Ways of Reducing Coverage and Sampling Error As Part of the Total Survey Error Framework for Establishment Surveys in Europe: Recent Developments
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Esomar/Grbn Guideline for Online Sample Quality
ESOMAR/GRBN GUIDELINE FOR ONLINE SAMPLE QUALITY ESOMAR GRBN ONLINE SAMPLE QUALITY GUIDELINE ESOMAR, the World Association for Social, Opinion and Market Research, is the essential organisation for encouraging, advancing and elevating market research: www.esomar.org. GRBN, the Global Research Business Network, connects 38 research associations and over 3500 research businesses on five continents: www.grbn.org. © 2015 ESOMAR and GRBN. Issued February 2015. This Guideline is drafted in English and the English text is the definitive version. The text may be copied, distributed and transmitted under the condition that appropriate attribution is made and the following notice is included “© 2015 ESOMAR and GRBN”. 2 ESOMAR GRBN ONLINE SAMPLE QUALITY GUIDELINE CONTENTS 1 INTRODUCTION AND SCOPE ................................................................................................... 4 2 DEFINITIONS .............................................................................................................................. 4 3 KEY REQUIREMENTS ................................................................................................................ 6 3.1 The claimed identity of each research participant should be validated. .................................................. 6 3.2 Providers must ensure that no research participant completes the same survey more than once ......... 8 3.3 Research participant engagement should be measured and reported on ............................................... 9 3.4 The identity and personal -
SAMPLING DESIGN & WEIGHTING in the Original
Appendix A 2096 APPENDIX A: SAMPLING DESIGN & WEIGHTING In the original National Science Foundation grant, support was given for a modified probability sample. Samples for the 1972 through 1974 surveys followed this design. This modified probability design, described below, introduces the quota element at the block level. The NSF renewal grant, awarded for the 1975-1977 surveys, provided funds for a full probability sample design, a design which is acknowledged to be superior. Thus, having the wherewithal to shift to a full probability sample with predesignated respondents, the 1975 and 1976 studies were conducted with a transitional sample design, viz., one-half full probability and one-half block quota. The sample was divided into two parts for several reasons: 1) to provide data for possibly interesting methodological comparisons; and 2) on the chance that there are some differences over time, that it would be possible to assign these differences to either shifts in sample designs, or changes in response patterns. For example, if the percentage of respondents who indicated that they were "very happy" increased by 10 percent between 1974 and 1976, it would be possible to determine whether it was due to changes in sample design, or an actual increase in happiness. There is considerable controversy and ambiguity about the merits of these two samples. Text book tests of significance assume full rather than modified probability samples, and simple random rather than clustered random samples. In general, the question of what to do with a mixture of samples is no easier solved than the question of what to do with the "pure" types. -
The Effect of Sampling Error on the Time Series Behavior of Consumption Data*
Journal of Econometrics 55 (1993) 235-265. North-Holland The effect of sampling error on the time series behavior of consumption data* William R. Bell U.S.Bureau of the Census, Washington, DC 20233, USA David W. Wilcox Board of Governors of the Federal Reserve System, Washington, DC 20551, USA Much empirical economic research today involves estimation of tightly specified time series models that derive from theoretical optimization problems. Resulting conclusions about underly- ing theoretical parameters may be sensitive to imperfections in the data. We illustrate this fact by considering sampling error in data from the Census Bureau’s Retail Trade Survey. We find that parameter estimates in seasonal time series models for retail sales are sensitive to whether a sampling error component is included in the model. We conclude that sampling error should be taken seriously in attempts to derive economic implications by modeling time series data from repeated surveys. 1. Introduction The rational expectations revolution has transformed the methodology of macroeconometric research. In the new style, the researcher typically begins by specifying a dynamic optimization problem faced by agents in the model economy. Then the researcher derives the solution to the optimization problem, expressed as a stochastic model for some observable economic variable(s). A trademark of research in this tradition is that each of the parameters in the model for the observable variables is endowed with a specific economic interpretation. The last step in the research program is the application of the model to actual economic data to see whether the model *This paper reports the general results of research undertaken by Census Bureau and Federal Reserve Board staff. -
9Th GESIS Summer School in Survey Methodology August 2020
