A concise, intuitive monograph that demystifies statistical sampling theory-especially as applied to elections and survey research-using real-world examples, simulations, and Excel-based tools. It's designed to be accessible to readers with only high school algebra.
Rezensionen / Stimmen
This book is a game-changer for anyone learning about statistical sampling. Professor Grofman takes a subject that's usually complex and math-heavy, and he makes it intuitive and accessible. The way he breaks down key ideas, provides hands-on Excel simulations, and captures core principles in his Introduction to the Laws of Statistical Sampling is truly brilliant. If you're a student looking to really understand sampling - not just memorize formulas - this is the book for you. And if you're an instructor searching for a fresh approach to bring sampling to life, look no further. -- Tracy M. Walker This book teaches statistical sampling and difference of means testing through elections and polling a data, a concept that should be easy and familiar for students to pick up. -- Nathan W. Prager
Reihe
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Maße
Höhe: 216 mm
Breite: 140 mm
ISBN-13
979-8-3488-3230-8 (9798348832308)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Bernard Grofman is Distinguished Research Professor of Political Science and Social Psychology, School of Social Sciences, University of California, Irvine. A member of the American Academy of Arts and Science, he was the inaugural Jack W. Peltason Endowed Chair of Democracy Studies at UCI and has also been an Adjunct Professor of Economics at UCI and a visiting scholar-in-residence at universities in nearly a dozen countries.
Series Editor Introduction
Acknowledgements
About the Author
Chapter 1: An Overview
1.1 Distinctive Features of the Approach to Sampling and Inference in This Volume
1.2 The Structure of this Book
1.3 Notation
1.4 Basic Metrics
APPENDIX to Chapter 1: A Few Useful EXCEL Functions and Tools
Chapter 2: Sampling Distributions
2.1 Ideal Types of Univariate Data Distributions
2.2 The Normal Distribution and the Standardized Normal Distribution
2.3 Approximately Normal Distributions
2.4 Cumulative Distributions and Finding Percentile Ranks Using EXCEL
2.5 The Binomial Distribution
2.6 The t-Distribution
2.7 Other Approximately Normal Distributions
2.8 Skewness and Kurtosis
2.9 Not all Univariate Distributions are Approximately Normal
APPENDIX to Chapter 2: Theorem Proofs
Chapter 3: Sampling and Hypothesis Testing
3.1 Sampling and Hypothesis Testing
3.2 An Inventory of the Ten Laws of Statistical Sampling
3.3 Sampling From a Normal Distribution with Binomial Variance
APPENDIX to Chapter 3: Distinguishing the Standard Error of the Mean From the Sample Error
Chapter 4: Using EXCEL to Answer the First Five of our Six Questions
4.1 Five Paradigmatic Questions About Sampling in Two-Candidate Elections
Chapter 5: Difference of Means
5.1 Question 6. "When can we reject the claim that two distributions are drawn from the same population?"
5.2 Experiments as the Basis for Generating Data for a Difference of Means Test
5.3 Statistical Significance versus Substantive Significance: The Importance of Sample Size
5.4 Illustrating Ideological Polarization and Partisan Sorting with Polling Data
5.5 Warnings about Causation and Selection Bias Effects
Chapter 6: Unifying Perspectives on Sampling and Hypothesis Testing Involving a Univariate Distribution
6.1 Similarities Across Statistical Tools
6.2 Concluding Thoughts
APPENDIX 1 to Chapter 6 - Parallels Between the Ideas in this Book and Regression Analysis
APPENDIX 2 to Chapter 6: A Short List of Suggestions for Further Reading
References
Index
