Statistics in Criminal Justice w/ Study Guide

by Weisburd, David; Britt, Chester

• ISBN13: 9780534595081
• ISBN10: 0534595081
• Edition: 2nd
• Format: Paperback
• Copyright: 2002-12-20
• Publisher: Wadsworth Publishing

Summary

"Statistics in Criminal Justice takes an approach that emphasizes the uses of statistics in research in crime and justice. This text is meant for students and professionals who want to gain a basic understanding of statistics in this field. The text takes a building-block approach, meaning that each chapter helps to prepare the student for the chapters that follow. It also means that the level of sophistication of the text increases as the text progresses. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. However, it takes a serious approach to statistics, which is relevant to the real world of research in crime and justice. This approach is meant to provide the reader with an accessible but sophisticated understanding of statistics that can be used to examine real-life criminal justice problems. The goal of the text is to give the student a basic understanding of statistics and statistical concepts that will leave the student with the confidence and the tools for tackling more complex problems on their own, Statistics in Criminal Justice is meant not only as an introduction for students but as a reference for researchers."--BOOK JACKET.

Table of Contents

preface	viii
Introduction: Statistics as a Research Tool	1	(12)
The Purpose of Statistics Is to Clarify and Not Confuse	3	(1)
Statistics Are Used to Solve Problems	4	(1)
Basic Principles Apply Across Statistical Techniques	5	(2)
The Uses of Statistics	7	(6)
Measurement: The Basic Building Block of Research	13	(20)
Science and Measurement: Classification as a First Step in Research	14	(1)
Levels of Measurement	15	(7)
Relating Interval, Ordinal, and Nominal Scales: The Importance of Collecting Data at the Highest Level Possible	22	(1)
What Is a Good Measure?	23	(10)
Representing and Displaying Data	33	(26)
What Are Frequency Distributions and Histograms?	34	(6)
Extending Histograms to Multiple Groups: Using Bar Charts	40	(7)
Using Bar Charts with Nominal or Ordinal Data	47	(1)
Pie Charts	48	(1)
Time Series Data	49	(10)
Describing the Typical Case: Measures of Central Tendency	59	(27)
The Mode: Central Tendency in Nominal Scales	60	(2)
The Median: Taking into Account Position	62	(6)
The Mean: Adding Value to Position	68	(8)
Statistics in Practice: Comparing the Median and the Mean	76	(10)
How Typical Is the Typical Case?: Measuring Dispersion	86	(29)
Measures of Dispersion for Nominal-and Ordinal-Level Data	87	(7)
Measuring Dispersion in Interval Scales: The Range, Variance, and Standard Deviation	94	(21)
The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics	115	(20)
The Dilemma: Making Statements About Populations from Sample Statistics	116	(3)
The Research Hypothesis	119	(2)
The Null Hypothesis	121	(2)
Risks of Error in Hypothesis Testing	123	(2)
Risks of Error and Statistical Levels of Significance	125	(2)
Departing from Conventional Significance Criteria	127	(8)
Defining the Observed Significance Level of a Test: A Simple Example Using the Binomial Distribution	135	(24)
The Fair Coin Toss	137	(4)
Different Ways of Getting Similar Results	141	(3)
Solving More Complex Problems	144	(1)
The Binomial Distribution	145	(4)
Using the Binomial Distribution to Estimate the Observed Significance Level of a Test	149	(10)
Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses	159	(25)
The Problem: The Impact of Problem-Oriented Policing on Disorderly Activity at Violent-Crime Hot Spots	160	(2)
Assumptions: Laying the Foundations for Statistical Inference	162	(6)
Selecting a Sampling Distribution	168	(2)
Significance Level and Rejection Region	170	(5)
The Test Statistic	175	(1)
Making a Decision	175	(9)
