Statistical Design and Inference for the Social Sciences

著者名：Vandegrift, Donald

SAGE Publications, Inc（2026/01/27発売）

• 言語：ENG
• ISBN：9781071848579
• eISBN：9781071848609

Description

Donald Vandegrift′s Statistical Design and Inference for the Social Sciences equips students with the skills to think critically about data—not just calculate it. Rather than focusing on rote computation, this text emphasizes how to build strong, evidence-based arguments using real-world data and thoughtful comparisons. Students learn to align their research questions with appropriate measures, designs, and statistical tools—developing the judgment needed to evaluate public policies, assess social science research, and make informed decisions. With a strong foundation in causal reasoning and a practical approach to software use, the book helps students move beyond formulas to understand the logic behind statistical choices.

Table of Contents

Preface
Acknowledgments
Foreward
Chapter 1: Making the Right Comparison: Understanding the Rules and Limitations of Quantitative Reasoning
Positive and Normative Statements
Deduction and Induction
Using Deduction and Induction Together
Cause and Association
Linking Deduction with Induction – Measurement Validity
A Note of Caution on Measurement
Linking Deduction with Induction – Measurement Reliability
Exercises
Chapter 2: Making the Right Comparison: Observations, Variable Types, Data Displays, and Data Conversions
Data Sets and Variable Types
Variable Types and Data Displays
Choice of Divisor in Creating Ratios
Other Types of Data Conversions: Adjusting for Inflation
Other Types of Data Conversions: Adjusting for Seasonality
Other Types of Data Conversions: Adjusting for Noise
Exercises
Chapter 3: Using Stata and Excel to Create Line, Bar, and Scatter Diagrams
Using Stata
Using Excel
Exercises
Chapter 4: Summarizing Variables using Measures of Central Tendency and Dispersion
Measures of Central Tendency – The Mean
Measures of Central Tendency – The Median
Measures of Central Tendency – The Mode
Measures of Dispersion – The Range
Measures of Dispersion – The Mean Absolute Deviation
Measures of Dispersion – The Variance and Standard Deviation
Populations and Samples
Appendix
Measures of Central Tendency and Dispersion Using Statistical Software
Measures of Central Tendency and Dispersion in Stata
Histograms in Stata
Measures of Central Tendency and Dispersion in Excel
Histograms in Excel
Exercises
Chapter 5: Research Design and Statistical Fallacies
Random Assignment and Wellness Programs
Broader Lessons from Comparing Studies on the Effectiveness of Wellness Programs
Inferring Cause When RCTs Are Not Possible
Wrongly Inferring Association: Regression Fallacy and Maturation
Wrongly Inferring Association: Ecological and Reductionist Fallacies
Wrongly Inferring Association: Simpson’s Paradox
Wrongly Inferring Association: Cherry Picking
Wrongly Inferring Cause: Selection Bias and Sample Mortality
Wrongly Inferring Cause: Bidirectional Causality
Exercises
Chapter 6: Constructing Informative Comparisons and Inferring Cause
John Snow’s Evidence
John Snow, Cholera, and General Rules for Quantitative Comparisons
Descriptive, Correlational, and Causal Research
The Difficulty of Establishing Cause Varies with Context
Sorting Data and Making Comparisons to Produce Evidence on Cause
Data Sorting and Cause: An Example
Difference-in-Differences Analysis
Difference-in-Differences: An Example
Discontinuity Analysis
Discontinuity Analysis: An Example
Exercises
Chapter 7: Sampling Distributions and Statistical Inference
Basic Probability
Random Variables and Their Probability Distributions
Discrete Probability Functions
Probability Density Functions
The Uniform Probability Distribution
The Normal Probability Distribution
The Sampling Distribution and the Central Limit Theorem
Confidence Intervals
Confidence Intervals for Means Using the z Distribution (s Known)
Confidence Intervals for Proportions Using the z Distribution
