Description

# Measuring Society

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

## Author: Nagaraja Chaitra H.

Language: Anglais## Subjects for *Measuring Society*:

Publication date: 07-2019

· 14x21.6 cm · Hardback

Publication date: 08-2019

· 14x21.6 cm · Paperback

## Description

/li>## Contents

/li>## Biography

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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.

- Introduction
- Simple Probability Samples
- Stratified Sampling
- Ratio and Regression Estimation
- Cluster Sampling with Equal Probabilities
- Sampling with Unequal Probabilities
- Complex Surveys
- Nonresponse
- Variance Estimation in Complex Surveys
- Categorical Data Analysis in Complex Surveys
- Regression with Complex Survey Data
- Two-Phase Sampling
- Estimating Population Size
- Rare Populations and Small Area Estimation
- Survey Quality

A Sample Controversy

Requirements of a Good Sample

Selection Bias

Measurement Error

Questionnaire Design

Sampling and Nonsampling Errors

Exercises

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

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

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

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

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

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

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

Linearization (Taylor Series) Methods

Random Group Methods

Resampling and Replication Methods

Generalized Variance Functions

Confidence Intervals

Chapter Summary

Exercises

Chi-Square Tests with Multinomial Sampling

Effects of Survey Design on Chi-Square Tests

Corrections to *χ*2 Tests

Loglinear Models

Chapter Summary

Exercises

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

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

Capture–Recapture Estimation

Multiple Recapture Estimation

Chapter Summary

Exercises

Sampling Rare Populations

Small Area Estimation

Chapter Summary

Exercises

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.