STATISICS IN CRIMINOLOGY AND CRIMINAL JUSTICE ANALYSIS AND INTERPRETATIONPDF电子书下载

1 The Logic of Comparisons and Analysis 2

Introduction: WhyAnalyze Data? 3

Some Statistical History 3

Uses of Statistics 4

Theory Construction at a Glance 5

What Is Theory? 5

Theoryand Research 5

The Process of Scientific Inquiry 6

Observation and Inquisitiveness 6

Primary Questions 8

Research Questions 8

Research: Movement from Theory to Data and Back 8

Formulating Hypotheses 9

Constructing the Research Design 10

Conceptualization 10

Operationalization 11

Gathering the Data 12

Statistical Analysis: The Art of Making Comparisons 14

Foundations of Valid Comparisons 14

Comparing Appropriate Phenomena 15

Using Comparable Measures 15

Choosing Analysis Methods That Best Summarize the Data 16

Drawing Conclusions 16

Communicating the Results 17

Data and Purposes of This Book 17

Key Terms 19

Exercises 20

References 20

For Further Reading 21

Notes 21

2 Variables and Measurement 22

The Variable Defined 23

Transforming Characteristics into Data: The Process of Measurement 23

How Variables Can Differ 25

Levels of Measurement 26

Scale Continuity 36

Use in the Research Process 37

Conclusions 40

Key Terms 40

Exercises 40

References 44

For Further Reading 45

Notes 45

3 Understanding Data Through Organization 46

Frequency Distributions: A Chart of a Different Color 49

Conventions for Building Distributions 49

Frequency Distributions 52

Percentage Distributions 54

Combination Distributions 56

Graphical Representation of Frequencies 56

Pie Charts 56

Histograms and Bar Charts 57

Polygons and Area Charts 61

Analyzing Univariate Statistics 62

Analyzing Change 64

Line Charts 64

Ogives 64

Analyzing Bivariate and Multivariate Data 65

Scatter Plots 65

Normal Probability Plots 67

Path Diagrams 68

Analyzing Geographic Distributions 69

Pin, Spot, or Point Maps 69

Choropleth Maps 70

Conclusion 74

Key Terms 75

Exercises 75

References 77

Notes 77

4 Measures of CentralTendency 78

Univariate Descriptive Statistics 79

Measures of Central Tendency 79

Mode 80

Median 85

Mean 90

Selecting the Most Appropriate Measure of Central Tendency 92

Conclusion 94

Key Terms 95

Summary of Equations 95

Exercises 96

References 100

Notes 101

5 Measuresof Dispersion 102

Deviation and Dispersion 103

Measures of Dispersion 105

Range 105

Index of Dispersion 108

Mean Absolute Deviation 110

Variance 111

Standard Deviation 116

Uses for the Variance and Standard Deviation 117

Selecting the Most Appropriate Measure of Dispersion 117

Conclusion 117

Key Terms 118

Summaryof Equations 118

Exercises 118

References 123

For Further Reading 123

Note 123

6 The Form of a Distribution 124

Momentsof a Distribution 125

Number of Modes 125

Skewness 126

Analysis of Skew 127

Kurtosis 129

Analysis of Kurtosis 129

The Importance of Skew and Kurtosis 129

Design of the Normal Curve 130

Points to Remember About the Normal Curve 135

Conclusion 136

Key Terms 136

Summary0fEquati0ns 136

Exercises 136

References 141

For Further Reading 141

Note 141

7 Introduction to Bivariate Descriptive Statistics 142

Bivariate Tables and Analysis 143

Statistical Tables versus Presentation Tables 145

Constructing BivariateTables 147

Ordinal Level Table Construction 148

Nominal LevelTable Construction 152

Analysis of BivariateTables 152

Conclusion 153

Key Terms 153

Exercises 153

Notes 155

8 Measures of Existence and Statistical Significance 156

Nominal Level Measures of Existence 157

Tables, Percentages, and Differences 158

Chi-Square 162

Requirements for Using Chi-Square 170

Limitations of Chi-Square 172

FinalNote on Chi-Square 173

Tests of Existence for Ordinal and Interval Level Data 173

Calculation and Interpretation for Ordinal Data 174

Spearman's Rho and Pearson's r 174

An Issue of Significance 179

Conclusion 179

Key Terms 180

Summary of Equations 180

Exercises 180

References 188

For Further Reading 188

Notes 188

9 Measures of Strength of a Relationship 190

What Is Association? 191

Nominal Level Data 195

Ordinal Level Data 199

Tau 204

Gamma 212

Somers' d 214

Spearman's Rho 216

Interval Level Data 220

Pearson's r 221

Conclusion: Selecting the Most Appropriate Measure of Strength 228

Key Terms 229

Summary of Equations 229

Exercises 230

References 236

Note 237

10 Measures of Direction and Nature of a Relationship 239

Direction of the Association 239

Establishing Direction for Ordinal Level Data 239

Establishing Direction for Interval and Ratio Level Data 242

Nature of the Association 244

Establishing the Nature of the Distribution for Nominal and Ordinal Level Data 244

