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Experimental Methods in Survey Research
Techniques that Combine Random Sampling with Random Assignment
Paul J. Lavrakas (Edited by), PJ Lavrakas (Author), Michael W. Traugott (Edited by), Courtney Kennedy (Edited by), Allyson L. Holbrook (Edited by), Edith D. de Leeuw (Edited by), Brady T. West (Edited by)
9781119083740, Wiley
Hardback, published 15 November 2019
544 pages
26.2 x 18.3 x 3 cm, 1.315 kg
A thorough and comprehensive guide to the theoretical, practical, and methodological approaches used in survey experiments across disciplines such as political science, health sciences, sociology, economics, psychology, and marketing This book explores and explains the broad range of experimental designs embedded in surveys that use both probability and non-probability samples. It approaches the usage of survey-based experiments with a Total Survey Error (TSE) perspective, which provides insight on the strengths and weaknesses of the techniques used. Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment addresses experiments on within-unit coverage, reducing nonresponse, question and questionnaire design, minimizing interview measurement bias, using adaptive design, trend data, vignettes, the analysis of data from survey experiments, and other topics, across social, behavioral, and marketing science domains. Each chapter begins with a description of the experimental method or application and its importance, followed by reference to relevant literature. At least one detailed original experimental case study then follows to illustrate the experimental method’s deployment, implementation, and analysis from a TSE perspective. The chapters conclude with theoretical and practical implications on the usage of the experimental method addressed. In summary, this book: Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment is an ideal reference for survey researchers and practitioners in areas such political science, health sciences, sociology, economics, psychology, public policy, data collection, data science, and marketing. It is also a very useful textbook for graduate-level courses on survey experiments and survey methodology.
List of Contributors xix Preface by Dr. Judith Tanur xxv About the Companion Website xxix 1 Probability Survey-Based Experimentation and the Balancing of Internal and External Validity Concerns 1 1.1 Validity Concerns in Survey Research 3 1.2 Survey Validity and Survey Error 5 1.3 Internal Validity 6 1.4 Threats to Internal Validity 8 1.5 External Validity 11 1.6 Pairing Experimental Designs with Probability Sampling 12 1.7 Some Thoughts on Conducting Experiments with Online Convenience Samples 12 1.8 The Contents of this Book 15 References 15 Part I Introduction to Section on Within-Unit Coverage 19 2 Within-Household Selection Methods: A Critical Review and Experimental Examination 23 2.1 Introduction 23 2.2 Within-Household Selection and Total Survey Error 24 2.3 Types of within-Household Selection Techniques 24 2.4 Within-Household Selection in Telephone Surveys 25 2.5 Within-Household Selection in Self-Administered Surveys 26 2.6 Methodological Requirements of Experimentally Studying Within-Household Selection Methods 27 2.7 Empirical Example 30 2.8 Data and Methods 31 2.9 Analysis Plan 34 2.10 Results 35 2.11 Discussion and Conclusions 40 References 42 3 Measuring within-Household Contamination: The Challenge of Interviewing More Than One Member of a Household 47 3.1 Literature Review 47 3.2 Data and Methods 50 Investigators 53 Field/Project Directors 53 3.3 The Sequence of Analyses 55 3.4 Results 55 3.5 Effect on Standard Errors of the Estimates 57 3.6 Effect on Response Rates 58 3.7 Effect on Responses 61 3.8 Substantive Results 64 References 64 Part II Survey Experiments with Techniques to Reduce Nonresponse 67 4 Survey Experiments on Interactions and Nonresponse: A Case Study of Incentives and Modes 69 4.1 Introduction 69 4.2 Literature Overview 70 4.3 Case Study: Examining the Interaction between Incentives and Mode 73 4.4 Concluding Remarks 83 Acknowledgments 85 References 86 5 Experiments on the Effects of Advance Letters in Surveys 89 5.1 Introduction 89 5.2 State of the Art on Experimentation on the Effect of Advance Letters 93 5.3 Case Studies: Experimental Research on the Effect of Advance Letters 95 5.4 Case Study I: Violence against Men in Intimate Relationships 96 5.5 Case Study II: The Neighborhood Crime and Justice Study 100 5.6 Discussion 106 5.7 Research Agenda for the Future 107 References 108 Part III Overview of the Section on the Questionnaire 111 6 Experiments on the Design and Evaluation of Complex Survey Questions 113 6.1 Question Construction: Dangling Qualifiers 115 6.2 Overall Meanings of Question Can Be Obscured by Detailed Words 117 6.3 Are Two Questions Better than One? 119 6.4 The Use of Multiple Questions to Simplify Response Judgments 121 6.5 The Effect of Context or Framing on Answers 122 6.6 Do Questionnaire Effects Vary Across Sub-groups of Respondents? 