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Graduate Course Proposal Form Submission Detail - NGR6106
Tracking Number - 1988
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Current Status:
Approved, Permanent Archive - 2005-10-06
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Detail Information
- Date & Time Submitted: 2004-02-16
- Department: Nursing
- College: NR
- Budget Account Number: 6201-000-20
- Contact Person: Judith Karshmer
- Phone: 9749229
- Email: jkarshme@has.usf.edu
- Prefix: NGR
- Number: 6106
- Full Title: Cluster -Analytic Techniques Health Science Research
- Credit Hours: 1
- Section Type: C -
Class Lecture (Primarily)
- Is the course title variable?: N
- Is a permit required for registration?: N
- Are the credit hours variable?: N
- Is this course repeatable?:
- If repeatable, how many times?: 0
- Abbreviated Title (30 characters maximum): Cluster Analysis
- Course Online?: -
- Percentage Online:
- Grading Option:
R - Regular
- Prerequisites: NGR 7841
- Corequisites:
- Course Description: Theoretical foundations and applications of cluster-analytic concepts: proximity, hierarchical agglomeration and division, various optimization algorithms of discrete groups of similar entities based on similarities among their features.
- Please briefly explain why it is necessary and/or desirable to add this course: National standards for nursing graduate education requires this course to be added to the program study
- 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? Will be offered each Spring
- Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Twice
- What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) PhD. and experience teaching Statistic for nursing research
- Objectives: 1) analyze data using these mathematical (although nonstatistical) methods;
2) program analyses using cluster analysis software;
3) interpret these methods as applied in published health-science literature.
- Learning Outcomes: 1) analyze data using these mathematical (although nonstatistical) methods;
2) program analyses using cluster analysis software;
3) interpret these methods as applied in published health-science literature.
- Major Topics: I. Introduction to clustering
A. Reasons for clustering
B. What is a cluster?
C. Examples in health science research
II. Measurement of Proximity
A. Similarity measures
1. For continuous data
2. For categorical data
B. Dissimilarity and distance measures
1. For continuous data
2. For categorical data
C. Standardization and weighting
III. Hierarchical techniques
A. Agglomerative methods
B. Divisive methods
C. Stopping rules
D. Graphical interpretation
IV. Optimization techniques
A. Choosing the number of clusters
B. Optimization algorithms
V. Miscellaneous topics
A. Testing for absence of structure
B. Fuzzy clustering
C. Finite mixture densities
- Textbooks: Everitt, B.S., Landau, S., & Leese, M. (2001). Cluster analysis (4th ed.). London, Arnold Publishers.
- Course Readings, Online Resources, and Other Purchases:
- Student Expectations/Requirements and Grading Policy:
- Assignments, Exams and Tests:
- Attendance Policy:
- Policy on Make-up Work:
- Program This Course Supports:
- Course Concurrence Information:
- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.