Designing and Conducting Business Surveys

GER SNIJKERS, PhD, is Senior Researcher and Advisor in Business Survey Data Collection Methodology at Statistics Netherlands, after having held various research and project management positions. From 2006-2011, he held the position of Professor of Business Survey Methodology at Utrecht University. GUSTAV HARALDSEN, Mag. Art., is Senior Methodological Advisor in the Department of Data Collection and Methods at Statistics Norway. He previously headed the Division for Interviewing Surveys and the Division of Data Collection Methods. JACQUI JONES, BA (Hons), MSc, is Deputy Director of the Business Indicators and Balance of Payments Division in the UK Office of National Statistics (ONS). Prior to this, she worked in the Methodology Directorate at ONS. DIANE K. WILLIMACK, MS, MA, is Chief of the Response Improvement Research Staff at the Economic Programs Directorate of the U.S. Census Bureau. She previously served as a mathematical statistician and survey methodologist at the National Agricultural Statistics Service (U.S. Department of Agriculture). Additional contributors include Paul Smith (ONS), Deirdre Giesen (Statistics Netherlands), Li Chun Zhang (Statistics Norway), and Mike Hidiroglou (Statistics Canada).

Preface xix

Contributors xxiii

Abbreviations xxv

1. Surveys and Business Surveys 1
Jacqui Jones, Ger Snijkers, and Gustav Haraldsen

1.1. The Objective of this Book, 1

1.2. The Statistical Production Process, 2

1.3. Surveys, 4

1.3.1. Definition of Surveys, 4

1.3.2. Survey Constraints, Considerations, and Tradeoffs, 5

1.3.3. A Brief Historical Look at Surveys, 8

1.4. Types of Business Data Outputs, 10

1.4.1. Official Statistics, 10

1.4.2. Other Types of Business Data Outputs, 14

1.5. Use of Business Data Outputs, 16

1.5.1. National, Regional, and Local Economic Performance, 17

1.5.2. Individual Business Monitoring and Policymaking, 18

1.5.3. Everyday Decisions, 18

1.6. Obtaining Business Data, 19

1.6.1. Business Data Collection Methods, 19

1.7. Business Surveys, 22

1.7.1. Types of Business Surveys, 22

1.7.2. The Business Survey–Output Production Process, 23

1.7.3. Perspectives of the Business Survey Process, 26

1.8. Overview of the Book, 28

1.8.1. The Audience, 28

1.8.2. Organization of the Book, 29

1.8.3. Chapter Summaries, 29

Acknowledgments, 33

Appendix, 33

2. The Business Context and its Implications for the Survey Response Process 39
Diane K. Willimack and Ger Snijkers

2.1. The Business Context from the Perspective of the Organizational Sciences, 40

2.1.1. Business Goals and Behaviors and Implications for Survey Response, 40

2.1.2. Dimensions of Social Behavior in Organizations Pertinent to the Survey Response Task, 45

2.1.3. The Response Process Viewed as Work, 49

2.2. A Comprehensive Approach Integrating the Business Context and the Survey Response Process, 51

2.2.1. The Decision to Participate, 54

2.2.2. Performing Response Tasks, 60

2.2.3. Release of the Questionnaire, 75

2.2.4. Role of the Survey Organization in the Business Survey Response Process: Implications for Survey Design, 75

2.3. Summary, 79

Acknowledgments, 82

3. Quality Issues in Business Surveys 83
Gustav Haraldsen

3.1. Survey Quality from a User/producer Perspective, 84

3.1.1. Users and User Evaluations of Business Surveys, 86

3.1.2. The Total Survey Error Approach, 88

3.2. Sample-Related Quality Issues, 96

3.2.1. A Glimpse into Transaction Studies, 96

3.2.2. Specifying Observational Units, 97

3.2.3. The Convenience Sample Element, 99

3.2.4. Sampling Error Issues, 101

3.2.5. Reporting Units and Informants, 102

3.2.6. Response Rate Issues, 103

