Survey Methodology and Missing Data

1. Introduction 2. Concept of survey and key survey terms2.1 What is survey?2.2 Five populations in surveys2.3 Purpose of populations2.4 Cross-sectional survey micro data2.5 X variables, auxiliary variables in more details2.6 Summary of the terms and the symbols of Chapter 22.7 Transformations3. Designing a questionnaire and survey modes3.1 What is questionnaire design?3.2 One or more modes in one survey?3.3 Questionnaire and questioning3.4 Designing questions for the questionnaire3.5 Developing questions for the survey3.6 Satisficing3.7 Straightlining3.8 Examples on questions and scales4. Sampling principles and missingness mechanisms4.1 Basic concepts, both for probability and non-probability sampling4.2 Missingness mechanisms4.3 Non-probability sampling cases4.4 Probability sampling framework4.5 Sampling and inclusion probabilities4.6 Illustration of the stratified three-stage sampling4.7 Basic weights of stratified three-stage sampling4.8 Two types of sampling weights5. Design effects at sampling phase5.1 DEFF due to clustering = DEFFc5.2 DEFF due to varying inclusion probabilities = DEFFp5.3 The entire design effect - DEFF, and the gross sample size5.4 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 treatment7.1 Reasons for unit nonresponse7.2 Coding of item nonresponse7.3 Missingness indicator and missingness rate7.4 Response propensity models8. Weighting adjustments due to unit missingness8.1 Actions of weighting and reweighting8.2 Introduction to re-weighting methods8.3 Post-stratification8.4 Response propensity weighting8.5. Comparisons of weights in other surveys8.6 Linear calibration8.7 Non-linear calibration8.8 Summary of all the weights9. Special cases in weighting9.1 Sampling of individuals, estimates for clusters such as households9.2 If analysis weights only are available but the proper weights are required9.3 Sampling and weights for households, estimates for individuals or other lower level9.4 Panel of two years10. Statistical editing10.1 Edit Rules and ordinary checks10.2 Other edit checks10.3 Satisficing in editing10.4 Selective editing10.5 Graphical editing10.6 Tabular editing10.7 Handling screening data in editing10.8 Editing not always completely done for public use data11. Introduction to statistical imputation11.1 Imputation and its purpose11.2 Targets for imputation should be specified clearly11.3 What can be imputed due to missingness?11.4 `Aggregate imputation'11.5 Most common tools for missing item handling without proper imputation11.6 Several imputations for the same micro data12. Imputation methods for single variables12.1 Imputation process12.2. Imputation model12.3. Imputation task12.4. Nearness metrics of real-donor methods12.5. Post-Editing after the model-donor method possibly12.6 Single and multiple imputation12.7 Examples of Deterministic imputation methods for a continuous variable12.8 Example of deterministic imputation methods for a binary variable12.9 Example of the continuous variable when the imputation model is poor12.10 Interval estimates13. Summary and key tasks of survey data cleaning14. Basic survey data analysis14.1 `Survey instruments' in the analysis14.2 Simple and demanding examples14.2.1 The sampling weights vary much14.2.2 Feeling about household's income nowadays with two types of weights14.2.3 Examples based on the test data (Chapter 6)14.2.4 Using sampling weights for cross-country survey data without country results14.2.5 The PISA literacy scores14.2.6 Multivariate linear regression with survey instruments14.2.7 The binary regression model with logit link14.3 Concluding remarks about the results based on simple and complex methodology