Indicators for Support for Economic Integration in Latin America
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Hypothesis Testing with Two Categorical Variables 203
Chapter 10 distribute or Hypothesis Testing With Two Categoricalpost, Variables Chi-Square copy, Learning Objectives • Identify the correct types of variables for use with a chi-square test of independence. • Explainnot the difference between parametric and nonparametric statistics. • Conduct a five-step hypothesis test for a contingency table of any size. • Explain what statistical significance means and how it differs from practical significance. • Identify the correct measure of association for use with a particular chi-square test, Doand interpret those measures. • Use SPSS to produce crosstabs tables, chi-square tests, and measures of association. 202 Part 3 Hypothesis Testing Copyright ©2016 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. he chi-square test of independence is used when the independent variable (IV) and dependent variable (DV) are both categorical (nominal or ordinal). The chi-square test is member of the family of nonparametric statistics, which are statistical Tanalyses used when sampling distributions cannot be assumed to be normally distributed, which is often the result of the DV being categorical rather than continuous (we will talk in detail about this). Chi-square thus sits in contrast to parametric statistics, which are used when DVs are continuous and sampling distributions are safely assumed to be normal. The t test, analysis of variance, and correlation are all parametric. Before going into the theory and math behind the chi-square statistic, read the Research Examples for illustrations of the types of situations in which a criminal justice or criminology researcher would utilize a chi- square test. -
Political Socialization, Public Opinion, and Polling
Political Socialization, Public Opinion, and Polling asic Tenants of American olitical Culture iberty/Freedom People should be free to live their lives with minimal governmental interference quality Political, legal, & of opportunity, but not economic equality ndividualism Responsibility for own decisions emocracy Consent of the governed Majority rule, with minority rights Citizen responsibilities (voting, jury duty, etc.) olitical Culture vs. Ideology Most Americans share common beliefs in he democratic foundation of government culture) yet have differing opinions on the proper role & scope of government ideology) olitical Socialization Process through which an individual acquires particular political orientations, beliefs, and values at factors influence political opinion mation? Family #1 source for political identification Between 60-70% of all children agree with their parents upon reaching adulthood. If one switches his/her party affiliation it usually goes from Republican/Democrat to independent hat factors influence political opinion mation? School/level of education “Building good citizens”- civic education reinforces views about participation Pledge of Allegiance Volunteering Civic pride is nation wide Informed student-citizens are more likely to participate/vote as adults Voting patterns based on level of educational attainment hat factors influence political opinion rmation? Peer group/occupation Increases in importance during teen years Occupational influences hat factors influence political opinion mation? Mass media Impact -
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 -
Lecture 1: Why Do We Use Statistics, Populations, Samples, Variables, Why Do We Use Statistics?
1pops_samples.pdf Michael Hallstone, Ph.D. [email protected] Lecture 1: Why do we use statistics, populations, samples, variables, why do we use statistics? • interested in understanding the social world • we want to study a portion of it and say something about it • ex: drug users, homeless, voters, UH students Populations and Samples Populations, Sampling Elements, Frames, and Units A researcher defines a group, “list,” or pool of cases that she wishes to study. This is a population. Another definition: population = complete collection of measurements, objects or individuals under study. 1 of 11 sample = a portion or subset taken from population funny circle diagram so we take a sample and infer to population Why? feasibility – all MD’s in world , cost, time, and stay tuned for the central limits theorem...the most important lecture of this course. Visualizing Samples (taken from) Populations Population Group you wish to study (Mostly made up of “people” in the Then we infer from sample back social sciences) to population (ALWAYS SOME ERROR! “sampling error” Sample (a portion or subset of the population) 4 This population is made up of the things she wishes to actually study called sampling elements. Sampling elements can be people, organizations, schools, whales, molecules, and articles in the popular press, etc. The sampling element is your exact unit of analysis. For crime researchers studying car thieves, the sampling element would probably be individual car thieves – or theft incidents reported to the police. For drug researchers the sampling elements would be most likely be individual drug users. Inferential statistics is truly the basis of much of our scientific evidence. -
Assessment of Socio-Demographic Sample Composition in ESS Round 61
