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Statistics for Criminology and Criminal Justice 4ed

by Ronet Bachman and Raymond Paternoster SAGE Publications, Inc
Pub Date:
02/2016
ISBN:
9781506326108
Format:
Pbk 544 pages
Price:
AU$156.00 NZ$161.74
Product Status: In Stock Now
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Available as eBook
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Statistics for Criminology and Criminal Justice, Fourth Edition offers students a practical and comprehensive introduction to statistics and highlights the integral role research and statistics play in the study of criminology and criminal justice. Packed with real-world case studies and contemporary examples utilising the most current crime data and empirical research available, students not only learn how to perform and understand statistical analyses, but also recognise the connection between statistical analyses use in everyday life and its importance to criminology and criminal justice.


 


Written by two well-known experts in the field, Ronet D. Bachman and Raymond Paternoster continueto facilitate learning by presenting statistical formulas with step-by-step instructions for calculation. This“how to calculate and interpret statistics”approach avoids complicated proofs and discussions of statistical theory, without sacrificing statistical rigor. The Fourth Edition is replete with new examples exploring key issues in today’s world, motivating students to investigate research questions related to criminal justice and criminology with statistics and conduct research of their own along the way.


 


Give your students the SAGE edge!


 


SAGE edge offers a robust online environment featuring an impressive array of free tools and resources for review, study, and further exploration, keeping both instructors and students on the cutting edge of teaching and learning.


 


 


 

