| Autore |
Weisburd David
|
| Edizione | [Fifth edition.] |
| Pubbl/distr/stampa |
Cham, Switzerland : , : Springer, , [2022]
|
| Descrizione fisica |
1 online resource (552 pages)
|
| Disciplina |
364.021
|
| Soggetto topico |
Criminology
Social sciences - Statistical methods
|
| ISBN |
3-030-67738-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- 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.
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| Record Nr. | UNINA-9910523896203321 |
Info
Advanced statistics in criminology and criminal justice / / David Weisburd, David B. Wilson, Alese Wooditch, and Chester Britt
