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Sampling Design and Analysis

ISBN-10: 0534353614

ISBN-13: 9780534353612

Edition: 1999

Authors: Sharon L. Lohr

List price: $369.95
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Sharon L. Lohr's SAMPLING: DESIGN AND ANALYSIS provides a modern introduction to the field of sampling. With a multitude of applications from a variety of disciplines, the book concentrates on the statistical aspects of taking and analyzing a sample. Overall, the book 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.
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Book details

List price: $369.95
Copyright year: 1999
Publisher: Brooks/Cole
Publication date: 12/15/1998
Binding: Hardcover
Pages: 450
Size: 7.50" wide x 9.75" long x 1.00" tall
Weight: 2.090
Language: English

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.

Introduction A Sample Controversy
Requirements of a Good Sample
Selection Bias
Measurement Bias
Questionnaire Design
Sampling and Nonsampling Errors
Simple Probability Samples Types of Probability Samples
Framework for Probability Sampling
Simple Random Sampling
Confidence Intervals
Sample Size Estimation
Systematic Sampling
Randomization Theory Results for Simple Random Sampling
A Model for Simple Random Sampling
When Should a Simple Random Sample Be Used?
Ratio and Regression Estimation Ratio Estimation
Regression Estimation
Estimation in Domains
Models for Ration and Regression Estimation
Stratified Sampling What is Stratified Sampling?
Theory of Stratified Sampling
Sampling Weights
Allocating Observations to Strata
Defining Strata
A Model for Stratified Sampling
Quota Sampling
Cluster Sampling with Equal Probabilities Notation for Cluster Sampling
One-Stage Cluster Sampling
Two-Stage Cluster Sampling
Using Weights in Cluster Samples
Designing a Cluster Sample
Systematic Sampling
Models for Cluster Sampling
Summary Exercises
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
Complex Surveys Assembling Design Components
Sampling Weights
Estimating a Distribution Function
Plotting Data from a Complex Survey
Design Effects
The National Crime Victimization Survey
Sampling and Experiment Design
Nonresponse Effects of Ignoring Nonresponse
Designing Surveys to Reduce Nonsampling Errors
Callbacks and Two-Phase Sampling
Mechanisms for Nonresponse
Weighting Methods for Nonresponse
Parametric Models for Nonresponse
What is An Acceptable Response Rate?
Variance Estimation in Complex Surveys Linearization (Taylor Series) Methods
Random Group Methods
Resampling and Replication Methods
Generalized Variance Functions
Confidence Intervals
Summary and Software
Categorical Data Analysis in Complex Surveys Chi-square Tests with Multinomial Sampling
Effects of Survey Design on Chi-Square Tests
Corrections to Chi-Square Tests
Loglinear Models
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
Other Topics in Sampling Two-Phase Sampling
Capture-Recapture Estimation
Estimation in Domains, Revisited
Sampling for Rare Events
Randomized Response
The Survey Program
Probability Concepts Used in Sampling
Random Variables and Expected Value
Conditional Probability
Conditional Expectation
Data Sets
Computer Codes Used for Examples
Statistical Table
Author Index
Subject Index