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