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. By focusing on evidence-based arguments and thoughtful design, students learn to connect questions with the right tools, assess policies, and evaluate research. Practical and clear, the book helps students move beyond formulas to understand the logic behind statistical choices.
Arvustused
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 ones 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. Its 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
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: Simpsons 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 Snows 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
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.