3.2.7. Quality Effects of Postsurvey Adjustments, 106

3.3. Measurement-Related Quality Issues, 106

3.3.1. Validity Challenges during Survey Specification, 107

3.3.2. Sources of Measurement Error in Business Surveys, 108

3.3.3. Measurement Issues Summarized, 118

3.4. Sample and Measurement Issues Combined, 120

Acknowledgment, 125

4. Planning the Survey 127
Ger Snijkers, Gustav Haraldsen, and Jacqui Jones

4.1. Introduction, 127

4.2. Business Surveys and Project Management Principles, 128

4.3. Specifying and Planning the Survey, 131

4.3.1. Specifying the Survey, 132

4.3.2. Planning the Survey in Detail: The Planning Process, 135

4.3.3. Planning the Fieldwork, 142

4.3.4. Planning the Designing, Building, and Testing of the Survey Components, 143

4.3.5. Planning the Data Analysis, Dissemination, and Archiving, 151

4.3.6. Planning the Survey Evaluation, 152

4.4. Required Resources, 153

4.5. The Timetable, 153

4.6. Planning Managing, Controlling, Monitoring, and Evaluation of the Survey Project, 154

4.7. Risk Management, 156

4.8. Finalizing the Project Plan and Getting Started, 162

4.9. Summary and Progress Status Overview, 162

Acknowledgment, 163

5. Sampling and Estimation for Business Surveys 165
Paul Smith

5.1. Basic Principles, 165

5.2. Registers and Frames, 167

5.2.1. Business Registers, 167

5.2.2. Constructing a Frame (without a Business Register), 174

5.2.3. Register and Frame Quality, 175

5.2.4. Dual-Frame Surveys, 176

5.3. Sample Design, 177

5.3.1. Some Basic Ideas, 177

5.3.2. Stratified Sampling, 178

5.3.3. Probability Proportional to Size (PPS) Sampling, 195

5.3.4. Cutoff Sampling, 196

5.3.5. Sample Rotation and Overlap, 197

5.3.6. Special Sample Designs, 199

5.4. Estimation, 202

5.4.1. Horvitz–Thompson Estimation, 202

5.4.2. Some Basic Ideas, 203

5.4.3. Ratio Estimation, 205

5.4.4. Combined Ratio Estimation, 207

5.4.5. More Complex Estimators, 208

5.4.6. Accuracy of Auxiliary Information, 208

5.4.7. Optimal Uses of Auxiliary Information, 209

5.4.8. Estimating Discontinuities, 209

5.5. Outliers, 210

5.5.1. Inspection and “Surprise Strata”, 211

5.5.2. Winsorization, 212

5.5.3. Other Approaches, 213

5.6. Variance Estimation, 214

5.7. Small Area Estimation, 216

5.8. Summary, 217

Acknowledgments, 218

6. Understanding and Coping with Response Burden 219
Gustav Haraldsen, Jacqui Jones, Deirdre Giesen, and Li-Chun Zhang

6.1. A Cost versus a Cost Benefit Approach, 220

6.1.1. Politicians’ Bird’s-Eye View, 221

6.1.2. Commercial Worries, 222

6.1.3. Perceived Response Burden, 223

6.2. Measuring and Monitoring Business Response Burden, 226

6.2.1. Measuring and Calculating Actual Response Burden, 229

6.2.2. Measuring Perceived Response Burden, 232

6.2.3. Calculations, 234

6.3. An Analytical Approach to Response Burden, 235

6.3.1. Disentangling Some Sources of Response Burden, 238

6.3.2. Suggesting a Curvilinear Correlation between Response Burden and Response Quality, 239

6.4. Measures to Reduce Response Burden, 241

6.4.1. Survey Reduction Measures, 241

6.4.2. Sample Coordination, 243

6.4.3. Survey Communication Measures, 248

6.4.4. Questionnaire Improvement Measures, 249

6.4.5. Tailored Evaluations of Response Burden Reduction Measures, 251

Acknowledgments, 252

7. Methods for the Development, Testing, and Evaluation of Data Collection Instruments 253
Diane K. Willimack

7.1. A Framework for Questionnaire Development, Testing, and Evaluation, 254

7.1.1. Business Survey Response Model and Measurement Error, 256

7.1.2. Constraints and Limitations to Questionnaire Development, Testing, and Evaluation in the Business Survey Context, 258

