Preface.

1. Introduction.

2. Basic Sampling Techniques.

2.1 Basic definitions.

2.2 The Province’91 population.

2.3 Simple random sampling and design effect.

2.4 Systematic sampling and intra-class correlation.

2.5 Selection with probability proportional to size.

3. Further Use of Auxiliary Information.

3.1 Stratified sampling.

3.2 Cluster sampling.

3.3 Model-assisted estimation.

3.4 Efficiency comparison using design effects.

4. Handling Nonsampling Errors.

4.1 Reweighting.

4.2 Imputation.

4.3 Chapter summary and further reading.

5. Linearization and Sample Reuse in Variance Estimation.

5.1 The Mini-Finland Health Survey.

5.2 Ratio estimators.

5.3 Linearization method.

5.4 Sample reuse methods.

5.5 Comparison of variance estimators.

5.6 The Occupational Health C are Survey.

5.7 Linearization method for covariance-matrix estimation.

5.8 Chapter summary and further reading.

6. Model-assisted Estimation for Domains.

6.1 Framework for domain estimation.

6.2 Estimator type and model choice.

6.3 Construction of estimators and model specification.

6.4 Further comparison of estimators.

6.5 Chapter summary and further reading.

7. Analysis of One-way and Two-way Tables.

7.1 Introductory example.

7.2 Simple goodness-of-fit test.

7.3 Preliminaries for tests for two-way tables.

7.4 Test of homogeneity.

7.5 Test of independence.

7.6 Chapter summary and further reading.

8. Multivariate Survey Analysis.

8.1 Range of methods.

8.2 Types of models and options for analysis.

8.3 Analysis of categorical data.

8.4 Logistic and linear regression.

8.5 Chapter summary and further reading.

9. More Detailed Case Studies.

9.1 Monitoring quality in a long-term transport survey.

9.2 Estimation of mean salary in a business survey.

9.3 Model selection in a socioeconomic survey.

9.4 Multi-level modelling in an educational survey.