Statistics for criminology and criminal justice:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles
SAGE
[2022]
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| Edition: | 5th edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxii, 590 Seiten Illustrationen, Diagramme |
| ISBN: | 9781544375700 |
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BRIEF CONTENTS Preface xvl Acknowledgments xix About the Authors xxii Chapter 1 · The Importance of Statistics in the Criminological Sciences or Why Do I Have to Learn This Stuff? 1 PARTI · UNIVARIATE ANALYSIS: DESCRIBING VARIABLE DISTRIBUTIONS 23 CHAPTER 2 · Levelsof Measurement and Aggregation 24 CHAPTER 3 · Data Visualization Techniques: Ways of Understanding Data Distributions CHAPTER 4 · Measures of Central Tendency 45 79 CHAPTER 5 · Measures of Dispersion 105 PART II · MAKING INFERENCES IN UNIVARIATE ANALYSIS: GENERALIZING FROM A SAMPLETOTHE POPULATION 149 CHAPTER 6 · Probability, Probability Distributions, and an Introduction to Inferential Statistics 150 CHAPTER 7 · Point Estimation and Confidence Intervals 198 CHAPTER 8 · From Estimation to Statistical Tests: Hypothesis Testing for One Population Mean and Proportion 223 PART III . BIVARIATE ANALYSIS: RELATIONSHIPS BETWEEN TWO VARIABLES 263 CHAPTER 9 · Testing Hypotheses With Categorical Data 264 CHAPTER 10 · Hypothesis Tests Involving Two Population Means or Proportions 301
CHAPTER 11 · Hypothesis Testing Involving Three or More Population Means: Analysis of Variance 345 CHAPTER 12 · Bivariate Correlation and Regression 377 PART IV · MULTIVARIABLE ANALYSIS: PREDICTING ONE DEPENDENT VARIABLE WITH TWO OR MORE INDEPENDENT VARIABLES 433 CHAPTER 13 · Controlling for a Third Variable: Multiple OLS Regression 434 CHAPTER 14 · Regression Analysis With a Dichotomous Dependent Variable: Logit Models 483 Appendix A: Review of Basic Mathematical Operations 523 Appendix B: Statistical Tables 533 Appendix C: Solutions to Odd-Numbered Practice Problems 544 Glossary 571 References 578 Index 582
DETAILED CONTENTS Preface xvi Acknowledgments xix About the Authors xxii CHAPTER 1 · The Importance of Statistics in the Criminological Sciences or Why Do I Have to Learn This Stuff? Learning Objectives Introduction Setting the Stage for Statistical Inquiry The Role of Statistical Methods in Criminology and Criminal Justice 1 1 1 3 3 ► CASE STUDY: Youth Violence 4 Descriptive Research 5 ► CASE STUDY: How Prevalent Is Youth Violence? 5 Explanatory Research 6 ► CASE STUDY: What Factors Are Related to Youth Delinquency and Violence? 6 Evaluation Research 7 ► CASE STUDY: How Effective Are School Bullying and Violence Prevention Programs? Populations and Samples How Do We Obtain a Sample? Probability Sampling Techniques Simple Random Samples Systematic Random Samples Multistage Cluster Samples Weighted or Stratified Samples Nonprobability Sampling Techniques Availability Samples Quota Samples Purposive or Judgment Samples Descriptive and Inferential Statistics Validity in Criminological Research Measurement Validity Reliability Causal Validity Summary Key Terms Practice Problems SPSS Exercises Excel Exercises Stata Exercises 7 8 9 10 10 10 11 11 12 13 13 14 15 15 16 17 17 18 18 19 19 20 21
PARTI * UNIVARIATE ANALYSIS: DESCRIBING VARIABLE DISTRIBUTIONS 23 CHAPTER 2 · Levels of Measurement and Aggregation 24 Learning Objectives Introduction Levels of Measurement Nominal Level of Measurement Ordinal Level of Measurement Interval Level of Measurement Ratio Level of Measurement The Case of Dichotomies Comparing Levels of Measurement Ways of Presenting Variables Counts and Rates Proportions and Percentages ► CASE STUDY: The Importance of Rates for Victimization Data Units of Analysis Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises 24 24 25 28 30 31 31 33 33 34 34 34 35 38 40 40 40 40 41 42 44 CHAPTER 3 · Data Visualization Techniques: Ways of Understanding Data Distributions 45 Learning Objectives Introduction The Tabular and Graphical Display of Qualitative Data Frequency Tables 45 45 47 48 ► CASE STUDY: An Analysis of Hate Crimes Using Tables Pie and Bar Charts The Tabular and Graphical Display of Quantitative Data Ungrouped Distributions 48 49 53 53 ► CASE STUDY: Police Response Time 53 Histograms Line Graphs or Polygons Grouped Frequency Distributions 56 56 58 ► CASE STUDY: Community Service Sentence Lengths Refinements to a Grouped Frequency Distribution The Shape of a Distribution Summary Key Terms Key Formulas Practice Problems SPSS Exercises 58 65 68 71 72 72 72 75
