Statistics in criminal justice.

By:

Weisburd, David

Contributor(s):

Britt, Chester L

Material type: Text

TextLanguage: English Publication details: College Park, MD : Springer, c2014Edition: 4th edDescription: xv, 783 pISBN:

9780387341125

Subject(s):

DDC classification:

343.1/519.5 WEI

Online resources:

Contents:

iv CHAPTER NUMBER : CHAPTER TITLE Contents Preface xiii Chapter one Introduction: Statistics as a Research Tool 1 The Purpose of Statistics Is to Clarify 3 Statistics Are Used to Solve Problems 4 Basic Principles Apply Across Statistical Techniques 5 The Uses of Statistics 7 Chapter two Measurement: The Basic Building Block of Research 13 Science and Measurement: Classification as a First Step in Research 14 Levels of Measurement 15 Relating Interval, Ordinal, and Nominal Scales: The Importance of Collecting Data at the Highest Level Possible 22 What Is a Good Measure? 23 Chapter three Representing and Displaying Data 36 What Are Frequency Distributions and Histograms? 37 Extending Histograms to Multiple Groups: Using Bar Charts 43 Using Bar Charts with Nominal or Ordinal Data 50 Pie Charts 51 Time Series Data 52 Chapter four Describing the Typical Case: Measures of Central Tendency 65 The Mode: Central Tendency in Nominal Scales 66 The Median: Taking into Account Position 68 The Mean: Adding Value to Position 74 Statistics in Practice: Comparing the Median and the Mean 82 Chapter five How Typical Is the Typical Case?: Measuring Dispersion 94 Measures of Dispersion for Nominal- and Ordinal-Level Data 95 Measuring Dispersion in Interval Scales: The Range, Variance, and Standard Deviation 102 vii viii CONTENTS Chapter six The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics 125 The Dilemma: Making Statements About Populations from Sample Statistics 126 The Research Hypothesis 129 The Null Hypothesis 131 Risks of Error in Hypothesis Testing 133 Risks of Error and Statistical Levels of Significance 135 Departing from Conventional Significance Criteria 137 Chapter seven Defining the Observed Significance Level of a Test: A Simple Example Using the Binomial Distribution 145 The Fair Coin Toss 147 Different Ways of Getting Similar Results 151 Solving More Complex Problems 154 The Binomial Distribution 155 Using the Binomial Distribution to Estimate the Observed Significance Level of a Test 159 Chapter eight Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses 171 The Problem: The Impact of Problem-Oriented Policing on Disorderly Activity at Violent-Crime Hot Spots 172 Assumptions: Laying the Foundations for Statistical Inference 174 Selecting a Sampling Distribution 180 Significance Level and Rejection Region 182 The Test Statistic 187 Making a Decision 187 Chapter nine Chi-Square: A Test Commonly Used for Nominal-Level Measures 197 Testing Hypotheses Concerning the Roll of a Die 198 Relating Two Nominal-Scale Measures in a Chi-Square Test 206 Extending the Chi-Square Test to Multicategory Variables: The Example of Cell Allocations in Prison 212 Extending the Chi-Square Test to a Relationship Between Two Ordinal Variables: Identification with Fathers and Delinquent Acts 217 The Use of Chi-Square When Samples Are Small: A Final Note 222 Chapter ten The Normal Distribution and Its Application to Tests of Statistical Significance 234 The Normal Frequency Distribution, or Normal Curve 235 Applying Normal Sampling Distributions to Nonnormal Populations 247 Comparing a Sample to an Unknown Population: The Single-Sample z-Test for Proportions 252 Comparing a Sample to an Unknown Population: The Single-Sample t-Test for Means 257 CONTENTS Chapter eleven Comparing Means and Proportions in Two Samples 269 Comparing Sample Means 270 Comparing Sample Proportions: The Two-Sample t-Test for Differences of Proportions 282 The t-Test for Dependent Samples 288 A Note on Using the t-Test