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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
The classic survey design reference, updated for the digital age For over two decades, Dillman's classic text on survey design has aided both students and professionals in effectively planning and conducting mail, telephone, and, more recently, Internet surveys. The new edition is thoroughly updated and revised, and covers all aspects of survey research. It features expanded coverage of mobile phones, tablets, and the use of do-it-yourself surveys, and Dillman's unique Tailored Design Method is also thoroughly explained. This invaluable resource is crucial for any researcher seeking to increase response rates and obtain high-quality feedback from survey questions. Consistent with current emphasis on the visual and aural, the new edition is complemented by copious examples within the text and accompanying website. This heavily revised Fourth Edition includes: * Strategies and tactics for determining the needs of a given survey, how to design it, and how to effectively administer it * How and when to use mail, telephone, and Internet surveys to maximum advantage * Proven techniques to increase response rates * Guidance on how to obtain high-quality feedback from mail, electronic, and other self-administered surveys * Direction on how to construct effective questionnaires, including considerations of layout * The effects of sponsorship on the response rates of surveys * Use of capabilities provided by newly mass-used media: interactivity, presentation of aural and visual stimuli. * The Fourth Edition reintroduces the telephone--including coordinating land and mobile. Grounded in the best research, the book offers practical how-to guidelines and detailed examples for practitioners and students alike.
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Table of Contents
Title Page
Copyright
Dedication
Additional Resources
Preface
Chapter 1: Sample Surveys in Our Electronic World
Four Cornerstones of Quality Surveys
What Is Different About Surveying in the 2010s?
Why Emphasize Mixed-Mode Data Collection?
What Is Tailored Design and Why Is It Needed?
Conclusion
Chapter 2: Reducing People's Reluctance to Respond to Surveys
Example of a Survey With a High Response Rate
Using Social Exchange Concepts to Motivate Potential Respondents
Putting the Parts Together: Some Guidelines for Applying Social Exchange
Mixed-Mode Designs Provide New Opportunities for Applying Social Exchange
Returning to the WSU Doctoral Student Experience Survey: Why It Obtained Such a High Response Rate
Conclusion
List of Guidelines
Chapter 3: Covering the Population and Selecting Who to Survey
Essential Definitions and Their Use
Current Coverage and Access Considerations
Common Sampling Frames and Assessing How Well They Cover the Population
Probability Sampling
Postsurvey Adjustments and Calculating Sampling Error
Nonprobability Sampling
Conclusion
Chapter 4: The Fundamentals of Writing Questions
Issues to Consider When Starting to Craft Survey Questions
The Anatomy of a Survey Question and Types of Question Formats
Guidelines for Choosing Words and Forming Questions
Conclusion
List of Guidelines
Chapter Chapter 5: How to Write Open- and Closed-Ended Questions
Guidelines for Writing Open-Ended Questions
General Guidelines for Writing All Types of Closed-Ended Questions
Guidelines for Nominal Closed-Ended Questions
Guidelines for Ordinal Closed-Ended Questions
Conclusion
List of Guidelines
Chapter Chapter 6: Aural Versus Visual Design of Questions and Questionnaires
The Importance of Visual Design in Self-Administered Surveys
Visual Design Concepts and Their Application to Surveys
General Guidelines for the Visual Presentation of Survey Questions
Conclusion
List of Guidelines
Chapter 7: Ordering Questions and Testing for Question Order Effects
Question Order
Testing Questions and Questionnaires
Conclusion
List of Guidelines
Chapter 8: Telephone Questionnaires and Implementation
Types of Telephone-Only Surveys Today
Guidelines for Designing Telephone Questionnaires
Guidelines for Administering Telephone Questionnaires
Guidelines for Establishing Calling Rules and Procedures
Quality Control and Testing Guidelines for Telephone Surveys
Conclusion
List of Guidelines
Chapter 9: Web Questionnaires and Implementation
Guidelines for Designing Web and Mobile Questionnaires
Guidelines for Web and Mobile Survey Implementation
Quality Control and Testing Guidelines for Web and Mobile Surveys
Conclusion
List of Guidelines
Chapter 10: Mail Questionnaires and Implementation
Guidelines for Designing Paper Questionnaires
Guidelines for Implementing Mail Questionnaires
Quality Control and Testing Guidelines for Mail Surveys
Conclusion
List of Guidelines
Chapter 11: Mixed-Mode Questionnaires and Survey Implementation
When Single-Mode Surveys Are Not Acceptable
Why Consider a Mixed-Mode Survey Design
Guidelines for Designing Questionnaires That Will Minimize Measurement Differences Across Survey Modes
Expanding the Research Base for Designing Mixed-Mode Surveys
Guidelines for Using Multiple Contact Modes to Achieve More Effective Communication With Potential Respondents
Guidelines for Providing Alternative Response Modes
From Individual Guidelines to Practical Study Designs
Guidelines for Testing Mixed-Mode Surveys
Conclusion
List of Guidelines
Chapter 12: Responding to Societal Change and Preparing for What Lies Ahead
Panels and Longitudinal Surveys
Nonprobability Sampling
New Mobile Devices and Technology
Supplementing Questionnaires With Measurement Using Electronic Devices
Big Data and Administrative Records
Data Security
Specialized Purpose Surveys
International and Cross-Cultural Surveys
The Challenge of Connecting With Empowered but Diverse Respondents
References
Author Index
Subject Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
Figure 3.7
Figure 3.8
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Figure 4.13
Figure 4.14
Figure 4.15
Figure 4.16
Figure 4.17
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Figure 5.10
Figure 5.11
Figure 5.12
Figure 5.13
Figure 5.14
Figure 5.15
Figure 5.16
Figure 5.17
Figure 5.18
Figure 5.19
Figure 5.20
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12.