Assessment of socio-demographic sample composition in ESS Round 61 Achim Koch GESIS – Leibniz Institute for the Social Sciences, Mannheim/Germany, June 2016 Contents 1. Introduction 2 2. Assessing socio-demographic sample composition with external benchmark data 3 3. The European Union Labour Force Survey 3 4. Data and variables 6 5. Description of ESS-LFS differences 8 6. A summary measure of ESS-LFS differences 17 7. Comparison of results for ESS 6 with results for ESS 5 19 8. Correlates of ESS-LFS differences 23 9. Summary and conclusions 27 References 1 The CST of the ESS requests that the following citation for this document should be used: Koch, A. (2016). Assessment of socio-demographic sample composition in ESS Round 6. Mannheim: European Social Survey, GESIS. 1. Introduction The European Social Survey (ESS) is an academically driven cross-national survey that has been conducted every two years across Europe since 2002. The ESS aims to produce high- quality data on social structure, attitudes, values and behaviour patterns in Europe. Much emphasis is placed on the standardisation of survey methods and procedures across countries and over time. Each country implementing the ESS has to follow detailed requirements that are laid down in the “Specifications for participating countries”. These standards cover the whole survey life cycle. They refer to sampling, questionnaire translation, data collection and data preparation and delivery. As regards sampling, for instance, the ESS requires that only strict probability samples should be used; quota sampling and substitution are not allowed. Each country is required to achieve an effective sample size of 1,500 completed interviews, taking into account potential design effects due to the clustering of the sample and/or the variation in inclusion probabilities. -
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. -
Chapter 3: Simple Random Sampling and Systematic Sampling
Chapter 3: Simple Random Sampling and Systematic Sampling Simple random sampling and systematic sampling provide the foundation for almost all of the more complex sampling designs that are based on probability sampling. They are also usually the easiest designs to implement. These two designs highlight a trade-off inherent in all sampling designs: do we select sample units at random to minimize the risk of introducing biases into the sample or do we select sample units systematically to ensure that sample units are well- distributed throughout the population? Both designs involve selecting n sample units from the N units in the population and can be implemented with or without replacement. Simple Random Sampling When the population of interest is relatively homogeneous then simple random sampling works well, which means it provides estimates that are unbiased and have high precision. When little is known about a population in advance, such as in a pilot study, simple random sampling is a common design choice. Advantages: • Easy to implement • Requires little advance knowledge about the target population Disadvantages: • Imprecise relative to other designs if the population is heterogeneous • More expensive to implement than other designs if entities are clumped and the cost to travel among units is appreciable How it is implemented: • Select n sample units at random from N available in the population All units within the population must have the same probability of being selected, therefore each and every sample of size n drawn from the population has an equal chance of being selected. There are many strategies available for selecting a random sample. -
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. -
Categorical Data Analysis
Categorical Data Analysis Related topics/headings: Categorical data analysis; or, Nonparametric statistics; or, chi-square tests for the analysis of categorical data. OVERVIEW For our hypothesis testing so far, we have been using parametric statistical methods. Parametric methods (1) assume some knowledge about the characteristics of the parent population (e.g. normality) (2) require measurement equivalent to at least an interval scale (calculating a mean or a variance makes no sense otherwise). Frequently, however, there are research problems in which one wants to make direct inferences about two or more distributions, either by asking if a population distribution has some particular specifiable form, or by asking if two or more population distributions are identical. These questions occur most often when variables are qualitative in nature, making it impossible to carry out the usual inferences in terms of means or variances. For such problems, we use nonparametric methods. Nonparametric methods (1) do not depend on any assumptions about the parameters of the parent population (2) generally assume data are only measured at the nominal or ordinal level. There are two common types of hypothesis-testing problems that are addressed with nonparametric methods: (1) How well does a sample distribution correspond with a hypothetical population distribution? As you might guess, the best evidence one has about a population distribution is the sample distribution. The greater the discrepancy between the sample and theoretical distributions, the more we question the “goodness” of the theory. EX: Suppose we wanted to see whether the distribution of educational achievement had changed over the last 25 years. We might take as our null hypothesis that the distribution of educational achievement had not changed, and see how well our modern-day sample supported that theory. -
