Full Description
Packed with real-world case studies and contemporary examples utilizing the most current crime data and empirical research available, students not only learn how to perform and understand statistical analyses, but also recognize the connection between statistical analyses use in everyday life and its importance to criminology and criminal justice.The authors continue to 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.This edition will also be accompanied by a SAGE Edge site. New to this edition:Includes current crime data and new research examples New learning objectives guide students through each chapter, reinforcing the most important concepts for students to understand before proceeding to the next chapter.New SPSS exercises that correspond to relevant chapter material give students hand-on experience using real data
Contents
CHAPTER 1SETTING THE STAGE FOR STATISTICAL INQUIRYPOPULATIONS AND SAMPLESPROBABILITY SAMPLING TECHNIQUESNONPROBABILITY SAMPLING TECHNIQUESDESCRIPTIVE AND INFERENTIAL STATISTICSPART 1: Univariate Analysis: Describing Variable DistributionsCHAPTER 3: UNDERSTANDING DATA DISTRIBUTIONSTHE SHAPE OF A DISTRIBUTIONTIME PLOTSTHE MODETHE MEDIANTHE MEANMEASURING DISPERSION FOR NOMINAL- AND ORDINAL-LEVEL VARIABLESMEASURING DISPERSION FOR INTERVAL- AND RATIO-LEVEL VARIABLESTHE STANDARD DEVIATION AND VARIANCECOMPUTATIONAL FORMULAS FOR VARIANCE AND STANDARD DEVIATIONGRAPHING DISPERSION WITH EXPLORATORY DATA ANALYSIS (EDA)PART 2: Making Inferences in Univariate Analysis: Generalizing From a Sample to the PopulationHYPOTHESIS TESTINGPROBABILITY DISTRIBUTIONSA DISCRETE PROBABILITY DISTRIBUTION-THE BINOMIAL DISTRIBUTIONHYPOTHESIS TESTING WITH THE BINOMIAL DISTRIBUTIONA CONTINUOUS PROBABILITY DISTRIBUTION-THE STANDARD NORMAL DISTRIBUTIONSAMPLES, POPULATIONS, SAMPLING DISTRIBUTIONS, AND THE CENTRAL LIMIT THEOREMCHAPTER 7: POINT ESTIMATION AND CONFIDENCE INTERVALSMAKING INFERENCES FROM POINT ESTIMATES: COFIDENCE INTERVALSESTIMATING A POPULATION MEAN FROM LARGE SAMPLESESTIMATING CONFIDENCE INTERVALS FOR A MEAN FROM SMALL SAMPLESESTIMATING CONFIDENCE INTERVALS FOR PROPORTIONS AND PERCENTS WITH A LARGE SAMPLEPOPULATION MEAN AND PROPORTIONHYPOTHESIS TESTING FOR POPULATION MEANS USING A LARGE SAMPLE: THE z TESTDIRECTIONAL AND NONDIRECTIONAL HYPOTHESIS TESTSHYPOTHESIS TESTING FOR POPULATION MEANS USING SMALL SAMPLES: THE t TESTHYPOTHESIS TESTING FOR POPULATION PROPORTIONS AND PERCENTS USING LARGE SAMPLESPART 3: Bivariate Analysis: Relationships Between Two VariablesCHAPTER 9: TESTING HYPOTHESIS WITH CATEGORICAL DATAA SIMPLE-TO-USE COMPUTATIONAL FORMULA FOR THE CHI-SQUARE TEST OF INDEPENDENCEBETWEENTWO CATEGORICAL VARIABLESEXPLAINING THE DIFFERENCE BETWEEN TWO SAMPLE MEANSTESTING A HYPOTHESIS ABOUT THE DIFFERENCE BETWEEN TWO MEANS: INDEPENDENT SAMPLESHYPOTHESIS TESTS FOR THE DIFFERENCE BETWEEN TWO PROPORTIONS: LARGE SAMPLESCONDUCTING A HYPOTHESIS TEST WITH ANOVACHAPTER 12: BIVARIATE CORRELATION AND REGRESSIONGRAPHING THE BIVARIATE DISTRIBUTION BETWEEN TWO QUANTITATIVE VARIABLES: SCATTERPLOTSTHE PEARSON CORRELATION COEFFICIENTDETERMINATIONTHE LEAST-SQUARES REGRESSION LINE AND SLOPE COEFFICIENTCOMPARISON OF b AND rTESTING FOR THE SIGNIFICANCE OF b AND rIN THE DATAPART 4: Multivariate Analysis: Relationships Between More Than Two VariablesCHAPTER 13: CONTROLLING FOR A THIRD VARIABLE: MULTIPLE OLS REGRESSIONTHE MULTIPLE REGRESSION EQUATIONCOMPARING THE STRENGTH OF A RELATIONSHIP USING BETA WEIGHTSPARTIAL CORRELATION COEFFICIENTSCHAPTER 14: REGRESSION WITH A DICHOTOMOUS DEPENDENT VARIABLE: LOGIT MODELSVARIABLE-THE LINEAR PROBABILITY MODELTHE LOGIT REGRESSION MODEL WITH ONE INDEPENDENT VARIABLEMULTIPLE LOGISTIC REGRESSION: MODELS WITH TWO INDEPENDENT VARIABLESAPPENDIX A: Review of Basic Mathematical OperationsAPPENDIX B: Statistical TablesAPPENDIX C: Solutions for Odd-Numbered Practice Problems
