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.
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.
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.
Inhaltsangabe
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
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
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