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 6.17
Figure 6.18
Figure 6.19
Figure 6.20
Figure 6.21
Figure 6.22
Figure 6.23
Figure 6.24
Figure 6.25
Figure 6.26
Figure 6.27
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 7.5
Figure 7.6
Figure 7.7
Figure 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Figure 8.5
Figure 8.6
Figure 8.7
Figure 8.8
Figure 8.9
Figure 9.1
Figure 9.2
Figure 9.3
Figure 9.4
Figure 9.5
Figure 9.6
Figure 9.7
Figure 9.8
Figure 9.9
Figure 9.10
Figure 9.11
Figure 9.12
Figure 9.13
Figure 9.14
Figure 10.1
Figure 10.2
Figure 10.3
Figure 10.4
Figure 10.5
Figure 10.6
Figure 10.7
Figure 10.8
Figure 10.9
Figure 10.10
Figure 10.11
Figure 10.12
Figure 10.13
Figure 10.14
Figure 10.15
Figure 11.1
Figure 11.2
Figure 11.3
Figure 11.4
Figure 11.5
Figure 11.6
Figure 11.7
Figure 11.8
Figure 11.9
Figure 11.10
Figure 11.11
Figure 11.12
Figure 11.13
Figure 11.14
Figure 11.15
FOURTH EDITION
INTERNET, PHONE, MAIL, AND MIXED-MODE SURVEYS
The Tailored Design Method
Don A. Dillman
Jolene D. Smyth
Leah Melani Christian
Cover design: Wiley
Cover image: © iStockphoto/khalus
Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.
ISBN: 978-1-118-45614-9 (cloth)
ISBN: 978-1-118-92129-6 (ebk)
ISBN: 978-1-118-92130-2 (ebk)
To John Tarnai (1947–2012)
For his leadership of the Social and Economic Sciences Research Center at Washington State University, the laboratory for our collaborative efforts to develop and test the methods described in this book.
Janet Harkness (1948–2012)
For encouraging the further development of these methods as Director of the Survey Research and Methodology (SRAM) Program at the University of Nebraska–Lincoln.
Additional Resources
We are excited to share new developments in survey methods with our readers in this fourth edition of Internet, Phone, Mail, and Mixed-Mode Surveys. There were issues we could not address in the pages of the book because of space limitations and the constraints of the print format. Our solution, in part at the urging of our great editorial team at John Wiley & Sons, was to create a Book Companion Website for this new edition of the book: www.wiley.com/go/dillman.
On the web page, we have provided a set of materials that we hope readers will find informative and useful. We chose materials we thought would help readers see how the ideas we discussed in the book can be brought together in practical ways. The website contains:
Checklist and summary of principles
: 184 guidelines for designing drawn from the book that can be used as a brief refresher or even as a checklist when one is designing one's own questionnaire. The guidelines are organized under topical headings for quicker searching.
Visual design video presentation
, “Understanding Visual Design for Questions and Questionnaires” (47 minutes) that is suitable for classroom presentation. In this video we demonstrate key visual design concepts and their application to questionnaire design. The video format allows us to integrate a number of helpful examples and illustrations that would not work in the static pages of a book. We anticipate that this will be a highly valuable resource for those trying to better understand the visual design of surveys and those trying to figure out how to format their questions into a questionnaire.
Sets of real-world example survey materials
: Each set includes a brief overview of the goals and design of the study, a copy of the questionnaire(s), copies of all implementation materials, and in some cases, copies of envelopes. These example materials illustrate how procedures have been brought together to create comprehensive designs that are consistent with our social exchange framework, are tailored to the specific study and population, and incorporate the visual design concepts presented in the book. The examples include both single- and mixed-mode surveys. These sample materials will be useful to those looking for examples of how we have applied ideas from the book to our surveys, as well as those looking for ideas about how to put together their own surveys.
An example of a 7″ × 8.5″ questionnaire
for those looking for an example of how this smaller booklet size can work.
Before-and-after images from a redesign of the USDA-sponsored Agricultural Resource Management survey
that demonstrates the application of many of the visual design ideas discussed in the book. This example shows how multiple visual design concepts and design strategies can be brought together to simplify an incredibly complex survey.
An
example of a cognitive interview report
that demonstrates how this method can be used to inform questionnaire design. This report describes the motivation behind the interviews, procedures followed, and results and discussion. Readers can use it to better understand how this method works, see a real example of its application, and inform their own study design and procedures, or as an example of how a cognitive interview report can be put together.
Color versions of select figures
where we think the color will help convey the central idea better than can be done in the black-and-white format used in the print edition of the book.
We hope that you find these materials helpful. We wish to acknowledge the invaluable help of Morgan Millar in pulling these materials together, especially the example survey materials. Morgan compiled most of these example surveys and wrote most of the survey descriptions. As with the rest of the book, this website has benefited greatly from her assistance.