Excel Exercises Stata Exercises CHAPTER 4 · Measures of Central Tendency Learning Objectives Introduction The Mode ► CASE STUDY: The Modal Category of Mortality in Prisons ► CASE STUDY: The Modal Number of Prior Arrests Advantages and Disadvantages of the Mode The Median ► CASE STUDIES:The Median Police Response Time and Vandalism Offending The Median for Grouped Data Advantages and Disadvantages of the Median The Mean ► CASE STUDY: Calculating the Mean Motor Vehicle Theft Rate for Cities ► CASE STUDY: Calculating the Mean Police Response Time The Mean for Grouped Data Advantages and Disadvantages of the Mean Compared to the Median Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 5 · Measures of Dispersion Learning Objectives Introduction Measuring Dispersion for Nominal- and Ordinal-Level Variables The Variation Ratio ► CASE STUDY: Types of Patrolling Practices Measuring Dispersion for Interval- and Ratio-Level Variables The Range and Interquartile Range ► CASE STUDY: Calculating the Range of Homicide Rates ► CASE STUDY: Calculating the Interquartile Range of the Number of Escapes by Prison The Variance and Standard Deviation 76 77 79 79 79 80 80 82 84 84 85 87 88 89 89 91 92 95 97 97 97 98 100 102 103 105 105 105 107 107 108 110 110 111 112 114 ► CASE STUDY: Calculating the Variance and Standard Deviation of Judges' Sentences 120 ► CASE STUDY: Self-Control for Delinquent Youth 124 Calculating the Variance and Standard Deviation With Grouped Data 125 ► CASE STUDY: Hours of Community Service Sentenced to Those Convicted
of Misdemeanor Fraud 126
Computational Formulas for Variance and Standard Deviation Graphing Dispersion With Exploratory Data Analysis (EDA) Boxplots 128 132 132 ► CASE STUDY: Prisoners Sentenced to Death by State 132 138 ► CASE STUDY: Constructing a Boxplot for Police Officers Killed Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises 140 141 141 142 144 146 147 PART II · MAKING INFERENCES IN UNIVARIATE ANALYSIS: GENERALIZING FROM A SAMPLE TO THE POPULATION 149 CHAPTER 6 · Probability, Probability Distributions, and an Introduction to Inferential Statistics 150 Learning Objectives Introduction Probability. What Is It Good for? Absolutely Everything! The Rules of Probability What Is Independence? Probability Distributions A Discrete Probability Distribution—The Binomial Distribution Hypothesis Testing With the Binomial Distribution ► CASE STUDY: Predicting the Probability of a Stolen Car Getting Recovered A Continuous Probability Distribution—The Normal Probability Distribution The Area Under the Normal Curve The Standard Normal Distribution and Standard Scores Samples, Populations, Sampling Distributions, and the Central Limit Theorem ► CASE STUDY: The Probability of a Stolen Car Recovered II Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 7 · Point Estimation and Confidence Intervals Learning Objectives Introduction 150 150 152 153 157 161 162 165 166 173 175 177 181 185 187 188 188 188 191 193 195 198 198 198
Making Inferences from Point Estimates: Confidence Intervals Properties of Good Estimates Estimating a Population Mean From Large Samples ► CASE STUDY: Estimating Alcohol Consumption for College Students ► CASE STUDY: Prior Arrests Estimating Confidence Intervals for a Mean With a Small Sample 199 202 203 204 206 207 ► CASE STUDY: Work-Role Overload in Policing 209 Estimating Confidence Intervals for Proportions and Percentages With a Large Sample 213 ► CASE STUDY: Estimating the Effects of Community Policing 214 ► CASE STUDY: Clearing Homicides 215 Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 8 · From Estimation to Statistical Tests: Hypothesis Testing for One Population Mean and Proportion Learning Objectives Introduction Hypothesis Testing for Population Means Using a Large Sample: The z Test 216 216 217 217 218 219 221 223 223 223 225 ► CASE STUDY: Testing the Mean Reading Level From a Prison Literacy Program 225 ► CASE STUDY: Testing the Mean Sentence Length for Robbery 234 Directional and Nondirectional Hypothesis Tests ► CASE STUDY: Mean Socialization Levels of Violent Offenders Hypothesis Testing for Population Means Using a Small Sample: The t Test 237 241 243 ► CASE STUDY: Assets Seized by ATF 244 ► CASE STUDY: Rate of Law Enforcement Personnel 245 Hypothesis Testing for Population Proportions and Percentages Using Large Samples 247 ► CASE STUDY: AttitudesToward Gun Control 248 ► CASE STUDY: Random Drug Testing of Inmates Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata
