General Information

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Table of Contents

Chapter 1: Conceptualizing data quality: Respondent attributes, study architecture and institutional practices

1. Conceptualizing response quality
2. Study architecture
3. Institutional quality control practices
4. Data screening methodology
5. Chapter outline

Chapter 2: Empirical findings on quality and comparability of survey data

1. Response quality
2. Approaches to detecting systematic response errors
3. Questionnaire architecture
4. Cognitive maps in cross-cultural perspective
5. Conclusion

Chapter 3: Statistical techniques for data screening

1. Principal component analysis
2. Categorical principal component analysis
3. Multiple correspondence analysis
4. Conclusion

Chapter 4: Institutional quality control practices

1. Detecting procedural deficiencies
2. Data duplication
3. Detecting faked and partly faked interviews
4. Data entry errors
5. Conclusion

Chapter 5: Substantive or methodology-induced factors? A comparison of PCA, CatPCA and MCA solutions

1. Descriptive analysis of personal feelings domain
2. Rotation and structure of data
3. Conclusion

Chapter 6: Item difficulty and response quality

1. Descriptive analysis of political efficacy domain
2. Detecting patterns with subset multiple correspondence analysis
3. Moderator effects
4. Conclusion

Chapter 7: Questionnaire architecture

1. Fatigue effect
2. Question order effects
3. Measuring data quality: The dirty data index
4. Conclusion

Chapter 8: Cognitive competencies and response quality

1. Data and measures
2. Response quality, task simplification, and complexity of cognitive maps
3. Conclusion

Chapter 9: Conclusion

References

Index