{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":24,"n_cols":5,"rows_kept":24,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[12,2,12,3],[15,2,15,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910523896203321"]
[null,null,"Autore",null,"Weisburd David"]
[null,null,"Titolo",null,"Advanced statistics in criminology and criminal justice / / David Weisburd, David B. Wilson, Alese Wooditch, and Chester Britt"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-67738-9"]
[null,null,"Edizione",null,"[Fifth edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (552 pages)"]
[null,null,"Disciplina",null,"364.021"]
[null,null,"Soggetti",null,"Criminology"]
[{"$nulls":4},"Social sciences - Statistical methods"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"an Independent Variable -- Interpreting Nonlinear Coefficients -- Note on Statistical Significance -- Transforming the Dependent Variable -- Review of Nonlinear Relationships -- Interaction Effects."]
[{"$nulls":4},"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."]
[{"$nulls":4},"Single Coefficients -- Multiple Coefficients -- Overall Model -- A Concluding Observation About Multinomial Logistic Regression Models -- Ordinal Logistic Regression -- Interpretation of Ordinal Logistic Regression Coefficients -- Substantive Example: Severity of Punishment Decisions -- Interpreting the Coefficients -- Statistical Significance -- Parallel Slopes Tests -- Score Test -- Brant Test -- Partial Proportional Odds -- Severity of Punishment Example -- Chapter Summary -- Key Terms -- Formulas -- Exercises -- Computer Exercises -- SPSS -- Multinomial Logistic Regression -- Ordinal Logistic Regression -- Stata -- Multinomial Logistic Regression -- Ordinal Logistic Regression -- Partial Proportional Odds -- R -- Multinomial Logistic Regression -- Ordinal Logistic Regression -- Partial Proportional Odds -- Problems -- References -- Chapter 6: Count-Based Regression Models -- The Poisson Distribution -- Poisson Regression -- Incident Rate Ratios (IRRs) -- Significance Testing -- Exposure and Offsets -- An Example: California 1999 Uniform Crime Report Data -- Over-Dispersion in Count Data -- Quasi-Poisson and Negative Binomial Regression -- An Example: Reanalysis of the California 1999 Uniform Crime Report Data -- Zero-Inflated Poisson and Negative Binomial Regression -- Chapter Summary -- Key Terms -- Symbols and Formulas -- Exercises -- Computer Exercises -- SPSS -- Poisson Regression -- Quasi-Poisson Regression -- Negative Binomial Regression -- Zero-Inflated Poisson/Negative Binomial Regression -- Stata -- Poisson Regression -- Quasi-Poisson Regression -- Negative Binomial Regression -- Zero-Inflated Poisson/Negative Binomial Regression -- R -- Poisson Regression -- Quasi-Poisson Regression -- Negative Binomial "]
[{"$nulls":4},"Regression -- Zero-Inflated Poisson/Negative Binomial Regression -- Problems -- References -- Chapter 7: Multilevel Regression Models."]
[{"$nulls":4},"A Simple Multilevel Model -- Fixed-Effects and Random-Effects -- A Substantive Example: Bail Decision-Making Study -- Intraclass Correlation and Explained Variance -- Deciding Between and Fixed- and Random-Effects Model -- Statistical Significance -- Bail Decision-Making Study -- Random Intercept Model with Fixed Slopes -- Statistical Significance -- Centering Independent Variables -- Bail Decision-Making Study -- Between and Within Effects -- Testing for Between and Within Effects -- Bail Decision-Making Study -- Random Coefficient Model -- Variance Estimates -- Bail Decision-Making Study -- Adding Cluster (Level 2) Characteristics -- A Substantive Example: Race and Sentencing Across Pennsylvania Counties -- Multilevel Negative Binomial Regression -- Chapter Summary -- Key Terms -- Symbols and Formulas -- Exercises -- Computer Exercises -- SPSS -- Stata -- Random Intercept Models -- Random Coefficient Models -- R -- Random Intercept Models -- Random Coefficient Models -- Problems -- References -- Chapter 8: Statistical Power -- Statistical Power -- Setting the Level of Statistical Power -- Components of Statistical Power -- Statistical Significance and Statistical Power -- Directional Hypotheses -- Sample Size and Statistical Power -- Effect Size and Statistical Power -- Estimating Statistical Power and Sample Size for a Statistically Powerful Study -- Difference of Means Test -- ANOVA -- Correlation -- Least Squares Regression -- Summing Up: Avoiding Studies Designed for Failure -- Chapter Summary -- Key Terms -- Symbols and Formulas -- Computer Exercises -- Stata -- Two-Sample Difference of Means Test -- ANOVA -- Correlation -- OLS Regression -- R -- Two-Sample Difference of Means Test -- ANOVA -- Correlation -- OLS Regression -- Problems -- References -- Chapter 9: Randomized Experiments -- The Structure of a Randomized Experiment."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"-- R -- Estimating Propensity Score -- Matching Cases -- Assessing Matches -- Estimating Treatment Effect -- Problems."]
[null,null,null,"Sommario/riassunto","This book provides the student, researcher or practitioner with the tools to understand many of the most commonly used advanced statistical analysis tools in criminology and criminal justice, and also to apply them to research problems. The volume is structured around two main topics, giving the user flexibility to find what they need quickly. The first is \"the general linear model\" which is the main analytic approach used to understand what influences outcomes in crime and justice. It presents a series of approaches from OLS multivariate regression, through logistic regression and multi-nomial regression, hierarchical regression, to count regression. The volume also examines alternative methods for estimating unbiased outcomes that are becoming more common in criminology and criminal justice, including analyses of randomized experiments and propensity score matching. It also examines the problem of statistical power, and how it can be used to better design studies. Finally, it discusses meta analysis, which is used to summarize studies; and geographic statistical analysis, which allows us to take into account the ways in which geographies may influence our statistical conclusions."]