In addition to these materials, the editors at Wiley have arranged to provide on the Book Companion Website short PowerPoint presentations of the key concepts in each chapter as well as test questions for each chapter for use by instructors.
Preface
Writing the fourth edition of this book nearly four decades after preparing the first edition has brought into perspective how survey science has evolved. It has also led us to reflect on how each edition needed to be refocused in order to fit with dramatically changing times.
The first edition was written on a typewriter, when personal computers, fax machines, the Internet, and cell phones were mostly unimagined by those wanting to do surveys. The title of this 1978 book, Mail and Telephone Surveys: The Total Design Method, suggested what was then a revolutionary idea—sample surveys of the general public, which prior to that time were viewed as synonymous with in-person interviews, could be done in other ways. It proposed standardized step-by-step methods for conducting such surveys by either mail or by telephone. Those procedures contained the seeds of a bold idea, “For very little investment of money, almost any academic institution or agency can establish the capability for conducting credible mail and telephone surveys” (Dillman, 1978, p. 275).
Nearly 20 years elapsed before work began on the second edition. During those years dozens of experiments and field tests involving different survey populations were undertaken to refine the 1978 mail data collection procedures and test new ones. The main outcome was to realize the necessity of tailoring specific data collection strategies to different populations, survey situations, and topics rather than using the one-size-fits-all approach described in that first book. The title of the 2000 edition, Mail and Internet Surveys: The Tailored Design Method, concisely summarized the fundamental changes introduced there. More than half of the new book was devoted to tailored designs such as alternative ways to deliver questionnaires, how to achieve greater speed and efficiency, challenges specific to government surveys, and how to survey businesses. The last chapter to be drafted, and the first to go out of date, was about Internet and interactive voice response surveys, which seemed ready to revolutionize surveying. In addition, the idea of mixed-mode survey designs, using the strengths of one mode to assist another, was introduced. To make room for these changes, telephone data collection methods were removed. This book was about a 95% revision of the first edition.
Only 6 years elapsed before work began in earnest on the third edition with two new coauthors, Jolene Smyth and Leah Christian. The three of us had begun working together as a team in 2002 to systematically research the effects of visual layout and design on the ways people answered survey questions and how responses differed across aural and visual modes of response. In this edition, we were first able to articulate what we had learned as guidelines for designing questionnaires. It was also apparent that there were multiple barriers to the conduct of mixed-mode surveys, ranging from how surveyors tended to structure questions for use in particular modes to inherent differences between aural and visual communication that might not be amenable to solutions for some types of questions. This edition began and ended with a discussion about the turbulence being felt among surveyors with declining response rates, coverage problems with telephone surveys, and a concern that the Internet was not yet ready to replace telephone as a stand-alone data collection mode, especially for household surveys. When bringing closure on this substantial rewrite in early 2008, we were also examining soon-to-be published results from a new kind of experiment we had done, which was a significant departure from the measurement and question wording issues that constituted much of our focus in this revision. These preliminary results seemed to show that we could use address-based sampling (our best source of coverage for household surveys in the United States) with mail contact and effectively encourage many people to respond over the Internet. These results (Smyth, Dillman, Christian, & O'Neill 2010) were included in this 2009 edition as having potential for surveying the general public by Internet using a mixed-mode design.
Work began on the fourth edition of this book, only 4 years after publication of the previous edition, and it was quickly apparent to us that the revisions would need to be nearly as substantial as the changes between the second and third editions. The telephone as an independent survey mode was continuing to face difficulties, and seemed on the verge of being rejected for certain national as well as state and smaller area surveys. It was also clear that the Internet had still not yet achieved the use and comfort levels that would allow it to be a sole data collection mode for many, and perhaps most, surveys. In addition, new challenges to designing and getting people to respond to Internet surveys had arisen because of the quick adoption of smartphones and tablets as devices for accessing the Internet. And mail, which was once our least expensive mode but had the poorest coverage, had become the mode with the best coverage of households but had also become a higher-cost mode. These were the new issues we were grappling with in the constantly changing survey landscape.
The most significant change in this edition is bringing the telephone back into the book after leaving it out of the 2000 and 2009 editions. This decision may seem curious at a time when most surveyors are moving away from the telephone mode. But it is apparent to us that the telephone is still necessary for certain types of surveys and, perhaps more importantly, that there are many ways it can be used in mixed-mode designs to overcome the weaknesses of single contact and/or response mode surveys. Including the telephone in this edition reflects our commitment to integrating some of the main themes of the previous edition—tailored design and mixed-mode surveys—throughout the book, rather than assigning them to individual chapters. In this edition we have also expanded the theoretical underpinnings of our approach to asking people to cooperate with survey requests and updated the social exchange framework used in all previous editions, placing more emphasis on trust and its response consequences in today's rapid-fire communication environment. Rethinking this framework was critical to laying a base for showing how different modes of contact, different response modes, and their coordinated use each provides potential for improving survey response rates and response quality.
