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Stay on current Cengage siteSharon L. Lohr's SAMPLING: DESIGN AND ANALYSIS, 2ND EDITION, provides a modern introduction to the field of survey sampling intended for a wide audience of statistics students. Practical and authoritative, the book is listed as a standard reference for training on real-world survey problems by a number of prominent surveying organizations. Lohr concentrates on the statistical aspects of taking and analyzing a sample, incorporating a multitude of applications from a variety of disciplines. The text gives guidance on how to tell when a sample is valid or not, and how to design and analyze many different forms of sample surveys. Recent research on theoretical and applied aspects of sampling is included, as well as optional technology instructions for using statistical software with survey data.

1. INTRODUCTION.

A Sample Controversy. Requirements of a Good Sample. Selection Bias.

Measurement Error. Questionnaire Design. Sampling and Nonsampling Errors. Exercises.

2. SIMPLE PROBABILITY SAMPLES.

Types of Probability Samples. Framework for Probability Sampling. Simple Random Sampling. Sampling Weights. Confidence Intervals. Sample Size Estimation. Systematic Sampling. Randomization Theory Results for Simple Random Sampling.

A Prediction Approach for Simple Random Sampling. When Should a Simple Random Sample Be Used? Chapter Summary. Exercises.

3. STRATIFIED SAMPLING.

What Is Stratified Sampling? Theory of Stratified Sampling. Sampling Weights in Stratified Random Sampling. Allocating Observations to Strata.

Defining Strata. Model-Based Inference for Stratified Sampling. Quota Sampling.

Chapter Summary. Exercises.

4. RATIO AND REGRESSION ESTIMATION.

Ratio Estimation in a Simple Random Sample. Estimation in Domains.

Regression Estimation in Simple Random Sampling. Poststratification.

Ratio Estimation with Stratified Samples. Model-Based Theory for Ratio and Regression Estimation. Chapter Summary. Exercises.

5. CLUSTER SAMPLING WITH EQUAL PROBABILITIES.

Notation for Cluster Sampling. One-Stage Cluster Sampling. Two-Stage Cluster Sampling. Designing a Cluster Sample. Systematic Sampling. Model-Based Inference in Cluster Sampling. Chapter Summary. Exercises.

6. SAMPLING WITH UNEQUAL PROBABILITIES.

Sampling One Primary Sampling Unit. One-Stage Sampling with Replacement.

Two-Stage Sampling with Replacement. Unequal Probability Sampling Without Replacement. Examples of Unequal Probability Samples. Randomization Theory Results and Proofs. Models and Unequal Probability Sampling. Chapter Summary.

Exercises.

7. COMPLEX SURVEYS.

Assembling Design Components. Sampling Weights. Estimating a Distribution Function. Plotting Data from a Complex Survey. Univariate Plots. Design Effects.

The National Crime Victimization Survey. Sampling and Experiment Design.

Chapter Summary. Exercises.

8. NONRESPONSE.

Effects of Ignoring Nonresponse. Designing Surveys to Reduce Nonsampling Errors.

Callbacks and Two-Phase Sampling. Mechanisms for Nonresponse.

Weighting Methods for Nonresponse. Imputation. Parametric Models for Nonresponse. What Is an Acceptable Response Rate? Chapter Summary. Exercises.

9. VARIANCE ESTIMATION IN COMPLEX SURVEYS.

Linearization (Taylor Series) Methods. Random Group Methods. Resampling and Replication Methods. Generalized Variance Functions. Confidence Intervals.

Chapter Summary. Exercises.

10. CATEGORICAL DATA ANALYSIS IN COMPLEX SURVEYS.

Chi-Square Tests with Multinomial Sampling. Effects of Survey Design on Chi-Square Tests. Corrections to x2 Tests. Loglinear Models. Chapter Summary. Exercises.

11. REGRESSION WITH COMPLEX SURVEY DATA.

Model-Based Regression in Simple Random Samples. Regression in Complex Surveys. Should Weights Be Used in Regression? Mixed Models for Cluster Samples. Logistic Regression. Generalized Regression Estimation for Population Totals. Chapter Summary. Exercises.

