Polling Prediction and Testing: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Polling is a powerful tool that can be used to measure public opinion and predict future events. However, it is important to understand the limitations of polling and to use it carefully. This book provides a comprehensive to the theory and practice of polling prediction and testing in the social sciences. It covers both traditional methods and cutting-edge techniques, with a special focus on the use of Bayesian methods and hierarchical models. The book is written in a clear and accessible style, and it includes numerous examples and exercises to help readers learn the material.

Understanding Elections through Statistics: Polling, Prediction, and Testing (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

by Ole J. Forsberg

5 out of 5

Language	:	English
File size	:	18246 KB
Print length	:	226 pages
Screen Reader	:	Supported

FREE

Chapter 1: to Polling

This chapter provides an overview of the polling process. It discusses the different types of polls, the methods used to collect data, and the challenges involved in designing and conducting a poll. The chapter also introduces the basic concepts of sampling and estimation. Using real data and examples, the authors show how to analyze the results of a poll and evaluate its accuracy.

Chapter 2: Sampling Methods

This chapter discusses the different methods used to select a sample for a poll. It covers probability sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, as well as non-probability sampling methods, such as convenience sampling and quota sampling. The chapter also discusses the advantages and disadvantages of each method and provides guidance on how to choose the best method for a particular study.

Chapter 3: Data Collection Methods

This chapter discusses the different methods used to collect data for a poll. It covers face-to-face interviews, telephone interviews, mail surveys, and online surveys. The chapter also discusses the advantages and disadvantages of each method and provides guidance on how to choose the best method for a particular study.

Chapter 4: Questionnaire Design

This chapter discusses the principles of questionnaire design. It covers the different types of questions, the ordering of questions, and the use of question wording. The chapter also discusses the importance of pretesting a questionnaire and provides guidance on how to do so. Avoiding bias in a questionnaire is also covered.

Chapter 5: Data Analysis

This chapter discusses the different methods used to analyze poll data. It covers descriptive statistics, such as frequency tables and cross-tabulations, as well as inferential statistics, such as hypothesis testing and regression analysis. The chapter also discusses the importance of data visualization and provides guidance on how to create effective data visualizations.

Chapter 6: Prediction and Forecasting

This chapter discusses the different methods used to predict future events based on poll data. It covers both traditional methods, such as time series analysis and regression analysis, as well as cutting-edge techniques, such as machine learning and Bayesian methods. The chapter also discusses the challenges involved in making predictions and provides guidance on how to evaluate the accuracy of predictions.

Chapter 7: Testing Hypotheses

This chapter discusses the different methods used to test hypotheses based on poll data. It covers both traditional methods, such as t-tests and chi-square tests, as well as cutting-edge techniques, such as Bayesian hypothesis testing and hierarchical modeling. The chapter also discusses the importance of power analysis and provides guidance on how to conduct power analyses.

Chapter 8: Bayesian Methods

This chapter provides an Bayesian methods in the context of polling prediction and testing. Bayesian methods offer a number of advantages over traditional frequentist methods, such as the ability to incorporate prior information and to make predictions about future events. The chapter also discusses the challenges involved in using Bayesian methods and provides guidance on how to overcome these challenges.

Chapter 9: Hierarchical Models

This chapter provides an to hierarchical models in the context of polling prediction and testing. Hierarchical models are a powerful tool for modeling complex data structures, such as data collected from clustered samples or repeated measures. The chapter also discusses the challenges involved in using hierarchical models and provides guidance on how to overcome these challenges.

This book provides a comprehensive to the theory and practice of polling prediction and testing in the social sciences. It covers both traditional methods and cutting-edge techniques, with a special focus on the use of Bayesian methods and hierarchical models. The book is written in a clear and accessible style, and it includes numerous examples and exercises to help readers learn the material.

References

[1] Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

[2] Kruschke, J. K. (2014). ng Bayesian data analysis: A tutorial with R, JAGS, and STAN. Academic Press.

[3] Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons.

[4] Quinn, K. M., & Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge University Press.

[5] Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An to basic and advanced multilevel modeling. SAGE Publications.

Understanding Elections through Statistics: Polling, Prediction, and Testing (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

by Ole J. Forsberg

5 out of 5

Language	:	English
File size	:	18246 KB
Print length	:	226 pages
Screen Reader	:	Supported

FREE