World Values Survey 1981-2006
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Statistical Inference: How Reliable Is a Survey?
Math 203, Fall 2008: Statistical Inference: How reliable is a survey? Consider a survey with a single question, to which respondents are asked to give an answer of yes or no. Suppose you pick a random sample of n people, and you find that the proportion that answered yes isp ˆ. Question: How close isp ˆ to the actual proportion p of people in the whole population who would have answered yes? In order for there to be a reliable answer to this question, the sample size, n, must be big enough so that the sample distribution is close to a bell shaped curve (i.e., close to a normal distribution). But even if n is big enough that the distribution is close to a normal distribution, usually you need to make n even bigger in order to make sure your margin of error is reasonably small. Thus the first thing to do is to be sure n is big enough for the sample distribution to be close to normal. The industry standard for being close enough is for n to be big enough so that 1 − p 1 − p n > 9 and n > 9 p p both hold. When p is about 50%, n can be as small as 10, but when p gets close to 0 or close to 1, the sample size n needs to get bigger. If p is 1% or 99%, then n must be at least 892, for example. (Note also that n here depends on p but not on the size of the whole population.) See Figures 1 and 2 showing frequency histograms for the number of yes respondents if p = 1% when the sample size n is 10 versus 1000 (this data was obtained by running a computer simulation taking 10000 samples). -
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. -
Summary of Human Subjects Protection Issues Related to Large Sample Surveys
Summary of Human Subjects Protection Issues Related to Large Sample Surveys U.S. Department of Justice Bureau of Justice Statistics Joan E. Sieber June 2001, NCJ 187692 U.S. Department of Justice Office of Justice Programs John Ashcroft Attorney General Bureau of Justice Statistics Lawrence A. Greenfeld Acting Director Report of work performed under a BJS purchase order to Joan E. Sieber, Department of Psychology, California State University at Hayward, Hayward, California 94542, (510) 538-5424, e-mail [email protected]. The author acknowledges the assistance of Caroline Wolf Harlow, BJS Statistician and project monitor. Ellen Goldberg edited the document. Contents of this report do not necessarily reflect the views or policies of the Bureau of Justice Statistics or the Department of Justice. This report and others from the Bureau of Justice Statistics are available through the Internet — http://www.ojp.usdoj.gov/bjs Table of Contents 1. Introduction 2 Limitations of the Common Rule with respect to survey research 2 2. Risks and benefits of participation in sample surveys 5 Standard risk issues, researcher responses, and IRB requirements 5 Long-term consequences 6 Background issues 6 3. Procedures to protect privacy and maintain confidentiality 9 Standard issues and problems 9 Confidentiality assurances and their consequences 21 Emerging issues of privacy and confidentiality 22 4. Other procedures for minimizing risks and promoting benefits 23 Identifying and minimizing risks 23 Identifying and maximizing possible benefits 26 5. Procedures for responding to requests for help or assistance 28 Standard procedures 28 Background considerations 28 A specific recommendation: An experiment within the survey 32 6. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
Evaluating Survey Questions Question
What Respondents Do to Answer a Evaluating Survey Questions Question • Comprehend Question • Retrieve Information from Memory Chase H. Harrison Ph.D. • Summarize Information Program on Survey Research • Report an Answer Harvard University Problems in Answering Survey Problems in Answering Survey Questions Questions – Failure to comprehend – Failure to recall • If respondents don’t understand question, they • Questions assume respondents have information cannot answer it • If respondents never learned something, they • If different respondents understand question cannot provide information about it differently, they end up answering different questions • Problems with researcher putting more emphasis on subject than respondent Problems in Answering Survey Problems in Answering Survey Questions Questions – Problems Summarizing – Problems Reporting Answers • If respondents are thinking about a lot of things, • Confusing or vague answer formats lead to they can inconsistently summarize variability • If the way the respondent remembers something • Interactions with interviewers or technology can doesn’t readily correspond to the question, they lead to problems (sensitive or embarrassing may be inconsistemt responses) 1 Evaluating Survey Questions Focus Groups • Early stage • Qualitative research tool – Focus groups to understand topics or dimensions of measures • Used to develop ideas for questionnaires • Pre-Test Stage – Cognitive interviews to understand question meaning • Used to understand scope of issues – Pre-test under typical field -
MRS Guidance on How to Read Opinion Polls