Much more is understood now about the different processes of communicating aurally and visually than when previous editions were written, and our comfort with blending aural and visual modes together has increased. Thus, an entire chapter is now devoted to these issues. It brings together the past 15 years of published research and will be invaluable to those designing both single- and mixed-mode surveys. Stand-alone telephone, web, and mail data collection methods are presented in individual chapters, because they are still relevant for certain survey situations; those chapters are also a prelude to their integration in mixed-mode designs.
This book ends on a note of uncertainty about exactly what lies ahead but also conveys our belief that the fundamental ideas of social exchange and tailored design that have evolved through all editions of this book will continue to be relevant and helpful for figuring out how to conduct surveys in the face of significant cultural and technological changes. Survey methods will undoubtedly continue to change and successful change will depend upon reconciling the needs and desires of surveyors with those of the people being surveyed. The ideas of social exchange and tailored design will be useful in figuring out how to do that. This edition draws heavily upon our own research experiences and experiments. Some of this research was conducted when we were working together at Washington State University with assistance from the Social and Economic Sciences Research Center (SESRC), but this edition also draws heavily on our separate experiences and research foci since that time. This includes Don's continued work at the SESRC, Jolene's experiences at the Survey Research and Methodology Program, the Department of Sociology, and the Bureau of Sociological Research at the University of Nebraska–Lincoln and Leah's experiences at the Pew Research Center and Nielsen.
For the first time we have developed a companion website for this book that contains additional materials. On the website you will find example survey materials (i.e., questionnaires, contact materials, descriptions of implementation, etc.) for web, mail, telephone, and mixed-mode surveys; resources developed to demonstrate good survey visual design; color versions of many of the figures from throughout the book; and a cognitive interview example report. Readers can access these materials at www.wiley.com/go/dillman.
This book is dedicated to two consummate professionals—John Tarnai and Janet Harkness—both of whom were taken from us too early. Each has influenced our work in ways neither may have realized.
As the Assistant Director and Director of the SESRC from 1981 to 2012, John, more than any individual, nurtured the development of the web, mail, and telephone data collection capabilities of the SESRC, which provided the survey infrastructure that made it possible for us to conduct dozens of experiments that are reported in this book. Without his entrepreneurial leadership, our joint research could not have been done. His quiet demeanor and insights inspired us to do our best work and to share our survey experiences openly with others. He also collaborated on one of the first efforts to articulate the need for mixed-mode survey designs (Dillman & Tarnai, 1988), which set the tone for 25 years of follow-up experiments on the strengths and limitations of such designs that made this book possible.
Janet Harkness, served as a faculty member and later the Director of the Survey Research and Methodology Program at the University of Nebraska–Lincoln from 2005 to 2012, and in that role was a strong supporter of much of the research reported in this edition of the book. In her research Janet was grappling with many incredibly complex issues involved in cross-national and cross-cultural survey research; her contributions in these areas will continue to influence our field for decades to come as more and more surveys are conducted across cultural and national borders.
Survey methodology and our abilities as a profession to tackle new ideas has benefited from the work of these colleagues. We thank them for inspiring us both personally and professionally.
For more than a decade the National Center for Science and Engineering Statistics (NCSES) has funded much of our work to invent and apply new mixed-mode methodologies and test their applicability to government surveys. For this we are especially grateful to the NCSES Division Director, Lynda Carlson, who initiated this work, and her successor, John Gawalt, who continued it and the many NCSES staff who worked with us. This funding provided support for many graduate students whose much appreciated contributions to this research appear in the book references—Michael Stern, Arina Gertseva, Taj Mahon-Haft, Nicholas Parsons, Bryan Rookey, Allison O'Neill, Benjamin Messer, Morgan Millar, and Michelle Edwards. We also wish to acknowledge the contributions of graduate students in the Sociology Department Survey Practicum at Washington State University, and in Data Collection Methods and Questionnaire Design courses at the University of Nebraska–Lincoln.
Don would also like to thank the many staff of the SESRC who regularly, and often with great patience, solved the innumerable design challenges associated with the experimentation necessary for testing many of the ideas presented here. Special thanks goes to Tom Allen, study director for most experiments, for his ability to solve the visual design and communication issues associated with working across survey modes, and Rita Koontz, SESRC Administrative Manager, for her commitment to making the SESRC an effective and much appreciated work environment. He would also like to thank Edith deLeeuw for conversations that influenced rewriting the theoretical approach used in this book.
Jolene would like to thank Kristen Olson for being a wonderful colleague and friend who shares her time generously and is always willing to talk through ideas and undertake new research projects. She would also like to thank Amanda Richardson and the staff of the Bureau of Sociological Research for the many insightful survey discussions that have influenced her thinking in recent years, and Dan Hoyt and Julia McQuillan for their ongoing support and leadership. Finally, Jolene has had the privilege of working directly with many wonderful graduate students in recent years who have made valuable contributions to her research and thinking. She appreciates each and every one and would like to especially thank Nuttirudee Charoenruk, Alian Kasabian, Amanda Libman, Rebecca Powell, Kay Ricci, Ashley Richards, Mathew Stange, Lauren Walton, Heather Wood, and Quan Zhou.
Leah would like to thank Scott Keeter, her mentor and collaborator at the Pew Research Center, as well as Jim Bell and the many other colleagues who eagerly tackled the methodological challenges the center faced. Special thanks go to Leah's new colleagues at Nielsen, who provided encouragement and guidance as she spent time on the final manuscript.
