J. Huggi~isund Stephen 1'. Mnck Tlie Impact of Nonresponse on the Unemployment Rate in the Current Population Survey (CPS) Ciyde Tucker arzd Brian A. Harris-Kojetin An Evaluation of Unit Nonresponse Bias in the Italian Households Budget Survey Claudio Ceccarelli, Giuliana Coccia and Fahio Crescetzzi Nonresponse in the 1996 Income Survey (Supplement to the Microcensus) Eva Huvasi anci Acfhnz Marron The Stability ol' Nonresponse Rates According to Socio-Dernographic Categories Metku Znletel anci Vasju Vehovar Understanding Household Survey Nonresponse Through Geo-demographic Coding Schemes Jolin King Response Distributions when TDE is lntroduced Hikan L. -
Coverage Bias in European Telephone Surveys: Developments of Landline and Mobile Phone Coverage Across Countries and Over Time
www.ssoar.info Coverage Bias in European Telephone Surveys: Developments of Landline and Mobile Phone Coverage across Countries and over Time Mohorko, Anja; Leeuw, Edith de; Hox, Joop Veröffentlichungsversion / Published Version Zeitschriftenartikel / journal article Empfohlene Zitierung / Suggested Citation: Mohorko, A., Leeuw, E. d., & Hox, J. (2013). Coverage Bias in European Telephone Surveys: Developments of Landline and Mobile Phone Coverage across Countries and over Time. Survey Methods: Insights from the Field, 1-13. https://doi.org/10.13094/SMIF-2013-00002 Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer CC BY-NC-ND Lizenz This document is made available under a CC BY-NC-ND Licence (Namensnennung-Nicht-kommerziell-Keine Bearbeitung) zur (Attribution-Non Comercial-NoDerivatives). For more Information Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden see: Sie hier: https://creativecommons.org/licenses/by-nc-nd/4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/deed.de Diese Version ist zitierbar unter / This version is citable under: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-333873 Survey Methods: Insights from the Field Anja Mohorko, Department of Methodology and Statistics, Utrecht University Edith de Leeuw, Department of Methodology and Statistics, Utrecht University Joop Hox, Department of Methodology and Statistics, Utrecht University 25.01.2013 How to cite this article: Mohorko, A., de Leeuw, E., & Hox, J. (2013). Coverage Bias in European Telephone Surveys: Developments of Landline and Mobile Phone Coverage across Countries and over Time. Survey Methods: Insights from the Field. Retrieved from http://surveyinsights.org/?p=828 Abstract With the decrease of landline phones in the last decade, telephone survey methodologists face a new challenge to overcome coverage bias. -
A Total Survey Error Perspective on Comparative Surveys
A Total Survey Error Perspective on Comparative Surveys October 2nd 2014 – ITSEW – Washington DC Beth-Ellen Pennell, University of Michigan Lars Lyberg, Stockholm University Peter Mohler, University of Mannheim Kristen Cibelli Hibben, University of Michigan (presenting author) Gelaye Worku, Stockholm University © 2014 by the Regents of the University of Michigan Presentation Outline 1. Background 2. Integrated Total Survey Error Model Contribution to Errors: Representation: Coverage, Sampling, Nonresponse errors Measurement: Validity, Measurement errors Measurement error – expanded view 3. Conclusion © 2014 by the Regents of the University of Michigan Background 1. The scope, scale, and number of multicultural, multiregional or multinational (3M) surveys continues to grow a. Examples: European Social Survey (20+ countries), the World Values Survey (75+ countries), the Gallup World Poll (160 countries) 2. The purpose is comparability – An estimate for one country should be reasonably comparable to another. 3. However, comparability in 3M surveys is affected by various error sources that we are used to considering plus some additional ones. © 2014 by the Regents of the University of Michigan Background, continued… 1. 3M surveys have an error typology that differs from monocultural surveys. a. Error sources in 3M surveys are typically amplified b. 3M surveys often require additional operations – i.e. adaptation, translation 2. Few attempts have been made to adapt the TSE framework to 3M surveys (except see: Smith’s (2011) refined TSE model). 3. The proposed TSE typology integrates error sources – and associated methodological and operational challenges that may limit comparability. © 2014 by the Regents of the University of Michigan Total Survey Error (Groves et al., 2004) © 2014 by the Regents of the University of Michigan An Integrated TSE Model Some general points: 1. -
Economic and Social Council Distr.: General 23 April 2010 English
