Sampling Methodologies with Applications

Sampling Methodologies with Applications

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Description

Sampling methods are integral to the design of surveys and experiments, to the validity of results, and thus to the study of statistics, social science, and a variety other disciplines that use statistical data. Yet most of the available texts on the subject are either quite advanced and theoretical or too applied, descriptive, and lacking statistical results.

Sampling Methodologies with Applications offers a balanced, practical treatment of the techniques and applications of the commonly used procedures for sampling from finite populations. It keeps mathematics to a minimum, but does not avoid them entirely: it features the principle results within the text but provides their derivations in the Appendices to each chapter. In an easily followed, step-by-step presentation, the author motivates each topic with illustrations followed by examples and exercises. All of these are constructed from everyday, practical situations covering a wide variety of topics, from scholastic aptitude tests to healthcare expenditures and presidential elections.

Why wade through advanced, theoretical tomes when what you need is straightforward, practical information? Why risk missing important statistical results often omitted from more basic texts? Sampling Methodologies with Applications has everything you need, presented clearly and logically for quick access to topics central to actual practice.
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Product details

  • Paperback | 336 pages
  • 154.94 x 226.06 x 20.32mm | 476.27g
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 403 equations; 38 Tables, black and white; 20 Illustrations, black and white
  • 158488214X
  • 9781584882145
  • 2,614,805

Table of contents

INTRODUCTION
Censuses and Surveys
Types of Surveys
Sampling Frame
Questionnaires, Interviews, and Sample Sizes
Probability Sampling
Nonprobability Sampling
Sampling in Practice
SIMPLE RANDOM SAMPLING: ESTIMATION OF MEANS AND TOTALS
Population Total, Mean and Variance
Sampling without Replacement
Sample Mean and Variance
Properties of Simple Random Sampling
Unbiasedness of the Sample Mean and Variance
Standard Error of the Sample Mean
Distribution of the Sample Mean
Confidence Limits for the Mean
Estimating the Total
Coefficient of Variation of the Sample Mean
Sample Size Determination
Sample Sizes for Individual Groups
SIMPLE RANDOM SAMPLING: RELATED TOPICS
Bias, Variance and Mean Square Error (MSE) of an Estimator
Precision, Accuracy and Consistency
Covariance and Correlation
Linear Combination of Two Characteristics
Systematic Sampling
Nonresponse
Inference Regarding the Means
Sampling with Replacement
PROPORTIONS, PERCENTAGES, AND COUNTS
Two Classes
Total Number or Count
Confidence Limits
Sample Sizes for Specified Requirements
Political and Public Polls
Estimation for more than Two Classes
Combining Estimates from Surveys
Covariances of Proportions
Difference between Proportions
Sample Sizes for more than Two Classes
Estimating Population Size
STRATIFICATION
Notation
Estimation for a Single Stratum
Estimation of the Population Mean and Total
Confidence Limits
Proportions and Totals
Population Total and Proportion
Proportional and Equal Allocation of the Sample
Neyman Allocation
Gains from Neyman Allocation
Summary on the Precisions of the Allocations
Sample Size Allocation to Estimate Proportions
Sample Size to Estimate Means and Totals
Sample Sizes to Estimate Proportions
Sample Sizes for Minimizing Variance or Cost
Further Topics
SUBPOPULATIONS
Totals, Means, and Variances
Estimation of the Means and their Differences
Totals of Subpopulations
Sample Sizes for Estimating the Means and Totals
Proportions and Counts
Subpopulations of a Stratum
Totals and Means of Subpopulations in Strata
Stratification: Proportions and Counts of Subpopulations
CLUSTER SAMPLING
Clusters of Equal Sizes
Estimation of the Means
Comparison with Simple Random Sampling
Estimation of the Standard Error
Optimum Cluster and Sample Sizes
Unequal Size Clusters
Alternative Estimation with Unequal Sizes
Proportions and Percentages
Stratification
Unequal Probability Selection
Horvitz-Thompson Estimator
Alternative Approaches
SAMPLING IN TWO STAGES
Equal Size First Stage Units
Estimating the Mean
Sample Size Determination - Equal size PSUs
Unequal Size Primary Units
Sample Sizes - Unequal Size PSUs
Ratio Adjustment for the Sizes
Proportions and Totals
Unequal Probability Selection
RATIOS AND RATIO ESTIMATORS
Bias and Variance of the Sample Ratio
Confidence Limits for the Ratio of Means
Ratio Estimators for the Mean and Total
Confidence Limits for the Mean and Total
Differences between Ratios, Means, or Totals
Regression through the Origin and the BLUEs
Ratio Estimation versus Stratification
Ratio Estimation with Stratification
Bias Reduction
Two-phase or Double Sampling Ratio Estimators
Ratio Estimator with Unequal Probability Selection
Multivariate Ratio Estimator
REGRESSION ESTIMATION
The Regression Estimator
Estimation from the Sample
Classical Linear Regression
Differences between Regression Estimators
Regression Estimation versus Stratification
Stratification and Regression Estimator
Multiple Regression Estimator
Double Sampling Regression Estimator
Generalized Regression and Calibration Estimators
NONRESPONSE AND REMEDIES
Effects of Survey Topics and Interviewing Methods
Response Rates
Bias and MSE
Estimating Proportions
Subsampling the Nonrespondents
Estimating the Missing Observations
Ratio and Regression Estimators
Poststratification and Weighting
Response Probabilities and Weighting
Imputation
Related Topics
FURTHER TOPICS
Linearization
The Jackknife
The Bootstrap
Balanced Repeated Replication (BRR)
Small Area Estimation
Complex Surveys
SOLUTIONS TO EXERCISES
BIBLIOGRAPHY
APPENDIX OF TABLES

NOTE: Each chapter also contains an Introduction, Exercises and Appendix
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