Principles of Survey Methodology
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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. -
On Becoming a Pragmatic Researcher: the Importance of Combining Quantitative and Qualitative Research Methodologies
DOCUMENT RESUME ED 482 462 TM 035 389 AUTHOR Onwuegbuzie, Anthony J.; Leech, Nancy L. TITLE On Becoming a Pragmatic Researcher: The Importance of Combining Quantitative and Qualitative Research Methodologies. PUB DATE 2003-11-00 NOTE 25p.; Paper presented at the Annual Meeting of the Mid-South Educational Research Association (Biloxi, MS, November 5-7, 2003). PUB TYPE Reports Descriptive (141) Speeches/Meeting Papers (150) EDRS PRICE EDRS Price MF01/PCO2 Plus Postage. DESCRIPTORS *Pragmatics; *Qualitative Research; *Research Methodology; *Researchers ABSTRACT The last 100 years have witnessed a fervent debate in the United States about quantitative and qualitative research paradigms. Unfortunately, this has led to a great divide between quantitative and qualitative researchers, who often view themselves in competition with each other. Clearly, this polarization has promoted purists, i.e., researchers who restrict themselves exclusively to either quantitative or qualitative research methods. Mono-method research is the biggest threat to the advancement of the social sciences. As long as researchers stay polarized in research they cannot expect stakeholders who rely on their research findings to take their work seriously. The purpose of this paper is to explore how the debate between quantitative and qualitative is divisive, and thus counterproductive for advancing the social and behavioral science field. This paper advocates that all graduate students learn to use and appreciate both quantitative and qualitative research. In so doing, students will develop into what is termed "pragmatic researchers." (Contains 41 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document. On Becoming a Pragmatic Researcher 1 Running head: ON BECOMING A PRAGMATIC RESEARCHER U.S. -
E-Survey Methodology
Chapter I E-Survey Methodology Karen J. Jansen The Pennsylvania State University, USA Kevin G. Corley Arizona State University, USA Bernard J. Jansen The Pennsylvania State University, USA ABSTRACT With computer network access nearly ubiquitous in much of the world, alternative means of data col- lection are being made available to researchers. Recent studies have explored various computer-based techniques (e.g., electronic mail and Internet surveys). However, exploitation of these techniques requires careful consideration of conceptual and methodological issues associated with their use. We identify and explore these issues by defining and developing a typology of “e-survey” techniques in organiza- tional research. We examine the strengths, weaknesses, and threats to reliability, validity, sampling, and generalizability of these approaches. We conclude with a consideration of emerging issues of security, privacy, and ethics associated with the design and implications of e-survey methodology. INTRODUCTION 1999; Oppermann, 1995; Saris, 1991). Although research over the past 15 years has been mixed on For the researcher considering the use of elec- the realization of these benefits (Kiesler & Sproull, tronic surveys, there is a rapidly growing body of 1986; Mehta & Sivadas, 1995; Sproull, 1986; Tse, literature addressing design issues and providing Tse, Yin, Ting, Yi, Yee, & Hong, 1995), for the laundry lists of costs and benefits associated with most part, researchers agree that faster response electronic survey techniques (c.f., Lazar & Preece, times and decreased costs are attainable benefits, 1999; Schmidt, 1997; Stanton, 1998). Perhaps the while response rates differ based on variables three most common reasons for choosing an e-sur- beyond administration mode alone. -
Those Missing Values in Questionnaires
Those Missing Values in Questionnaires John R. Gerlach, Maxim Group, Plymouth Meeting, PA Cindy Garra, IMS HEALTH; Plymouth Meeting, PA Abstract alternative values are not bona fide SAS missing values; consequently, a SAS procedure, expectedly, will include Questionnaires are notorious for containing these alternative values, thereby producing bogus results. responses that later become missing values during processing and analyses. Contrary to a non-response that Typically, you can re-code the original data, so results in a typical, bona fide, missing value, questions that the missing values become ordinary missing values, might allow several alternative responses, necessarily thus allowing SAS to process only appropriate values. Of outside the usual range of appropriate responses. course, there's a loss of information since the original Whether a question represents continuous or categorical missing values did not constitute a non-response. Also, data, a good questionnaire offers meaningful alternatives, such pre-processing might include a myriad of IF THEN / such as: "Refused to Answer" and, of course, the ELSE statements, which can be very tedious and time- quintessential "Don't Know." Traditionally, these consuming to write, and difficult to maintain. Thus, given alternative responses have numeric values such as 97, a questionnaire that has over a hundred variables with 998, or 9999 and, therefore, pose problems when trying varying levels of missing values, the task of re-coding to distinguish them from normal responses, especially these variables becomes very time consuming at best. when multiple missing values exist. This paper discusses Even worse, the permanent re-coding of alternative missing values in SAS and techniques that facilitate the responses to ordinary missing numeric values in SAS process of dealing with multi-valued, meaningful missing precludes categorical analysis that requires the inclusion values often found in questionnaires. -
10 Questions Opinion Polls
Questions you might have on 10opinion polls 1. What is an opinion poll? An opinion poll is a survey carried out to measure views on a certain topic within a specific group of people. For example, the topic may relate to who Kenyans support in the presidential race, in which case, the group of people interviewed will be registered voters. 2. How are interviewees for an opinion poll selected? The group of people interviewed for an opinion poll is called a sample. As the name suggests, a sample is a group of people that represents the total population whose opinion is being surveyed. In a scientific opinion poll, everyone has an equal chance of being interviewed. 3. So how come I have never been interviewed for an opinion poll? You have the same chance of being polled as anyone else living in Kenya. However, chances of this are very small and are estimated at about 1 in 14,000. This is because there are approximately 14 million registered voters in Kenya and, for practical and cost reasons, usually only between 1,000 and 2,000 people are interviewed for each survey carried out. 4. How can such a small group be representative of the entire population? In order to ensure that the sample/survey group is representative of the population, the surveyors must ensure that the group reflects the characteristics of the whole. For instance, to get a general idea of who might win the Kenyan presidential election, only the views of registered voters in Kenya will be surveyed as these are the people who will be able to influence the election. -
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 -
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System
Handbook of Recommended Practices for Questionnaire Development and Testing in the European Statistical System Release year: 2006 Authors: G. Brancato, S. Macchia, M. Murgia, M. Signore, G. Simeoni - Italian National Institute of Statistics, ISTAT K. Blanke, T. Körner, A. Nimmergut - Federal Statistical Office Germany, FSO P. Lima, R. Paulino - National Statistical Institute of Portugal, INE J.H.P. Hoffmeyer-Zlotnik - German Center for Survey Research and Methodology, ZUMA Version 1 Acknowledgements We are grateful to the experts from the network countries who supported us in all relevant stages of the work: Anja Ahola, Dirkjan Beukenhorst, Trine Dale, Gustav Haraldsen. We also thank all colleagues from European and overseas NSIs who helped us in understanding the current practices and in the review of the draft version of the handbook. Executive summary Executive Summary Questionnaires constitute the basis of every survey-based statistical measurement. They are by far the most important measurement instruments statisticians use to grasp the phenomena to be measured. Errors due to an insufficient questionnaire can hardly be compensated at later stages of the data collection process. Therefore, having systematic questionnaire design and testing procedures in place is vital for data quality, particularly for a minimisation of the measurement error. Against this background, the Directors General of the members of the European Statistical System (ESS) stressed the importance of questionnaire design and testing in the European Statistics Code of Practice, endorsed in February 2005. Principle 8 of the Code states that “appropriate statistical procedures, implemented from data collection to data validation, must underpin quality statistics.” One of the indicators referring to this principle requires that “questionnaires are systematically tested prior to the data collection.” Taking the Code of Practice as a starting point, this Recommended Practice Manual aims at further specifying the requirements of the Code of Practice. -
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. -
Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes*
Journal of International Education and Leadership Volume 8 Issue 2 Fall 2018 http://www.jielusa.org/ ISSN: 2161-7252 Meta-Analysis of the Relationships Between Different Leadership Practices and Organizational, Teaming, Leader, and Employee Outcomes* Carl J. Dunst Orelena Hawks Puckett Institute Mary Beth Bruder University of Connecticut Health Center Deborah W. Hamby, Robin Howse, and Helen Wilkie Orelena Hawks Puckett Institute * This study was supported, in part, by funding from the U.S. Department of Education, Office of Special Education Programs (No. 325B120004) for the Early Childhood Personnel Center, University of Connecticut Health Center. The contents and opinions expressed, however, are those of the authors and do not necessarily reflect the policy or official position of either the Department or Office and no endorsement should be inferred or implied. The authors report no conflicts of interest. The meta-analysis described in this paper evaluated the relationships between 11 types of leadership practices and 7 organizational, teaming, leader, and employee outcomes. A main focus of analysis was whether the leadership practices were differentially related to the study outcomes. Studies were eligible for inclusion if the correlations between leadership subscale measures (rather than global measures of leadership) and outcomes of interest were reported. The random effects weighted average correlations between the independent and dependent measures were used as the sizes of effects for evaluating the influences of the leadership practices on the outcome measures. One hundred and twelve studies met the inclusion criteria and included 39,433 participants. The studies were conducted in 31 countries in different kinds of programs, organizations, companies, and businesses. -
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 ..........................................................................................................