Graduate Course Proposal Form Submission Detail - CCJ6707
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Approved by SCNS
Submission Type: Change
Course Change Information (for course changes only): We are reducing the credit hours from 4 to 3 credit hours (indicated in box "j") for this course as part of a complete revision of the Department of Criminology graduate curriculum and to better reflect the course requirements.
Comments: For PhD in Crim. Credit hour change. to Chair 5/2/14. Approved 5/19/14. To USF Sys 5/20/14. to SCNS 5/28/14. Apprd eff 11/1/14
- Department and Contact Information
Tracking Number Date & Time Submitted 2756 2012-02-09 Department College Budget Account Number Criminology BC 122100000 Contact Person Phone Lorie Fridell 8139746862 firstname.lastname@example.org
- Course Information
Prefix Number Full Title CCJ 6707 Quantitative Analysis in Criminology II Is the course title variable? N Is a permit required for registration? N Are the credit hours variable? N Is this course repeatable? N If repeatable, how many times? 0 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) R - Regular Abbreviated Title (30 characters maximum) Quant Analysis in Crim II Course Online? Percentage Online C - Face-to-face (0% online) 100
Introduction to multivariate regression analyses for criminology students.
A. Please briefly explain why it is necessary and/or desirable to add this course.
Needed for program/concentration/certificate change
B. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service?
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
Expertise in Statistics.
- Other Course Information
The objectives of this is to demonstrate the conceptual and analytic tools necessary to understand, conduct, and think critically about methodologically rigorous research.
B. Learning Outcomes
Upon completion of this course, a student will be able to:
• Identify the key assumptions underlying OLS regression and the situations in which OLS regression is appropriate.
• Demonstrate an understanding of and the ability to apply ordinary least squares regression.
• Identify the key assumptions underlying regression models of limited dependent variables and the situations in which these models are appropriate.
• Demonstrate an understanding of and the ability to apply regression models for limited dependent variables.
C. Major Topics
Multivariate regression analyses
Allison, Paul D. 2012. Logistic regression using SAS System (2nd edition). Cary, NC: SAS Institute
McClendon, McKee J. 1994. Multiple regression and causal analysis. Long Grove, IL: Waveland
E. Course Readings, Online Resources, and Other Purchases
F. Student Expectations/Requirements and Grading Policy
G. Assignments, Exams and Tests
H. Attendance Policy
I. Policy on Make-up Work
J. Program This Course Supports
- Course Concurrence Information