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Beschreibung
This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.
This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.
Über den Autor
Seppo Laaksonen is a professor of statistics at the University of Helsinki, Finland, and has worked at various survey institutes including Statistics Finland, Eurostat and The Finnish Center for Social and Health Research. The former scientific secretary (2001-2003) and vice president of the International Association of Survey Statisticians (2007-2009), he has been a member of the sampling expert team of the European Social Survey since 2001. He has also been involved in a number of European research projects and is a consultant for surveys in Moldova, Ethiopia, Slovenia, the United Kingdom and Hungary.
Zusammenfassung
Offers researchers and practitioners a comprehensive introduction to survey methodology
Presents survey data collection and cleaning methods
Explains new advanced survey methodologies and tools
Provides concrete empirical examples from real survey data sets
Covers statistical editing, imputation, nonresponse and nonresponse adjustments
Inhaltsverzeichnis
1. Introduction
2. Concept of survey and key survey terms
2.1 What is survey?
2.2 Five populations in surveys
2.3 Purpose of populations
2.4 Cross-sectional survey micro data
2.5 X variables, auxiliary variables in more details
2.6 Summary of the terms and the symbols of Chapter 2
2.7 Transformations
3. Designing a questionnaire and survey modes
3.1 What is questionnaire design?
3.2 One or more modes in one survey?3.3 Questionnaire and questioning
3.4 Designing questions for the questionnaire
3.5 Developing questions for the survey
3.6 Satisficing
3.7 Straightlining
3.8 Examples on questions and scales
4. Sampling principles and missingness mechanisms
4.1 Basic concepts, both for probability and non-probability sampling
4.2 Missingness mechanisms
4.3 Non-probability sampling cases
4.4 Probability sampling framework4.5 Sampling and inclusion probabilities
4.6 Illustration of the stratified three-stage sampling
4.7 Basic weights of stratified three-stage sampling
4.8 Two types of sampling weights
5. Design effects at sampling phase
5.1 DEFF due to clustering = DEFFc
5.2 DEFF due to varying inclusion probabilities = DEFFp
5.3 The entire design effect - DEFF, and the gross sample size
5.4 How to decide the sample size and allocate the gross sample into strata?
6. Sampling design data file
6.1 Principles of the sampling design data file
6.2 Test data used in several examples in the book
7. Missingness, its reasons and treatment
7.1 Reasons for unit nonresponse
7.2 Coding of item nonresponse
7.3 Missingness indicator and missingness rate7.4 Response propensity models
8. Weighting adjustments due to unit missingness
8.1 Actions of weighting and reweighting
8.2 Introduction to re-weighting methods
8.3 Post-stratification
8.4 Response propensity weighting
8.5. Comparisons of weights in other surveys
8.6 Linear calibration
8.7 Non-linear calibration
8.8 Summary of all the weights
9. Special cases in weighting
9.1 Sampling of individuals, estimates for clusters such as households
9.2 If analysis weights only are available but the proper weights are required
9.3 Sampling and weights for households, estimates for individuals or other lower level
9.4 Panel of two years
10. Statistical editing10.1 Edit Rules and ordinary checks
10.2 Other edit checks
10.3 Satisficing in editing
10.4 Selective editing
10.5 Graphical editing
10.6 Tabular editing
10.7 Handling screening data in editing
10.8 Editing not always completely done for public use data
11. Introduction to statistical imputation
11.1 Imputation and its purpose
11.2 Targets for imputation should be specified clearly
11.3 What can be imputed due to missingness?
11.4 'Aggregate imputation'
11.5 Most common tools for missing item handling without proper imputation
11.6 Several imputations for the same micro data
12. Imputation methods for single variables
12.1 Imputation process
12.2. Imputation model
12.3. Imputation task
12.4. Nearness metrics of real-donor methods
12.5. Post-Editing after the model-donor method possibly
12.6 Single and multiple imputation12.7 Examples of Deterministic imputation methods for a continuous variable
12.8 Example of deterministic imputation methods for a binary variable
12.9 Example of the continuous variable when the imputation model is poor
12.10 Interval estimates
13. Summary and key tasks of survey data cleaning14. Basic survey data analysis
14.1 'Survey instruments' in the analysis
14.2 Simple and demanding examples
14.2.1 The sampling weights vary much
14.2.2 Feeling about household's income nowadays with two types of weights
14.2.3 Examples based on the test data (Chapter 6)
14.2.4 Using sampling weights for cross-country survey data without country results
14.2.5 The PISA literacy scores
14.2.6 Multivariate linear regression with survey instruments
14.2.7 The binary regression model with logit link
14.3 Concluding remarks about the results based on simple and complex methodology
Details
| Medium: | Buch |
|---|---|
| Inhalt: |
xii
224 S. 10 s/w Illustr. 53 farbige Illustr. 224 p. 63 illus. 53 illus. in color. |
| ISBN-13: | 9783319790107 |
| ISBN-10: | 3319790102 |
| Sprache: | Englisch |
| Herstellernummer: | 978-3-319-79010-7 |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Laaksonen, Seppo |
| Auflage: | 1st ed. 2018 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG |
| Maße: | 241 x 160 x 19 mm |
| Von/Mit: | Seppo Laaksonen |
| Erscheinungsdatum: | 18.07.2018 |
| Gewicht: | 0,524 kg |
Über den Autor
Seppo Laaksonen is a professor of statistics at the University of Helsinki, Finland, and has worked at various survey institutes including Statistics Finland, Eurostat and The Finnish Center for Social and Health Research. The former scientific secretary (2001-2003) and vice president of the International Association of Survey Statisticians (2007-2009), he has been a member of the sampling expert team of the European Social Survey since 2001. He has also been involved in a number of European research projects and is a consultant for surveys in Moldova, Ethiopia, Slovenia, the United Kingdom and Hungary.
