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Statistical Design and Inference for the Social Sciences
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Statistical Design and Inference for the Social Sciences

January 2026 | 520 pages | SAGE Publications, Inc
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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.
 
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
Key features

KEY FEATURES:

  • Teaches students to think critically with data—not just crunch numbers. This text emphasizes judgment, argumentation, and design, helping students understand why they're using a statistical method, not just how to do it.
  • Bridges the gap between theory and application. Students learn to construct meaningful comparisons, evaluate claims, and use statistical tools in real-world contexts—skills that extend far beyond the classroom.
  • Designed for early-career social science students. With only algebra as a prerequisite, the book is accessible to students new to statistics and relevant across disciplines like sociology, political science, and economics.
  • Focuses on research design and measurement—often the missing pieces. By prioritizing how to frame questions, choose comparisons, and interpret results, the book addresses the most common stumbling blocks in student research.
  • Supports hands-on learning with flexible software options. Integrated instruction for Excel and Stata, plus downloadable datasets, make it easy for instructors to tailor assignments and for students to build practical skills.
  • Prepares students for the data-driven world. With a focus on causal reasoning, data visualization, and critical thinking, this book equips students with the analytical mindset needed for both academic success and modern careers.

Sample Materials & Chapters

Preface, Chapters 1-2

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