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Survey Methodology and Missing Data - Tools and Techniques for Practitioners
Seppo Laaksonen
Verlag Springer-Verlag, 2018
ISBN 9783319790114 , 228 Seiten
Format PDF, OL
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Geräte
Preface
5
Contents
8
1: Introduction
12
References
15
2: Concept of Survey and Key Survey Terms
16
2.1 What Is a Survey?
16
2.2 Five Populations in Surveys
17
A Multiframe Example
19
2.3 The Purpose of Populations
20
2.4 Cross-Sectional Survey Micro Data
21
2.4.1 Specific Examples of Problems in the Data File
22
2.5 X Variables-Auxiliary Variables in More Detail
26
2.6 Summary of the Terms and the Symbols in Chap. 2
29
2.7 Transformations
29
Example 2.1 Summary Variable with Linear Transformations
30
Example 2.2 Summary/Compound Variable Using Exploratory Factor Analysis and Factor Scores
33
References
37
3: Designing a Questionnaire and Survey Modes
38
3.1 What Is Questionnaire Design?
39
3.2 One or More Modes in One Survey?
41
Examples of Mixed-Mode Surveys
42
Estonian Pilot Mixed-Mode Survey 2012 for the ESS
42
Mode Effects in Estimates
43
3.3 Questionnaire and Questioning
44
3.4 Designing Questions for the Questionnaire
46
3.5 Developing Questions for the Survey
47
Example 3.1 Instance in Which the Scale Was Kept Similar to Earlier Social Surveys
49
Example 3.2 Screening Example of the Finnish Security Survey
50
3.6 Satisficing
51
3.7 Straightlining
53
Example 3.3 Textual Versus Coded Categories
54
3.8 Examples of Questions and Scales
55
Example 3.4 Two Alternative Lengths of Scales
55
Example 3.5 Different Scales for `Happiness´ in the Two Questionnaires
56
References
58
4: Sampling Principles, Missingness Mechanisms, and Design Weighting
59
4.1 Basic Concepts for Both Probability and Nonprobability Sampling
60
4.2 Missingness Mechanisms
62
4.3 Nonprobability Sampling Cases
63
4.4 Probability Sampling Framework
68
4.5 Sampling and Inclusion Probabilities
68
Implicit Stratification
71
PPS with Replacement, with a Valid Inclusion Probability
72
Example 4.1 ESS Sampling of Dwellings
74
Example 4.2 Inclusion Probabilities and Weights of the Test Data with Three-Stage Cluster Design
76
4.6 Illustration of Stratified Three-Stage Sampling
78
4.7 Basic Weights of Stratified Three-Stage Sampling
78
Example 4.3 Basic Weights of the Test Data for the Cluster Domain (see Sect. 6.2)
80
4.8 Two Types of Sampling Weights
81
Example 4.4 The Weights of the 2012 PISA Survey
82
References
86
5: Design Effects at the Sampling Phase
87
5.1 DEFF Because of Clustering, DEFFc
89
5.2 DEFF Because of Varying Inclusion Probabilities, DEFFp
92
Example 5.1 Design Effects Because of Unequal Weights in the Test Data, by Eight Strata
92
Example 5.2 Design Effects Because of Unequal Weights Based on the Design Weights in Some Countries of the ESS, Round 6; Count...
