Muutke küpsiste eelistusi

E-raamat: Basic Statistics in Criminology and Criminal Justice

, , ,
  • Formaat: PDF+DRM
  • Ilmumisaeg: 23-Feb-2021
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030479671
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 122,88 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Basic Statistics in Criminology and Criminal JusticeSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 23-Feb-2021
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030479671
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This introductory textbook takes a building-block approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and professionals who want to gain a basic understanding of common statistical methods used in criminology and criminal justice before advancing to more complex statistical analyses in future volumes. 

This book emphasizes comprehension and interpretation. As the statistical methods discussed become more complex and demanding to compute, it integrates statistical software. It provides readers with an accessible understanding of popular statistical programs used to examine real-life crime and justice problems (including SPSS, Stata, and R). In addition, the book includes supplemental resources such as a glossary of key terms, practice questions, and sample data.

Basic Statistics in Criminology and Criminal Justice aims to give students and researchers a core understanding of statistical concepts and methods that will leave them with the confidence and tools to tackle the statistical problems in their own research work.

Preface v
Chapter one Introduction: Statistics as a Research Tool
1(12)
The Purpose of Statistics Is to Clarify
3(1)
Statistics Are Used to Solve Problems
4(1)
Basic Principles Apply Across Statistical Techniques
5(2)
The Uses of Statistics
7(1)
Descriptive Statistics
7(1)
Inferential Statistics
8(2)
Chapter Summary
10(1)
Key Terms
10(1)
References
11(2)
Chapter two Measurement: The Basic Building Block of Research
13(26)
Science and Measurement: Classification as a First Step in Research
15(1)
Levels of Measurement
15(1)
Nominal Scales
16(2)
Ordinal Scales
18(1)
Interval and Ratio Scales
19(3)
Relating Interval, Ordinal, and Nominal Scales: The Importance of Collecting Data at the Highest Level Possible
22(1)
What Is a Good Measure?
23(3)
Chapter Summary
26(1)
Key Terms
27(1)
Exercises
28(3)
Computer Exercises
31(6)
SPSS
32(2)
Stata
34(2)
R
36(1)
Problems
37(1)
References
37(2)
Chapter three Representing and Displaying Data
39(34)
Frequency Distributions, Bar Charts, and Histograms
40(2)
The Bar Chart
42(1)
The Grouped Bar Chart
43(7)
Histograms for Continuous and Discrete Data
50(3)
Box plots for Interval and Ratio Data
53(4)
Time Series Data
57(3)
Chapter Summary
60(1)
Key Terms
61(1)
Symbols and Formulas
62(1)
Exercises
63(1)
Computer Exercises
64(7)
SPSS
65(2)
Stata
67(2)
R
69(1)
Problems
70(1)
References
71(2)
Chapter four Describing the Typical Case: Measures of Central Tendency
73(36)
The Mode: Central Tendency in Nominal Scales
74(3)
The Median: Taking into Account Position
77(6)
The Mean: Adding Value to Position
83(3)
Comparing Results Gained Using the Mean and Median
86(3)
Other Characteristics of the Mean
89(2)
Using the Mean for Non-interval/Ratio Scales
91(1)
Statistics in Practice: Comparing the Median and the Mean
92(3)
Chapter Summary
95(1)
Key Terms
96(1)
Symbols and Formulas
96(1)
Exercises
97(4)
Computer Exercises
101(6)
SPSS
101(2)
Stata
103(1)
R
104(2)
Problems
106(1)
References
107(2)
Chapter five How Typical Is the Typical Case? Measuring Dispersion
109(36)
Measures of Dispersion for Nominal- and Ordinal-Level Data
110(1)
The Proportion in the Modal Category
111(1)
The Percentage in the Modal Category
112(1)
The Variation Ratio
113(2)
Index of Qualitative Variation
115(3)
Measuring Dispersion in Interval/Ratio Scales: The Range, Interquartile Range, Variance, and Standard Deviation
118(3)
The Variance
121(3)
The Standard Deviation
124(4)
Characteristics of the Variance and Standard Deviation
128(1)
The Coefficient of Relative Variation
129(2)
A Note on the Mean Deviation
131(2)
Chapter Summary
133(1)
Key Terms
134(1)
Symbols and Formulas
135(2)
Exercises
137(3)
