Sampling Methodologies with Applications

Sampling Methodologies with Applications

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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.show more

Product details

  • Paperback | 336 pages
  • 154.94 x 226.06 x 20.32mm | 476.27g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 20 black & white illustrations, 38 black & white tables
  • 158488214X
  • 9781584882145
  • 2,409,278

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 Appendixshow more

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