124 6.7 Discussion 126 References 128 7 Impact of Response Scale Features on Survey Responses to Behavioral Questions 131 7.1 Introduction 131 7.2 Previous Work on Scale Design Features 132 7.3 Methods 134 7.4 Results 136 7.5 Discussion 141 Acknowledgment 143 7.A Question Wording 143 7.A.1 Experimental Questions (One Question Per Screen) 143 7.A.2 Validation Questions (One Per Screen) 144 7.A.3 GfK Profile Questions (Not Part of the Questionnaire) 145 7.B Test of Interaction Effects 145 References 146 8 Mode Effects Versus Question Format Effects: An Experimental Investigation of Measurement Error Implemented in a Probability-Based Online Panel 151 8.1 Introduction 151 8.2 Experiments and Probability-Based Online Panels 153 8.3 Mixed-Mode Question Format Experiments 154 8.4 Summary and Discussion 161 Acknowledgments 162 References 162 9 Conflicting Cues: Item Nonresponse and Experimental Mortality 167 9.1 Introduction 167 9.2 Survey Experiments and Item Nonresponse 167 9.3 Case Study: Conflicting Cues and Item Nonresponse 170 9.4 Methods 170 9.5 Issue Selection 171 9.6 Experimental Conditions and Measures 172 9.7 Results 173 9.8 Addressing Item Nonresponse in Survey Experiments 174 9.9 Summary 178 References 179 10 Application of a List Experiment at the Population Level: The Case of Opposition to Immigration in the Netherlands 181 10.1 Fielding the Item Count Technique (ICT) 183 10.2 Analyzing the Item Count Technique (ICT) 185 10.3 An Application of ICT: Attitudes toward Immigrants in the Netherlands 186 10.4 Limitations of ICT 190 References 192 Part IV Introduction to Section on Interviewers 195 11 Race- and Ethnicity-of-Interviewer Effects 197 11.1 Introduction 197 11.2 The Current Research 205 11.3 Respondents and Procedures 207 11.4 Measures 207 11.5 Analysis 210 11.6 Results 211 11.7 Discussion and Conclusion 219 References 221 12 Investigating Interviewer Effects and Confounds in Survey-Based Experimentation 225 12.1 Studying Interviewer Effects Using a Post hoc Experimental Design 226 12.2 Studying Interviewer Effects Using A Priori Experimental Designs 230 12.3 An Original Experiment on the Effects of Interviewers Administering Only One Treatment vs. Interviewers Administrating Multiple Treatments 232 12.4 Discussion 239 References 242 Part V Introduction to Section on Adaptive Design 245 13 Using Experiments to Assess Interactive Feedback That Improves Response Quality in Web Surveys 247 13.1 Introduction 247 13.2 Case Studies – Interactive Feedback in Web Surveys 251 13.3 Methodological Issues in Experimental Visual Design Studies 258 References 269 14 Randomized Experiments for Web-Mail Surveys Conducted Using Address-Based Samples of the General Population 275 14.1 Introduction 275 14.2 Study Design and Methods 278 14.3 Results 281 14.4 Discussion 285 References 287 Part VI Introduction to Section on Special Surveys 291 15 Mounting Multiple Experiments on Longitudinal Social Surveys: Design and Implementation Considerations 293 15.1 Introduction and Overview 293 15.2 Types of Experiments that Can Be Mounted in a Longitudinal Survey 294 15.3 Longitudinal Experiments and Experiments in Longitudinal Surveys 295 15.4 Longitudinal Surveys that Serve as Platforms for Experimentation 296 15.5 The Understanding Society Innovation Panel 298 15.6 Avoiding Confounding of Experiments 299 15.7 Allocation Procedures 301 15.8 Refreshment Samples 304 15.9 Discussion 305 15.A Appendix: Stata Syntax to Produce Table 15.3 Treatment Allocations 306 References 306 16 Obstacles and Opportunities for Experiments in Establishment Surveys Supporting Official Statistics 309 16.1 Introduction 309 16.2 Some Key Differences between Household and Establishment Surveys 310 16.3 Existing Literature Featuring Establishment Survey Experiments 312 16.4 Key Considerations for Experimentation in Establishment Surveys 314 