for Ordinal Scales 293 Chapter twelve Comparing Means Among More Than Two Samples: Analysis of Variance 306 Analysis of Variance 307 Defining the Strength of the Relationship Observed 328 Making Pairwise Comparisons Between the Groups Studied 331 A Nonparametric Alternative: The Kruskal-Wallis Test 334 Chapter thirteen Measures of Association for Nominal and Ordinal Variables 351 Distinguishing Statistical Significance and Strength of Relationship: The Example of the Chi-Square Statistic 352 Measures of Association for Nominal Variables 355 Measures of Association for Ordinal-Level Variables 367 Choosing the Best Measure of Association for Nominal- and Ordinal-Level Variables 385 Chapter fourteen Measuring Association for Interval-Level Data: Pearson’s Correlation Coefficient 398 Measuring Association Between Two Interval-Level Variables 399 Pearson’s Correlation Coefficient 401 Spearman’s Correlation Coefficient 419 Testing the Statistical Significance of Pearson’s r 421 Testing the Statistical Significance of Spearman’s r 428 Chapter fifteen An Introduction to Bivariate Regression 439 Estimating the Influence of One Variable on Another: The Regression Coefficient 440 Prediction in Regression: Building the Regression Line 445 Evaluating the Regression Model 453 The F-Test for the Overall Regression 467 Chapter sixteen Multivariate Regression 481 The Importance of Correct Model Specifications 482 Correctly Specifying the Regression Model 494 ix x CONTENTS Chapter seventeen Multivariate Regression: Additional Topics 514 Non-linear Relationships 516 Interaction Effects 522 An Example: Race and Punishment Severity 525 An Example: Punishment Severity 533 The Problem of Multicollinearity 534 Chapter eighteen Logistic Regression 548 Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable? 550 Logistic Regression 555 Interpreting Logistic Regression Coefficients 567 Comparing Logistic Regression Coefficients 577 Evaluating the Logistic Regression Model 583 Statistical Significance in Logistic Regression 587 Chapter nineteen Multivariate Regression with Multiple Category Nominal or Ordinal Measures: Extending the Basic Logistic Regression Model 601 Multinomial Logistic Regression 603 Ordinal Logistic Regression 615 Substantive Example: Severity of Punishment Decisions 619 Chapter twenty Multilevel Regression Models 637 Variance Components Model 640 Random Intercept Model 646 Random Coefficient Model 655 Adding Cluster (Level 2) Characteristics 660 Chapter twenty one Special Topics: Randomized Experiments 674 The Structure of a Randomized Experiment 676 The Main Advantage of Experiments: Isolating Causal Effects 677 Internal Validity 682 Sample Size, Equivalence, and Statistical Power 683 Statistical Power and Block Randomization 691 Using Covariates to Increase Statistical Power in Experimental Studies 693 Examining Interaction Terms in Experimental Research 695 Chapter twenty two Special Topics: Confidence Intervals 702 Confidence Intervals 704 Constructing Confidence Intervals 708 CONTENTS Chapter twenty three Special Topics: Statistical Power 726 Statistical Power 728 Components of Statistical Power 731 Estimating Statistical Power and Sample Size for a Statistically Powerful Study 738 Summing Up: Avoiding Studies Designed for Failure 747 Appendix Appendix Appendix Appendix Appendix Appendix Appendix Appendix 1 2 3 4 5 6 7 8 Factorials 759 Critical Values of ␹2 Distribution 760 Areas of the Standard Normal Distribution 761 Critical Values of Student’s t Distribution 762 Critical Values of the F-Statistic 763 Critical Value for P (Pcrit), Tukey’s HSD Test 766 Critical Values for Spearman’s Rank-Order Correlation Coefficient 767 Fisher r-to-Z* Transformation 768 Glossary 770 Index 778 xi http://www.springer.com/978-1-4614-9169-9

Tags from this library: No tags from this library for this title.