The intensive writing process benefitted greatly from the help of several individuals. We appreciate Kristen Olsen critically reviewing the sampling and coverage chapter and Amanda Richardson providing a thorough review of the telephone chapter. In addition, Mathew Stange provided assistance with some of the figures. We especially want to thank Morgan Millar, who brought her expertise with survey methods and excellent editorial skills to bear on all aspects of reviewing, preparing, and submitting the final manuscript. Her attention to detail, organization, and encouragement ensured we were able to deliver a final manuscript.
Finally, we want to thank our families. Joye Jolly Dillman has memorably experienced with Don the writing of all four editions of this book as spouse, parent, and Washington State University faculty colleague. His appreciation for her support is both deep and long lasting.
Kristi and Tyson Chambers were both invaluable sources of support and inspiration during the writing of this book. They did more than their share of the chores when Jolene was tied to the computer, stayed patient with the process, and always seemed to have the right answer, usually a laugh or a hug, at the right time. She hopes they know how much she loves and appreciates them.
Eugene MacIntyre has helped Leah throughout her work on this book; she deeply appreciates his unwavering support. She also thanks Leilani, who lights every day and reminds Leah of all the really important things in life, and who gave up very important playtime with Mommy so she could work on the book.
Don A. DillmanWashington State UniversityPullman, WashingtonJolene D. SmythUniversity of Nebraska–LincolnLincoln, NebraskaLeah Melani ChristianNielsenAtlanta, Georgia
Chapter 1Sample Surveys in Our Electronic World
Hundreds of times every day someone decides to create a survey. The variety of organizations and individuals who make this decision is enormous, ranging from individual college students to the largest corporations. Community service organizations, nonprofit foundations, educators, voluntary associations, special interest groups, research scientists, and government agencies also all collect needed information by conducting surveys. The topics of these surveys vary greatly, from questions about health, education, employment, and political preferences to inquiries about television viewing, the use of electronic equipment, and interest in buying a new car, among many other things.
The reasons for deciding to conduct a survey are as diverse as the range of survey sponsors and topics. Sometimes, the justification is that the sponsors do not know the opinions or beliefs of those they want to survey. More typically, the sponsor has interests that go much deeper, wanting to know not just how many individuals in a group have a particular attitude, but how that attitude varies with other respondent characteristics that will be asked in the survey, such as across men and women or across different age or socioeconomic groups.
While the need to know something that is unknown drives the decision to conduct most surveys, the uses of survey results are as diverse as those who sponsor them. For example, one of us recently completed a community survey that was used to decide what facilities to include in a new neighborhood park that was about to be developed. University leaders use results from surveys of students to revise their undergraduate and graduate education programs. Public opinion pollsters use results from surveys of likely voters to predict who will win national and local elections. The Federal Reserve uses estimates of the unemployment rate produced monthly in the Current Population Survey to help set economic policy. Data from this same survey are used by individuals and businesses throughout the United States to make investment, hiring, and policy decisions. Market researchers use surveys to provide insights into consumer attitudes and behaviors. Nonprofit groups use surveys to measure attitudes about issues that are important to them and support for possible programs the group might pursue.
Surveys are both large and small. For example, over the course of a year the U.S. Census Bureau asks a few million households to respond to the American Community Survey. Others ask only a few hundred or even fewer individuals to respond. The survey response mode also varies, with some surveys being conducted by a single mode—in-person, web, telephone, or paper—while others provide multiple modes for answering questions. Sometimes respondents are asked to respond only once, while in other surveys a single individual may be asked to answer questions repeatedly over months or years, and surveys may be conducted in just a few weeks or over several months or years. In some cases people are asked to provide information about themselves or their households, and in other cases they are asked to provide information about a particular business or other organization with which they are affiliated.
Despite this diversity, all surveys still have a lot in common. Each is motivated by the desire to collect information to answer a particular question or solve a particular problem. In some cases the desired information is not available from any other source. In other cases, the information may be available, but it cannot be connected to other important information—such as other characteristics or related attitudes and behaviors—that need to be known in order to solve the problem or answer the question.
In most surveys only some of those in the population of interest are asked to respond. That is, the survey is based on a sample rather than being a census of every member of the target population. In addition, those who respond are asked questions they are expected to answer by choosing from among predetermined response categories or, occasionally by providing open-ended answers in their own words. These commonalities and the enormous amount of money and effort now spent on surveys point to their importance as a tool for learning about people's characteristics, opinions, and behaviors, and using those results to inform and direct public policy, business decisions, and for many other purposes.
Other nonsurvey means, both quantitative and qualitative, are available to social scientists, marketing professionals, government officials, special interest groups, and others for collecting useful information that will produce insight into the attitudes and behaviors of people and the groups they are a part of. These include unstructured interviews, focus groups, participant observation, content analyses, simulations, small group experiments, and analyses of administrative records or organic data such as birth and death records, sales transactions, records of online searches, social media, and other online behavior. Each of these methods can yield different types of information, and for some questions they are more appropriate than surveys or may be used in combination with surveys to answer the research question or community problem.
