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El. knyga: Survey Methodology and Missing Data: Tools and Techniques for Practitioners

  • Formatas: EPUB+DRM
  • Išleidimo metai: 05-Jul-2018
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319790114
Kitos knygos pagal šią temą:
Survey Methodology and Missing Data: Tools and Techniques for PractitionersDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Išleidimo metai: 05-Jul-2018
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783319790114
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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. 

1 Introduction
1(4)
References
4(1)
2 Concept of Survey and Key Survey Terms
5(22)
2.1 What Is a Survey?
5(1)
2.2 Five Populations in Surveys
6(3)
2.3 The Purpose of Populations
9(1)
2.4 Cross-Sectional Survey Micro Data
10(5)
2.4.1 Specific Examples of Problems in the Data File
11(4)
2.5 X Variables--Auxiliary Variables in More Detail
15(3)
2.6 Summary of the Terms and the Symbols in Chap. 2
18(1)
2.7 Transformations
18(9)
References
26(1)
3 Designing a Questionnaire and Survey Modes
27(22)
3.1 What Is Questionnaire Design?
28(2)
3.2 One or More Modes in One Survey?
30(3)
3.3 Questionnaire and Questioning
33(2)
3.4 Designing Questions for the Questionnaire
35(1)
3.5 Developing Questions for the Survey
36(4)
3.6 Satisficing
40(2)
3.7 Straightlining
42(2)
3.8 Examples of Questions and Scales
44(5)
References
47(2)
4 Sampling Principles, Missingness Mechanisms, and Design Weighting
49(28)
4.1 Basic Concepts for Both Probability and Nonprobability Sampling
50(2)
4.2 Missingness Mechanisms
52(1)
4.3 Nonprobability Sampling Cases
53(5)
4.4 Probability Sampling Framework
58(1)
4.5 Sampling and Inclusion Probabilities
58(10)
4.6 Illustration of Stratified Three-Stage Sampling
68(1)
4.7 Basic Weights of Stratified Three-Stage Sampling
68(3)
4.8 Two Types of Sampling Weights
71(6)
References
76(1)
5 Design Effects at the Sampling Phase
77(14)
5.1 DEFF Because of Clustering, DEFFc
79(3)
5.2 DEFF Because of Varying Inclusion Probabilities, DEFFp
82(1)
5.3 The Entire Design Effect: DEFF and Gross Sample Size
83(1)
5.4 How Should the Sample Size Be Decided, and How Should the Gross Sample Be Allocated into Strata?
84(7)
References
89(2)
6 Sampling Design Data File
91(8)
6.1 Principles of the Sampling Design Data File
92(2)
6.2 Test Data Used in Several Examples in this Book
94(5)
References
97(2)
7 Missingness, Its Reasons and Treatment
99(12)
7.1 Reasons for Unit Non-response
101(1)
7.2 Coding of Item Non-responses
102(1)
7.3 Missingness Indicator and Missingness Rate
102(4)
7.4 Response Propensity Models
106(5)
References
110(1)
8 Weighting Adjustments Because of Unit Non-response
111(24)
8.1 Actions of Weighting and Reweighting
112(1)
8.2 Introduction to Reweighting Methods
112(1)
8.3 Post-stratification
113(4)
8.4 Response Propensity Weighting
117(5)
8.5 Comparisons of Weights in Other Surveys
122(2)
8.6 Linear Calibration
124(3)
8.7 Non-linear Calibration
127(4)
8.8 Summary of All the Weights
131(4)
References
133(2)
9 Special Cases in Weighting
135(6)
9.1 Sampling of Individuals and Estimates for Clusters Such as Households
136(1)
9.2 Cases Where Only Analysis Weights Are Available Although Proper Weights Are Required
137(1)
9.3 Sampling and Weights for Households and Estimates for Individuals or Other Subordinate Levels
137(1)
9.4 Panel Over Two Years
138(3)
Reference
140(1)
10 Statistical Editing
141(14)
10.1 Edit Rules and Ordinary Checks
142(2)
10.2 Some Other Edit Checks
144(1)
10.3 Satisficing in Editing
145(1)
10.4 Selective Editing
145(1)
10.5 Graphical Editing
146(1)
10.6 Tabular Editing
147(1)
10.7 Handling Screening Data during Editing
147(1)
10.8 Editing of Data for Public Use
147(8)
References
153(2)
11 Introduction to Statistical Imputation
155(16)
11.1 Imputation and Its Purpose
157(2)
11.2 Targets for Imputation Should Be Clearly Specified
159(1)
11.3 What Can Be Imputed as a Result of Missingness?
160(1)
11.4 `Aggregate Imputation'
160(2)
11.5 The Most Common Tools for Handling Missing Items Without Proper Imputation
162(4)
11.6 Several Imputations for the Same Micro Data
166(5)
References
169(2)
12 Imputation Methods for Single Variables
171(26)
12.1 Imputation Process
172(1)
12.2 The Imputation Model
173(2)
12.3 Imputation Tasks
175(2)
12.4 Nearness Metrics for Real-Donor Methods
177(1)
12.5 Possible Editing After the Model-Donor Method
178(1)
12.6 Single and Multiple Imputation
179(3)
12.7 Examples of Deterministic Imputation Methods for a Continuous Variable
182(8)
12.8 Examples of Deterministic Imputation Methods for a Binary Variable
190(1)
12.9 Example for a Continuous Variable When the Imputation Model Is Poor
191(2)
12.10 Interval Estimates
193(4)
References
194(3)
13 Summary and Key Survey Data-Collection and Cleaning Tasks
197(4)
14 Basic Survey Data Analysis
201(18)
14.1 `Survey Instruments' in the Analysis
202(1)
14.2 Simple and Demanding Examples
203(13)
14.2.1 Sampling Weights That Vary Greatly
203(1)
14.2.2 Current Feeling About Household Income, with Two Types of Weights
204(1)
14.2.3 Examples Based on the Test Data
205(3)
14.2.4 Example Using Sampling Weights for Cross-Country Survey Data Without Country Results
208(1)
14.2.5 The PISA Literacy Scores
209(2)
14.2.6 Multivariate Linear Regression with Survey Instruments
211(3)
14.2.7 A Binary Regression Model with a Logit Link
214(2)
14.3 Concluding Remarks About Results Based on Simple and Complex Methodology
216(3)
References
217(2)
Further Reading 219(4)
Index 223
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.
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