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Measuring Society ASA-CRC Series on Statistical Reasoning in Science and Society Series

Langue : Anglais

Auteur :

Couverture de l’ouvrage Measuring Society

Collecting and analyzing data on unemployment, inflation, poverty and inequality help us describe the complex world in which we live. When published by the government, they are called official statistics. They are reported by the media, used by politicians to lend weight to their arguments and by economic commentators to opine about the state of society. Despite such widescale use, explanations about how these measures are constructed are seldom provided for a non-technical reader.

This book is a short, accessible guide to six topics: jobs, house prices, inequality, prices for goods and services, poverty, and deprivation. Each relates to concepts we use on a personal level to form an understanding of the society in which we live: We need a job, a place to live, and food to eat.

Using data from the United States, we answer three basic questions?why, how and for whom these statistics have been constructed. We add some context and flavor by discussing the historical background. The intention is to provide the reader with a good grasp of these measures.

Chaitra H. Nagaraja is Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham, Chaitra was a researcher at the U.S. Census Bureau. While there, she worked on projects relating to the American Community Survey.

  1. Introduction
  2. A Sample Controversy

    Requirements of a Good Sample

    Selection Bias

    Measurement Error

    Questionnaire Design

    Sampling and Nonsampling Errors

    Exercises

  3. Simple Probability Samples
  4. 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

  5. Stratified Sampling
  6. 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

  7. Ratio and Regression Estimation
  8. 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

  9. Cluster Sampling with Equal Probabilities
  10. 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

  11. Sampling with Unequal Probabilities
  12. 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

  13. Complex Surveys
  14. Assembling Design Components

    Sampling Weights

    Estimating a Distribution Function

    Plotting Data from a Complex Survey

    Design Effects

    The National Crime Victimization Survey

    Sampling and Design of Experiments

    Chapter Summary

    Exercises

  15. Nonresponse
  16. 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

  17. Variance Estimation in Complex Surveys
  18. Linearization (Taylor Series) Methods

    Random Group Methods

    Resampling and Replication Methods

    Generalized Variance Functions

    Confidence Intervals

    Chapter Summary

    Exercises

  19. Categorical Data Analysis in Complex Surveys
  20. Chi-Square Tests with Multinomial Sampling

    Effects of Survey Design on Chi-Square Tests

    Corrections to χ2 Tests

    Loglinear Models

    Chapter Summary

    Exercises

  21. Regression with Complex Survey Data
  22. Model-Based Regression in Simple Random Samples

    Regression in Complex Surveys

    Using Regression to Compare Domain Means

    Should Weights Be Used in Regression?

    Mixed Models for Cluster Samples

    Logistic Regression

    Generalized Regression Estimation for Population Totals

    Chapter Summary

    Exercises

  23. Two-Phase Sampling
  24. Theory for Two-Phase Sampling

    Two-Phase Sampling with Stratification

    Ratio and Regression Estimation in Two-Phase Samples

    Jackknife Variance Estimation for Two-Phase Sampling

    Designing a Two-Phase Sample

    Chapter Summary

    Exercises

  25. Estimating Population Size
  26. Capture–Recapture Estimation

    Multiple Recapture Estimation

    Chapter Summary

    Exercises

  27. Rare Populations and Small Area Estimation
  28. Sampling Rare Populations

    Small Area Estimation

    Chapter Summary

    Exercises

  29. Survey Quality

          Coverage Error

          Nonresponse Error

          Measurement Error

          Sensitive Questions

          Processing Error

         Total Survey Quality

         Chapter Summary

         Exercises

Appendix A. Probability Concepts Used in Sampling

Probability

Random Variables and Expected Value

Conditional Probability

Conditional Expectation

References

Author Index

Subject Index

 

Chaitra H. Nagaraja is Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham, Chaitra was a researcher at the U.S. Census Bureau. While there, she worked on projects relating to the American Community Survey.

Date de parution :

14x21.6 cm

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Prix indicatif 79,30 €

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Date de parution :

14x21.6 cm

Disponible chez l'éditeur (délai d'approvisionnement : 13 jours).

31,44 €

Ajouter au panier
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