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