Exercises 250 252 253 253 253 255 257 260
PART III · BIVARIATE ANALYSIS: RELATIONSHIPS BETWEEN TWO VARIABLES CHAPTER 9 · Testing Hypothesis With Categorical Data Learning Objectives Introduction Contingency Tables and the Two-Variable Chi-Square Test of Independence 263 264 264 264 265 ► CASE STUDY: Gender, Emotions, and Delinquency 265 к CASE STUDY: Liking School and Delinquency The Chi-Square Test of Independence A Simple-to-Use Computational Formula for the Chi-Square Test of Independence ► CASE STUDY: Socioeconomic Status of Neighborhoods and Police Response Time Measures of Association: Determining the Strength of the Relationship Between Two Categorical Variables Nominal-Level Variables 269 270 275 276 280 280 ► CASE STUDY: Police Role and Weapon Use 280 ► CASE STUDY: Type of Counsel and Sentence 282 Ordinal-Level Variables ► CASE STUDY: Adolescents’Employment and Drug and Alcohol Use к CASE STUDY: Age of Onset for Delinquency and Future Offending Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 1O · Hypothesis Tests Involving Two Population Means or Proportions Learning Objectives Introduction Explaining the Difference Between Two Sample Means Sampling Distribution of Mean Differences Testing a Hypothesis About the Difference Between Two Means: Independent Samples When We Can Assume Equal Variances: Pooled Variance Estimate (σ, = σ2) ► CASE STUDY: Murder Rates in States With and Without the Death Penalty 285 286 287 290 290 290 291 294 296 298 301 301 301 302 305 307 308 309 ► CASE STUDY: Social Disorganization and Crime 313 ► CASE STUDY: Boot Camps and
Recidivism 315 When We Cannot Assume Equal Variances: Separate Variance Estimate (ог * σ2) 317
► CASE STUDY: Formal Sanctions and Intimate Partner Assault ► CASE STUDY: Gender and Sentencing Matched-Groups or Dependent-Samples t Test 318 321 322 ► CASE STUDY: Problem-Oriented Policing and Crime 325 ► CASE STUDY: Empathy Training to Reduce Bullying 329 Hypothesis Tests for the Difference Between Two Proportions: Large Samples ► CASE STUDY: Education and Recidivism Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 11 · Hypothesis Testing Involving Three or More Population Means: Analysis of Variance Learning Objectives Introduction The Logic of Analysis of Variance The Problem With Using a t Test With Three or More Means ► CASE STUDY: Police Responses to Intimate Partner Violence Tÿpes of Variance: Total, Between-Groups, and Within-Group Conducting a Hypothesis Test With ANOVA After the F Test: Testing the Difference Between Pairs of Means Tukey’s Honest Significance Difference (HSD) Test A Measure of Association With ANOVA Eta Squared (Correlation Ratio) A Second ANOVA Example: Caseload Size and Success on Probation A Third ANOVA Example: Region of the Country and Homicide Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises CHAPTER 12 · Bivariate Correlation and Regression Learning Objectives Introduction Graphing the Bivariate Distribution Between Two Quantitative Variables: Scatterplots ► CASE STUDY: Predicting State-Level Crime Rates The Pearson Correlation Coefficient 332 334 336 336 336 337 339 340 342 345 345 345 346 346 347 348 353 356 356 358 359 360 362 367 367
367 368 369 371 374 377 377 377 378 385 390
A More Precise Way to Interpret a Correlation: The Coefficient of Determination The Least-Squares Regression Line and the Slope Coefficient 397 397 ► CASE STUDY: Age and Delinquency Using the Regression Line for Prediction 398 404 к CASE STUDY: Predicting State Crime Rates Comparison of b and r Testing for the Significance of b and r 405 410 411 ► CASE STUDY: Murder and Poverty Rates 414 ► CASE STUDY: Robbery Rates and Rural Population 415 ► CASE STUDY: Murder Rates and Rural Population 415 The Problems of Limited Variation, Nonlinear Relationships, and Outliers in the Data Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises 416 422 422 423 423 426 428 430 PART IV · MULTIVARIABLE ANALYSIS: PREDICTING ONE DEPENDENT VARIABLE WITH TWO OR MORE INDEPENDENT VARIABLES 433 CHAPTER 13 · Controlling for a Third Variable: Multiple OLS Regression 434 Learning Objectives Introduction What Do We Mean by Controlling for Other Important Variables? Illustrating Statistical Control With Partial Tables 434 434 435 438 ► CASE STUDY: Boot Camps and Recidivism The Multiple Regression Equation 438 439 ► CASE STUDY: Predicting Delinquency Comparing the Strength of a Relationship Using Beta Weights Partial Correlation Coefficients Multiple Coefficient of Determination, R2 Calculating Change in R2 Hypothesis Testing in Multiple Regression Another Example: Prison Density, Mean Age, and Rate of Inmate Violence ► CASE STUDY: Using a Dichotomous Independent Variable: Predicting Murder Rates in States Summary Key Terms Key Formulas Practice Problems 442 446 447