CHAPTER 1: THE PURPOSE OF STATISTICS IN THE CRIMINOLOGICAL SCIENCES
SETTING THE STAGE FOR STATISTICAL INQUIRY
THE ROLE OF STATISTICAL METHODS IN CRIMINOLOGY AND CRIMINAL JUSTICE
POPULATIONS AND SAMPLES
HOW DO WE OBTAIN A SAMPLE?
PROBABILITY SAMPLING TECHNIQUES
NONPROBABILITY SAMPLING TECHNIQUES
DESCRIPTIVE AND INFERENTIAL STATISTICS
VALIDITY IN CRIMINOLOGY RESEARCH
PART 1: Univariate Analysis: Describing Variable Distributions
CHAPTER 2: LEVELS OF MEASUREMENT AND AGGREGATION
LEVELS OF MEASUREMENT
WAYS OF PRESENTING VARIABLE
UNITS OF ANALYSIS
CHAPTER 3: UNDERSTANDING DATA DISTRIBUTIONS
THE TABULAR AND GRAPHICAL DISPLAY OF QUALITATIVE DATA
THE SHAPE OF A DISTRIBUTION
TIME PLOTS
CHAPTER 4: MEASURES OF CENTRAL TENDENCY
THE MODE
THE MEDIAN
THE MEAN
CHAPTER 5: MEASURES OF DISPERSION
MEASURING DISPERSION FOR NOMINAL- AND ORDINAL-LEVEL VARIABLES
MEASURING DISPERSION FOR INTERVAL- AND RATIO-LEVEL VARIABLES
THE STANDARD DEVIATION AND VARIANCE
COMPUTATIONAL FORMULAS FOR VARIANCE AND STANDARD DEVIATION
GRAPHING DISPERSION WITH EXPLORATORY DATA ANALYSIS (EDA)
PART 2: Making Inferences in Univariate Analysis: Generalizing From a Sample to the Population
CHAPTER 6: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND AN INTRODUCTION TO HYPOTHESIS TESTING
PROBABILITY. WHAT IS IT GOOD FOR? ABSOLUTELY EVERYTHING!
THE RULES OF PROBABILITY
PROBABILITY DISTRIBUTIONS
A DISCRETE PROBABILITY DISTRIBUTION'THE BINOMIAL DISTRIBUTION
HYPOTHESIS TESTING WITH THE BINOMIAL DISTRIBUTION
A CONTINUOUS PROBABILITY DISTRIBUTION'THE STANDARD NORMAL DISTRIBUTION
SAMPLES, POPULATIONS, SAMPLING DISTRIBUTIONS, AND THE CENTRAL LIMIT THEOREM
CHAPTER 7: POINT ESTIMATION AND CONFIDENCE INTERVALS
MAKING INFERENCES FROM POINT ESTIMATES: COFIDENCE INTERVALS
PROPERTIES OF GOOD ESTIMATES
ESTIMATING A POPULATION MEAN FROM LARGE SAMPLES
ESTIMATING CONFIDENCE INTERVALS FOR A MEAN FROM SMALL SAMPLES
ESTIMATING CONFIDENCE INTERVALS FOR PROPORTIONS AND PERCENTS WITH A LARGE SAMPLE
CHAPTER 8: FROM ESTIMATION TO STATISTICAL TESTS: HYPOTHESIS TESTING FOR ONE POPULATION MEAN AND PROPORTION
HYPOTHESIS TESTING FOR POPULATION MEANS USING A LARGE SAMPLE: THE z TEST
DIRECTIONAL AND NONDIRECTIONAL HYPOTHESIS TESTS
HYPOTHESIS TESTING FOR POPULATION MEANS USING SMALL SAMPLES: THE t TEST
HYPOTHESIS TESTING FOR POPULATION PROPORTIONS AND PERCENTS USING LARGE SAMPLES
PART 3: Bivariate Analysis: Relationships Between Two Variables
CHAPTER 9: TESTING HYPOTHESIS WITH CATEGORICAL DATA
CONTINGENCY TABLES AND THE TWO VARIABLE CHI-SQUARE TEST OF INDEPENDENCE
THE CHI-SQUARE TEST OF INDEPENDENCE
A SIMPLE-TO-USE COMPUTATIONAL FORMULA FOR THE CHI-SQUARE TEST OF INDEPENDENCE
MEASURES OF ASSOCIATION: DETERMINING THE STRENGTH OF THE RELATIONSHIP BETWEEN
TWO CATEGORICAL VARIABLES
CHAPTER 10: HYPOTHESIS TESTS INVOLVING TWO POPULATION MEANS OR PROPORTIONS
EXPLAINING THE DIFFERENCE BETWEEN TWO SAMPLE MEANS
SAMPLING DISTRIBUTION OF MEAN DIFFERENCES
TESTING A HYPOTHESIS ABOUT THE DIFFERENCE BETWEEN TWO MEANS: INDEPENDENT SAMPLES
MATCHED-GROUPS OR DEPENDENT SAMPLES t TEST
HYPOTHESIS TESTS FOR THE DIFFERENCE BETWEEN TWO PROPORTIONS: LARGE SAMPLES
CHAPTER 11: HYPOTHESIS TESTING INVOLVING THREE OR MORE POPULATION MEANS: ANALYSIS OF VARIANCE
THE LOGIC OF ANALYSIS OF VARIANCE
TYPES 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
A MEASURE OF ASSOCIATION WITH ANOVA
A SECOND ANOVA EXAMPLE: CASELOAD SIZE AND SUCCESS ON PROBATION
A THIRD ANOVA EXAMPLE: REGION OF THE COUNTRY AND HOMICIDE
CHAPTER 12: BIVARIATE CORRELATION AND REGRESSION
GRAPHING THE BIVARIATE DISTRIBUTION BETWEEN TWO QUANTITATIVE VARIABLES: SCATTERPLOTS
THE PEARSON CORRELATION COEFFICIENT
A MORE PRECISE WAY TO INTERPRET A CORRELATION: THE COEFFICIENT OF DETERMINATION
THE LEAST-SQUARES REGRESSION LINE AND SLOPE COEFFICIENT
COMPARISON OF b AND r
TESTING FOR THE SIGNIFICANCE OF b AND r
THE PROBLEMS OF LIMITED VARIATION, NONLINEAR RELATIONSHIPS, AND OUTLIERS IN THE DATA
PART 4: Multivariate Analysis: Relationships Between More Than Two Variables
CHAPTER 13: CONTROLLING FOR A THIRD VARIABLE: MULTIPLE OLS REGRESSION
WHAT DO WE MEAN BY CONTROLLING FOR OTHER IMPORTANT VARIABLES?
THE MULTIPLE REGRESSION EQUATION
COMPARING THE STRENGTH OF A RELATIONSHIP USING BETA WEIGHTS
PARTIAL CORRELATION COEFFICIENTS
HYPOTHESIS TESTING IN MULTIPLE REGRESSION
ANOTHER EXAMPLE: PRISON DENSITY, MEAN AGE, AND RATE OF INMATE VIOLENCE
CHAPTER 14: REGRESSION WITH A DICHOTOMOUS DEPENDENT VARIABLE: LOGIT MODELS
ESTIMATING AN OLS REGRESSION MODEL WITH A DICHOTOMOUS DEPENDENT VARIABLE'THE LINEAR PROBABILITY MODEL
THE LOGIT REGRESSION MODEL WITH ONE INDEPENDENT VARIABLE
MULTIPLE LOGISTIC REGRESSION: MODELS WITH TWO INDEPENDENT VARIABLES
APPENDIX A: Review of Basic Mathematical Operations
APPENDIX B: Statistical Tables
APPENDIX C: Solutions for Odd-Numbered Practice Problems

“Detailed and systematic presentation of statistical concepts”

Ronet Bachman, PhD, is Professor in the Department of Sociology and Criminal Justice at the University of Delaware. Her most recent federally funded research was a mixed methods study that investigated the long term trajectories of offending behaviour using official data of a prison cohort released in the early 1990s and then interviewed in 2009. Raymond Paternoster, PhD is a Professor in the Department of Criminology and Criminal Justice at the University of Maryland. In addition to his interest in statistics, he also pursues questions related to offender decision making and rational choice theory, desistance from crime, and capital punishment.