Graduate Course Proposal Form Submission Detail - PSY6219
Campus: St Petersburg
Submission Type: New
Course Change Information (for course changes only):
- Department and Contact Information
Tracking Number Date & Time Submitted 5268 2015-09-18 Department College Budget Account Number Psychology AP 511255 Contact Person Phone Mark Pezzo 7278734020 firstname.lastname@example.org
- Course Information
Prefix Number Full Title PSY 6219 Advanced Statistical Methodology 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) Advanced Statistics Course Online? Percentage Online C - Face-to-face (0% online) 0
PSY 6217 & PSY 6218 or permission of the instructor
Advanced multivariate statistical methods in social science emphasizing multiple regression, factor analysis, and structural equations modeling.
A. Please briefly explain why it is necessary and/or desirable to add this course.
Replacing Selected Topics with Permanent number; already listed in program
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?
Although this course is an elective, based on previous semesters enrollment (in SOP 6266 selected topics), we expect approximately 50% of our graduate students will opt to take this course. Additionally, the Psychology Department is developing a new Data Analysis graduate certificate, which will have this as a required course.
- Fall 2014 100% of second year graduate students enrolled/completed this course.
- Fall 2015 ~50% of second year graduate students enrolled in course. Additionally, students outside of the graduate program inquired about taking course although they had already graduated.
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
Yes, 2 times
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
PhD in Psychology or related field
- Other Course Information
1.To acquire knowledge of multivariate statistical techniques (e.g., multiple regression, path analysis, factor analysis, structural equation modeling).
2.To develop familiarity with software (SPSS and LISREL) for conducting multivariate analyses.
3.To understand a theoretical framework distinguishing measurements from latent constructs and how this framework guides conduct and interpretation of multivariate analyses.
4.To translate relationships expressed in symbolic form (e.g., matrix algebra and path diagrams) to verbal form and vice versa.
5.To judge the appropriateness of various multivariate techniques for addressing questions or testing hypotheses in the social sciences
B. Learning Outcomes
o Demonstrate understanding of core concepts of advanced statistical techniques listed below.
o Demonstrate ability to determine which statistical test is appropriate for a given situation.
o Demonstrate ability to use SPSS to calculate statistical tests
o Demonstrate ability to calculate statistical tests by hand
C. Major Topics
Simple, Part, and Semi-partial Correlation; Simple regression, Multiple Regression; Moderators & Mediators, Path Analysis, Factor Analysis, and Structural Equation Modeling.
1. Tabachnick, B.G. & Fidell, L.S. Using multivariate statistics. Any edition from 3rd through 6th.
2. Boslaugh, S. (2005). An intermediate guide to SPSS programming: Using syntax for data management. Sage Publications Inc, Thousand Oaks CA.
E. Course Readings, Online Resources, and Other Purchases
1. SPSS Software Package USF Computer Store (http://www.usf.edu/it/computer-store/.) or free on the USF Application Gateway site (apps.usf.edu)
2. LISREL Software Package Free student version at Scientific Software International, Inc. (http://www.ssicentral.com).
3. Alwin, D. F. & Hauser, R.M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40, 37-47.
4. Bauer, D. J. (2011). Evaluating individual differences in psychological processes. Current Directions In Psychological Science, 20, 115-118.
5. Baron, R.M. & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51 (6), 1173-1182.
6. Beckstead, J.W. (2002). Confirmatory factor analysis of the Maslach Burnout InventoryBeck2 = Beckstead, J.W. (2002). Modeling attitudinal antecedents of nurses' decisions to report impaired colleagues. Western Journal of Nursing Research. 24 (5), 537-551.
7. Beckstead, J.W. (2012). Isolating and examining sources of suppression and multicollinearity in multiple linear regression. Multivariate Behavioral Research, 47, 224-246.
8. Hayes, A.F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408-420.
9. Hoyle, R. H. (2012). Path analysis and structural equation modeling with latent variables. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology (Vol. 2, pp. 333-367). Washington, DC: American Psychological Association.
10. MacCallum, R.C. Widaman, K.F., Zhang,S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, (1) 84-89.
11. McDonald, R.P., & Ho, M.R. (2002). Principles and practice in reporting structural equation analysis. Psychological Methods, 7 (1) 64-82.
12. MacKinnon, D.P., Krull, J.L., & Lockwood, C.M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1 (4) 173-181.
13. Trochim, W. M. K. (2006). General Linear Model. Retrieved from http://www.socialresearchmethods.net/kb/genlin.php.
F. Student Expectations/Requirements and Grading Policy
1. Final grades will be based on the following distribution of percentages:
90% and above = A
80% 89% = B
70% 79% = C
60% 69% = D
Below 60 = F.
2.Although curving of course grades is not anticipated, the instructor reserves the right to curve the grades upward if he deems the overall distribution of grades to be unacceptably low. Extra lectures or help sessions may be scheduled upon demand in order to review material or work through
G. Assignments, Exams and Tests
1. Two, in-class essay exams will be given. Prior to the exams, students will be given sample questions. Some of the questions on the exams will be identical to those in the sample; others will be different. However, it is the instructor's intent that if students understand the answers to the sample questions, they ought to be able to answer the questions on the exam correctly.
2. Midterm Exam 35% of course grade. Final Exam 35% of course grade. Scheduled during finals week.
3. Students should expect weekly homework assignments. These are used to develop their skills in applying statistical concepts, logical reasoning, and programing. These will be assigned at least one week before they are due. Not all these will be collected, however, those that are will constitute 30% of the course grade. Points will be awarded for correctly conducting and interpreting analyses. Late assignments will be penalized one letter grade per day. All homework assignments must be satisfactorily completed for a passing course grade.
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. Its 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
1. Late assignments will be penalized one letter grade per day.
2. Academic Integrity - Behaviors that are contrary to University standards will not be tolerated. Such behaviors include, but may not be limited to, cheating, plagiarism, and lying to the professor about course-related material. Any student found guilty of any such behavior will receive a failing grade for the course and may be reported to the Dean of the College of Arts and Sciences for disciplinary action. Students are expected to work independently. Working with others in any way to complete requirements constitutes cheating.
J. Program This Course Supports
Masters of Arts in Psychology
- Course Concurrence Information
We anticipate possible interest in this course from students in the Environmental Science and Policy (ESPG) graduate program.