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Sampling Design and Analysis



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Cengage Learning
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  • Sampling Design and Analysis
    Sampling Design and Analysis


Sharon 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.

Table of Contents

A Sample Controversy
Requirements of a Good Sample
Selection Bias
Measurement Error
Questionnaire Design
Sampling and Nonsampling Errors
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 Model for Simple Random Sampling
When Should a Simple Random Sample Be Used?
Chapter Summary
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
Chapter Summary
Ratio and Regression Estimation
Ratio Estimation
Regression Estimation
Estimation in Domains
Models for Ratio and Regression Estimation
Chapter Summary
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
Chapter Summary
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
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
Chapter Summary
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?
Chapter Summary
Variance Estimation in Complex Surveys
Linearization (Taylor Series) Methods
Random Group Methods
Resampling and Replication Methods
Generalized Variance Functions
Confidence Intervals
Chapter Summary
Categorical Data Analysis in Complex Surveys
Chi-Squared Tests with Multinomial Sampling
Effects of Survey Design on Chi-Squared Tests
Corrections to Chi-Squared Tests
Loglinear Models
Chapter Summary
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
Two Phase Sampling
Two-Phase Sampling for Stratification
Two-Phase Sampling for Ratio Estimation
Theory of Two-Phase Sampling
Chapter Summary
Estimating Population Size
Capture-Recapture Estimates
Contingency Tables for Capture-Recapture Experiments
Assessing Undercoverage
Chapter Summary
Rare Populations and Small Areas
What is a Rare Population? Sampling for Rare Events
Estimation in Domains, Revisited
Small Area Estimation
Chapter Summary
Survey Quality
Coverage Error
Nonresponse Error
Measurement Error
Sensitive Questions
Processing Error
Sampling Error
Interaction of Error Sources
The Future of Sampling
Chapter Summary
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
Table of Contents provided by Publisher. All Rights Reserved.

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