7.2. Developing Content, Measurements, and Survey Questions, 260

7.2.1. The Roles of Subject Area Specialists, Stakeholders, and Data Users, 262

7.2.2. Methods for Content Development and Specifying Measurements, 262

7.3. Testing Questions and Questionnaires, 267

7.3.1. The Response Model as a Framework for Testing, 267

7.3.2. Methods, 269

7.4. Evaluating Survey Questions and Questionnaires, 276

7.4.1. Methods Used during Data Collection, 276

7.4.2. Methods Used after Data Collection, 278

7.4.3. Methods for Identifying Questionnaire Problems in Recurring Surveys, 280

7.5. Development, Testing, and Evaluation of Electronic Data Collection Instruments, 282

7.5.1. Development Strategies for Electronic Instruments, 282

7.5.2. Usability Testing, 285

7.5.3. Evaluating Web Survey Data Collection Instruments, 286

7.6. Analyzing Qualitative Data, 287

7.6.1. Data Capture, 288

7.6.2. Types of Analysis, 288

7.6.3. Data Reduction, 289

7.6.4. Data Display, 290

7.6.5. Drawing Conclusions and Verification, 290

7.7. Multimethod Approaches to Development, Testing, and Evaluation, 290

7.8. Organization and Logistics, 293

7.8.1. Survey Organizational Structures for Instrument Development, Testing, and Evaluation, 293

7.8.2. Pretesting Logistics, 294

7.9. Summary, 295

Acknowledgments, 298

Appendix 7.A.1 Example of Conclusions from Qualitative Data Analysis: Public Trust in Statistics: A Conceptual

Model of Trust Determinants, 298

Appendix 7.A.2 Case Study: Logistics of Questionnaire Pretesting for Economic Survey Programs at the US Census Bureau, 299

