Advanced Statistics in Criminology and Criminal Justice

Contents
Chapter 1: Introduction
	Proportionality Review and the Supreme Court of New Jersey: A Cautionary Tale
	Generalized Linear Models
	Special Topics
	References
Chapter 2: Multiple Regression
	Overview of Simple Regression
	Extending Simple Regression to Multiple Regression
	Assumptions of Multiple Regression
		Independence
		Normally Distributed Errors
		Homoscedasticity of Errors
		Linearity
	Measurement Error in the Independent Variables
	Regression Diagnostics
	Dealing with Outliers and Influential Cases
	Testing the Significance of Individual Regression Coefficients
	Assessing Overall Model Fit and Comparing Nested Models
		R2 and Adjusted R2
	Comparing Regression Coefficients Within a Single Model: The Standardized Regression Coefficient
	Correctly Specifying the Regression Model
	Model Specification and Building
	An Example of a Multiple Regression Model
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
			Standardized Regression Coefficients (Betas)
			F-Test for a Subset of Variables
			Residual Plot
		Stata
			Standardized Regression Coefficients (Betas)
			F-Test for a Subset of Variables
			Residual Plot
		R
			Standardized Regression Coefficients (Betas)
			F-Test for a Subset of Variables
			Residual Plot
		Problems
	References
Chapter 3: Multiple Regression: Additional Topics
	Nominal Variables with Three or More Categories in Multiple Regression
	Nonlinear Relationships
		Finding a Nonlinear Relationship: Graphical Assessment
		Incorporating Nonlinear Relationships into an OLS Model Using a Quadratic Term of an Independent Variable
			Interpreting Nonlinear Coefficients
			Note on Statistical Significance
		Transforming the Dependent Variable
		Review of Nonlinear Relationships
	Interaction Effects
		Interaction of a Dummy Variable and a Scaled Variable
	An Example: Race and Punishment Severity
		Interaction Effects Between Two Scaled Variables
	An Example: Punishment Severity
	The Problem of Multicollinearity
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
			Dummy Coding Nominal Variables
			Computing Nonlinear and Interaction Terms
			Nonlinear Terms
			Interaction Terms
			Estimating the Regression Model
			Collinearity Diagnostics
		Stata
			Dummy Coding Nominal Variables
			Computing Nonlinear and Interaction Terms
			Nonlinear Terms
			Interaction Terms
			Estimating the Regression Model
			Collinearity Diagnostics
		R
			Dummy Coding Nominal Variables
			Computing Nonlinear and Interaction Terms
			Nonlinear Terms
			Interaction Terms
			Estimating the Regression Model
			Collinearity Diagnostics
		Problems
	References
Chapter 4: Logistic Regression
	Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable?
	Logistic Regression
	A Substantive Example: Adoption of Compstat in U.S. Police Agencies
	Interpreting Logistic Regression Coefficients
		The Odds Ratio
		The Derivative at Mean
	Comparing Logistic Regression Coefficients
		Using Probability Estimates to Compare Coefficients
		Standardized Logistic Regression Coefficients
	Evaluating the Logistic Regression Model
		Percent of Correct Predictions
		Pseudo-R2
	Statistical Significance in Logistic Regression
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
		Stata
		R
		Problems
	References
Chapter 5: Multiple Regression with Multiple Category Nominal or Ordinal Measures
	Multinomial Logistic Regression
		A Substantive Example: Case Dispositions in California
		The Missing Set of Coefficients
		Statistical Inference
			Single Coefficients
			Multiple Coefficients
			Overall Model
		A Concluding Observation About Multinomial Logistic Regression Models
	Ordinal Logistic Regression
		Interpretation of Ordinal Logistic Regression Coefficients
		Substantive Example: Severity of Punishment Decisions
		Interpreting the Coefficients
		Statistical Significance
		Parallel Slopes Tests
		Score Test
		Brant Test
		Partial Proportional Odds
		Severity of Punishment Example
	Chapter Summary
	Key Terms
	Formulas
	Exercises
	Computer Exercises
		SPSS
			Multinomial Logistic Regression
			Ordinal Logistic Regression
		Stata
			Multinomial Logistic Regression
			Ordinal Logistic Regression
			Partial Proportional Odds
		R
			Multinomial Logistic Regression
			Ordinal Logistic Regression
			Partial Proportional Odds
		Problems
	References
Chapter 6: Count-Based Regression Models
	The Poisson Distribution
	Poisson Regression
		Incident Rate Ratios (IRRs)
		Significance Testing
		Exposure and Offsets
		An Example: California 1999 Uniform Crime Report Data
	Over-Dispersion in Count Data
	Quasi-Poisson and Negative Binomial Regression
		An Example: Reanalysis of the California 1999 Uniform Crime Report Data
	Zero-Inflated Poisson and Negative Binomial Regression
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
			Poisson Regression
			Quasi-Poisson Regression
			Negative Binomial Regression
			Zero-Inflated Poisson/Negative Binomial Regression
		Stata
			Poisson Regression
			Quasi-Poisson Regression
			Negative Binomial Regression
			Zero-Inflated Poisson/Negative Binomial Regression
		R
			Poisson Regression
			Quasi-Poisson Regression
			Negative Binomial Regression
			Zero-Inflated Poisson/Negative Binomial Regression
		Problems
	References
Chapter 7: Multilevel Regression Models
	A Simple Multilevel Model
		Fixed-Effects and Random-Effects
		A Substantive Example: Bail Decision-Making Study
