The principles and concepts of survey measurement quality Issues of survey quality have become increasingly more prominent in recent years. As more and more professionals who are not necessarily trained as survey researchers take on tasks associated with surveys, the need arises for a grounded, basic introduction to current survey methods and quality issues associated with them. Introduction to Survey Quality summarizes the history of survey research and outlines the essential concepts for data quality. With an emphasis on total survey error, authors Paul Biemer and Lars Lyberg review…mehr
The principles and concepts of survey measurement quality Issues of survey quality have become increasingly more prominent in recent years. As more and more professionals who are not necessarily trained as survey researchers take on tasks associated with surveys, the need arises for a grounded, basic introduction to current survey methods and quality issues associated with them. Introduction to Survey Quality summarizes the history of survey research and outlines the essential concepts for data quality. With an emphasis on total survey error, authors Paul Biemer and Lars Lyberg review well-established, as well as recently developed principles and concepts in the field, and examine important issues that are still unresolved and being actively pursued in the current survey methods literature. Spanning a range of topics dealing with the quality of data collected through the survey process, they focus on such key issues as: * Major sources of survey error, examining the origins of each error source and the most successful methods for reducing errors from those sources * Methods most often used in practice for evaluating the effects of the source on total survey error * Implications of improving survey quality for organizational management and cost Introduction to Survey Quality is written for a broad audience that includes experienced survey researchers who would benefit from a better understanding of survey data quality as well as others with little or no prior training in survey methods. It is both a useful road map to the issues of survey measurement encountered in survey work and an essential guide to practical methods for improving the quality of survey data.
PAUL P. BIEMER, PhD, is a distinguished Fellow at RTIInternational, and Assistant Director for Survey Research at theOdum Institute for Research in Social Science at the University ofNorth Carolina at Chapel Hill. LARS E. LYBERG, PhD, is Chief Scientist at StatisticsSweden. They both have co-edited, with others, Measurement Errorsin Surveys, Survey Measurement and Process Quality, and TelephoneSurvey Methodology (all published by Wiley).
Inhaltsangabe
Preface.
Chapter 1. The Evolution of Survey Process Quality.
1.1 The Concept of a Survey.
1.2 Types of Surveys.
1.3 Brief History of Survey Methodology.
1.4 The Quality Revolution.
1.5 Definitions of Quality and Quality in StatisticalOrganizations.
1.6 Measuring Quality.
1.7 Improving Quality.
1.8 Quality in a Nutshell.
Chapter 2. The Survey Process and Data Quality.
2.1 Overview of the Survey Process.
2.2 Data Quality and Total Survey Error.
2.3 Decomposing Nonsampling Error into Its Component Parts.
2.4 Gauging the Magnitude of Total Survey Error.
2.5 Mean Squared Error.
2.6 An Illustration of the Concepts.
Chapter 3. Coverage and Nonresponse Error.
3.1 Coverage Error.
3.2 Measures of Coverage Bias.
3.3 Reducing Coverage Bias.
3.4 Unit Nonresponse Error.
3.5 Calculating Response Rates.
3.6 Reducing Nonresponse Bias.
Chapter 4. The Measurement Process and Its Implications forQuestionnaire Design.
4.1Components of Measurement Error.
4.2 Errors Arising from the Questionnaire Design.
4.3 Understanding the Response Process.
Chapter 5. Errors Due to Interviewers and Interviewing.
5.1 Role of the Interviewer.
5.2 Interviewer Variability.
5.3 Design Factors that Influence Interviewer Effects.
5.4 Evaluation of Interviewer Performance.
Chapter 6. Data Collection Modes and Associated Errors.
6.1 Modes of Data Collection.
6.2 Decision Regarding Mode.
6.3 Some Examples of Mode Effects.
Chapter 7. Data Processing: Errors and Their Control.
7.1 Overview of Data Processing Steps.
7.2 Nature of Data Processing Error.
7.3 Data Capture Errors.
7.4 Post-Data Capture Editing.
7.5 Coding.
7.6 File Preparation.
7.7 Applications of Continuous Quality Improvement: The Case ofCoding.
7.8 Integration Activities.
Chapter 8. Overview of Survey Error Evaluation Methods.
8.1 Purposes of Survey Error Evaluation.
8.2 Evaluation Methods for Designing and Pretesting Surveys.
8.3 Methods for Monitoring and Controlling Data Quality.
8.4 Postsurvey Evaluations.
8.5 Summary of Evaluation Methods.
Chapter 9. Sampling Error.
9.1 Brief History of Sampling.
9.2 Nonrandom Sampling Methods.
9.3 Simple Random Sampling.
9.4 Statistical Inference in the Presence of NonsamplingErrors.
9.5 Other Methods of Random Sampling.
9.6 Concluding Remarks.
Chapter 10.1 Practical Survey Design for Minimizing Total SurveyError.
10.1 Balance Between Cost, Survey Error, and Other QualityFeatures.
10.2 Planning a Survey for Optimal Quality.
10.3 Documenting Survey Quality.
10.4 Organizational Issues Related to Survey Quality.
