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 1.3.2.1. Survey Errors and the Process Quality Approach, 5 1.3.2.2. Response Burden, 7 1.3.2.3. Trust, 7 1.3.3. A Brief Historical Look at Surveys, 8 1.4. Types of Business Data Outputs, 10 1.4.1. Official Statistics, 10 1.4.1.1. National Accounts, 11 1.4.1.2. Structural Business Statistics, 12 1.4.1.3. Short-Term Statistics, 13 1.4.1.4. International and European Comparisons, 13 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 1.7.2.1. Business Survey Constraints, Considerations, and Tradeoffs, 23 1.7.2.2. Unique Features of Business Surveys, 25 1.7.3. Perspectives of the Business Survey Process, 26 1.7.3.1. Survey Organization Perspective, 26 1.7.3.2. Respondent Perspective, 27 1.7.3.3. User Perspective, 28 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 2.1.3.1. How Work is Accomplished, 49 2.1.3.2. Social Behavioral Dimensions of Work, 49 2.1.3.3. Accomplishing the Work of Survey Response, 50 2.2. A Comprehensive Approach Integrating the Business Context and the Survey Response Process, 51 2.2.1. The Decision to Participate, 54 2.2.1.1. External Environment Factors, 54 2.2.1.2. Business/Management Factors, 55 2.2.2. Performing Response Tasks, 60 2.2.2.1. Organizational Context for Performing Response Tasks, 61 2.2.2.2. Organizational Context and Social Behavior, 66 2.2.2.3. Organizational Context and the Cognitive Response Process, 69 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 3.1.2.1. Quality Constraints, 90 3.1.2.2. Survey-Related Effects, 93 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 3.2.6.1. The Significance of Legal Obligations, 104 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 3.3.2.1. The Business Survey Context, 110 3.3.2.2. Question and Questionnaire Elements, 112 3.3.2.3. The Content of Business Questionnaires, 116 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 4.3.2.1. The Project Plan, 135 4.3.2.2. The Business Survey Production Environment, 138 4.3.3. Planning the Fieldwork, 142 4.3.4. Planning the Designing, Building, and Testing of the Survey Components, 143 4.3.4.1. Planning Mode(s) of Data Collection and Questionnaire Design, 143 4.3.4.2. Planning the Sample Design and Estimation, 146 4.3.4.3. Planning the Survey Communication Strategy Design, 147 4.3.4.4. Planning the Data Collection Process, 148 4.3.4.5. Planning the Data Processing Procedures, 150 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 5.2.1.1. Sources for Constructing Registers, 170 5.2.1.2. Classification, 170 5.2.1.3. Frames from Registers, 171 5.2.1.4. Updating Registers, 171 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 5.3.2.1. Stratification Variables, 179 5.3.2.2. Defining the Strata, 180 5.3.2.3. Sample Allocation, 188 5.3.2.4. Extra Details of Allocation, 191 5.3.2.5. Multivariate and Multipurpose Sampling, 193 5.3.2.6. Numerical Methods and Software for Stratification, 194 5.3.2.7. Sample Selection, 194 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 5.3.6.1. Product Surveys and Other Detailed Breakdowns of Activity, 199 5.3.6.2. Price Index Surveys, 200 5.3.6.3. Surveys for Rare Characteristics ("Needle in a Haystack" Surveys), 201 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 6.4.2.1. Common Frame and Regime, 244 6.4.2.2. Effects on Perceived Response Burden, 245 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 7.2.2.1. Top--Down Theory-Driven Approaches, 263 7.2.2.2. Bottom--Up Data-Driven Approaches, 264 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 8.3.1.1. Creating Trust, 314 8.3.1.2. Assigning Tasks, 315 8.3.2. Common Access to Several Web Surveys, 317 8.3.2.1. Simple and Advanced Business Web Portals, 317 8.3.2.2. Visual Design of the Web Portal, 320 8.3.2.3. Data Collection Instruments for Small Companies and Small Questionnaires, 323 8.3.2.4. Mobile Data Collection Instruments in a Mixed-Mode