Public Opinion Survey: Residents of Armenia
Public Opinion Survey: Residents of Armenia February 2021 Detailed Methodology • The survey was conducted on behalf of “International Republican Institute’s” Center for Insights in Survey Research by Breavis (represented by IPSC LLC). • Data was collected throughout Armenia between February 8 and February 16, 2021, through phone interviews, with respondents selected by random digit dialing (RDD) probability sampling of mobile phone numbers. • The sample consisted of 1,510 permanent residents of Armenia aged 18 and older. It is representative of the population with access to a mobile phone, which excludes approximately 1.2 percent of adults. • Sampling frame: Statistical Committee of the Republic of Armenia. Weighting: Data weighted for 11 regional groups, age, gender and community type. • The margin of error does not exceed plus or minus 2.5 points for the full sample. • The response rate was 26 percent which is similar to the surveys conducted in August-September 2020. • Charts and graphs may not add up to 100 percent due to rounding. • The survey was funded by the U.S. Agency for International Development. 2 Weighted (Disaggregated) Bases Disaggregate Disaggregation Category Base Share 18-35 years old n=563 37% Age groups 36-55 years old n=505 34% 56+ years old n=442 29% Male n=689 46% Gender Female n=821 54% Yerevan n=559 37% Community type Urban n=413 27% Rural n=538 36% Primary or secondary n=537 36% Education Vocational n=307 20% Higher n=665 44% Single n=293 19% Marital status Married n=1,059 70% Widowed or divorced n=155 10% Up -
World Values Surveys and European Values Surveys, 1999-2001 User Guide and Codebook
ICPSR 3975 World Values Surveys and European Values Surveys, 1999-2001 User Guide and Codebook Ronald Inglehart et al. University of Michigan Institute for Social Research First ICPSR Version May 2004 Inter-university Consortium for Political and Social Research P.O. Box 1248 Ann Arbor, Michigan 48106 www.icpsr.umich.edu Terms of Use Bibliographic Citation: Publications based on ICPSR data collections should acknowledge those sources by means of bibliographic citations. To ensure that such source attributions are captured for social science bibliographic utilities, citations must appear in footnotes or in the reference section of publications. The bibliographic citation for this data collection is: Inglehart, Ronald, et al. WORLD VALUES SURVEYS AND EUROPEAN VALUES SURVEYS, 1999-2001 [Computer file]. ICPSR version. Ann Arbor, MI: Institute for Social Research [producer], 2002. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2004. Request for Information on To provide funding agencies with essential information about use of Use of ICPSR Resources: archival resources and to facilitate the exchange of information about ICPSR participants' research activities, users of ICPSR data are requested to send to ICPSR bibliographic citations for each completed manuscript or thesis abstract. Visit the ICPSR Web site for more information on submitting citations. Data Disclaimer: The original collector of the data, ICPSR, and the relevant funding agency bear no responsibility for uses of this collection or for interpretations or inferences based upon such uses. Responsible Use In preparing data for public release, ICPSR performs a number of Statement: procedures to ensure that the identity of research subjects cannot be disclosed. -
Synergy of Quantitative and Qualitative Marketing Research − Capi and Observation Diary1
ECONOMETRICS. EKONOMETRIA Advances in Applied Data Analysis Year 2018, Vol. 22, No. 1 ISSN 1507-3866; e-ISSN 2449-9994 SYNERGY OF QUANTITATIVE AND QUALITATIVE MARKETING RESEARCH − CAPI AND OBSERVATION DIARY1 Marcin Lipowski, Zbigniew Pastuszak, Ilona Bondos Maria Curie-Skłodowska University in Lublin, Lublin, Poland e-mails: [email protected]; [email protected]; [email protected] © 2018 Marcin Lipowski, Zbigniew Pastuszak, Ilona Bondos This is an open access article distributed under the Creative Commons Attribution-NonCommercial- -NoDerivs license (http://creativecommons.org/licenses/by-nc-nd/3.0/) DOI: 10.15611/eada.2018.1.04 JEL Classification: M31, C83 Abstract: The purpose of the publication is to indicate the need for a well thought-out combination of quantitative marketing research with qualitative research. The result of this research approach should be a fuller understanding of the research problem and the ability to interpret results more closely, while maintaining the reliability of the whole research process. In the theoretical part of the article, the essence of quantitative and qualitative research, with particular emphasis on the limitations and strengths of both research approaches, is presented. The increasing popularity of qualitative research does not absolve researchers from the prudent attitude towards the whole marketing research process − including the need to verify hypotheses or research questions. Excessive simplification in the approach to qualitative research can distort the essence of marketing research. In the empirical part of the article, the authors presented an example of combining quantitative marketing research with qualitative research − for this purpose, the results of their research will be used (scientific grant from National Science Centre).