Computer Exercises
140(3)
SPSS
140(1)
Stata
141(1)
R
142(1)
Problems
142(1)
Reference
143(2)
Chapter six The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics
145(22)
The Dilemma: Making Statements About Populations from Sample Statistics
146(3)
The Research Hypothesis
149(3)
The Null Hypothesis
152(1)
Risks of Error in Hypothesis Testing
153(2)
Risks of Error and Statistical Levels of Significance
155(2)
Departing from Conventional Significance Criteria
157(2)
Chapter Summary
159(1)
Key Terms
160(1)
Symbols
161(1)
Exercises
162(4)
References
166(1)
Chapter seven Defining the Observed Significance Level of a Test: A Simple Example Using the Binomial Distribution
167(30)
The Fair Coin Toss
169(1)
Sampling Distributions and Probability Distributions
169(3)
The Multiplication Rule
172(2)
Different Ways of Getting Similar Results
174(3)
Solving More Complex Problems
177(2)
The Binomial Distribution
179(4)
Using the Binomial Distribution to Estimate the Observed Significance Level of a Test
183(4)
Chapter Summary
187(1)
Key Terms
188(1)
Symbols and Formulas
188(1)
Exercises
189(3)
Computer Exercises
192(5)
SPSS
193(1)
Stata
194(1)
R
195(1)
Problems
195(2)
Chapter eight Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses
197(28)
The Problem: The Impact of Problem-Oriented Policing on Disorderly Activity at Violent-Crime Hot Spots
198(2)
Assumptions: Laying the Foundations for Statistical Inference
200(1)
Level of Measurement
200(1)
Shape of the Population Distribution
200(1)
Sampling Method
201(4)
The Hypotheses
205(1)
Stating All of the Assumptions
206(1)
Selecting a Sampling Distribution
206(2)
Significance Level and Rejection Region
208(1)
Choosing a One-Tailed or a Two-Tailed Rejection Region
209(4)
The Test Statistic
213(1)
Making a Decision
213(1)
Chapter Summary
214(1)
Key Terms
215(1)
Exercises
216(4)
Computer Exercises
220(3)
SPSS
220(1)
Stata
221(1)
R
222(1)
Problems
222(1)
References
223(2)
Chapter nine Chi-Square: A Test Commonly Used for Nominal-Level Measures
225(42)
Testing Hypotheses Concerning the Roll of a Die
226(1)
The Chi-Square Distribution
227(2)
Calculating the Chi-Square Statistic
229(1)
Linking the Chi-Square Statistic to Probabilities: The Chi-Square Table
230(1)
A Substantive Example: The Relationship Between Assault Victims and Offenders
231(3)
Relating Two Nominal-Scale Measures in a Chi-Square Test
234(1)
A Substantive Example: Type of Sanction and Recidivism Among Convicted White-Collar Criminals
235(7)
Extending the Chi-Square Test to Multicategory Variables: The Example of Cell Allocations in Prison
242(2)
The Sampling Distribution
244(1)
Significance Level and Rejection Region
244(1)
The Test Statistic
244(2)
The Decision
246(3)
Extending the Chi-Square Test to a Relationship Between Two Ordinal Variables: Identification with Fathers and Delinquent Acts
249(1)
The Sampling Distribution
250(1)
Significance Level and Rejection Region
251(1)
The Test Statistic
251(1)
The Decision
252(1)
The Use of Chi-Square When Samples Are Small: A Final Note
253(1)
Chapter Summary
254(1)
Key Terms
254(1)
Symbols and Formulas
255(1)
Exercises
256(5)
Computer Exercises
261(5)
SPSS
262(1)
Stata
263(1)
R
264(1)
Problems
265(1)
References
266(1)
Chapter ten The Normal Distribution and Its Application to Tests of Statistical Significance
267(48)
The Normal Frequency Distribution (Normal Curve)
269(2)
Characteristics of the Normal Frequency Distribution
271(1)
z-Scores
272(3)
Developing Tests of Statistical Significance Based on the Standard Normal Distribution: The Single-Sample z-Test for Known Population
275(7)
Applying Normal Sampling Distributions to Non-normal Populations
282(5)
Comparing a Sample to an Unknown Population: The Single-Sample z-Test for Proportions
287(1)
Computing the Mean and Standard Deviation for the Sampling Distribution of a Proportion
287(2)
Testing Hypotheses with the Normal Distribution: The Case of a New Prison Program
289(3)
Limitations of the z-Test on a Proportion
292(1)
Comparing a Sample to an Unknown Population: The Single-Sample t-Test for Means
293(2)
Testing Hypotheses with the t Distribution
295(3)
Confidence Intervals
298(4)
Constructing Confidence Intervals
302(1)
Confidence Intervals for Sample Means
303(1)