Design, 324 8.3.3. Layout and Functional Tailoring for Internal Data Collection, 325 8.3.3.1. Guiding the Questionnaire to the Right Respondent(s), 325 8.3.3.2. Guidelines and Tailoring Tools in Web Questionnaires, 325 8.3.3.3. Intercomputer Communication, 331 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 8.4.3.1. How Question Order Affects Comprehension, 340 8.4.3.2. Implicit Expectations, 341 8.4.3.3. Looking for Hints, 341 8.4.3.4. Talking with Web Questionnaires, 345 8.5. Minimizing Response Burden, 346 8.5.1. Time Saving Measures, 347 8.5.1.1. IT Tools, 348 8.5.1.2. Presentations, 350 8.5.2. Reducing the Cognitive Burden, 351 8.5.2.1. From Preprinting to Dependent Dialogs, 351 8.5.2.2. Manageable Matrices, 353 8.5.2.3. Computerized Matrices, 355 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 9.2.3.1. Prefield Survey Communication Stage, 365 9.2.3.2. Field Survey Communication Stage, 368 9.2.3.3. Postfield Survey Communication Stage, 371 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 9.4.2.1. Informed Consent and Confidentiality, 383 9.4.2.2. Mandatory Surveys, 387 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 9.5.3.1. Motivation Strategies, 401 9.5.3.2. Facilitation Strategies, 407 9.6. Business Survey Communication Measures, Practices, and Guidelines, 407 9.6.1. Prefield Communication Measures, 408 9.6.1.1. Measures for Increasing the Chances of Establishing Contact, 408 9.6.1.2. Measures for Introducing the Survey and Seeking Cooperation, 410 9.6.1.3. Reducing Response Burden, 412 9.6.2. Field Communication Measures, 412 9.6.2.1. Making Contact, Dispatching the Questionnaire, and Informing the Business about the Survey, 412 9.6.2.2. Measures for Increasing Survey Participation, 414 9.6.2.3. Providing Assistance with Questions and Complaints: Help Desk, Website, Web Care, and Ombudsman, 416 9.6.2.4. Nonresponse Follow-ups and Refusal Conversion, 418 9.6.2.5. Measures for Maintaining a Relationship, 420 9.6.3. Special Cases: Large Businesses and Mixed-Mode Designs, 421 9.6.3.1. Dealing with Large and Multisurveyed Businesses, 421 9.6.3.2. Introducing Web Mixed-Mode Designs, 422 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 11.4.4.1. Data Validation Edits, 490 11.4.4.2. Statistical Edits, 491 11.4.5. Treatment of Microdata Errors, 492 11.4.6. Identifying Errors at the Macroediting Stage, 496 11.4.6.1. Macroediting Methods, 496 11.4.7. Imputation, 498 11.4.7.1. Deterministic Imputation Methods, 499 11.4.7.2. Stochastic Imputation Methods, 500 11.4.8. Minimizing, Measuring, and Monitoring Errors in Editing and Imputation, 500 11.4.8.1. Editing Quality Process Measures, 501 11.4.8.2. Imputation Quality Process Measures, 502 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 12.2.2.1. Analyzing Different Types of Data, 510 12.2.2.2. Aggregate and Microlevel Data Analysis, 510 12.2.2.3. Analysis of Time Series Data, 511 12.2.2.4. Analyzing Single- or Multicomponent Series, 511 12.2.2.5. Analyzing Data with Different Sample Designs, 512 12.2.2.6. Pitfalls to Avoid in Statistical Analysis, 512 12.2.3. Analytical Techniques for Improving Statistical Interpretation, 513 12.2.3.1. Seasonal Adjustment, 513 12.2.3.2. Index Numbers, 518 12.2.3.3. Values and Volumes, 521 12.2.3.4. Consumer Price Index (CPI), 524 12.2.3.5. Retail Prices Index (RPI), 525 12.2.3.6. Producer Price Index (PPI), 525 12.2.3.7. Services Producer Price Index (SPPI), 526 12.2.3.8. Import and Export Indices, 526 12.2.3.9. Implied Deflators, 527 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 12.3.3.1. Microdata Disclosure Control Methods, 532 12.3.3.2. Tabular Disclosure Control Methods, 533 12.3.4. Presenting the Statistics, 536 12.3.4.1. Writing Statistical Commentary, 536 12.3.4.2. Identifying the Headline Message, 537 12.3.4.3. Information to Support Disseminated Statistics, 537 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