9th GESIS Summer School in Survey Methodology August 2020 Syllabus for short course C: “Applied Systematic Review and Meta-Analysis” Lecturers: Dr. Jessica Daikeler Sonila Dardha E-mail: [email protected] [email protected] Homepage: https://www.gesis.org/en/institute/ https://www.researchgate.net/profile/Sonila_Dard /staff/person/Jessica.wengrzik ha Date: 12-14 August 2020 Time: 10:00-16:00 Time zone: CEST, course starts on Wednesday at 10:00 Venue: Online via Zoom About the Lecturers: Jessica is a survey methodologist and works at GESIS in the Survey Operations and Survey Statistics teams in the Survey Design and Methodology department. Jessica wrote her dissertation on "The Application of Evidence- Based Methods in Survey Methodology" with Prof. Michael Bosnjak (University of Trier & ZPID) and Prof. Florian Keusch (University of Mannheim). At GESIS she is involved in the application of evidence-based methods, in par- ticular experiments, systematic reviews and meta-analyses. She has lots of experience with different systematic review and meta-analysis projects. Her research is currently focused on data quality in web and mobile surveys, link of nonresponse and measurement errors, data linkage and, of course, methods for the accumulation of evi- dence. Sonila is a survey methodologist researching interviewer effects, response patterns, nonresponse error and 3MC surveys, and a survey practitioner coordinating cross-country comparative projects. She is currently pursuing her PhD in Survey Research Methodology at City, University of London, the home institution of the European Social Survey (ESS ERIC). Sonila has previously obtained three Master degrees, one of which in M.Sc in Statistics (Quan- titative Analysis for the Social Sciences) from KU Leuven, Belgium. -
13 Collecting Statistical Data
13 Collecting Statistical Data 13.1 The Population 13.2 Sampling 13.3 Random Sampling 1.1 - 1 • Polls, studies, surveys and other data collecting tools collect data from a small part of a larger group so that we can learn something about the larger group. • This is a common and important goal of statistics: Learn about a large group by examining data from some of its members. 1.1 - 2 Data collections of observations (such as measurements, genders, survey responses) 1.1 - 3 Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data 1.1 - 4 Population the complete collection of all individuals (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all of the individuals to be studied 1.1 - 5 Census Collection of data from every member of a population Sample Subcollection of members selected from a population 1.1 - 6 A Survey • The practical alternative to a census is to collect data only from some members of the population and use that data to draw conclusions and make inferences about the entire population. • Statisticians call this approach a survey (or a poll when the data collection is done by asking questions). • The subgroup chosen to provide the data is called the sample, and the act of selecting a sample is called sampling. 1.1 - 7 A Survey • The first important step in a survey is to distinguish the population for which the survey applies (the target population) and the actual subset of the population from which the sample will be drawn, called the sampling frame. -
5.1 Survey Frame Methodology
Regional Course on Statistical Business Registers: Data sources, maintenance and quality assurance Perak, Malaysia 21-25 May, 2018 4 .1 Survey fram e methodology REVIEW For sampling purposes, a snapshot of the live register at a particular point in t im e is needed. The collect ion of active statistical units in the snapshot is referred to as a frozen frame. REVIEW A sampling frame for a survey is a subset of t he frozen fram e t hat includes units and characteristics needed for t he survey. A single frozen fram e should be used for all surveys in a given reference period Creat ing sam pling fram es SPECIFICATIONS Three m ain t hings need t o be specified t o draw appropriate sampling frames: ▸ Target population (which units?) ▸ Variables of interest ▸ Reference period CHOICE OF STATISTICAL UNIT Financial data Production data Regional data Ent erpri ses are Establishments or Establishments or typically the most kind-of-activity local units should be appropriate units to units are typically used if regional use for financial data. the most appropriate disaggregation is for production data. necessary. Typically a single t ype of unit is used for each survey, but t here are except ions where t arget populat ions include m ult iple unit t ypes. CHOICE OF STATISTICAL UNIT Enterprise groups are useful for financial analyses and for studying company strategies, but they are not normally the target populations for surveys because t hey are t oo diverse and unstable. SURVEYS OF EMPLOYMENT The sam pling fram es for t hese include all active units that are em ployers. -
American Community Survey Accuracy of the Data (2018)
American Community Survey Accuracy of the Data (2018) INTRODUCTION This document describes the accuracy of the 2018 American Community Survey (ACS) 1-year estimates. The data contained in these data products are based on the sample interviewed from January 1, 2018 through December 31, 2018. The ACS sample is selected from all counties and county-equivalents in the United States. In 2006, the ACS began collecting data from sampled persons in group quarters (GQs) – for example, military barracks, college dormitories, nursing homes, and correctional facilities. Persons in sample in (GQs) and persons in sample in housing units (HUs) are included in all 2018 ACS estimates that are based on the total population. All ACS population estimates from years prior to 2006 include only persons in housing units. The ACS, like any other sample survey, is subject to error. The purpose of this document is to provide data users with a basic understanding of the ACS sample design, estimation methodology, and the accuracy of the ACS data. The ACS is sponsored by the U.S. Census Bureau, and is part of the Decennial Census Program. For additional information on the design and methodology of the ACS, including data collection and processing, visit: https://www.census.gov/programs-surveys/acs/methodology.html. To access other accuracy of the data documents, including the 2018 PRCS Accuracy of the Data document and the 2014-2018 ACS Accuracy of the Data document1, visit: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. 1 The 2014-2018 Accuracy of the Data document will be available after the release of the 5-year products in December 2019. -