Confidence Intervals for Sample Proportions
304(1)
Chapter Summary
305(1)
Key Terms
306(1)
Symbols and Formulas
307(2)
Exercises
309(4)
References
313(2)
Chapter eleven Comparing Means and Proportions in Two Samples to Test Hypotheses About Population Parameters
315(58)
Comparing Means
317(1)
The Case of Anxiety Among Police Officers and Firefighters
317(3)
The Sampling Distribution
320(8)
Constructing Confidence Intervals for Differences of Means
328(1)
Bail in Los Angeles County: Another Example of the Two-Sample t-Test for Hypotheses About Population Mean Differences
328(3)
The Sampling Distribution
331(5)
Comparing Proportions: The Two-Sample z-Test for Differences Between Population Proportions
336(1)
The Case of Drug Testing and Pretrial Misconduct
337(2)
The Sampling Distribution
339(2)
The f-Test for Dependent (Paired) Samples
341(1)
The Effect of Police Presence on High-Crime Street Segments
341(3)
The Sampling Distribution
344(3)
Nonparametric Alternative to the t-Test
347(1)
Mann-Whitney U: Nonparametric Test for Two Independent Samples
348(1)
Bail in Los Angeles County Redux: The Mann-Whitney U
349(3)
Wilcoxon Signed-Rank Test for Dependent Samples
352(1)
The Effect of Police Presence Near High-Crime Street Segments Redux: The Wilcoxon Signed-Rank Test
352(2)
Effect Size Measures for Comparing Two Means: Cohen's d
354(2)
A Note on Using the t-Test for Ordinal Scales with a Limited Number of Categories
356(1)
Chapter Summary
357(1)
Key Terms
358(1)
Symbols and Formulas
359(2)
Exercises
361(6)
Computer Exercises
367(3)
SPSS
368(1)
Stata
368(1)
R
369(1)
Problems
369(1)
References
370(3)
Chapter twelve Comparing Means Among More Than Two Samples to Test Hypotheses about Populations: Analysis of Variance
373(52)
Analysis of Variance
374(4)
Computing the Variance Between and Within Groups
378(5)
A Substantive Example: Age and White-Collar Crimes
383(9)
Another ANOVA Example: Race and Bail Amounts Among Felony Drug Defendants
392(4)
Defining the Strength of the Relationship Observed
396(3)
Making Pairwise Comparisons Between the Groups Studied
399(1)
Tukey's Honestly Significant Difference (HSD) Test
400(2)
Bonferroni Post Hoc Pairwise t-Tests
402(3)
A Nonparametric Alternative: The Kruskal-Wallis Test
405(1)
The Sampling Distribution
406(1)
Significance Level and Rejection Region
406(1)
The Test Statistic
406(2)
The Decision
408(1)
Chapter Summary
408(1)
Key Terms
409(1)
Symbols and Formulas
410(2)
Exercises
412(4)
Computer Exercises
416(7)
SPSS
416(2)
Stata
418(2)
R
420(1)
Problems
421(2)
References
423(2)
Chapter thirteen Measures of Association for Nominal and Ordinal Variables
425(54)
Distinguishing Statistical Significance and Strength of Relationship: The Example of the Chi-Square Statistic
426(3)
Measures of Association for Nominal Variables
429(1)
Measures of Association Based on the Chi-Square Statistic
429(7)
Proportional Reduction in Error Measures: Tau and Lambda
436(7)
Statistical Significance of Measures of Association for Nominal Variables
443(2)
Measures of Association for Ordinal-Level Variables
445(6)
Gamma
451(1)
Kendall's τb and τc
452(2)
Somers' d
454(1)
A Substantive Example: Affectional Identification with Father and Level of Delinquency
454(5)
Note on the Use of Measures of Association for Ordinal Variables
459(1)
Statistical Significance of Measures of Association for Ordinal Variables
459(5)
Ch oosing the Best Measure of Association for Nominal- and Ordinal-Level Variables
464(1)
Chapter Summary
465(1)
Key Terms
466(1)
Symbols and Formulas
467(3)
Exercises
470(4)
Computer Exercises
474(3)
SPSS
474(1)
Stata
475(1)
R
475(1)
Problems
476(1)
References
477(2)
Chapter fourteen Measuring Association for Scaled Data: Pearson's Correlation Coefficient
479(52)
Measuring Association Between Two Interval- or Ratio-Level Variables
480(2)
Pearson's Correlation Coefficient
482(4)
The Calculation
486(2)
A Substantive Example: Crime and Unemployment in California
488(2)
Nonlinear Relationships and Pearson's r
490(6)
Beware of Outliers
496(4)
Spearman's Correlation Coefficient
500(3)
Testing the Statistical Significance of Pearson's r
503(1)
Statistical Significance of r. The Case of Age and Number of Arrests
503(4)