Establishing the Nature of the Distribution for Interval and Ratio Level Data 247

Conclusions 248

Key Terms 249

Exercises 249

11 Introduction to Multivariate Statistics 254

When Two Variables Just Aren't Enough 255

Interaction Among Variables 255

Causation 258

Association 258

Temporal Ordering 259

Elimination of Confounding Variables 261

Additional Concepts in Multivariate Analysis 262

Robustness 262

Error 263

Parsimony 264

Conclusion 265

Key Terms 265

Summaryof Equations 265

Exercises 265

References 266

Note 267

12 Multiple Regression I: Ordinary Least Squares Regression 269

Regression 269

Assumptions 271

Analysis and Interpretation 274

Steps in OLS Regression Analysis 278

Other OLS Regression Information 283

Limitations of OLS Regression 283

Independent Variables with Lower Levels of Measurement and Nonlinear Relationships 283

Dummy Variables 284

Interaction Terms 285

Nonlinear Relationships and Transformations 287

Parabolic Functions 287

Logarithmic Functions 291

Multicollinearity 292

Assessing Multicollinearity 293

Adjusting for Multicollinearity 295

Conclusion 295

Key Terms 296

Key Formulas 296

Exercises 297

References 298

For Further Reading 299

Notes 299

13 Multiple Regression Ⅱ: Limited Dependent Variables 301

Dealing with Limited Dependent Variables 301

OLS Assumptions That Are Violated by Dichotomous Variables 302

Logistic Regression 305

Interpreting Logit Results 306

Interactive Effects and Other Types of Logit 313

Criticisms of Logistic Regression 315

P0iss0n and Negative Binomial Regression 316

A Note About Dispersion in Poisson and Negative Binomial Regression 317

Interpreting Poisson and Negative Binomial Regression 317

Criticisms of Poisson and Negative Binomial Models 320

Other Multiple Regression Techniques 320

Probit Regression 320

Tobit Regression 321

Multicollinearity and Alternative Regression Techniques 321

Conclusion 322

Key Terms 322

Exercises 322

References 323

14 Factor Analysis and Structural Equation Modeling 325

Introduction 325

Factor Analysis 325

Assumptions 327

Analysis and Interpretation 328

Structural Equation Modeling 341

Variables in Structural Equation Modeling 342

SEM Assumptions 342

Advantages of SEM 342

SEM Analysis 344

Conclusion 348

Key Terms 348

Key Equations 349

Questions and Exercises 349

References 349

15 Introduction to Inferential Analysis 352

Terminology and Assumptions 354

Normal Curve 355

Probability 357

Sampling 359

Probability Sampling 360

Nonprobability Sampling 363

Sampling Distributions 364

Central Limit Theorem 366

Confidence Intervals 367

Calculating Confidence Intervals 367

Interpreting Confidence Intervals 369

Conclusion 370

Key Words 370

Summary of Equations 371

Exercises 371

References 371

16 Hypothesis Testing 372

Null and Research Hypotheses 374

Steps in Hypothesis Testing 375

Type I and Type Ⅱl Errors 380

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

Power of Tests 383

Conclusion 385

Key Terms 385

Summary of Equations 385

Exercises 385

References 386

For Further Reading 387

17 HypothesisTests 389

ZTest 389

Calculation and Example 390

Interpretation and Application: Known Probability of Error 392

One- versus Two-Sample ZTests 396

t-test 396

Assumptions of a t-test 397

Calculation and Example 398

SPSS Analysis for Ztests and t-tests 400

Chi-squareTest forlndependence 406

Conclusion 407

Key Terms 407

Summary of Equations 407

Exercises 408

References 409

Note 409

18 Analysis of Variance (ANOVA) 411

ANOVA 411

Assumptions 412

Calculation and Interpretation 413

Post HocTests 418

Conclusion 420

Key Terms 420

Summary of Equations 420

Exercises 420

References 421

For Further Reading 421

Notes 421

19 Putting It All Together 423

The Relationship Between Statistics, Methodology, and Theory 423

Describe It or Make Inferences 424

Abuses of Statistics 426

When You Are On Your Own 427

Conclusion 428

References 429

Notes 429

AppendixA Math Review and Practice Test 431

AppendixB StatisticalTables 435

AppendixC The GreekAlphabet 441

AppendixD Variables in Data Sets 443

Index 477