Overview

Statistical Design and Inference for the Social Sciences goes beyond the math to teach students how to use data to support meaningful, causal arguments.

Full Product Details

Author:   Donald Vandegrift
Publisher:   SAGE Publications Inc
Imprint:   SAGE Publications Inc
Weight:   0.770kg
ISBN:  

9781071848579

ISBN 10:   1071848577
Pages:   520
Publication Date:   14 April 2026
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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

Reviews

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

Author Information

Donald Vandegrift is a Professor of Economics at The College of New Jersey in Ewing, NJ where he teaches courses in statistics and economics. He received a BA from the College of William and Mary and a Ph.D. from the University of Connecticut. His primary areas of research are urban issues and experimental/behavioral economics. His urban research considers the amenity value and economic development effects of large institutions, crime and policing, and the economic effects of transport projects and land-use regulation. This research has appeared in Landscape and Urban Planning, Journal of Quantitative Criminology, Urban Affairs Review, Journal of Regional Science, Annals of Regional Science, Health & Place, and Research in Transportation Economics, among others. His experimental/behavioral research considers the effect of compensation schemes on risk taking, unproductive activities (i.e., sabotage), decisions to compete, and behavioral norms. This research has appeared in Journal of Economic Behavior and Organization, Experimental Economics, Labour Economics, Journal of Neuroscience, Psychology, and Economics, Journal of Research in Personality, and Journal of Institutional and Theoretical Economics. Grants from the National Science Foundation, the Lincoln Institute of Land Policy, and the Institute for Humane Studies have supported his research.

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