Confidence Intervals for Means Using the t Distribution (s Unknown)
Choosing the Right Procedure to Calculate a Confidence Interval
Exercises
Chapter 8: One-Sample Hypothesis Tests
The Basic Structure of Hypothesis Tests
The Null and the Alternative Hypotheses
One-Tailed and Two-Tailed Hypothesis Tests
Type I and Type II Errors
One- and Two-Sample Hypothesis Tests
Sampling Distributions and the Structure of One-Sample Hypothesis Tests
Understanding Test Statistics for One-Sample Hypothesis Tests
Executing One-Sample Hypothesis Tests for a Population Mean Using the z Distribution
Executing One-Sample Hypothesis Tests for a Population Proportion Using the z Distribution
Executing One-Sample Hypothesis Tests for a Population Mean Using the t Distribution
Summarizing the Steps for One-Sample Hypothesis Tests
Hypothesis Tests and Confidence Intervals
Appendix
Confidence Intervals and Hypothesis Tests Using Statistical Software
Confidence Intervals and Hypothesis Tests in Stata Using Univariate Measures
Confidence Intervals and Hypothesis Tests in Stata Using Sample Observations
Confidence Intervals and Hypothesis Tests in Excel Using Sample Observations
Exercises
Chapter 9: Two-Sample Hypothesis Tests of Means
Two-Sample Hypothesis Tests and Cause
Undefined Populations and External Validity
Dependent and Independent Samples
One-Sample Hypothesis Tests and Two-Sample Hypothesis Tests
Two-Sample Hypothesis Tests of Means: Independent Samples
Two-Sample Hypothesis Test of Means: Dependent Samples
Executing Two-Sample Hypothesis Tests on Means: Murders
Summarizing the Two-Sample Hypothesis Tests of Means
Appendix
Two-Sample Hypothesis Tests of Means Using Statistical Software
Two-Sample Hypothesis Tests of Means in Stata Using Univariate Measures
Two-Sample Hypothesis Tests of Means in Stata Using Sample Observations
Two-Sample Hypothesis Tests of Means in Excel Using Sample Observations
Exercises
Chapter 10: Two-Sample Hypothesis Tests of Proportions
Two-Sample Hypothesis Test for Proportions: Independent Samples
Two-Sample Hypothesis Test for Proportions: Dependent Samples
Summarizing the Two-Sample Hypothesis Tests of Proportions
Appendix
Two-Sample Hypothesis Tests of Proportions Using Statistical Software
Two-Sample Hypothesis Tests of Proportions in Stata Using Univariate Measures
Two-Sample Hypothesis Tests of Proportions in Stata Using Sample Observations
Two-Sample Hypothesis Tests of Proportions in Excel Using Sample Observations
Exercises
Chapter 11: Correlation and Simple Linear Regression
Correlation
Calculating the Correlation Coefficient and Testing the Hypothesis ? = 0
Simple Linear Regression
Simple Linear Regression as Estimating Relationships Using (x, y) Coordinates
Calculating Coefficients in a Simple Linear Regression
Testing Coefficients of a Simple Linear Regression
Calculating R^2
Appendix
Correlation and Simple Linear Regression Using Statistical Software
Correlation in Stata
Simple Linear Regression in Stata
Correlation in Excel
Simple Linear Regression in Excel
Exercises
Chapter 12: Simple Linear Regression: Assumptions and Extensions
Assumptions of Simple Linear Regression
Nonlinear Relationships and Log Transformation in Simple Linear Regression
Dichotomous Independent Variables in Simple Linear Regression
Detecting and Correcting Serial Autocorrelation
Detecting and Correcting Heteroskedasticity
Transforming Variables to Support Causal Claims: Time Lags and Changes
Appendix
Simple Linear Regression Procedures Using Statistical Software
Executing Log-Transform Simple Linear Regression in Stata
Detecting and Correcting Serial Autocorrelation in Stata
Detecting and Correcting Heteroskedasticity in Stata
Using Stata to Transform Variables and Generate Evidence on Cause
Executing Log-Transform Simple Linear Regression in Excel
Detecting Serial Correlation in Excel
Detecting Heteroskedasticity in Excel
Using Excel to Transform Variables and Generate Evidence on Cause
Exercises
Glossary