Grofman | Introduction to the Laws of Statistical Sampling | Buch | 979-8-3488-3230-8 | www.sack.de

Buch, Englisch, 144 Seiten, Format (B × H): 139 mm x 215 mm, Gewicht: 180 g

Reihe: Quantitative Applications in the Social Sciences

Grofman

Introduction to the Laws of Statistical Sampling

With Illustrations From Election Polling
1. Auflage 2026
ISBN: 979-8-3488-3230-8
Verlag: SAGE Publications Inc

With Illustrations From Election Polling

Buch, Englisch, 144 Seiten, Format (B × H): 139 mm x 215 mm, Gewicht: 180 g

Reihe: Quantitative Applications in the Social Sciences

ISBN: 979-8-3488-3230-8
Verlag: SAGE Publications Inc


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.

Grofman Introduction to the Laws of Statistical Sampling jetzt bestellen!

Autoren/Hrsg.


Weitere Infos & Material


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


Grofman, Bernard
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.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.