Statistical Design and Inference for the Social Sciences

Donald Vandegrift - The College of New Jersey, USA

January 2026 | SAGE Publications, Inc

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.

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ISBN: 9781071848579 Paperback Suggested Retail Price: $183.00 Bookstore Price: $146.40

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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

Uploading Data into Stata

Executing Basic Commands in Stata

Saving Your Work

Using Excel

Uploading Data into Excel

Executing Basic Commands in Excel

Saving Your Work

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 – Mean Absolute Deviation

Measures of Dispersion – 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

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

Testing Population Variances

Two-Sample Hypothesis Tests of Proportions in Stata Using Sample Observations

Executing Two-Sample Hypothesis Tests on Means: Murders

Summarizing the Two-Sample Hypothesis Tests for Differences in 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

Executing Two-Sample Hypothesis Tests on Proportions: Leg Restraints and Leadership

Summarizing the Two-Sample Hypothesis Tests for Differences in 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 R2

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

A soup to nuts introduction to statistics for the social researcher grounded in theory, real-life application, and critical analysis.

Lanora Callahan

Marist College

This is a book that effectively integrates topics of research design, particularly focused on issues of measurement, causality, and appropriate questions and comparisons, with a reasonably rigorous, formal, and technical introduction to foundational concepts in probability and statistics and the logic and application of the most commonly used statistical tests in the social sciences, primarily to advanced undergraduate social science (particularly economics) majors but could be used as an introductory text for social science or public policy graduate students, particularly those who are changing fields and may be relatively new to quantitative research methods.

Robert Shand

American University

This book emphasizes research design as the cornerstone of the research enterprise. It ties the standard statistical topics to the elements of research design.

Wendy Martinek

Binghamton University

This is an introductory text into statistical methods and analytic thought. It attempts to teach thought processes and analytic reasoning as the basis for statistical methods, and thus, would be most appropriate at the beginning of one's studies.

Christiana Coyle

New York University

This is inferential statistical tests book for the social sciences. Compared to the traditional statistical book, this book has more discussion on the design of the test and the validity of the data and test.

Xin Zhang

Austin Peay State University

The book goes beyond basic statistics and discusses issues surrounding causal inference, which is absent in introductory statistics books. It reviews basic statistical concepts and procedure and introduce challenges of causation that people constantly confront in data analysis.

Xiaofeng Liu

University of South Carolina

While there are many statistics and methods textbooks for sale, this one stands out for its clear narrative, excellent examples, integration of Excel into the learning narrative, and appropriate exercises. It's perfect for my MPA and MS students.

Jonathan Engel

CUNY

Student will realize statistics is a useful tool that is relevant to their tasks in everyday work. This book uses real world stories with publicly accessible data and emphasizes practical skills.

Hee Soun Jang

University of North Texas

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.

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Statistical Design and Inference for the Social Sciences

Sage College Publishing

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