		Intraclass Correlation and Explained Variance
		Deciding Between and Fixed- and Random-Effects Model
		Statistical Significance
		Bail Decision-Making Study
	Random Intercept Model with Fixed Slopes
		Statistical Significance
		Centering Independent Variables
		Bail Decision-Making Study
		Between and Within Effects
		Testing for Between and Within Effects
		Bail Decision-Making Study
	Random Coefficient Model
		Variance Estimates
		Bail Decision-Making Study
	Adding Cluster (Level 2) Characteristics
		A Substantive Example: Race and Sentencing Across Pennsylvania Counties
		Multilevel Negative Binomial Regression
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
		Stata
			Random Intercept Models
			Random Coefficient Models
		R
			Random Intercept Models
			Random Coefficient Models
		Problems
	References
Chapter 8: Statistical Power
	Statistical Power
		Setting the Level of Statistical Power
	Components of Statistical Power
		Statistical Significance and Statistical Power
			Directional Hypotheses
		Sample Size and Statistical Power
		Effect Size and Statistical Power
	Estimating Statistical Power and Sample Size for a Statistically Powerful Study
		Difference of Means Test
		ANOVA
		Correlation
		Least Squares Regression
	Summing Up: Avoiding Studies Designed for Failure
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Computer Exercises
		Stata
			Two-Sample Difference of Means Test
			ANOVA
			Correlation
			OLS Regression
		R
			Two-Sample Difference of Means Test
			ANOVA
			Correlation
			OLS Regression
		Problems
	References
Chapter 9: Randomized Experiments
	The Structure of a Randomized Experiment
	The Main Advantage of Experiments: Isolating Causal Effects
	Internal Validity
	Selected Design Types and Associated Statistical Methods
		The Two-Group Randomized Design
		Three or More Group Randomized Design
		Factorial Design
			Two-Way ANOVA for Between-Subjects Designs
			An Example: Perceptions of Children During a Police Interrogation
		Mixed Within- and Between-Subjects Factorial Designs
	Block Randomized Designs
		Block Randomization and Statistical Power
		Examining Interaction in a Block Randomized Experiment
	Using Covariates to Increase Statistical Power in Experimental Studies
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
			Independent Sample t-Test
			One-Way ANOVA
			Two-Way Factorial (Type I SS)
			Two-Way Factorial (Type II SS)
			Two-Way Factorial (Type III SS)
		Stata
			Independent Sample t-Test
			One-Way ANOVA
			Two-Way Factorial (Type I SS)
			Two-Way Factorial (Type II SS)
			Two-Way Factorial (Type III SS)
		R
			Independent Sample t-Test
			One-Way ANOVA
			Two-Way Factorial (Type I SS)
			Two-Way Factorial (Type II SS)
			Two-Way Factorial (Type III SS)
		Problems
	References
Chapter 10: Propensity Score Matching
	The Underlying Logic Behind Propensity Score Matching
	Selection of Model for Predicting Propensity for Treatment
	Matching Methods
		The Case of Work Release in Prison: A Substantive Example
	Assessing the Quality of the Matches
	Sensitivity Analysis for Average Treatment Effects
	Limitations of Propensity Score Matching
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		Stata
			Estimating Propensity Score
			Matching Cases
			Assessing Matches
			Estimating Treatment Effect
		R
			Estimating Propensity Score
			Matching Cases
			Assessing Matches
			Estimating Treatment Effect
		Problems
	References
Chapter 11: Meta-analysis
	A Historical Note
	The Logic of Meta-analysis
	The Effect Size
		The Standardized Mean Difference: Cohen´s d and Hedges´ g
		Risk Ratio
		Odds Ratio
		Correlation Coefficient
		Converting Between Effect Size Indices
			Converting Effect Sizes into Cohen´s d
			Converting Effect Sizes into Odds Ratios
			Converting Effect Sizes into Risk Ratios
			Converting Effect Sizes into Correlations
	Meta-analysis of Effect Sizes
		Fixed-Effect Meta-analysis
			The Mean Effect Size and Associated Statistics
			Homogeneity Testing
			The Random-Effect Model
		Example: Police-Led Diversion of Youth
	Forest Plots
	Moderator Analysis
		Analog-to-the-ANOVA Moderator Analysis
		Example Analog-to-the-ANOVA Moderator Analysis: Police-Led Diversion of Youth
		Meta-regression Moderator Analysis
		Example Meta-regression Moderator Analysis: Restorative Justice Programs for Youth
	Handling Statistically Dependent Effect Sizes: Robust Standard Errors
	Publication Selection Bias
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		SPSS
		Stata
		R
		Problems
	References
Chapter 12: Spatial Regression
	Why Can´t We Use OLS Regression with Spatial Data?
	How Do We Define Spatial Relationships?
	What Is Spatial Regression?
		What Is a Spatial Lag Model?
		What Is a Spatial Error Model?
	Which Type of Spatial Regression Should I Use?
		Assess Spatial Autocorrelation
		Which Type of Spatial Regression Model Should I Conduct?
	Spatial Regression Example
		Step 1: Conduct OLS Regression
		Step 2: Construct a Spatial Weights Matrix
		Step 3: Test for Spatial Autocorrelation
		Step 4: Select and Conduct Spatial Regression Model
	Chapter Summary
	Key Terms
	Symbols and Formulas
	Exercises
	Computer Exercises
		R
		OLS Regression
			Visualize OLS Regression Residuals Spatially
			Distance-Based Spatial Weights Matrix
			Contiguity-Based Spatial Weights Matrix
			Moran´s I Test of Residuals
			Lagrange Multiplier Diagnostics
			Spatial Lag/Error Regression
		Problems
	References
Glossary
Index