8. Questionnaire Communication in Business Surveys 303
Gustav Haraldsen

8.1. Communication Modes, 303

8.2. Elements in Concert, 306

8.3. Tailoring for Businesses, 311

8.3.1. Setting the Scene, 313

8.3.2. Common Access to Several Web Surveys, 317

8.3.3. Layout and Functional Tailoring for Internal Data Collection, 325

8.4. Phrasing Questions in Business Surveys, 332

8.4.1. From Requirements to Inquiries, 332

8.4.2. Clarifying Terms and Tasks, 335

8.4.3. Listening to Questionnaires, 340

8.5. Minimizing Response Burden, 346

8.5.1. Time Saving Measures, 347

8.5.2. Reducing the Cognitive Burden, 351

8.6. Concluding Reflections, 357

Acknowledgments, 357

9. Business Survey Communication 359
Ger Snijkers and Jacqui Jones

9.1. Introduction, 359

9.2. The Role, Objectives, and Process of Business Survey Communication, 360

9.2.1. Role, 360

9.2.2. Objectives, 361

9.2.3. Process, 362

9.3. Tailoring to the Business Context, 373

9.3.1. Actors and Their Roles in the Response Process, 373

9.3.2. Business Size and Economic Sector, 374

9.3.3. Factors Affecting the Response Process, 375

9.3.4. Questions and Complaints, 377

9.4. The Survey Organization Context, 377

9.4.1. Staff in the Communication Process and Their Roles, 377

9.4.2. Survey Organization Policies and Procedures, 383

9.4.3. Survey Communication in Perspective, 388

9.5. Designing an Effective Business Survey Communication Strategy, 389

9.5.1. Planning and Designing the Communication Strategy, 390

9.5.2. Effectiveness of Communication Modes, 398

9.5.3. Effectively Motivating and Facilitating, 400

9.6. Business Survey Communication Measures, Practices, and Guidelines, 407

9.6.1. Prefield Communication Measures, 408

9.6.2. Field Communication Measures, 412

9.6.3. Special Cases: Large Businesses and Mixed-Mode Designs, 421

9.7. Summary and Example of a Communication Strategy, 426

Acknowledgment, 430

10. Managing the Data Collection 431
Ger Snijkers and Gustav Haraldsen

10.1. Introduction, 431

10.2. Implementing the Survey, 432

10.3. Active Fieldwork Management, 434

10.4. Paradata, 436

10.5. Monitoring the Quality of the Response, 440

10.5.1. A Classification of Data Collection Outcome Codes, 441

10.5.2. Response and Return Rates at Unit Level, 445

10.5.3. Weights of Businesses, 447

10.5.4. Distribution of the Response: Representativeness, 448

10.5.5. Quality of the Measurements, 449

10.6. Monitoring the Survey Production Process, 451

10.7. Survey Evaluation and Quality Report, 454

10.8. Summary, 456

Acknowledgment, 457

11. Capturing, Coding, and Cleaning Survey Data 459
Jacqui Jones and Mike Hidiroglou

11.1. Introduction, 459

11.2. Data Capture, 461

11.2.1. Step One: Initial Data Capture, 461

11.2.2. Minimizing, Measuring, and Monitoring Errors in Initial Data Capture, 462

11.2.3. Step Two: Data Transfer, 464

11.2.4. Step Three: Data Capture Processing, 465

11.2.5. Capturing Data from Paper Questionnaires, 466

11.2.6. Electronic Scanning, 468

11.2.7. Design of Paper Self-Completion Questionnaires for Electronic Scanning, 468

11.2.8. Instructions for Completing a Paper Self-Completion Questionnaire to be Scanned, 471

11.2.9. Minimizing, Measuring, and Monitoring Errors in Data Capture Processing, 473

11.3. Data Coding, 475

11.3.1. Coding Descriptions to Classifications, 478

11.3.2. Questionnaire Design Implications, 478

11.3.3. Two Examples of Data Coding Tools, 480

11.3.4. Minimizing, Measuring, and Monitoring Errors in Data Coding, 481

11.4. Editing and Imputation, 483

11.4.1. An Overview of Editing and Imputation in Business Surveys, 483

11.4.2. Microediting, 485

11.4.3. Types of Microdata Error, 485

11.4.4. Methods for Identifying Microerrors, 489

11.4.5. Treatment of Microdata Errors, 492

11.4.6. Identifying Errors at the Macroediting Stage, 496

11.4.7. Imputation, 498

11.4.8. Minimizing, Measuring, and Monitoring Errors in Editing and Imputation, 500

11.4.9. Editing Strategies, 502

11.5. Concluding Comments, 503

Acknowledgments, 504

12. From Survey Data to Statistics 505
Jacqui Jones

12.1. Introduction, 505

12.2. Analysis, 505

12.2.1. Analysis and Different Types of Statistical Outputs, 507

12.2.2. Analytical Technique Considerations, 510

12.2.3. Analytical Techniques for Improving Statistical Interpretation, 513

12.3. Preparing for Dissemination, 527

12.3.1. Preparing Statistical Tables and Datasets, 528

12.3.2. The Importance of Maintaining Confidentiality, 530

12.3.3. Disclosure Control Methods, 532

12.3.4. Presenting the Statistics, 536

12.4. Dissemination, 539

12.4.1. Evaluating Statistical Dissemination, 539

12.5. Archiving, 540

12.6. Concluding Comments, 542

Acknowledgments, 543

References 545

Index 597