93
5.3 The Entire Design Effect: DEFF and Gross Sample Size
93
5.4 How Should the Sample Size Be Decided, and How Should the Gross Sample Be Allocated into Strata?
94
Example 5.3 Components of the Design Effect for the Variable `Plausible Value of Science Literacy´ in PISA, 2015
96
References
99
6: Sampling Design Data File
100
6.1 Principles of the Sampling Design Data File
101
6.2 Test Data Used in Several Examples in this Book
103
References
106
7: Missingness, Its Reasons and Treatment
107
7.1 Reasons for Unit Non-response
109
7.2 Coding of Item Non-responses
110
7.3 Missingness Indicator and Missingness Rate
110
7.4 Response Propensity Models
114
Example 7.1 Propensity Model for Item Response
115
Example 7.2 Response Propensity Probit Model of the Finnish Security Survey
116
References
118
8: Weighting Adjustments Because of Unit Non-response
119
Advance Reading
119
8.1 Actions of Weighting and Reweighting
120
8.2 Introduction to Reweighting Methods
120
8.3 Post-stratification
121
Example 8.1 Post-stratification in the Test Data of the SRS Domain
124
8.4 Response Propensity Weighting
125
Example 8.2 The Response Propensity Weighting of the Test SDDF Data
128
8.5 Comparisons of Weights in Other Surveys
130
8.6 Linear Calibration
132
Example 8.3 From the Basic Weights to Linear Calibration in the Test Data (Continued from Example 8.2)
133
8.7 Non-linear Calibration
135
Example 8.4 Comparison of Four Weights in Simulated Data
136
8.8 Summary of All the Weights
139
References
141
9: Special Cases in Weighting
143
9.1 Sampling of Individuals and Estimates for Clusters Such as Households
144
9.2 Cases Where Only Analysis Weights Are Available Although Proper Weights Are Required
145
9.3 Sampling and Weights for Households and Estimates for Individuals or Other Subordinate Levels
145
9.4 Panel Over Two Years
146
Example 9.1 Income Changes in a Two-year Panel
147
Reference
148
10: Statistical Editing
149
10.1 Edit Rules and Ordinary Checks
150
10.2 Some Other Edit Checks
152
10.3 Satisficing in Editing
153
10.4 Selective Editing
153
10.5 Graphical Editing
154
10.6 Tabular Editing
155
10.7 Handling Screening Data during Editing
155
10.8 Editing of Data for Public Use
155
Example 10.1 Cross-Tabulation of the Two Categorical Variables for Logical Checking (Two-Dimensional Edit Rule)
157
References
161
11: Introduction to Statistical Imputation
162
Advance Reading
163
11.1 Imputation and Its Purpose
164
11.2 Targets for Imputation Should Be Clearly Specified
166
11.3 What Can Be Imputed as a Result of Missingness?
167
11.4 `Aggregate Imputation´
167
11.5 The Most Common Tools for Handling Missing Items Without Proper Imputation
169
Example 11.1 Multivariate Linear Regression for `Happiness by age´ Using the ESS
170
11.6 Several Imputations for the Same Micro Data
173
Example 11.2 Possible Imputation Strategies in the Case of Item Non-responses of Five Variables
173
References
176
12: Imputation Methods for Single Variables
177
12.1 Imputation Process
178
12.2 The Imputation Model
179
12.3 Imputation Tasks
181
12.4 Nearness Metrics for Real-Donor Methods
183
12.5 Possible Editing After the Model-Donor Method
184
12.6 Single and Multiple Imputation
185
Example 12.1 PISA `Multiple Imputation´
187
12.7 Examples of Deterministic Imputation Methods for a Continuous Variable
188
Special Cases and an Example on Real Donors
191
12.8 Examples of Deterministic Imputation Methods for a Binary Variable
196
12.9 Example for a Continuous Variable When the Imputation Model Is Poor
197
12.10 Interval Estimates
199
References
200
13: Summary and Key Survey Data-Collection and Cleaning Tasks
202
14: Basic Survey Data Analysis
206
14.1 `Survey Instruments´ in the Analysis
207
14.2 Simple and Demanding Examples
208
14.2.1 Sampling Weights That Vary Greatly
208
14.2.2 Current Feeling About Household Income, with Two Types of Weights
209
14.2.3 Examples Based on the Test Data
210
14.2.4 Example Using Sampling Weights for Cross-Country Survey Data Without Country Results
213
14.2.5 The PISA Literacy Scores
214
14.2.6 Multivariate Linear Regression with Survey Instruments
216
14.2.7 A Binary Regression Model with a Logit Link
219
14.3 Concluding Remarks About Results Based on Simple and Complex Methodology
221
References
222
Further Reading
223
Journals Much Focused on Surveys
223
Survey Textbooks
223
Research Articles Dealing With Surveys: Calibration and Weighting
224
Research Articles Dealing With Surveys: Editing and Imputation
225
Research Articles Dealing With Surveys: Other Literature
225
Index
227
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