449 451 454 459 468 473 473 473 474
SPSS Exercises Excel Exercises Stata Exercises CHAPTER 14 · Regression Analysis With a Dichotomous Dependent Variable: Logit Models Learning Objectives Introduction Estimating an Ols Regression Model With a Dichotomous Dependent Variable—The Linear Probability Model ► CASE STUDY: Age and Bullying Behavior The Logit Regression Model With One Independent Variable Predicted Probabilities in Logit Models Significance Testing for Logistic Regression Coefficients Model Goodness-of-Fit Measures к CASE STUDY: Race and Capital Punishment Logistic Regression Models With Two Independent Variables ► CASE STUDY: Predicting Adult Offending With Age at Which Delinquency First Occurred and Gender ► CASE STUDY: Race of Victim, the Brutality of a Homicide, and Capital Punishment Summary Key Terms Key Formulas Practice Problems SPSS Exercises Excel Exercises Stata Exercises 477 479 480 483 483 483 484 490 490 493 497 498 500 503 503 509 514 514 514 514 516 518 520 Appendix A: Review of Basic Mathematical Operations 523 Appendix B: Statistical Tables 533 B.l Area Under the Standard Normal Curve (z Distribution) B.2 Table of Random Numbers B.3 The t Distribution B.4 Critical Values of the Chi-Square Statistic at the .05 and .01 Significance Level B.5 The F Distribution B.6 The Studentized Range Statistic, q 533 534 536 537 538 542 Appendix C: Solutions to Odd-Numbered Practice Problems 544 Glossary 571 References 578 Index 582 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T23:40:07Z |
| indexdate | 2024-07-20T07:54:42Z |
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| isbn | 9781544375700 |
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| spelling | Bachman, Ronet 1960- Verfasser (DE-588)138624550 aut Statistics for criminology and criminal justice Ronet D. Bachman, Raymond Paternoster, Theodore H. Wilson 5th edition Los Angeles SAGE [2022] xxii, 590 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Criminology Criminal justice, Administration of Methode (DE-588)4038971-6 gnd rswk-swf Kriminologie (DE-588)4033197-0 gnd rswk-swf Datenerhebung (DE-588)4155272-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Kriminologie (DE-588)4033197-0 s Datenerhebung (DE-588)4155272-6 s Methode (DE-588)4038971-6 s Statistik (DE-588)4056995-0 s DE-604 Paternoster, Raymond 1952-2017 (DE-588)1064353932 aut Wilson, Theodore H. (DE-588)1248047737 aut Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034992345&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bachman, Ronet 1960- Paternoster, Raymond 1952-2017 Wilson, Theodore H. Statistics for criminology and criminal justice Criminology Criminal justice, Administration of Methode (DE-588)4038971-6 gnd Kriminologie (DE-588)4033197-0 gnd Datenerhebung (DE-588)4155272-6 gnd Statistik (DE-588)4056995-0 gnd |
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| title | Statistics for criminology and criminal justice |
| title_auth | Statistics for criminology and criminal justice |
| title_exact_search | Statistics for criminology and criminal justice |
| title_exact_search_txtP | Statistics for criminology and criminal justice |
| title_full | Statistics for criminology and criminal justice Ronet D. Bachman, Raymond Paternoster, Theodore H. Wilson |
| title_fullStr | Statistics for criminology and criminal justice Ronet D. Bachman, Raymond Paternoster, Theodore H. Wilson |
| title_full_unstemmed | Statistics for criminology and criminal justice Ronet D. Bachman, Raymond Paternoster, Theodore H. Wilson |
| title_short | Statistics for criminology and criminal justice |
| title_sort | statistics for criminology and criminal justice |
| topic | Criminology Criminal justice, Administration of Methode (DE-588)4038971-6 gnd Kriminologie (DE-588)4033197-0 gnd Datenerhebung (DE-588)4155272-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Criminology Criminal justice, Administration of Methode Kriminologie Datenerhebung Statistik |
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