Assessing the quality of survey data
著者
書誌事項
Assessing the quality of survey data
（Research methods for social scientists）
SAGE, 2012
- : pbk
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注記
Includes bibliographical references (p. [164]-172) and index
内容説明・目次
内容説明
This is a book for any researcher using any kind of survey data. It introduces the latest methods of assessing the quality and validity of such data by providing new ways of interpreting variation and measuring error. By practically and accessibly demonstrating these techniques, especially those derived from Multiple Correspondence Analysis, the authors develop screening procedures to search for variation in observed responses that do not correspond with actual differences between respondents. Using well-known international data sets, the authors exemplify how to detect all manner of non-substantive variation having sources such as a variety of response styles including acquiescence, respondents' failure to understand questions, inadequate field work standards, interview fatigue, and even the manufacture of (partly) faked interviews.
目次
Conceptualizing Data Quality: Respondent Attributes, Study Architecture, and Institutional Practices
Conceptualizing Response Quality
Study Architecture
Institutional Quality Control Practices
Data Screening Methodology
Chapter Outline
Empirical Findings on Quality and Comparability of Survey Data
Response Quality
Approaches to Detecting Systematic Response Errors
Questionnaire Architecture
Cognitive Maps in Cross-Cultural Perspective
Conclusion
Statistical Techniques for Data Screening
Principal Component Analysis
Categorical Principal Component Analysis
Multiple Correspondence Analysis
Conclusion
Institutional Quality Control Practices
Detecting Procedural Deficiencies
Data Duplication
Detecting Faked and Partly Faked Interviews
Data Entry Errors
Conclusion
Substantive or Methodology-Induced Factors? A Comparison of PCA, CatPCA and MCA Solutions
Descriptive Analysis of Personal Feelings Domain
Rotation and Structure of Data
Conclusion
Item Difficulty and Response Quality
Descriptive Analysis of Political Efficacy Domain
Detecting Patterns with Subset Multiple Correspondence Analysis
Moderator Effects
Conclusion
Questionnaire Architecture
Fatigue Effect
Question Order Effects
Measuring Data Quality: The Dirty Data Index
Conclusion
Cognitive Competencies and Response Quality
Data and Measures
Response Quality, Task Simplification and Complexity of Cognitive Maps
Conclusion
Conclusion
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