Zusammenfassung
Offers researchers and practitioners a comprehensive introduction to survey methodology
Presents survey data collection and cleaning methods
Explains new advanced survey methodologies and tools
Provides concrete empirical examples from real survey data sets
Covers statistical editing, imputation, nonresponse and nonresponse adjustments
Inhaltsverzeichnis
1. Introduction
2. Concept of survey and key survey terms
2.1 What is survey?
2.2 Five populations in surveys
2.3 Purpose of populations
2.4 Cross-sectional survey micro data
2.5 X variables, auxiliary variables in more details
2.6 Summary of the terms and the symbols of Chapter 2
2.7 Transformations
3. Designing a questionnaire and survey modes
3.1 What is questionnaire design?
3.2 One or more modes in one survey?3.3 Questionnaire and questioning
3.4 Designing questions for the questionnaire
3.5 Developing questions for the survey
3.6 Satisficing
3.7 Straightlining
3.8 Examples on questions and scales
4. Sampling principles and missingness mechanisms
4.1 Basic concepts, both for probability and non-probability sampling
4.2 Missingness mechanisms
4.3 Non-probability sampling cases
4.4 Probability sampling framework4.5 Sampling and inclusion probabilities
4.6 Illustration of the stratified three-stage sampling
4.7 Basic weights of stratified three-stage sampling
4.8 Two types of sampling weights
5. Design effects at sampling phase
5.1 DEFF due to clustering = DEFFc
5.2 DEFF due to varying inclusion probabilities = DEFFp
5.3 The entire design effect - DEFF, and the gross sample size
5.4 How to decide the sample size and allocate the gross sample into strata?
6. Sampling design data file
6.1 Principles of the sampling design data file
6.2 Test data used in several examples in the book
7. Missingness, its reasons and treatment
7.1 Reasons for unit nonresponse
7.2 Coding of item nonresponse
7.3 Missingness indicator and missingness rate7.4 Response propensity models
8. Weighting adjustments due to unit missingness
8.1 Actions of weighting and reweighting
8.2 Introduction to re-weighting methods
8.3 Post-stratification
8.4 Response propensity weighting
8.5. Comparisons of weights in other surveys
8.6 Linear calibration
8.7 Non-linear calibration
8.8 Summary of all the weights
9. Special cases in weighting
9.1 Sampling of individuals, estimates for clusters such as households
9.2 If analysis weights only are available but the proper weights are required
9.3 Sampling and weights for households, estimates for individuals or other lower level
9.4 Panel of two years
10. Statistical editing10.1 Edit Rules and ordinary checks
10.2 Other edit checks
10.3 Satisficing in editing
10.4 Selective editing
10.5 Graphical editing
10.6 Tabular editing
10.7 Handling screening data in editing
10.8 Editing not always completely done for public use data
11. Introduction to statistical imputation
11.1 Imputation and its purpose
11.2 Targets for imputation should be specified clearly
11.3 What can be imputed due to missingness?
11.4 'Aggregate imputation'
11.5 Most common tools for missing item handling without proper imputation
11.6 Several imputations for the same micro data
12. Imputation methods for single variables
12.1 Imputation process
12.2. Imputation model
12.3. Imputation task
12.4. Nearness metrics of real-donor methods
12.5. Post-Editing after the model-donor method possibly
12.6 Single and multiple imputation12.7 Examples of Deterministic imputation methods for a continuous variable
12.8 Example of deterministic imputation methods for a binary variable
12.9 Example of the continuous variable when the imputation model is poor
12.10 Interval estimates
13. Summary and key tasks of survey data cleaning14. Basic survey data analysis
14.1 'Survey instruments' in the analysis
14.2 Simple and demanding examples
14.2.1 The sampling weights vary much
14.2.2 Feeling about household's income nowadays with two types of weights
14.2.3 Examples based on the test data (Chapter 6)
14.2.4 Using sampling weights for cross-country survey data without country results
14.2.5 The PISA literacy scores
14.2.6 Multivariate linear regression with survey instruments
14.2.7 The binary regression model with logit link
14.3 Concluding remarks about the results based on simple and complex methodology
Details
| Medium: | Buch |
|---|---|
| Inhalt: |
xii
224 S. 10 s/w Illustr. 53 farbige Illustr. 224 p. 63 illus. 53 illus. in color. |
| ISBN-13: | 9783319790107 |
| ISBN-10: | 3319790102 |
| Sprache: | Englisch |
| Herstellernummer: | 978-3-319-79010-7 |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Laaksonen, Seppo |
| Auflage: | 1st ed. 2018 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG |
| Maße: | 241 x 160 x 19 mm |
| Von/Mit: | Seppo Laaksonen |
| Erscheinungsdatum: | 18.07.2018 |
| Gewicht: | 0,524 kg |
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