United Nations ECE/CES/GE.41/2010/6 Economic and Social Council Distr.: General 23 April 2010 English Original: French Economic Commission for Europe Conference of European Statisticians Group of Experts on Population and Housing Censuses Thirteenth Meeting Geneva, 7–9 July 2010 Provisional agenda item 3 Plans for census quality evaluation Quality Assurance Programme of the 2011 Canadian Census of Population Note by Statistics Canada Summary This article outlines the quality assurance programme of the 2011 Canadian Census of Population and gives an overview of the main changes introduced for the 2011 Census. Conducted every five years, the Canadian Census of Population is a major undertaking whose planning and implementation spans a period of over eight years. The census is unique because it is the only source of detailed socio-economic and demographic data on small geographical areas including neighbourhoods and communities. In order to ensure that the population and dwelling counts and the data on the characteristics of the population and dwellings are of sufficiently high quality, a diversified quality assurance programme has been developed over the last few censuses. During these censuses, changes and additions were made to the quality assurance programme in the light of operational, technological and methodological changes as well as a changing reality. GE.10-22128 (E) 110510 140510 ECE/CES/GE.41/2010/6 1. At its meeting in Washington, D.C. (United States) on 19 and 20 October 2006, the Bureau of the Conference of European Statisticians (CES) adopted the renewed terms of reference of the Steering Group on Population and Housing Censuses and the plan of future CES activities in the field of population and housing censuses. -
Ethical Considerations in the Total Survey Error Context
Ethical Considerations in the Total Survey Error Context Julie de Jong 3MCII International Conference July 26-29, 2016 Chicago, Illinois © 2014 by the Regents of the University of Michigan Acknowledgements • Invited paper in Advances in Comparative Survey Methods: Multicultural, Multinational and Multiregional Contexts (3MC), edited by T.P. Johnson, B.E. Pennell, I.A.L. Stoop, & B. Dorer. New York, NY: John Wiley & Sons. • Special thanks to Kirsten Alcser, Christopher Antoun, Ashley Bowers, Judi Clemens, Christina Lien, Steve Pennell, and Kristen Cibelli Hibben who contributed substantially to earlier iterations of the chapter “Ethical Considerations” in the Cross-Cultural Survey Guidelines from which this chapter draws material. © 2014 by the Regents of the University of Michigan Overview – Legal and professional regulations and socio-political contexts can lead to differences in how ethical protocols are realized across countries. – Differences in the implementation of ethical standards contributes to the sources of total survey error (TSE) • TSE describes statistical properties of survey estimates by incorporating a variety of error sources and is used in the survey design stage, in the data collection process, and evaluation and post survey adjustments (Groves & Lyberg, 2010) • The TSE framework is generally used to design and evaluate a single-country survey – The framework has been extended to 3MC surveys and the impact of TSE on overall comparison error (Smith 2011) 3 © 2014 by the Regents of the University of Michigan Objectives -
Census Coverage Studies in Canada: a History with Emphasis on the 2011 Census
Section on Survey Research Methods – JSM 2010 Census Coverage Studies in Canada: A History with Emphasis on the 2011 Census David Dolson Statistics Canada, 100 Tunney’s Pasture Driveway, Ottawa, ON, Canada, K1A 0T6 Abstract For Canada’s 2011 Census of Population, Statistics Canada will again use the Reverse Record Check methodology for estimating undercoverage. Estimates are based upon classification of a sample of persons who “should” be enumerated as being in-scope enumerated, in-scope missed or out-of-scope. Overcoverage will be estimated using the Census Overcoverage Study, first implemented in 2006. This methodology is based upon matching the census database to itself using administrative records to aid in confirming the validity of matching pairs of enumerations. This paper will trace the history of census coverage studies in Canada, primarily focusing on the period since 1961 when the Reverse Record Check was first implemented. Emphasis will be placed on plans for 2011, outlining the methodology of each study and highlighting important changes from the 2006 coverage studies. Keywords: Reverse Record Check, Record Linkage 1. Introduction The first census in Canada including a formal coverage measurement program was that in 1961; undercoverage was estimated using the Reverse Record Check (RRC) methodology. This paper will first give a description of coverage measurement fundamentals and concepts as applied in Canada. The next section will review the methodology of the 1961 RRC and trace major developments in the coverage studies program over the following 45 years. The last section will provide a detailed description of plans for 2011 including the Dwelling Classification Survey, the RRC and the Census Overcoverage Study. -
Standards and Guidelines for Reporting on Coverage Martin Provost Statistics Canada R