Chi-Square: A Test Commonly Used for Nominal-Level Measures	184	(35)
Testing Hypotheses Concerning the Roll of a Die	185	(8)
Relating Two Nominal-Scale Measures in a Chi-Square Test	193	(6)
Extending the Chi-Square Test to Multicategory Variables: The Example of Cell Allocations in Prison	199	(5)
Extending the Chi-Square Test to a Relationship Between Two Ordinal Variables: Identification with Fathers and Delinquent Acts	204	(5)
The Use of Chi-Square When Samples Are Small: A Final Note	209	(10)
The Normal Distribution and Its Application to Tests of Statistical Significance	219	(35)
The Normal Frequency Distribution, or Normal Curve	220	(12)
Applying Normal Sampling Distributions to Nonnormal Populations	232	(5)
Comparing a Sample to an Unknown Population: The Single-Sample z-Test for Proportions	237	(5)
Comparing a Sample to an Unknown Population: The Single-Sample t-Test for Means	242	(12)
Comparing Means and Proportions in Two Samples	254	(36)
Comparing Sample Means	255	(12)
Comparing Sample Proportions: The Two-Sample t-Test for Differences of Proportions	267	(6)
The t-Test for Dependent Samples	273	(5)
A Note on Using the t-Test for Ordinal Scales	278	(12)
Comparing Means Among More Than Two Samples: Analysis of Variance	290	(43)
Analysis of Variance	291	(21)
Defining the Strength of the Relationship Observed	312	(3)
Making Pairwise Comparisons Between the Groups Studied	315	(3)
A Nonparametric Alternative: The Kruskal-Wallis Test	318	(15)
Measures of Association for Nominal and Ordinal Variables	333	(46)
Distinguishing Statistical Significance and Strength of Relationship: The Example of the Chi-Square Statistic	334	(3)
Measures of Association for Nominal Variables	337	(12)
Measures of Association for Ordinal Variables	349	(18)
Choosing the Best Measure of Association for Nominal- and Ordinal-Level Variables	367	(12)
Measuring Association for Interval-Level Data: Pearson's Correlation Coefficient	379	(40)
Measuring Association Between Two Interval-Level Variables	380	(2)
Pearson's Correlation Coefficient	382	(18)
Spearman's Correlation Coefficient	400	(2)
Testing the Statistical Significance of Pearson's r	402	(7)
Testing the Statistical Significance of Spearman's r	409	(10)
An Introduction to Bivariate Regression	419	(40)
Estimating the Influence of One Variable on Another: The Regression Coefficient	420	(5)
Prediction in Regression: Building the Regression Line	425	(8)
Evaluating the Regression Model	433	(14)
The F-Test for the Overall Regression	447	(12)
Multivariate Regression	459	(35)
The Importance of Correct Model Specifications	460	(12)
Correctly Specifying the Regression Model	472	(10)
The Problem of Multicollinearity	482	(12)
Logistic Regression	494	(52)
Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable?	496	(5)
Logistic Regression	501	(12)
Interpreting Logistic Regression Coefficients	513	(10)
Comparing Logistic Regression Coefficients	523	(6)
Evaluating the Logistic Regression Model	529	(4)
Statistical Significance in Logistic Regression	533	(13)
Special Topics: Confidence Intervals	546	(22)
Confidence Intervals	548	(4)
Constructing Confidence Intervals	552	(16)
Special Topics: Statistical Power	568	(22)
Statistical Power	570	(9)
Parametric versus Nonparametric Tests	579	(1)
Estimating Statistical Power: What Size Sample Is Needed for a Statistically Powerful Study?	579	(4)
Summing Up: Avoiding Studies Designed for Failure	583	(7)
appendix 1 Factorials	590	(1)
appendix 2 Critical Values of X2 Distribution	591	(1)
appendix 3 Areas of the Standard Normal Distribution	592	(1)
appendix 4 Critical Values of Student's t Distribution	593	(1)
appendix 5 Critical Values of the F-Statistic	594	(3)
appendix 6 Critical Value for P (Pcrit), Tukey's HSD Test	597	(1)
appendix 7 Critical Values for Spearman's Rank-Order Correlation Coefficient	598	(1)
appendix 8 Fisher r-to-Z* Transformation	599	(2)
Glossary	601	(7)
Index	608