Average rating: 0.0 (0 votes)

No physical items for this record

iv
CHAPTER
NUMBER
:
CHAPTER
TITLE
Contents
Preface
xiii
Chapter one
Introduction: Statistics as a Research Tool 1
The Purpose of Statistics Is to Clarify 3
Statistics Are Used to Solve Problems 4
Basic Principles Apply Across Statistical Techniques 5
The Uses of Statistics 7
Chapter two
Measurement: The Basic Building Block of Research 13
Science and Measurement: Classification as a First Step in Research 14
Levels of Measurement 15
Relating Interval, Ordinal, and Nominal Scales: The Importance of Collecting Data
at the Highest Level Possible 22
What Is a Good Measure? 23
Chapter three
Representing and Displaying Data 36
What Are Frequency Distributions and Histograms? 37
Extending Histograms to Multiple Groups: Using Bar Charts 43
Using Bar Charts with Nominal or Ordinal Data 50
Pie Charts 51
Time Series Data 52
Chapter four
Describing the Typical Case: Measures of Central Tendency 65
The Mode: Central Tendency in Nominal Scales 66
The Median: Taking into Account Position 68
The Mean: Adding Value to Position 74
Statistics in Practice: Comparing the Median and the Mean 82
Chapter five
How Typical Is the Typical Case?: Measuring Dispersion 94
Measures of Dispersion for Nominal- and Ordinal-Level Data 95
Measuring Dispersion in Interval Scales: The Range, Variance, and Standard Deviation 102
vii
viii
CONTENTS
Chapter six
The Logic of Statistical Inference: Making Statements
About Populations from Sample Statistics 125
The Dilemma: Making Statements About Populations from Sample Statistics 126
The Research Hypothesis 129
The Null Hypothesis 131
Risks of Error in Hypothesis Testing 133
Risks of Error and Statistical Levels of Significance 135
Departing from Conventional Significance Criteria 137
Chapter seven
Defining the Observed Significance Level of a Test:
A Simple Example Using the Binomial Distribution 145
The Fair Coin Toss 147
Different Ways of Getting Similar Results 151
Solving More Complex Problems 154
The Binomial Distribution 155
Using the Binomial Distribution to Estimate the Observed Significance Level of a Test 159
Chapter eight
Steps in a Statistical Test: Using the Binomial Distribution
to Make Decisions About Hypotheses 171
The Problem: The Impact of Problem-Oriented Policing on Disorderly Activity at Violent-Crime Hot Spots 172
Assumptions: Laying the Foundations for Statistical Inference 174
Selecting a Sampling Distribution 180
Significance Level and Rejection Region 182
The Test Statistic 187
Making a Decision 187
Chapter nine
Chi-Square: A Test Commonly Used for Nominal-Level Measures 197
Testing Hypotheses Concerning the Roll of a Die 198
Relating Two Nominal-Scale Measures in a Chi-Square Test 206
Extending the Chi-Square Test to Multicategory Variables: The Example of Cell Allocations in Prison 212
Extending the Chi-Square Test to a Relationship Between Two Ordinal Variables: Identification with Fathers
and Delinquent Acts 217
The Use of Chi-Square When Samples Are Small: A Final Note 222
Chapter ten
The Normal Distribution and Its Application to Tests
of Statistical Significance 234
The Normal Frequency Distribution, or Normal Curve 235
Applying Normal Sampling Distributions to Nonnormal Populations 247
Comparing a Sample to an Unknown Population: The Single-Sample z-Test for Proportions 252
Comparing a Sample to an Unknown Population: The Single-Sample t-Test for Means 257
CONTENTS
Chapter eleven
Comparing Means and Proportions in Two Samples 269
Comparing Sample Means 270
Comparing Sample Proportions: The Two-Sample t-Test for Differences of Proportions 282
The t-Test for Dependent Samples 288
A Note on Using the t-Test for Ordinal Scales 293
Chapter twelve
Comparing Means Among More Than Two Samples: Analysis of Variance 306