Journal of Econometrics an Empirical Total Survey Error Decomposition
Journal of Econometrics xxx (xxxx) xxx Contents lists available at ScienceDirect Journal of Econometrics journal homepage: www.elsevier.com/locate/jeconom An empirical total survey error decomposition using data combination ∗ Bruce D. Meyer a,b,c, Nikolas Mittag d, a Harris School of Public Policy Studies, University of Chicago, 1307 E. 60th Street, Chicago, IL 60637, United States of America b NBER, United States of America c AEI, United States of America d CERGE-EI, joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences, Politických v¥z¬· 7, Prague 1, 110 00, Czech Republic article info a b s t r a c t Article history: Survey error is known to be pervasive and to bias even simple, but important, estimates Received 16 July 2019 of means, rates, and totals, such as the poverty and the unemployment rate. In order Received in revised form 16 July 2019 to summarize and analyze the extent, sources, and consequences of survey error, we Accepted 5 March 2020 define empirical counterparts of key components of the Total Survey Error Framework Available online xxxx that can be estimated using data combination. Specifically, we estimate total survey Keywords: error and decompose it into three high level sources of error: generalized coverage Total survey error error, item non-response error and measurement error. We further decompose these Administrative data sources into lower level sources such as failure to report a positive amount and errors Measurement error in amounts conditional on reporting a positive value. For errors in dollars paid by two Linked data large government transfer programs, we use administrative records on the universe Data combination of program payments in New York State linked to three major household surveys to estimate the error components previously defined. -
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 -
The Evidence from World Values Survey Data
Munich Personal RePEc Archive The return of religious Antisemitism? The evidence from World Values Survey data Tausch, Arno Innsbruck University and Corvinus University 17 November 2018 Online at https://mpra.ub.uni-muenchen.de/90093/ MPRA Paper No. 90093, posted 18 Nov 2018 03:28 UTC The return of religious Antisemitism? The evidence from World Values Survey data Arno Tausch Abstract 1) Background: This paper addresses the return of religious Antisemitism by a multivariate analysis of global opinion data from 28 countries. 2) Methods: For the lack of any available alternative we used the World Values Survey (WVS) Antisemitism study item: rejection of Jewish neighbors. It is closely correlated with the recent ADL-100 Index of Antisemitism for more than 100 countries. To test the combined effects of religion and background variables like gender, age, education, income and life satisfaction on Antisemitism, we applied the full range of multivariate analysis including promax factor analysis and multiple OLS regression. 3) Results: Although religion as such still seems to be connected with the phenomenon of Antisemitism, intervening variables such as restrictive attitudes on gender and the religion-state relationship play an important role. Western Evangelical and Oriental Christianity, Islam, Hinduism and Buddhism are performing badly on this account, and there is also a clear global North-South divide for these phenomena. 4) Conclusions: Challenging patriarchic gender ideologies and fundamentalist conceptions of the relationship between religion and state, which are important drivers of Antisemitism, will be an important task in the future. Multiculturalism must be aware of prejudice, patriarchy and religious fundamentalism in the global South. -
Observational Studies and Bias in Epidemiology
The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Observational Studies and Bias in Epidemiology Manuel Bayona Department of Epidemiology School of Public Health University of North Texas Fort Worth, Texas and Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa Observational Studies and Bias in Epidemiology Contents Lesson Plan . 3 The Logic of Inference in Science . 8 The Logic of Observational Studies and the Problem of Bias . 15 Characteristics of the Relative Risk When Random Sampling . and Not . 19 Types of Bias . 20 Selection Bias . 21 Information Bias . 23 Conclusion . 24 Take-Home, Open-Book Quiz (Student Version) . 25 Take-Home, Open-Book Quiz (Teacher’s Answer Key) . 27 In-Class Exercise (Student Version) . 30 In-Class Exercise (Teacher’s Answer Key) . 32 Bias in Epidemiologic Research (Examination) (Student Version) . 33 Bias in Epidemiologic Research (Examination with Answers) (Teacher’s Answer Key) . 35 Copyright © 2004 by College Entrance Examination Board. All rights reserved. College Board, SAT and the acorn logo are registered trademarks of the College Entrance Examination Board. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: www.collegeboard.com. Copyright © 2004. All rights reserved. 2 Observational Studies and Bias in Epidemiology Lesson Plan TITLE: Observational Studies and Bias in Epidemiology SUBJECT AREA: Biology, mathematics, statistics, environmental and health sciences GOAL: To identify and appreciate the effects of bias in epidemiologic research OBJECTIVES: 1. Introduce students to the principles and methods for interpreting the results of epidemio- logic research and bias 2. -
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.