Survey Methodology and Missing Data: Tools and Techniques for Practitioners (Hardcover, 2018)

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 framework 4.5 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 rate 7.4 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 editing 10.1 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 imputation 12.7 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 cleaning 14. 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