|Computers in Social Science|
|Room||212 Haaren Hall|
What I cannot create, I do not understand. Richard P. Feynman
In this course you will review the statistical skills necessary to examine and evaluate criminal justice phenomena using methods of quantitative inquiry. You will gain a foundation in statistics and statistical reasoning. Statistical research is an inductive process. We let small samples of data tell us about the veracity of our theories. By necessity, much of this course will involve working with statuistical software.
This course will use basic spreadsheets to illustrate statistical calculations. This course will be more concerned with conceptual thinking and the understanding of the principals behind statistical thinking. Topics covered in this course include the fundamentals of probability, sampling methods, measures of central tendency and dispersion, univariate descriptions of variables, bivariate measures of associations, and an introduction to multivariate tests.
The course is developmental. We will begin by understanding where the data is held and passwords. The material is built on a scaffold and the knowledge is cumulative. Therefore, you must keep current with homework and other assignments and bring your questions about the material to class. Throughout the semester there will be weekly homework assignments, quizzes, and a midterm and final examination. Your grade will be based on the homework, quizzes, lab performance, a midterm, and a final exam. The homework is designed to build your statistical skills and your understanding of the material.
You will learn to what can be done to manipulate data, manage data, and use statistical software. The quizzes and exams will test your understanding and your ability to conceptualize the research questions in the context of police leadership. Should you miss a class, you are responsible for material covered and any homework assigned.
- Understand and explain the nature and structure of quantitative data including concepts such as variables, levels of measurement, and unit of analysis
- Formulate research hypotheses appropriate for statistical testing
- Build and define quantitative datasets from a large dispatch set given to the class.
- Conduct quality control of quantitative data
- Manipulate data
- Understand and explain basic concepts of probability, data distributions, sampling, inferences, and statistical significance
- Understand and perform univariate and bivariate statistical analyses
- Explain the nature, purposes, and limitations of various statistical techniques
- Conduct significance tests.
- Discuss statistical findings accurately and meaningfully using spreadsheets.
- Formulate research hypotheses appropriate for statistical tests.
- Understand and perform multivariate and bivariate statistical analyses
- Explain the nature, purposes, and limitations of various statistical techniques as applied to live data
- Important and transform data files
- Finding data
- Downloading data
- Cleaning data
- Excel basics
- Frequency Distribution Presentation, & Central Tendency I
- Measures of Dispersion
- Data Distribution and Variance
- Hypothesis Testing Unit of Analysis
- Basic Probability, Inference
- Significance Tests
- Geocoding data
- Incorporating geospatial data
- Chi Square, Expected values & Mean Testing Continued
- Analysis of Variance (ANOVA)
- Associations Nominal and Ordinal Data, Bivariate Correlation,
- Pearson’s r and Spearman’s Rho
- Bivariate Regression
- Multiple Regression
Special Baltimore Dispatch data
This semeseter we have been especially lucky to be permitted to learn through their examination of a novel and not yet publicly available data set. These data were gained through the Freedom of Information Act and entrusted to Professor Peter Moskos for analysis. They represent basic data on each and every dispatch made in Baltimore between 2013 and 2016. The data is (like almost all admin data) not perfectly clean. Students will receive notes on these imperfections or partial data sets so that they do not need to work with whole data set at one time.
Adam Fera’s Documents
How to guides
College Policy on Extra Work during the Semester
Any extra credit coursework opportunities during the semester for a student to improve his or her grade must be made available to all students at the same time. Furthermore, there is no obligation on the part of any instructor to offer extra credit work in any course. The term “extra credit work” refers to optional work that may be assigned by the instructor to all students in addition to the required work for the course that all students must complete. It is distinguished from substitute assignments or substitute work that may be assigned by the instructor to individual students, such as make-up assignments to accommodate emergencies or to accommodate the special circumstances of individual students.
College Policy for a Grade of Incomplete (INC)
An Incomplete grade may be given only to those students who would pass the course if they were to satisfactorily complete course requirements. It is within the discretion of the faculty member as to whether or not to give the grade of Incomplete. If a faculty member decides to give an Incomplete grade, he or she completes an Incomplete Grade drop-down form that will appear on the grading screen when the faculty member assigns the INC grade online. The faculty member will then provide the following information: the grade the student has earned so far; the assignment(s) that are missing; and the percentage of the final grade that the missing assignment(s) represents for this purpose. If the course takes place during the fall semester or winter session, then the incomplete work is due by the student no later than the end of the third week of the following spring semester. If the course takes place during the spring semester or summer session, then the incomplete work is due no later than the end of the third week of the following fall semester. It is within the discretion of the faculty member to extend this deadline under extraordinary circumstances. When completing the online Incomplete Grade Form, the faculty member agrees to grade the student’s outstanding coursework as specified on the form and to submit the student’s grade for the course any time from the date the student submits the completed work until the end of that fall or spring semester. This policy should be included on undergraduate course syllabi. If the student does not successfully complete the missing work, the faculty member may change the grade to a letter grade. If the faculty member does not submit a change of grade, the Incomplete grade automatically becomes the grade of “FIN” at the end of that semester. This policy does not apply to laboratory and studio courses, or to internship courses, for which neither the professor nor the department can reasonably accommodate a student’s missed lab or studio or internship work as described herein. The academic departments which offer such courses shall develop departmental policy for consideration by the College Council. Degree candidates should be aware that an INC grade received during their last semester in courses required for graduation will result in the postponement of graduation.