The feature of the probability sample survey that distinguishes it from these other methods of investigation is that it can provide a close estimate of the distribution of a characteristic in a population by surveying only some members of that population. If done correctly, it allows one to generalize results with great precision, from a few to the many, making it a very efficient method for learning about people and populations.
The efficiency and importance of the probability sample survey might best be illustrated by considering an alternative way to learn about a population—a census. Every 10 years the U.S. Census Bureau attempts to contact and survey every household in the United States, as required by our Constitution. The resulting information is used to reapportion the U.S. House of Representatives so that each member represents about the same number of U.S. residents. This massive survey, known as the Decennial Census, costs billions of dollars to conduct. A smaller organization that wants to know the opinions of all U.S. residents on a particular issue could hardly afford such an undertaking. But with a probability sample survey, it can learn those opinions for considerably lower costs by selecting only some members of the population to complete the survey.
Even on a smaller scale, few would be able to afford to survey every undergraduate student at a large university in order to assess students' satisfaction in the education they are receiving. If this were necessary, studies of student satisfaction would seldom, if ever, be done. But probability sample surveys allow us to be much more efficient with our resources by surveying only a sample of students in a way that enables us to generalize to the entire student population.
Whatever the target population or research question, limiting our data collection to a carefully selected sample of the population of interest allows us to concentrate limited resources (e.g., time and money for follow-up communications, data cleaning, and analysis) on fewer individuals, yet obtain results that are only slightly less precise than they would be if every member of the population were surveyed.
Our purpose in this book is to explain how to conduct effective probability sample surveys. We discuss the fundamental requirements that must be met if one wants to generalize results with statistical confidence from the few who are surveyed to the many they are selected to represent. We also describe specific procedures for designing surveys in which one can have high confidence in the results. Regardless of whether your interest in surveys is to understand one of the many national surveys that are conducted for policy purposes or to gain knowledge of how to design your own survey of organization members, college students, customers, or any other population, it is important to understand what it takes to do a good survey and the multiple sources of error that can reduce the accuracy of the survey results—or completely invalidate them.
Four Cornerstones of Quality Surveys
In general, survey error can be thought of as the difference between an estimate that is produced using survey data and the true value of the variables in the population that one hopes to describe. There are four main types of error that surveyors need to try to minimize in order to improve the survey estimates.
1.
Coverage Error
occurs when the list from which sample members are drawn does not accurately represent the population on the characteristic(s) one wants to estimate with the survey data (whether a voter preference, a demographic characteristic, or something else). A high-quality sample survey requires that every member of the population has a known, nonzero probability of being sampled, meaning they have to be accurately represented on the list from which the sample will be drawn. Coverage error is the difference between the estimate produced when the list is inaccurate and what would have been produced with an accurate list.
2.
Sampling Error
is the difference between the estimate produced when only a sample of units on the frame is surveyed and the estimate produced when every unit on the list is surveyed. Sampling error exists anytime we decide to survey only some, rather than all, members of the sample frame.
3.
Nonresponse Error
is the difference between the estimate produced when only some of the sampled units respond compared to when all of them respond. It occurs when those who do not respond are different from those who do respond in a way that influences the estimate.
4.
Measurement Error
is the difference between the estimate produced and the true value because respondents gave inaccurate answers to survey questions. It occurs when respondents are unable or unwilling to provide accurate answers, which can be due to poor question design, survey mode effects, interviewer and respondent behavior, or data collection mistakes.
We consider reducing the potential for these errors as the four cornerstones of conducting successful sample surveys. Surveyors should attempt to limit each to acceptable levels. None of them can be ignored. As such, each receives detailed attention in the chapters that follow. Because these sources of error are so essential for defining survey quality, we describe each of them here in more detail.
Coverage Error
As we previously mentioned, the strength of a probability sample survey is that it allows us to collect data from only a sample of the population but generalize results to the whole, thus saving considerable time, money, and effort that would be incurred if we had to survey everyone in the population. However, in order to draw a sample, one has to have a sample frame, or a list of members of the target population, and any errors in that list have the potential to introduce coverage error into the final estimates that are produced. If some units from the target population are not included on the sample frame (i.e., undercoverage) and they differ from those that are in ways that are important to the survey, the final estimates will contain error.
For example, all other error sources aside, a landline random digit dial telephone survey would likely overestimate the prevalence of higher socioeconomic status because the well-off are more likely than the poor to have landline telephone service (i.e., the well-off are more likely to be on the landline random digit dial sample frame) (Blumberg & Luke, 2013). In fact, one of the challenges now being faced in conducting household telephone surveys is that only about 58% of households still have landlines (Blumberg & Luke, 2013), the traditional source of random digit dialing samples, and those who have them are quite different from those who do not on a number of important characteristics. Using the landline telephone frame alone (without supplementing it with a cell phone frame) for a national household survey would leave out significant portions of the population who are likely to differ in important ways from those included on the frame.
Similarly, conducting a national household survey by Internet would leave out significant portions of the population because, as of May 2013, only 73% of American adults have Internet access in the home (Pew Internet & American Life Project, 2013b). In comparison, an Internet survey of undergraduate students at a university, where all students are required to use the Internet, would likely have little coverage error, provided a list of all students could be obtained. In Chapter 3 we discuss in detail the threat of coverage error, its likely sources, and how to limit it.