16.5 Examples of Experimentation in Establishment Surveys 318 16.6 Discussion and Concluding Remarks 323 Acknowledgments 324 References 324 Part VII Introduction to Section on Trend Data 327 17 Tracking Question-Wording Experiments across Time in the General Social Survey, 1984–2014 329 17.1 Introduction 329 17.2 GSS Question-Wording Experiment on Spending Priorities 330 17.3 Experimental Analysis 330 17.4 Summary and Conclusion 338 17.A National Spending Priority Items 339 References 340 18 Survey Experiments and Changes in Question Wording in Repeated Cross-Sectional Surveys 343 18.2 Background 344 18.3 Two Case Studies 347 18.4 Implications and Conclusions 362 Acknowledgments 364 References 364 Part VIII Vignette Experiments in Surveys 369 19 Are Factorial Survey Experiments Prone to Survey Mode Effects? 371 19.1 Introduction 371 19.2 Idea and Scope of Factorial Survey Experiments 372 19.3 Mode Effects 373 19.4 Case Study 378 19.5 Conclusion 388 References 390 20 Validity Aspects of Vignette Experiments: Expected “What-If” Differences between Reports of Behavioral Intentions and Actual Behavior 393 20.1 Outline of the Problem 393 20.2 Research Findings from Our Experimental Work 399 20.3 Discussion 411 References 413 Part IX Introduction to Section on Analysis 417 21 Identities and Intersectionality: A Case for Purposive Sampling in Survey-Experimental Research 419 21.1 Introduction 419 21.2 Common Techniques for Survey Experiments on Identity 420 21.3 How Limited are Representative Samples for Intersectionality Research? 426 21.4 Conclusions and Discussion 430 Author Biographies 431 References 431 22 Designing Probability Samples to Study Treatment Effect Heterogeneity 435 22.1 Introduction 435 22.2 Nesting a Randomized Treatment in a National Probability Sample: The NSLM 446 22.3 Discussion and Conclusions 451 Acknowledgments 453 References 453 23 Design-Based Analysis of Experiments Embedded in Probability Samples 457 23.1 Introduction 457 23.2 Design of Embedded Experiments 458 23.3 Design-Based Inference for Embedded Experiments with One Treatment Factor 460 23.4 Analysis of Experiments with Clusters of Sampling Units as Experimental Units 466 23.5 Factorial Designs 468 23.6 A Mixed-Mode Experiment in the Dutch Crime Victimization Survey 472 23.7 Discussion 477 Acknowledgments 478 References 478 24 Extending the Within-Persons Experimental Design: The Multitrait-Multierror (MTME) Approach 481 24.1 Introduction 481 24.2 The Multitrait-Multierror (MTME) Framework 482 24.3 Designing the MTME Experiment 487 24.4 Statistical Estimation for the MTME Approach 489 24.5 Measurement Error in Attitudes toward Migrants in the UK 491 24.6 Results 494 24.7 Conclusions and Future Research Directions 497 Acknowledgments 498 References 498 Index 501
Paul J. Lavrakas, Courtney Kennedy, Edith D. de Leeuw, Brady T. West, Allyson L. Holbrook, and Michael W. Traugott
Paul J. Lavrakas and Edith D. de Leeuw
Jolene D. Smyth, Kristen Olson, and Mathew Stange
Colm O’Muircheartaigh, Stephen Smith, and Jaclyn S.Wong
Edith D. de Leeuw and Paul J. Lavrakas
A. Bianchi and S. Biffignandi
Susanne Vogl, Jennifer A. Parsons, Linda K. Owens, and Paul J. Lavrakas
Allyson Holbrook and Michael W. Traugott
Paul Beatty, Carol Cosenza, and Floyd J. Fowler Jr.
Florian Keusch and Ting Yan
Edith D. de Leeuw, Joop Hox, and Annette Scherpenzeel
David J. Ciuk and Berwood A. Yost
Mathew J. Creighton, Philip S. Brenner, Peter Schmidt, and Diana Zavala-Rojas
Brady T. West and Edith D. de Leeuw
Allyson L. Holbrook, Timothy P. Johnson, and Maria Krysan
Paul J. Lavrakas, Jenny Kelly, and Colleen McClain
Courtney Kennedy and Brady T. West
Tanja Kunz and Marek Fuchs
Z. Tuba Suzer-Gurtekin, Mahmoud Elkasabi, James M. Lepkowski, Mingnan Liu, and Richard Curtin
Michael W. Traugott and Edith D. de Leeuw
Peter Lynn and Annette Jäckle
Diane K. Willimack and Jaki S. McCarthy
Michael W. Traugott and Paul J. Lavrakas
Tom W. Smith and Jaesok Son
Allyson L. Holbrook, David Sterrett, Andrew W. Crosby, Marina Stavrakantonaki, Xiaoheng Wang, Tianshu Zhao, and Timothy P. Johnson
18.1 Introduction 343
Allyson Holbrook and Paul J. Lavrakas
Katrin Auspurg, Thomas Hinz, and Sandra Walzenbach
Stefanie Eifler and Knut Petzold
Brady T. West and Courtney Kennedy
Samara Klar and Thomas J. Leeper
Elizabeth Tipton, David S. Yeager, Ronaldo Iachan, and Barbara Schneider
Jan A. van den Brakel
Alexandru Cernat and Daniel L. Oberski
Subject Areas: Mathematics [PB]
