Statistics in criminal justice2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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chapter one1

Introduction: Statistics as a Research Tool1

The Purpose of Statistics Is to Clarify and Not Confuse3

Statistics Are Used to Solve Problems4

Basic Principles Apply Across Statistical Techniques5

The Uses of Statistics7

chapter two13

Measurement: The Basic Building Block of Research13

Science and Measurement: Classification as a First Step in Research14

Levels of Measurement15

Relating Interval, Ordinal, and Nominal Scales: The Importance of Collecting Data at the Highest Level Possible22

What Is a Good Measure?23

chapter three33

Representing and Displaying Data33

What Are Frequency Distributions and Histograms?34

Extending Histograms to Multiple Groups: Using Bar Charts40

Using Bar Charts with Nominal or Ordinal Data47

Pie Charts48

Time Series Data49

chapter four59

Describing the Typical Case: Measures of Central Tendency59

The Mode: Central Tendency in Nominal Scales60

The Median: Taking into Account Position62

The Mean: Adding Value to Position68

Statistics in Practice: Comparing the Median and the Mean76

chapter five86

How Typical Is the Typical Case?: Measuring Dispersion86

Measures of Dispersion for Nominal-and Ordinal-Level Data87

Measuring Dispersion in Interval Scales: The Range, Variance, and Standard Deviation94

chapter six115

The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics115

The Dilemma: Making Statements About Populations from Sample Statistics116

The Research Hypothesis119

The Null Hypothesis121

Risks of Error in Hypothesis Testing123

Risks of Error and Statistical Levels of Significance125

Departing from Conventional Significance Criteria127

chapter seven135

Defining the Observed Significance Level of a Test:A Simple Example Using the Binomial Distribution135

The Fair Coin Toss137

DifferentWays of Getting Similar Results141

Solving More Complex Problems144

The Binomial Distribution145

Using the Binomial Distribution to Estimate the Observed Significance Level of a Test149

chapter eight159

Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses159

The Problem: The Impact of Problem-Oriented Policing on Disorderly Activity at Violent-Crime Hot Spots160

Assumptions: Laying the Foundations for Statistical Inference162

Selecting a Sampling Distribution168

Significance Level and Rejection Region170

The Test Statistic175

Making a Decision175

chapter nine184

Chi-Square: A Test Commonly Used for Nominal-Level Measures184

Testing Hypotheses Concerning the Roll of a Die185

Relating Two Nominal-Scale Measures in a Chi-Square Test193

Extending the Chi-Square Test to Multicategory Variables: The Example of Cell Allocations in Prison199

Extending the Chi-Square Test to a Relationship Between Two Ordinal Variables: Identification with Fathers and Delinquent Acts204

The Use of Chi-Square When Samples Are Small: A Final Note209

chapter ten219

The Normal Distribution and Its Application to Tests of Statistical Significance219

The Normal Frequency Distribution,or Normal Curve220

Applying Normal Sampling Distributions to Nonnormal Populations232

Comparing a Sample to an Unknown Population: The Single-Sample z-Test for Proportions237

Comparing a Sample to an Unknown Population: The Single-Sample t-Test for Means242

chapter eleven254

Comparing Means and Proportions in Two Samples254

Comparing Sample Means255

Comparing Sample Proportions: The Two-Sample t-Test for Differences of Proportions267

The t-Test for Dependent Samples273

A Note on Using the t-Test for Ordinal Scales278

chapter twelve290

Comparing Means Among More Than Two Samples: Analysis of Variance290

Analysis of Variance291

Defining the Strength of the Relationship Observed312

Making Pairwise Comparisons Between the Groups Studied315

A Nonparametric Alternative: The Kruskal-Wallis Test318

chapter thirteen333

Measures of Association for Nominal and Ordinal Variables333

Distinguishing Statistical Significance and Strength of Relationship:The Example of the Chi-Square Statistic334

Measures of Association for Nominal Variables337

Measures of Association for Ordinal Variables349

Choosing the Best Measure of Association for Nominal-and Ordinal-Level Variables367

chapter fourteen379

Measuring Association for Interval-Level Data:Pearson's Correlation Coefficient379

Measuring Association Between Two Interval-Level Variables380

Pearson's Correlation Coefficient382

Spearman's Correlation Coefficient400

Testing the Statistical Significance of Pearson's r402

Testing the Statistical Significance of Spearman's r409

chapter fifteen419

An Introduction to Bivariate Regression419

Estimating the Influence of One Variable on Another: The Regression Coefficient420

Prediction in Regression: Building the Regression Line425

Evaluating the Regression Model433

The F-Test for the Overall Regression447

chapter sixteen459

Multivariate Regression459

The Importance of Correct Model Specifications460

Correctly Specifying the Regression Model472

The Problem of Multicollinearity482

chapter seventeen494

Logistic Regression494

Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable?496

Logistic Regression501

Interpreting Logistic Regression Coefficients513

Comparing Logistic Regression Coefficients523

Evaluating the Logistic Regression Model529

Statistical Significance in Logistic Regression533

chapter eighteen546

Special Topics: Confidence Intervals546

Confidence Intervals548

Constructing Confidence Intervals552

chapter nineteen568

Special Topics: Statistical Power568

Statistical Power570

Parametric versus Nonparametric Tests579

Estimating Statistical Power: What Size Sample Is Needed for a Statistically Powerful Study?579

Summing Up: Avoiding Studies Designed for Failure583

appendix 1 Factorials590

appendix 2 Critical Values of x2 Distribution591

appendix 3 Areas of the Standard Normal Distribution592

appendix 4 Critical Values of Student's tDistribution593

appendix 5 Critical Values of the F-Statistic594

appendix 6 Critical Value forP (Pcrit), Tukey's HSD Test597

appendix 7 Critical Values for Spearman's Rank-Order Correlation Coefficient598

appendix 8 Fisher r-to-Z* Transformation599

Glossary601

Index608