Statistical Significance of r. Unemployment and Crime in California
507(1)
Testing the Statistical Significance of Spearman's r
508(1)
The Sampling Distribution
509(1)
Significance Level and Rejection Region
509(1)
The Test Statistic
510(1)
The Decision
510(1)
Using Pearson's r When One or Both Variables are Dichotomous or Ordinal
510(1)
The Correlation Coefficient for Two Dichotomous Variables
511(2)
The Correlation Coefficient for One Dichotomous Variable and One Interval- or Ratio-level Variable
513(4)
Confidence Intervals for the Correlation Coefficient
517(2)
Chapter Summary
519(1)
Key Terms
520(1)
Symbols and Formulas
520(2)
Exercises
522(3)
Computer Exercises
525(5)
SPSS
525(1)
Stata
526(1)
R
527(1)
Problems
528(2)
References
530(1)
Chapter fifteen An Introduction to Bivariate Regression
531(50)
Estimating the Influence of One Variable on Another: The Regression Coefficient
532(2)
Calculating the Regression Coefficient
534(2)
A Substantive Example: Unemployment and Burglary in California
536(1)
Prediction in Regression: Building the Regression Line
537(1)
The K-Intercept
538(1)
The Regression Line
539(2)
Predictions Beyond the Distribution Observed in a Sample
541(1)
Predicting Burglary Rates from Unemployment Rates in California
542(2)
Choosing the Best Line of Prediction Based on Regression Error
544(2)
Evaluating the Regression Model
546(1)
Percent of Variance Explained
546(3)
Percent of Variance Explained: Unemployment Rates and Burglary Rates in California
549(2)
Statistical Significance of the Regression Coefficient: The Case of Age and Number of Arrests
551(9)
Testing the Statistical Significance of the Regression Coefficient for Unemployment Rates and Burglary Rates in California
560(5)
The F-Test for the Overall Regression
565(1)
Age and Number of Arrests
566(1)
Unemployment Rates and Burglary Rates in California
567(1)
Chapter Summary
568(1)
Key Terms
569(1)
Symbols and Formulas
570(2)
Exercises
572(4)
Computer Exercises
576(4)
SPSS
576(1)
Stata
577(1)
R
578(1)
Problems
579(1)
Reference
580(1)
Appendix 1 Factorials 581(2)
Appendix 2 Critical Values of X1 Distribution 583(2)
Appendix 3 Areas of the Standard Normal Distribution 585(2)
Appendix 4 Critical Values of Student's t Distribution 587(2)
Appendix 5 Critical Values of the F-Statistic 589(2)
Appendix 6 Critical Value for P(Pcrit)--Tukey's HSD Test 591(2)
Appendix 7 Critical Values for Spearman's Rank-Order Correlation Coefficient 593(2)
Appendix 8 Fisher r-to-Z' Transformation 595(4)
Glossary 599(6)
Index 605
David Weisburd is a leading researcher and scholar in criminology and criminal justice. He is Distinguished Professor of Criminology, Law and Society at George Mason University in Virginia and Walter E. Meyer Professor of Law and Criminal Justice at the Hebrew University of Jerusalem. Professor Weisburd has received many awards and prizes for his contributions to criminology and criminal justice including the Stockholm Prize in Criminology and the Sutherland and Vollmer Awards from the American Society of Criminology.

Chester Britt was a leading researcher and scholar in the field of criminology. During his career, he taught at a number of universities and led departments at Northeastern University, Arizona State University, and the University of Iowa. His research addressed theories of criminal behavior and victimization, demography of crime and criminal careers, criminal justice decision-making, and quantitative research methods.

David B. Wilson is a Professorin the Criminology, Law and Society Department at George Mason University in Virginia. He is a social psychologist and leading applied statistician in the field of criminology, and was the recipient of the Mosteller Award from the Campbell Collaboration for his contributions to the science of systematic review and meta-analysis.





Alese Wooditch is an Assistant Professor of Criminal Justice at Temple University. She received her PhD from George Mason University. Professor Wooditch is interested in innovative spatial statistical analyses in the area of criminology and criminal justice experimental and computational criminology, and quantitative methodological issues.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97830304796712e.html
Märksõnad: