STATISICS IN CRIMINOLOGY AND CRIMINAL JUSTICE ANALYSIS AND INTERPRETATION2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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1 The Logic of Comparisons and Analysis2

Introduction: WhyAnalyze Data?3

Some Statistical History3

Uses of Statistics4

Theory Construction at a Glance5

What Is Theory?5

Theoryand Research5

The Process of Scientific Inquiry6

Observation and Inquisitiveness6

Primary Questions8

Research Questions8

Research: Movement from Theory to Data and Back8

Formulating Hypotheses9

Constructing the Research Design10

Conceptualization10

Operationalization11

Gathering the Data12

Statistical Analysis: The Art of Making Comparisons14

Foundations of Valid Comparisons14

Comparing Appropriate Phenomena15

Using Comparable Measures15

Choosing Analysis Methods That Best Summarize the Data16

Drawing Conclusions16

Communicating the Results17

Data and Purposes of This Book17

Key Terms19

Exercises20

References20

For Further Reading21

Notes21

2 Variables and Measurement22

The Variable Defined23

Transforming Characteristics into Data: The Process of Measurement23

How Variables Can Differ25

Levels of Measurement26

Scale Continuity36

Use in the Research Process37

Conclusions40

Key Terms40

Exercises40

References44

For Further Reading45

Notes45

3 Understanding Data Through Organization46

Frequency Distributions: A Chart of a Different Color49

Conventions for Building Distributions49

Frequency Distributions52

Percentage Distributions54

Combination Distributions56

Graphical Representation of Frequencies56

Pie Charts56

Histograms and Bar Charts57

Polygons and Area Charts61

Analyzing Univariate Statistics62

Analyzing Change64

Line Charts64

Ogives64

Analyzing Bivariate and Multivariate Data65

Scatter Plots65

Normal Probability Plots67

Path Diagrams68

Analyzing Geographic Distributions69

Pin, Spot, or Point Maps69

Choropleth Maps70

Conclusion74

Key Terms75

Exercises75

References77

Notes77

4 Measures of CentralTendency78

Univariate Descriptive Statistics79

Measures of Central Tendency79

Mode80

Median85

Mean90

Selecting the Most Appropriate Measure of Central Tendency92

Conclusion94

Key Terms95

Summary of Equations95

Exercises96

References100

Notes101

5 Measuresof Dispersion102

Deviation and Dispersion103

Measures of Dispersion105

Range105

Index of Dispersion108

Mean Absolute Deviation110

Variance111

Standard Deviation116

Uses for the Variance and Standard Deviation117

Selecting the Most Appropriate Measure of Dispersion117

Conclusion117

Key Terms118

Summaryof Equations118

Exercises118

References123

For Further Reading123

Note123

6 The Form of a Distribution124

Momentsof a Distribution125

Number of Modes125

Skewness126

Analysis of Skew127

Kurtosis129

Analysis of Kurtosis129

The Importance of Skew and Kurtosis129

Design of the Normal Curve130

Points to Remember About the Normal Curve135

Conclusion136

Key Terms136

Summary0fEquati0ns136

Exercises136

References141

For Further Reading141

Note141

7 Introduction to Bivariate Descriptive Statistics142

Bivariate Tables and Analysis143

Statistical Tables versus Presentation Tables145

Constructing BivariateTables147

Ordinal Level Table Construction148

Nominal LevelTable Construction152

Analysis of BivariateTables152

Conclusion153

Key Terms153

Exercises153

Notes155

8 Measures of Existence and Statistical Significance156

Nominal Level Measures of Existence157

Tables, Percentages, and Differences158

Chi-Square162

Requirements for Using Chi-Square170

Limitations of Chi-Square172

FinalNote on Chi-Square173

Tests of Existence for Ordinal and Interval Level Data173

Calculation and Interpretation for Ordinal Data174

Spearman's Rho and Pearson's r174

An Issue of Significance179

Conclusion179

Key Terms180

Summary of Equations180

Exercises180

References188

For Further Reading188

Notes188

9 Measures of Strength of a Relationship190

What Is Association?191

Nominal Level Data195

Ordinal Level Data199

Tau204

Gamma212

Somers' d214

Spearman's Rho216

Interval Level Data220

Pearson's r221

Conclusion: Selecting the Most Appropriate Measure of Strength228

Key Terms229

Summary of Equations229

Exercises230

References236

Note237

10 Measures of Direction and Nature of a Relationship239

Direction of the Association239

Establishing Direction for Ordinal Level Data239

Establishing Direction for Interval and Ratio Level Data242

Nature of the Association244

Establishing the Nature of the Distribution for Nominal and Ordinal Level Data244