What are opinion polls? MRS guidance on how to read opinion polls June 2016 1 June 2016 www.mrs.org.uk MRS Guidance Note: How to read opinion polls MRS has produced this Guidance Note to help individuals evaluate, understand and interpret Opinion Polls. This guidance is primarily for non-researchers who commission and/or use opinion polls. Researchers can use this guidance to support their understanding of the reporting rules contained within the MRS Code of Conduct. Opinion Polls – The Essential Points What is an Opinion Poll? An opinion poll is a survey of public opinion obtained by questioning a representative sample of individuals selected from a clearly defined target audience or population. For example, it may be a survey of c. 1,000 UK adults aged 16 years and over. When conducted appropriately, opinion polls can add value to the national debate on topics of interest, including voting intentions. Typically, individuals or organisations commission a research organisation to undertake an opinion poll. The results to an opinion poll are either carried out for private use or for publication. What is sampling? Opinion polls are carried out among a sub-set of a given target audience or population and this sub-set is called a sample. Whilst the number included in a sample may differ, opinion poll samples are typically between c. 1,000 and 2,000 participants. When a sample is selected from a given target audience or population, the possibility of a sampling error is introduced. This is because the demographic profile of the sub-sample selected may not be identical to the profile of the target audience / population. -
The World Values Survey in the New Independent States C
THE WORLD’S LARGEST SOCIAL SCIENCE INFRASTRUCTURE AND ACADEMIC SURVEY RESEARCH PROGRAM: THE WORLD VALUES SURVEY IN THE NEW INDEPENDENT STATES C. Haerpfer1, K. Kizilova2* 1University of Vienna, Austria 2V.N. Karazin Kharkiv National University, Kharkiv, Ukraine The World Values Survey (WVS) is an international research program developed to assess the im- pact of values stability or change over time on the social, political and economic development of countries and societies. It started in 1981 by Ronald Inglehart and his team, since then has involved more than 100 world societies and turned into the largest non-commercial cross-national empirical time-series inves- tigation of human beliefs and values ever executed on a global scale. The article consists of a few sections differing by the focus. The authors begin with the description of survey methodology and organization management that both ensure cross-national and cross-regional comparative character of the study (the survey is implemented using the same questionnaire, a face-to-face mode of interviews, and the same sample type in every country). The next part of the article presents a short overview of the project history and comparative surveys’ time-series (so called “waves” — periods between two and four years long during which collection of data in several dozens of countries using one same questionnaire is taking place; such waves are conducted every five years). Here the authors describe every wave of the WVS mentioning coordination and management activities that were determined by the extension of the project thematically and geographically. After that the authors identify the key features of the WVS in the New Independent States and mention some of the results of the study conducted in NIS countries in 1990—2014, such as high level of uncertainty in the choice of ideological preferences; rapid growth of declared religiosity; ob- served gap between the declared values and actual facts of social life, etc. -
Exploratory Factor Analysis with the World Values Survey Diana Suhr, Ph.D
Exploratory Factor Analysis with the World Values Survey Diana Suhr, Ph.D. University of Northern Colorado Abstract Exploratory factor analysis (EFA) investigates the possible underlying factor structure (dimensions) of a set of interrelated variables without imposing a preconceived structure on the outcome (Child, 1990). The World Values Survey (WVS) measures changes in what people want out of life and what they believe. WVS helps a worldwide network of social scientists study changing values and their impact on social and political life. This presentation will explore dimensions of selected WVS items using exploratory factor analysis techniques with SAS® PROC FACTOR. EFA guidelines and SAS code will be illustrated as well as a discussion of results. Introduction Exploratory factor analysis investigates the possible underlying structure of a set of interrelated variables. This paper discusses goals, assumptions and limitations as well as factor extraction methods, criteria to determine factor structure, and SAS code. Examples of EFA are shown using data collected from the World Values Survey. The World Values Survey (WVS) has collected data from over 57 countries since 1990. Data has been collected every 5 years from 1990 to 2010 with each data collection known as a wave. Selected items from the 2005 wave will be examined to investigate the factor structure (dimensions) of values that could impact social and political life across countries. The factor structure will be determined for the total group of participants. Then comparisons of the factor structure will be made between gender and between age groups. Limitations Survey questions were changed from wave to wave. Therefore determining the factor structure for common questions across waves and comparisons between waves was not possible. -