12. TWO-PHASE SAMPLING.

Theory for Two-Phase Sampling. Two-Phase Sampling with Stratification. Two-Phase Sampling with Ratio Estimation. Subsampling Nonrespondents. Designing a Two-Phase Sample. Chapter Summary. Exercises.

13. ESTIMATING POPULATION SIZE.

Capture-Recapture Estimates. Contingency Tables for Capture-Recapture Experiments. Assessing Undercoverage. Chapter Summary. Exercises.

14. RARE POPULATIONS AND SMALL AREA ESTIMATIONS.

Sampling for Rare Events. Small Area Estimation. Chapter Summary. Exercises.

15. SURVEY QUALITY.

Nonresponse Error. Measurement Error. Sensitive Questions. Processing Error.

Sampling Error. Interaction of Error Sources. The Future of Sampling. Chapter Summary. Exercises.

APPENDICES: PROBABILITY CONCEPTS USED IN SAMPLING.

Probability. Random Variables and Expected Value. Conditional Probability. Conditional Expectation.

REFERENCES.

- New Content--New chapters cover sampling rare populations, estimating a population size, and survey quality (which ties together much of the material in the earlier chapters). In addition, there is expanded treatment of computer-intensive methods such as jackknife and bootstrap, and discussion of new modes of data collection such as Internet surveys.
- SAS integration--SAS is incorporated in examples for analyzing data from complex surveys, with SAS code provided on the book's website.
- Current research--Recent research in survey methodology is incorporated, enhancing the book's real-world orientation. Examples include new approaches to linearization variance estimation, computer-intensive methods for variance estimation, small area estimation, nonresponse models, and plotting survey data.
- New applied examples--As is the case with most examples in the book, new ones are based on real surveys conducted in social sciences, epidemiology, and many other areas.
- Varied, categorized exercises to build skills--Exercises, many of which are new, are categorized into four groups: A, Introductory Exercises, many suitable for hand calculations; B, Working with Survey Data, with most requiring use of statistical software; C, Working with Theory, ideal for more mathematically oriented classes; and D, Projects and Activities, with activities suitable either for classroom use or as assigned student projects.

- Varied, categorized exercises to build skills--Exercises, many of which are new, are categorized into four groups: A, Introductory Exercises, many suitable for hand calculations; B, Working with Survey Data, with most requiring use of statistical software; C, Working with Theory, ideal for more mathematically oriented classes; and D, Projects and Activities, with activities suitable either for classroom use or as assigned student projects.
- Use of real data--As much as possible, examples and exercises come from social sciences, engineering, agriculture, medicine, and other disciplines.
- Topic coverage not found in other texts at this level--The book discusses analysis of complex surveys, nonresponse, and other important topics. These optional sections allow instructors the flexibility to pick and choose the additional topics they want to include with the core course content.
- Emphasis on the importance of graphing the data--This helps students avoid flawed data analysis.

**Sharon L. Lohr**

Arizona State University

Sharon Lohr (Ph.D. in statistics, University of Wisconsin–Madison) is the Thompson Industries Dean's Distinguished Professor of Statistics at Arizona State University, where she has taught since 1990. Dr. Lohr's research focuses on survey sampling, design of experiments, and applications of statistics to social sciences and education. She has published numerous articles in journals including The Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, Journal of Quantitative Criminology, Wisconsin Law Review, and The American Statistician. She has served as chair of the Survey Research Methods Section of the American Statistical Association, president of the Arizona Chapter of the American Statistical Association, Fellow of the American Statistical Association, Elected Member of the International Statistical Institute, and member of the Census Advisory Committee of Professional Associations and the Statistics Canada Advisory Board on Statistical Methodology. In 2003 she received the inaugural Gertrude M. Cox Award from the Washington Statistical Society and in 2009 was selected to present the Morris Hansen Lecture.