Assessing the quality of survey data /

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Bibliographic Details
Author / Creator:	Blasius, Jörg, 1957-
Imprint:	London ; Thousand Oaks, Calif. : Sage Publications, 2012.
Description:	xi, 174 p. : ill. ; 25 cm.
Language:	English
Subject:	Social surveys -- Methodology. Social sciences -- Statistical methods. Social sciences -- Research -- Methodology. Correspondence analysis (Statistics) Correspondence analysis (Statistics) Social sciences -- Research -- Methodology. Social sciences -- Statistical methods. Social surveys -- Methodology.
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/8689370

Hidden Bibliographic Details
Other authors / contributors:	Thiessen, Victor.
ISBN:	1849203326 9781849203326 1849203318 9781849203319
Notes:	Includes bibliographical references (p. [164]-172) and index.
Summary:	"This book introduces the latest methods for assessing the quality and validity of survey 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 show how to detect all manner of non-substantive variation from response styles including acquiescence, respondents' failure to understand questions, inadequate field work standards, interview fatigue, and even the manufacture of (partly) faked interviews."--Publisher's website.

Table of Contents:

About the authors
List of acronyms and sources of data
Preface
Chapter 1. Conceptualizing data quality: Respondent attributes, study architecture and institutional practices
1.1. Conceptualizing response quality
1.2. Study architecture
1.3. Institutional quality control practices
1.4. Data screening methodology
1.5. Chapter outline
Chapter 2. Empirical findings on quality and comparability of survey data
2.1. Response quality
2.2. Approaches to detecting systematic response errors
2.3. Questionnaire architecture
2.4. Cognitive maps in cross-cultural perspective
2.5. Conclusion
Chapter 3. Statistical techniques for data screening
3.1. Principal component analysis
3.2. Categorical principal component analysis
3.3. Multiple correspondence analysis
3.4. Conclusion
Chapter 4. Institutional quality control practices
4.1. Detecting procedural deficiencies
4.2. Data duplication
4.3. Detecting faked and partly faked interviews
4.4. Data entry errors
4.5. Conclusion
Chapter 5. Substantive or methodology-induced factors? A comparison of PCA, CatPCA and MCA solutions
5.1. Descriptive analysis of personal feelings domain
5.2. Rotation and structure of data
5.3. Conclusion
Chapter 6. Item difficulty and response quality
6.1. Descriptive analysis of political efficacy domain
6.2. Detecting patterns with subset multiple correspondence analysis
6.3. Moderator effects
6.4. Conclusion
Chapter 7. Questionnaire architecture
7.1. Fatigue effect
7.2. Question order effects
7.3. Measuring data quality: The dirty data index
7.4. Conclusion
Chapter 8. Cognitive competencies and response quality
8.1. Data and measures
8.2. Response quality, task simplification, and complexity of cognitive maps
8.3. Conclusion
Chapter 9. Conclusion
References
Index

Assessing the quality of survey data /

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