Standards and Guidelines for Reporting on Coverage Martin Provost Statistics Canada R. H. Coats Building, 15th Floor, Tunney’s Pasture, Ottawa ONT, Canada, K1A 0T6, [email protected] Key Words: Quality indicators, coverage, ideal, target, survey and frame populations 1. Introduction The level of coverage provided by a survey or a statistical program has a major impact on the overall quality of the survey process. It is therefore important that users of the survey data be informed of the coverage quality provided by the survey and of any potential problem associated with identified defects in terms of coverage. Those measures of coverage add to the set of quality indicators that together provide a complete picture that facilitate an adequate use of the survey data and an optimization of the survey resources to allow for its constant improvement. But how should one define coverage (based on what units, etc.)? And how should survey managers report on it? This paper outlines the content and describes the procedures, issues and topics which will have to be covered in standards and guidelines for reporting on coverage at Statistics Canada to standardize the terminology, the definitions and the measures relating to these coverage issues. These standards and guidelines have been proposed for inclusion in Statistics Canada's Quality Assurance Framework as a supplement to this agency's Policy on Informing Users of Data Quality and Methodology. 2. Statistics Canada’s Quality Assurance Framework Statistics Canada (STC) has always invested considerable effort in achieving an adequate level of quality for all of its products and services. -
Survey Coverage
MEMBERS OF THE FEDERAL COMMITTEE ON STATISTICAL METHODOLOGY (April 1990) Maria E. Gonzalez (Chair) office of Management and Budget Yvonne M. Bishop Daniel Kasprzyk Energy Information Bureau of the Census Administration Daniel Melnick Warren L. Buckler National Science Foundation Social Security Administration Robert P. Parker Charles E. Caudill Bureau of Economic Analysis National Agricultural Statistical Service David A. Pierce Federal Reserve Board John E. Cremeans Office of Business Analysis Thomas J. Plewes Bureau of Labor Statistics Zahava D. Doering Smithsonian Institution Wesley L. Schaible Bureau of Labor Statistics Joseph K. Garrett Bureau of the Census Fritz J. Scheuren Internal Revenue service Robert M. Groves Bureau of the Census Monroe G. Sirken National Center for Health C. Terry Ireland Statistics National Computer Security Center Robert D. Tortora Bureau of the Census Charles D. Jones Bureau of the Census PREFACE The Federal Committee on Statistical Methodology was organized by the Office of Management and Budget (OMB) in 1975 to investigate methodological issues in Federal statistics. Members of the committee, selected by OMB on the basis of their individual expertise and interest in statistical methods, serve in their personal capacity rather than as agency representatives. The committee conducts its work through subcommittees that are organized to study particular issues and that are open to any Federal employee who wishes to participate in the studies. working papers are prepared by the subcommittee members and reflect only their individual and collective ideas. The Subcommittee on Survey Coverage studied the survey errors that can seriously bias sample survey data because of undercoverage of certain subpopulations or because of overcoverage of other subpopulations. -
STANDARDS and GUIDELINES for STATISTICAL SURVEYS September 2006
OFFICE OF MANAGEMENT AND BUDGET STANDARDS AND GUIDELINES FOR STATISTICAL SURVEYS September 2006 Table of Contents LIST OF STANDARDS FOR STATISTICAL SURVEYS ....................................................... i INTRODUCTION......................................................................................................................... 1 SECTION 1 DEVELOPMENT OF CONCEPTS, METHODS, AND DESIGN .................. 5 Section 1.1 Survey Planning..................................................................................................... 5 Section 1.2 Survey Design........................................................................................................ 7 Section 1.3 Survey Response Rates.......................................................................................... 8 Section 1.4 Pretesting Survey Systems..................................................................................... 9 SECTION 2 COLLECTION OF DATA................................................................................... 9 Section 2.1 Developing Sampling Frames................................................................................ 9 Section 2.2 Required Notifications to Potential Survey Respondents.................................... 10 Section 2.3 Data Collection Methodology.............................................................................. 11 SECTION 3 PROCESSING AND EDITING OF DATA...................................................... 13 Section 3.1 Data Editing ........................................................................................................