Establishing the Nature of the Distribution for Interval and Ratio Level Data247

Conclusions248

Key Terms249

Exercises249

11 Introduction to Multivariate Statistics254

When Two Variables Just Aren't Enough255

Interaction Among Variables255

Causation258

Association258

Temporal Ordering259

Elimination of Confounding Variables261

Additional Concepts in Multivariate Analysis262

Robustness262

Error263

Parsimony264

Conclusion265

Key Terms265

Summaryof Equations265

Exercises265

References266

Note267

12 Multiple Regression I: Ordinary Least Squares Regression269

Regression269

Assumptions271

Analysis and Interpretation274

Steps in OLS Regression Analysis278

Other OLS Regression Information283

Limitations of OLS Regression283

Independent Variables with Lower Levels of Measurement and Nonlinear Relationships283

Dummy Variables284

Interaction Terms285

Nonlinear Relationships and Transformations287

Parabolic Functions287

Logarithmic Functions291

Multicollinearity292

Assessing Multicollinearity293

Adjusting for Multicollinearity295

Conclusion295

Key Terms296

Key Formulas296

Exercises297

References298

For Further Reading299

Notes299

13 Multiple Regression Ⅱ: Limited Dependent Variables301

Dealing with Limited Dependent Variables301

OLS Assumptions That Are Violated by Dichotomous Variables302

Logistic Regression305

Interpreting Logit Results306

Interactive Effects and Other Types of Logit313

Criticisms of Logistic Regression315

P0iss0n and Negative Binomial Regression316

A Note About Dispersion in Poisson and Negative Binomial Regression317

Interpreting Poisson and Negative Binomial Regression317

Criticisms of Poisson and Negative Binomial Models320

Other Multiple Regression Techniques320

Probit Regression320

Tobit Regression321

Multicollinearity and Alternative Regression Techniques321

Conclusion322

Key Terms322

Exercises322

References323

14 Factor Analysis and Structural Equation Modeling325

Introduction325

Factor Analysis325

Assumptions327

Analysis and Interpretation328

Structural Equation Modeling341

Variables in Structural Equation Modeling342

SEM Assumptions342

Advantages of SEM342

SEM Analysis344

Conclusion348

Key Terms348

Key Equations349

Questions and Exercises349

References349

15 Introduction to Inferential Analysis352

Terminology and Assumptions354

Normal Curve355

Probability357

Sampling359

Probability Sampling360

Nonprobability Sampling363

Sampling Distributions364

Central Limit Theorem366

Confidence Intervals367

Calculating Confidence Intervals367

Interpreting Confidence Intervals369

Conclusion370

Key Words370

Summary of Equations371

Exercises371

References371

16 Hypothesis Testing372

Null and Research Hypotheses374

Steps in Hypothesis Testing375

Type I and Type Ⅱl Errors380

Which Is Better, Type Ⅰ or Type Ⅱ Error?382

Power of Tests383

Conclusion385

Key Terms385

Summary of Equations385

Exercises385

References386

For Further Reading387

17 HypothesisTests389

ZTest389

Calculation and Example390

Interpretation and Application: Known Probability of Error392

One- versus Two-Sample ZTests396

t-test396

Assumptions of a t-test397

Calculation and Example398

SPSS Analysis for Ztests and t-tests400

Chi-squareTest forlndependence406

Conclusion407

Key Terms407

Summary of Equations407

Exercises408

References409

Note409

18 Analysis of Variance (ANOVA)411

ANOVA411

Assumptions412

Calculation and Interpretation413

Post HocTests418

Conclusion420

Key Terms420

Summary of Equations420

Exercises420

References421

For Further Reading421

Notes421

19 Putting It All Together423

The Relationship Between Statistics, Methodology, and Theory423

Describe It or Make Inferences424

Abuses of Statistics426

When You Are On Your Own427

Conclusion428

References429

Notes429

AppendixA Math Review and Practice Test431

AppendixB StatisticalTables435

AppendixC The GreekAlphabet441

AppendixD Variables in Data Sets443

Index477