Graduate Course Proposal Form Submission Detail - EDF7436
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Approved by SCNS
Submission Type: New
Course Change Information (for course changes only):
Comments: to GC 5/6/13 - elective. Approved. Cleared Syst Concurrence 7/31/13. to SCNS 8/5/13. Approved eff 9/1/13
- Department and Contact Information
Tracking Number Date & Time Submitted 3096 2013-02-24 Department College Budget Account Number Educational Measurement and Research ED 171100 Contact Person Phone YI-HSIN CHEN 8139744964 firstname.lastname@example.org
- Course Information
Prefix Number Full Title EDF 7436 Rasch Measurement Models 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 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) - Abbreviated Title (30 characters maximum) Rasch Measurement Models Course Online? Percentage Online C - Face-to-face (0% online) 100
EDF 6432 (Foundations of Measurement) or equivalent
Introduction to a family of Rasch models. Estimation procedures of item and ability parameters. Applications of Rasch models for dichotomous and polytomous data, such as item construction/selection and differential item functioning (DIF).
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?
This course is one of the Measurement concentration courses in the doctoral program.
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.)
Teaching qualification includes a doctorate degree in statistics, measurement, and research.
- Other Course Information
The primary purpose of the course is to introduce students to the terminologies, models, and computer programs of a family of Rasch models and further apply them to educational and psychological test data. The emphasis will be on models and applications most frequently used in social science research, especially in the fields of education and psychology. Computer applications of the models and procedures will be integrated into the course.
Successful completion of course requirements is expected to result in students’ increased ability to (a) comprehend and apply a family of Rasch models to analyze real sets of data; (b) intelligently read and evaluate research literature; (c) recognize the strengths/limitations of Rasch-related analyses in the conduct of disciplined inquiry; (d) communicate with peers and other professionals on Rasch-related research issues (e) conduct data analyses and reporting of results in a manner consistent with the ethical guidelines of professional associations such as the American Statistical Association (ASA), American Educational Research Association (AERA), and the American Psychological Association (APA).
B. Learning Outcomes
Students who successfully complete all course requirements will be able to:
1) become familiar with models of the Rasch family and understand mathematical underpinning of the models in Rasch models,
2) recognize the fields of applications of diverse Rasch models and be able to use computer programs to analyze empirical data,
3) read and evaluate current literature of Rasch models and its applications,
4) write reports in sufficient detail that other colleagues and researchers are able to understand and interpret them accordingly, and
5) work collaboratively with peers in the conduct of research activities (e.g., literature review, data analyses, interpreting statistical analyses of data, preparation of summary reports).
C. Major Topics
1. Measurement and Principles of Measurement
2. Technical aspects of the Rasch model
3. Basic principles of the Rasch model
4. Building a set of items (Dichotomous Data Analysis)
5. Invariance: Test equating and DIF
6. Measurement Using Likert Scales
7. The Partial Credit Model
8. Measuring Facets beyond Ability and Difficulty
9. Model Fit and Unidemensionality
10. Cognitive Assessment: Linear Logistic Test
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental Measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
E. Course Readings, Online Resources, and Other Purchases
Smith and Smith (Eds) (2007). Rasch Measurement: Advanced and Specialized Applications. Maple Grove, Minnesota: JAM Press.
Smith and Smith (Eds.) (2004). Introduction to Rasch Measurement. Maple Grove, Minnesota: JAM Press.
Special Websites and Journals for the Rasch Model:
Rasch SIG At AERA Administration Website: http://www.raschsig.org/
Institute for Objective Measurement, Inc. http://www.rasch.org
Journal of Applied Measurement http://www.jampress.org/
Rasch Measurement Transactions (Online) http://www.rasch.org/rmt/contents.htm
F. Student Expectations/Requirements and Grading Policy
a)Attendance & Participation (10%)
b)Weekly Reading Summary (20%)
c)Literature Review (10%)
d)Assignments 1 & 2 (30%)
e)Research Project (30%)
Criteria for grades are as follows: 90-100%=A; 80-89%=B; 70-79%=C; Below 70%=F;
G. Assignments, Exams and Tests
Weekly Reading Summary, Literature Review, Assignments 1 & 2, and Research Project
H. Attendance Policy
Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
I. Policy on Make-up Work
Late homework and papers with documented medical circumstance or permission from instructor will be accepted past due date without any penalty.
Academic Dishonesty and Plagiarism
“Plagiarism is defined as “literary theft” and consists of the unattributed quotation of the exact words of a published text, or the unattributed borrowing of original ideas by paraphrase from a published text. On written papers for which the student employs information gathered from books, articles, or oral sources, each direct quotation, as well as ideas and facts that are not generally known to the public at large must be attributed to its author by means of the appropriate citation procedure. Citations may be made in footnotes or within the body of the text. Plagiarism also consists of passing off as one’s own, segments or the total of another person’s work.
Punishment for Academic Dishonesty will depend on the seriousness of the offense and may include receipt of an “F” with a numerical value of zero on the item submitted, and the “F” shall be used to determine the final course grade. It is the option of the instructor to assign the student a grade of F or FF (the latter indicating dishonesty) in the course. ”
J. Program This Course Supports
Measurement, Statistics, and Evaluation
- Course Concurrence Information
I/O program in Psychology.