Location and Time
- Where: Rm: 10.65.36,New Building CUNY John Jay
- When: Saturday 9 - 11 am
The text is both optional and available free online:
- Using R for Simple Statistics
Computers in the classroom
This is a hands on class. You will be in front of a computer during class throughout the semester. Please feel free to use the computer to take notes or use the class Etherpad to discuss the course material, but do not use the computers for non-class related topics.
There will be a short homework assignments given after some classes. There will be at least four quizzes. There will be a project based on your assigned project. This should form the basis of your methods section for your thesis. Class participation will count for 10%.
If there is no test your grade for the day will be based on your participation and preparation will determine your grade for the day. Successful completion of all homework should guarantee a grade of ‘A’. Be warned, this is a demanding class.
The general rule of thumb regarding college studying is, and has been for a long time, that for each class, students should spend approximately 2-3 of study time for each hour that they spend in class. Many students carry a course load of 15 credits, or approximately 15 hours of class time each week.
If students are spending considerablely more than 7.5 hours per week on the homework, they are advised to speak with the instructor and adjustments will be made. If you put int the time, there is no reason not to get an outstanding mark. Do not try to game the system by not doing the homework and making it up on the exams. That strategy has not been successful yet.
I am available via email and will respond within 24 hours.
Students enrolled in this course are required to attend all lecture, recitation and laboratory sessions of the section for which they registered. (During summer session, two weeks of classes are covered each calendar week.) Excessive absences (defined above) will result in a reduction in the grade. Attendance is taken solely from roll sheets circulated at the beginning and/or end of each session. Lateness or early departure (resulting in missing no more than 15 minutes of a session) counts as 1⁄2 absence. Students missing more than 15 minutes of a session will be counted as absent. If the college is officially closed, thereby canceling all classes, an announcement will be found on 237-8000, and broadcast on AM stations WINS (1010), WOR (710), WCBS (880), WADD (1280), WMCA (570), WLIB (1190), and WFAS (1230), as well as FM stations WCBS (101.1) and WBLS (107.5). If a class will be cancelled for extraordinary circumstances, the instructor will email an announcement to the preferred email of enrolled students as soon as practicable. This has not happened in recent memory.
Active College E-Mail
Students are expected to maintain active and accessible college email and Blackboard accounts. Blackboard will be used to send emails and may be used to post announcements, handouts, additional study materials, text supplements, grades, etc. Use the CUNY Portal Login page help features for a forgotten username or password, or contact DoIT, 212-237-8200 for other help. Verify your CUNY email address is correctly listed on Blackboard and keep the mailbox from filling up and refusing delivery, because you will be responsible for the contents of any email sent to that account.
When emailing instructors for this course, start the email’s subject line with the course and section number (e.g., CSCI 372-01) followed by a brief description. Include your full name in the body of every email. Emails that do not contain these descriptive details may be considered spam, and remain unopened and unanswered. Students are expected to check email regularly.
Ada Statement: Students With Disabilities
Qualified students with disabilities will be provided reasonable academic accommodations if determined appropriate by the Office of Accessibility Services (OAS), 212- 237-8031, located in room L.66.00. Prior to granting disability accommodations, verification of a student’s eligibility must be timely received from OAS by the math department chairman, Professor Douglas Salane (firstname.lastname@example.org), and the instructor, from the OAS. It is the student’s responsibility to initiate contact with the OAS and to follow the established procedures for having the accommodation notice sent to both the course coordinator and the instructor.
Students who succeed in this course and graduate with a degree in Computer Science and Information Security may be hired by government or private agencies to analyze evidence and testify in a court of law, placing in jeopardy another person’s reputation and/or liberty. Dishonesty of any kind cannot and will not be tolerated. Students are expected to become thoroughly aware of the “John Jay College Policy on Academic Integrity” (and other college policies), available on the college’s Web site. Sanctions to the extent permitted by the policy will be imposed and any written material submitted may be transmitted by the instructor to Turnitin.com (or equivalent service) to help analyze its originality. See the Undergraduate Bulletin for the College’s Policy on Plagiarism and Cheating, which will be strictly enforced. Plagiarism includes copying ASA or homework answers from others. You are required to do your own work to avoid severe grade and disciplinary penalties. The College subscribes to Turnitin.com and Blackboard has a similar module called SafeAssign. Any written assignments submitted may be subject to evaluation by these or similar programs.
I was not originally contracted to teach this course. To the extent that it succedes this semester it will only be because of the generosity of my colleagues who have shared their course materials with me. I would like to acknowledge Valarie West and Lila Kazemian who shared their assigments and lectures and John Shane who shared his syllabus and always stood ready to share more if I needed it. Peter Moskos has provided the actual Computer Aided Dispatch data prior to its release to the public. The data is for us to use in the context of this class ONLY.