Sampling Error
The extent to which the precision of the survey estimates is limited because only some people from the sample frame are selected to do the survey (i.e., sampled) and others are not is known as sampling error. If we have a sample frame with complete coverage (i.e., the list matches the population perfectly), we can say that sampling error is the difference between the estimates produced and the true value because we survey only a sample of the population and not everyone. The power of probability sampling, which is also discussed in detail in Chapter 3, is that estimates with acceptable levels of precision can usually be made for the population by surveying only a small portion of the people in the population. For example, a researcher can sample only about 100 members of the U.S. general public and, if all 100 respond, achieve estimates with a margin of error of +/−10%. Successfully surveying a sample of 2,000 individuals reduces the margin of error to about +/−2%. Surveying 100 or even 2,000 people rather than the approximately 315 million people in the United States represents an enormous and desirable cost savings, but doing so means that one has to be willing to live with some sampling error in the estimates.
Sampling error is an unavoidable result of obtaining data from only some rather than all members on the sample frame and exists as a part of all sample surveys. For this reason, we describe the importance of reducing survey error to acceptable levels, rather than being able to eliminate it entirely. By contrast, censuses—in which all members on the sampling frame are selected to be surveyed—are not subject to sampling error.
Many novice surveyors find sampling error to be somewhat nonintuitive. They find it difficult to imagine only needing to survey a few hundred or thousand to learn about millions of households or individuals. Yet, during each presidential election in the United States, surveys of between 1,000 and 2,000 likely voters are conducted that correctly estimate (within the limits of sampling error) the votes for each candidate. For example, across polls conducted in the final week of the 2012 campaign, the average error for each candidate was about 2 percentage points. Just as nonintuitive for some beginning surveyors to grasp is that in order to predict the outcome of a local election for a particular state or medium sized U.S. city with perhaps 50,000 voters, nearly as many people need to be surveyed as are needed for predicting a national election.
The exact sampling error is easily calculated mathematically, as described in Chapter 3. However, the ease of making those calculations and the mathematical preciseness of the result leads to overreliance on it as a singular measure of the amount of error in a survey statistic. This tendency should be avoided. Sampling error calculations reflect the completed sample size, that is, only received responses are considered. The larger the number of responses, the greater the reported precision and statistical confidence. But they ignore the possibility for coverage error as well as the fact that many and sometimes most of the invited participants did not respond, which raises the potential for a third source of error, nonresponse error.
Nonresponse Error
Many sponsors think of a survey's response rate (the proportion of sampled individuals that respond to the survey) as the major indicator of survey quality. A major focus of this book is how to obtain high response rates to surveys. However, taken by itself, the response rate is only an indirect indicator of survey quality. The more important response quality indicator is nonresponse error, which occurs when the characteristics of respondents differ from those who chose not to respond in a way that is relevant to the study results. For example, if a survey on environmental attitudes obtained responses mostly from those individuals who have positive attitudes toward the environment and those who have negative attitudes are underrepresented, then that survey's results would be biased because of nonresponse error.
The common mistake sometimes made by novice surveyors is to consider response rate as an adequate indicator of whether nonresponse error exists. Comparisons across many surveys have shown that nonresponse error may occur in surveys with higher as well as lower response rates (Groves & Peytcheva, 2008). For example, in 1989 a study was conducted in Dallas County, Texas, to learn about people's thoughts and behaviors related to acquired immunodeficiency syndrome (AIDS). Sampled individuals were asked to complete a self-administered survey and have a blood sample drawn by a phlebotomist. This study achieved a remarkable 84% response rate: A rate that some might think is a clear indication of high quality. But to ascertain whether there was nonresponse bias, the researchers went back to a random sample of the nonrespondents and were able to get some to participate (some were not asked to give the blood sample at this stage). This effort revealed that the prevalence of human immunodeficiency virus (HIV) risk behaviors like intravenous (IV) drug use and male-to-male sex were underestimated in the original data collection effort. Only 3% of those who initially participated reported engaging in IV drug use compared to 7% of those who participated in the follow-up. Similarly, only about 5% of the initial participants reported engaging in male-to-male sex compared to about 17% of those in the follow-up (Centers for Disease Control and Prevention, 1991). Despite an impressive 84% response rate, the initial estimates were biased because those who responded differed from those who did not respond on characteristics of interest in this study.
While the study just described demonstrates that higher response rates do not guarantee minimal nonresponse error, it is important to recognize that higher response rates do reduce the likelihood of nonresponse error and thus provide greater credibility to surveys' results than do lower response rates. In addition, higher response rates result in larger completed samples, thereby increasing the precision of the estimates in that way. Thus, designing surveys in ways that produce higher response rates can be a helpful tool in reducing nonresponse error.
Response is a function of contact and cooperation. That is, in order to obtain a response, we first have to make contact with sample members and then we have to convince them to cooperate with our request to complete the survey. Using multiple contact attempts and varying the timing, delivery method, and mode of those attempts are a few ways we discuss in this book of increasing the likelihood of making contact with sample members. Respondent-friendly questionnaires, shorter (rather than longer) survey instruments, the use of incentives, follow-up requests that target likely nonrespondents, and switching survey modes are a few of the many features of survey design discussed in this book that are intended to increase the likelihood of sample members cooperating with our request. All of these strategies have the parallel objectives of increasing response while simultaneously reducing nonresponse error. Chapter 2 introduces the discussion of implementation procedures and a theory for guiding those decisions. The majority of this book, from Chapter 4 forward, focuses on many aspects of survey design that can reduce nonresponse as well as measurement error.