Analysis of Variance 307
Defining the Strength of the Relationship Observed 328
Making Pairwise Comparisons Between the Groups Studied 331
A Nonparametric Alternative: The Kruskal-Wallis Test 334
Chapter thirteen
Measures of Association for Nominal and Ordinal Variables 351
Distinguishing Statistical Significance and Strength of Relationship:
The Example of the Chi-Square Statistic 352
Measures of Association for Nominal Variables 355
Measures of Association for Ordinal-Level Variables 367
Choosing the Best Measure of Association for Nominal- and Ordinal-Level Variables 385
Chapter fourteen
Measuring Association for Interval-Level Data:
Pearson’s Correlation Coefficient 398
Measuring Association Between Two Interval-Level Variables 399
Pearson’s Correlation Coefficient 401
Spearman’s Correlation Coefficient 419
Testing the Statistical Significance of Pearson’s r 421
Testing the Statistical Significance of Spearman’s r 428
Chapter fifteen
An Introduction to Bivariate Regression 439
Estimating the Influence of One Variable on Another: The Regression Coefficient 440
Prediction in Regression: Building the Regression Line 445
Evaluating the Regression Model 453
The F-Test for the Overall Regression 467
Chapter sixteen
Multivariate Regression 481
The Importance of Correct Model Specifications 482
Correctly Specifying the Regression Model 494
ix
x
CONTENTS
Chapter seventeen
Multivariate Regression: Additional Topics 514
Non-linear Relationships 516
Interaction Effects 522
An Example: Race and Punishment Severity 525
An Example: Punishment Severity 533
The Problem of Multicollinearity 534
Chapter eighteen
Logistic Regression 548
Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable? 550
Logistic Regression 555
Interpreting Logistic Regression Coefficients 567
Comparing Logistic Regression Coefficients 577
Evaluating the Logistic Regression Model 583
Statistical Significance in Logistic Regression 587
Chapter nineteen
Multivariate Regression with Multiple Category Nominal or Ordinal Measures:
Extending the Basic Logistic Regression Model 601
Multinomial Logistic Regression 603
Ordinal Logistic Regression 615
Substantive Example: Severity of Punishment Decisions 619
Chapter twenty
Multilevel Regression Models
637
Variance Components Model 640
Random Intercept Model 646
Random Coefficient Model 655
Adding Cluster (Level 2) Characteristics 660
Chapter twenty one
Special Topics: Randomized Experiments
674
The Structure of a Randomized Experiment 676
The Main Advantage of Experiments: Isolating Causal Effects 677
Internal Validity 682
Sample Size, Equivalence, and Statistical Power 683
Statistical Power and Block Randomization 691
Using Covariates to Increase Statistical Power in Experimental Studies 693
Examining Interaction Terms in Experimental Research 695
Chapter twenty two
Special Topics: Confidence Intervals 702
Confidence Intervals 704
Constructing Confidence Intervals 708
CONTENTS
Chapter twenty three
Special Topics: Statistical Power 726
Statistical Power 728
Components of Statistical Power 731
Estimating Statistical Power and Sample Size for a Statistically Powerful Study 738
Summing Up: Avoiding Studies Designed for Failure 747
Appendix
Appendix
Appendix
Appendix
Appendix
Appendix
Appendix
Appendix
1
2
3
4
5
6
7
8
Factorials 759
Critical Values of ␹2 Distribution 760
Areas of the Standard Normal Distribution 761
Critical Values of Student’s t Distribution 762
Critical Values of the F-Statistic 763
Critical Value for P (Pcrit), Tukey’s HSD Test 766
Critical Values for Spearman’s Rank-Order Correlation Coefficient 767
Fisher r-to-Z* Transformation 768
Glossary 770
Index 778
xi
http://www.springer.com/978-1-4614-9169-9

There are no comments on this title.

to post a comment.