About PA-ADOPT
About OER
About the Author
Table of Contents
Preface
Chapter 1. Introduction to Crime Data Analysis, R and RStudio
	Data Analysis in the Criminal Justice System
	Importance of Statistics and Statistical Software Programs for Crime Analysts
	What Is R and RStudio?
	Setting Up R and RStudio
	Calculating Using R
		Step 1. Launch RStudio
		Step 2. Choose Script or Console
		Step 3. Type R Code
	R Packages (How To Install and Load Packages)
	Conclusion
Chapter 2. Introduction to Data Formations and Graphics
	The 2012 General Social Survey
	Importing the Data Using the Haven Package
	View Function
	Summary Function
	Categorical vs Numerical Variables
	Dplyr Package
	Ggplot2 Package
Chapter 3. Creating a New Variable and Producing Summary Statistics
	Uniform Crime Report
	Readxl Package
	Rename Function
	Crime Rates
	Mutate Function
	Select Function
	Arrange Function
	Cut Function
	Group_By Function
	Geom_Histogram()
Chapter 4. Central Tendency and Variability
	Central Tendency
	Variability
	Gapminder Data Package
	? And Data
	Subset Function
	Mean and Median
	Mode
	Variance and Standard Deviation
	Conclusion
	References
Chapter 5. Reliability of a Scale
	Reliability vs Validity
	National Crime Victimization Survey
	Test-Retest and Internal Consistency Methods
	Cronbach’s Alpha Coefficient
	Importing the Data in Stata Format
	Guardianship
	Psych Package
	Alpha Function
	Reporting the Results Regarding the Internal Consistency
	Conclusion
	References
Chapter 6. Chi-Squared Test
	Hypothesis Testing
	NHST Steps
	Chi-Squared Test
	NHST Steps for Chi-Squared Test
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Step 4: If the P-Value Is very small, Typically Less Than 5%, Reject the Null Hypothesis.
		Step 5: If the P-Value is not small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results
	Conclusion
	References
Chapter 7. T-Test
	Introduction to T-Test
	Cognitive Behavioral Therapy
	Independent-Samples T-Test
	NHST Steps for Independent-Samples T-Test
		Step 1: Formulate the null and alternative hypotheses.
		Step 2: Calculate the test statistic.
		Step 3: Determine the probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the p-value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results of an Independent-Samples T-Test
	Density Plot
	Paired-Samples T-Test
	NHST Steps for Paired-Samples T-Test
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the P-Value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results of a Paired-Samples T-Test
	Conclusion
	References
Chapter 8. Analysis of Variance
	Introduction to ANOVA
	Media Exposure and Perceptions of the Police
	One-Way Analysis of Variance
	NHST Steps for One-Way ANOVA
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the P-Value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results From One-Way ANOVA
	Post-Hoc Test
	Conclusion
	References
Chapter 9. Correlation
	Introduction to Correlation
	Pearson Product-Moment Correlation Coefficient
	Computing Correlation Using the USArrests Dataset
	NHST Steps for Pearson’s R Correlation Coefficient
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the P-Value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results for Pearson’s Product-Moment Correlation Coefficient
	Assumptions That Need To Be Met To Perform Correlation Analysis
	Scatter Plot
	Conclusion
	References
Chapter 10. Linear Regression
	Introduction to Regression
	Simple Linear Regression Vs. Multiple Linear Regression
	Ordinary Least Squares (OLS) Model
	Inmate Self-Reported Survey
	Assumptions of Linear Regression
	A Scatterplot of Low Self-Control and Risky Lifestyles
	Checking a Correlation Coefficient
	Conducting Simple Linear Regression Analysis
	NHST Steps for Simple Linear Regression Model
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the P-value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the Null Hypothesis.
	Reporting the Results From the Simple Linear Regression Model
	Model Significance for Simple Linear Regression
		Step 1: Formulate the Null and Alternative Hypotheses.
		Step 2: Calculate the Test Statistic.
		Step 3: Determine the Probability (P-Value) of Obtaining a Test Statistic at Least as Extreme as the Observed Value, Assuming no Relationship Exists.
		Steps 4 & 5: If the P-Value is Very Small, Typically Less Than 5%, Reject the Null Hypothesis, but if the P-Value is Not Small, Typically 5% or Greater, Retain the null Hypothesis.
	Reporting the Model Significance for the Simple Linear Regression Model
	Conducting Multiple Linear Regression
	Model Fit for Linear Regression
	Conclusion
	References