Measurement Error
Survey objectives are realized by asking questions to which respondents provide accurate answers. However, in designing a survey that will achieve valid and reliable measurement, one faces a gauntlet of measurement challenges. One of the challenges to asking a good survey question is making sure that it adequately measures the idea or concept of interest. An example occurred in a survey in which the sponsor wanted to obtain a measurement of household wealth. He had tentatively decided to use household income for the previous year as a measure of wealth until a colleague pointed out that annual income is likely to decrease sharply when a person retires, but wealth typically does not. Similarly, a community survey sponsor proposed using length of time individuals had lived in their current residence as a measure of length of time in the community, but soon discarded the idea because of the likelihood that many people may have moved from one residence to another in the same community. When a question does not measure what it was intended to, as in these cases, it is typically referred to as having specification error (also known as low construct validity). Considerable time and effort can be spent deciding what format of question to use, what type of scale to provide, how to label answer categories, whether to offer a “don't know” option, and any number of other details, but all of that effort is useless if the question does not measure the concept called for by the study objectives.
Once one has selected an acceptable way to measure a specific concept, there are many different ways that accuracy of the estimate may be compromised, resulting in measurement error.
The substance of the question may encourage a response that, because of perceived societal norms, puts the respondent in a more favorable light to the interviewer and/or survey sponsor. Questions about sex and illegal behaviors are examples.
The question may be unclear to the respondent because it uses words that are not understood or phrases that are confusing.
The question structure may encourage certain answers that another structure would not. For example, items that ask respondents to mark all that apply tend to result in fewer selections among later categories than those that ask for an explicit positive or negative answer for each item (i.e., a forced-choice or yes/no format).
The order in which questions are asked may produce different answers to specific questions than would another order.
The visual layout of a question may increase the likelihood that certain answers are chosen and others are not, or that some items are overlooked altogether.
Some types of respondents may be less likely to give accurate answers than others.
Perception of the expectations of interviewers or the sponsor may also influence answers.
Interviewer characteristics, such as gender or race, may influence the answers people provide.
The choice of survey mode may also influence answers to surveys. For example, research has consistently shown that scalar questions are likely to be answered differently in visual versus aural surveys.
These problems can result in two types of measurement error. The first is response bias, in which estimates are systematically shifted one way or the other. Two common examples are underestimating socially undesirable behaviors, like drug use and criminal activity, and overestimating socially desirable behaviors, like volunteering and voting. The second type of measurement error is response variance, which is akin to the idea of low reliability. That is, if the measurement were taken over and over multiple times, it would produce a different result each time.
A great deal of terminology is often used to indicate why some questions and not others exhibit measurement error, including social desirability, primacy/recency, acquiescence, clarity of figure/ground relationships, the Law of Pragnanz, the norm of evenhandedness, and much more. We mention these many sources of potential measurement differences because writing effective questions requires simultaneously working on many fronts in an effort to reduce measurement problems in surveys to obtain accurate answers to all questions. We discuss this further in Chapters 4, 5, 6, and 7.
Total Survey Error
The need to focus on many design considerations at once sometimes results in ignoring one source of error, a mistake that can have devastating repercussions for a survey. For example, a faculty member concerned with reports of classroom cheating decided to take advantage of the web survey software available in her university and design a survey of students to get their perceptions about whether classroom cheating was happening and to learn what they thought would be appropriate punishment. It was her hope that conducting a probability sample survey of students would produce data she could report to the appropriate university officials to inform new policies for dealing with cheating cases. To avoid the challenge of sending sample members e-mails with individual passwords that would allow only those sampled to respond, she sent generic e-mails and set up the survey website so that anyone who knew about the survey could complete it. She soon learned that the e-mails sent to the carefully selected sample of students had been forwarded to other students and that some students with particularly strong viewpoints had filled out the survey multiple times (i.e., stuffed the ballot box!), which breaks from the requirement for a probability sample that only the people selected for the survey can provide a response and that each person can respond only once. In trying to simplify the administration of this survey, the faculty member ended up making a decision that undermined the probability nature of the sample and discredited the survey's results.
We have also observed situations in which survey designers became excessively concerned over resolving issues with small consequences. Upon learning that a sample of household addresses for a community survey would only reach about 95% of the households in the community, one surveyor became obsessed with how to manually add the missing addresses. To do so would have required tremendous costs and effort, including cross-checking records and potential personal visits to areas in the community to check to see if there were addresses there. In this case, the error from missing 5% of households was likely to be small, and the resources that would be required to fix it were excessive in relation to the likely benefit. It would have been more beneficial to focus on reducing other potential errors.
In another situation this may not be the case. Surveyors designing a national survey that will produce data used to allocate government funds may decide that even though small, the extra precision obtained by enumerating the missing 5% of addresses is worth the extra effort because it will help ensure that federal funds are fairly distributed.
One mistake some survey designers make is to worry most about what error source they know best. The research-based knowledge for dealing with specific
