CJUS 3312: Statistics in Criminal Justice

Purpose: to help other instructors teaching the same course

Common Course ID: CJUS 3312
CSU Instructor Open Textbook Adoption Portrait

Abstract: This open textbook is being utilized in a Criminal Justice course for undergraduate or graduate students by Sishi Wu at California State University, San Bernardino. The open textbook provides intro-level statistical knowledge, various examples, as well as practices. The main motivation to adopt an open textbook was alleviate students' financial burden and reduce their apprehension towards statistics. Most student access the open textbook in PDF or HTML format.

CJUS 3312: Statistics in Criminal Justice
Brief Description of course highlights:  This course provides introduction to statistical reasoning and social science statistics commonly used in criminal justice. Includes descriptive, inferential, and predictive statistics, with emphasis on calculation and interpretation. 

Student Population: Include majors, typical incoming knowledge (i.e. prerequisites).  Most students taking this course are from the criminal justice major. This course has the following prerequisite: CJUS 3311 and MATH 1301 or MATH 1101 or equivalent and completion of Pre-Major requirements.

Learning or student outcomes: Institutional Learning Outcomes:
- Students engage in diverse methods of reasoning and inquiry to define problems, identify and evaluate potential solutions, and determine a course of action.
-  Students develop and use new approaches to thinking, problem solving, and expression.

Departmental Learning Outcomes: Students will be able to demonstrate common statistical techniques used in social science, particularly criminological research.
- Students Learning Outcome 1. Students will apply statistical reasoning and social science statistics commonly used in criminal justice to solve problems, engage in diverse methods of reasoning and inquiry to define problems, identify and evaluate potential solutions, and determine a course of action.
-  Student Learning Outcome 2. Students will use descriptive, inferential, and predictive statistics to analyze data, with emphasis on interpretation of statistical findings rather than calculation.

Course Learning Outcomes:  After completing the course, the student should:
- Obtain basic knowledge about data and statistics commonly used in criminal justice.
- Understand tables and charts commonly used in criminal justice.
- Conduct basic data analyses.
-  Interpret the results of data analyses.

Key challenges faced and how resolved:
Key challenge: Student anxiety and fear of statistics. Many students enter an undergraduate statistics course with significant anxiety and preconceived notions about the difficulty of the subject. This fear can hinder their ability to engage with the material and perform well in the course.
Resolution:
1. Open Textbook Adoption: I adopted an open-access textbook to alleviate financial stress and ensure all students had immediate access to the course material. This decision also allowed for the customization of content to better suit the needs of the students.
2. Incremental Learning: The course was structured to gradually introduce complex concepts, allowing students to build their understanding step-by-step. This scaffolding approach helped students feel more comfortable as the material became progressively more challenging.
3. Real-World Example: I used crime statistics from multiple sources and real-world examples (e.g., predicting sentencing outcomes) to demonstrate how statistical knowledge can be used to solve problems in criminology and criminal justice.
4.  Supportive Environment: I fostered a supportive classroom environment where questions were encouraged and mistakes were viewed as learning opportunities. This included offering detailed explanations, multiple examples, additional office hours, and providing timely feedback.

Syllabus and/or Sample assignment from the course or the adoption
Syllabus     Sample assignment

Textbook or OER/Low cost Title: 

Brief Description:  This book helps students form a foundation of statistical thinking and methods.
1. Statistics is an applied field with a wide range of practical applications.
2. You don’t have to be a math guru to learn from interesting, real data.
3.  Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

Textbook Structure
Part 1: Introduction to data. Data structures, variables, summaries, graphics, and basic data collection and study design techniques.
Part 2: Exploratory data analysis. Data visualization and summarization, with particular emphasis on multivariable relationships.
Part 3: Regression modeling. Modeling numerical and categorical outcomes with linear and logistic regression and using model results to describe relationships and make predictions.
Part 4: Foundations for inference. Case studies are used to introduce the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
Part 5: Statistical inference. Further details of statistical inference using randomization tests, bootstrap intervals, and mathematical models for numerical and categorical data.
Part 6: Inferential modeling. Extending inference techniques presented thus-far to linear and logistic regression settings and evaluating model performance.

Each part contains multiple chapters and ends with a case study. Building on the content covered in the part, the case study uses the tools and techniques to present a high-level overview.

Please provide a link to the resource: 
https://openintro-ims.netlify.app

Authors: Çetinkaya-Rundel, M., & Hardin, J.

Student access:  Official Website or download the PDF version.

Supplemental resources: : Solutions, sample exams and problem sets, sample syllabi, learning management system, slides and videos. 

Cost savings from that of a traditional textbook. Previous Used Textbooks: $50-$210 This Textbook: $0

License: The textbook is available under a Creative Commons Attribution-ShareAlike 3.0 Unported United States License. 

OER/Low Cost Adoption Process

Provide an explanation or what motivated you to use this textbook or OER/Low Cost option. One of the primary reasons for adopting this textbook is to relieve the financial burden on students. Second, this web native textbook can easily be updated to reflect the latest methods, theories, and applications.

How did you find and select the open textbook for this course? Recommended by a colleague.

Sharing Best Practices: The sustainability of open education relies on sharing with others. Please give suggestions for faculty who are just getting started with OER or Low Cost options.  List anything you wish that you had known earlier.

Communicate with scholars outside your university and outside your field, learn from their experiences and ask them to recommend resources popular in their institution/field.

Describe any key challenges you experienced, how they were resolved  and lessons learned Sometimes open textbooks are not tailored for your field (e.g., criminology and criminal justice). What I did was to use the textbook to help students understand key terms and concepts in statistics. But when it comes to examples and applications, I will use my own real-world examples that are closed related to my field. 

Instructor Name:  Sishi Wu
I am an Assistant Professor in the School of Criminology and Criminal Justice at California State University, San Bernardino. I teach stats, corrections, and law and courts.

Please provide a link to your university page.
https://www.csusb.edu/profile/sishi.wu

Please describe the courses you teach.
Statistics in Criminal Justice: This course provides introduction to statistical reasoning and social science statistics commonly used in criminal justice. Includes descriptive, inferential, and predictive statistics, with emphasis on calculation and interpretation. 

Correctional Theory and Institutions: This course provides students with an interdisciplinary review of the history of criminal punishment, and the main changes that have occurred and their causes. Students will learn the dominant justifications used for punishing offenders, such as deterrence, retribution and rehabilitation. Special attention is given to current penological practice such as prison, jail, probation, parole, other alternative ways of dealing with offenders and sentencing. Reform is then discussed within this historical and interdisciplinary context.

Legal Issues in Criminal Justice: This course provides students with an interdisciplinary review of public policy relating to criminal justice and individual liberties along with an examination of case law in the areas of general civil liability, constitutional rights, and administrative and management practices. Students will learn to analyze criminal justice policies from different perspectives, conduct legal analysis, and write a proposal for empirical research. Students will also be exposed to resources and databases related to law and criminal justice.

Describe your teaching philosophy and any research interests related to your discipline or teaching.    Teaching philosophy: I value content mastery and critical thinking in my teaching. I aim to share theoretical and applicable knowledge that will (1) be useful for students planning to pursue a career in a related field (e.g., law enforcement); (2) be informative for students who want to know their rights as citizens; (3) be interesting for students who are curious about law, courtrooms, and correction; and (4) be inspiring for students who would like to conduct additional research.

Courses taught: Statistics in Criminal Justice, Correctional Theory and Institutions, Legal Issues in Criminal Justice