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. -
A Meta-Analysis of the Effect of Concurrent Web Options on Mail Survey Response Rates
When More Gets You Less: A Meta-Analysis of the Effect of Concurrent Web Options on Mail Survey Response Rates Jenna Fulton and Rebecca Medway Joint Program in Survey Methodology, University of Maryland May 19, 2012 Background: Mixed-Mode Surveys • Growing use of mixed-mode surveys among practitioners • Potential benefits for cost, coverage, and response rate • One specific mixed-mode design – mail + Web – is often used in an attempt to increase response rates • Advantages: both are self-administered modes, likely have similar measurement error properties • Two strategies for administration: • “Sequential” mixed-mode • One mode in initial contacts, switch to other in later contacts • Benefits response rates relative to a mail survey • “Concurrent” mixed-mode • Both modes simultaneously in all contacts 2 Background: Mixed-Mode Surveys • Growing use of mixed-mode surveys among practitioners • Potential benefits for cost, coverage, and response rate • One specific mixed-mode design – mail + Web – is often used in an attempt to increase response rates • Advantages: both are self-administered modes, likely have similar measurement error properties • Two strategies for administration: • “Sequential” mixed-mode • One mode in initial contacts, switch to other in later contacts • Benefits response rates relative to a mail survey • “Concurrent” mixed-mode • Both modes simultaneously in all contacts 3 • Mixed effects on response rates relative to a mail survey Methods: Meta-Analysis • Given mixed results in literature, we conducted a meta- analysis -
(“Quasi-Experimental”) Study Designs Are Most Likely to Produce Valid Estimates of a Program’S Impact?
Which Comparison-Group (“Quasi-Experimental”) Study Designs Are Most Likely to Produce Valid Estimates of a Program’s Impact?: A Brief Overview and Sample Review Form Originally published by the Coalition for Evidence- Based Policy with funding support from the William T. Grant Foundation and U.S. Department of Labor, January 2014 Updated by the Arnold Ventures Evidence-Based Policy team, December 2018 This publication is in the public domain. Authorization to reproduce it in whole or in part for educational purposes is granted. We welcome comments and suggestions on this document ([email protected]). Brief Overview: Which Comparison-Group (“Quasi-Experimental”) Studies Are Most Likely to Produce Valid Estimates of a Program’s Impact? I. A number of careful investigations have been carried out to address this question. Specifically, a number of careful “design-replication” studies have been carried out in education, employment/training, welfare, and other policy areas to examine whether and under what circumstances non-experimental comparison-group methods can replicate the results of well- conducted randomized controlled trials. These studies test comparison-group methods against randomized methods as follows. For a particular program being evaluated, they first compare program participants’ outcomes to those of a randomly-assigned control group, in order to estimate the program’s impact in a large, well- implemented randomized design – widely recognized as the most reliable, unbiased method of assessing program impact. The studies then compare the same program participants with a comparison group selected through methods other than randomization, in order to estimate the program’s impact in a comparison-group design. -
Evidence from the Gallup World Poll
Journal of Economic Perspectives—Volume 22, Number 2—Spring 2008—Pages 53–72 Income, Health, and Well-Being around the World: Evidence from the Gallup World Poll Angus Deaton he great promise of surveys in which people report their own level of life satisfaction is that such surveys might provide a straightforward and easily T collected measure of individual or national well-being that aggregates over the various components of well-being, such as economic status, health, family circumstances, and even human and political rights. Layard (2005) argues force- fully such measures do indeed achieve this end, providing measures of individual and aggregate happiness that should be the only gauges used to evaluate policy and progress. Such a position is in sharp contrast to the more widely accepted view, associated with Sen (1999), which is that human well-being depends on a range of functions and capabilities that enable people to lead a good life, each of which needs to be directly and objectively measured and which cannot, in general, be aggregated into a single summary measure. Which of life’s circumstances are important for life satisfaction, and which—if any—have permanent as opposed to merely transitory effects, has been the subject of lively debate. For economists, who usually assume that higher incomes represent a gain to the satisfaction of individuals, the role of income is of particular interest. It is often argued that income is both relatively unimportant and relatively transi- tory compared with family circumstances, unemployment, or health (for example, Easterlin, 2003). Comparing results from a given country over time, Easterlin (1974, 1995) famously noted that average national happiness does not increase over long spans of time, in spite of large increases in per capita income.