NURS 7375-001 Regression Models in Nursing Science
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Students are expected to follow all policies related to COVID-19 found on the university webpage: https://wp.uthscsa.edu/coronavirus/.
NOTE: Our campus has enabled the CANVAS MOBILE LEARN application. CANVAS tools such as discussions, quizzes or videos May or May NOT function on all mobile devices. This is because mobile devices are available with rapidly changing and different configurations. Hence, students must not depend on only a mobile device to access course materials. Students must have access to a laptop or desktop device to access course materials and complete assignments.
This course is Web Enhanced with
Please be sure to check the Current Computer Requirements
This course requires the use of IBM SPSS Standard Graduate Pack (versions 24.0 through 28.0 areok; do not rent the Base or Premium Graduate Pack)for statistical analysis. Prior to this first class session, make sure that your rental license for IBM SPSS is current on your laptop and/or tablet.
Possible vendors for SPSS:
On the Hub:
GSBS Course Expectations RegardingCOVID-19
The GSBS expects all students to exhibit the highest standards of conduct, honesty, and professionalism both in and out of the classroom.
· All students and faculty are expected, as part of their professional responsibility, to remain masked during classroom activities in accordance with current CDC guidelines. In-person instruction is an integral aspect of your training, and we must maintain safety in the learning environment. Masks have been demonstrated to be effective in mitigating the spread of the virus. If you forget your mask, your instructor may have extra and there will be a supply in the GSBS Dean's office.
· Social distancing will also be used in accordance with current CDC guidelines. Students will be assigned seats in this class, so that, in the event of a positive COVID-19 diagnosis, contact tracing can be used.
· If students elect not to attend lectures, the course director has the latitude to implement the GSBS attendance policy and determine if these absences will be considered excused or unexcused.
· Students and faculty who are symptomatic(sniffles, fever, cough etc.) should remain at home and contact Wellness 360 (210-567-2788) for possible testing. Students should also contact their instructor(s) as soon as possible. Following a positive test, please adhere to the updated protocols outlined here.
In summary, the most important things you can do are to perform diligent symptom monitoring each morning before class and don’t come to class if you are ill! Also, if you have not yet been vaccinated for SARS-COV-2, UT Health can accommodate you as a walk-up patient TODAY at our MARC outpatient building on Floyd Curl Drive (First Floor Main Lobby).
For additional questions or further information, please contact Dr. Tim Raabe, Associate Dean of Academic Affairs, GSBS.
FACULTY CONTACT INFORMATION
Andrea E. Berndt, PhD,
Phone: (210) 567-5839
Office Hours: By appointment.
Virtual Office Hours will be selected and determined during the first class session.
This course presents regression analysis at an intermediate level. Course will focus on regression for continuous variables: specification, estimation, testing, and diagnostics. Logistic regression for binomial and multinomial variables, log-linear regression for count variables, and proportional hazards regression for duration variables will be explored. An introduction to multilevel regression will occur.
CREDIT AND TIME ALLOCATION
Credit Hour Allocation: 3 Semester Credit Hours
Clock Hour Allocation: 3 Clock Hours Class (45 hours class)
Upon completion of the Doctor of Philosophy (PhD) in Nursing Program students will:
- Advance the discipline of nursing through the generation of new knowledge and theory.
- Demonstrate excellence as a clinical researcher in the health sciences in a focal area of nursing.
- Synthesize theories from natural and/or behavioral sciences for application to a specified area of nursing.
- Advance evidence-based clinical practice.
- Assume nurse scientist roles within academic health centers and other health centers and other interdisciplinary health sciences and educational institutions.
- Evaluate the value and knowlege components of philosophical and ethical dimensions of issues confronting healthcare and nursing.
- Explain the principles for regression analyses.
- Select the appropriate regression approach for different data types.
- Appraise the statistics associated with each regression model.
- Formulate and execute models in statistical software.
- Assess model adequacy using regression diagnostics.
- Interpret, organize, and present results of regression analysis.
GRADING SCALE FOR GRADUATE COURSES
A = 4 points (90-100)
B = 3 points (80-89)
C = 2 points (75-79)
D = 1 point (66-74)
F = 0 points (65 or below)
CRITERIA FOR EVALUATION / GRADES
CRITERIA FOR EVALUATION / GRADES
|30% - Class Project|
60% - Data Assignments (3 @ 20% each)
10% - Article Reviews (1 @ 10% each)
100% - Total
Assignments are expected to be turned in on their due date.
For each day an assignment is turned in beyond the due date, 10 points will be deducted from the assignment grade.
If an assignment is turned in 4 days after the due date, 50 points will be deducted from the assignment grade
Attendance in class is an expectation of each student.
- If written assignments are made in a course they are required.
- Students are expected to submit written work on the scheduled date and time.
- The student must notify the course coordinator prior to the scheduled due date and time if they are unable to submit the written work as scheduled. Failure to make this notification in advance will result in a "zero" for that written work.
- If the excuse is accepted as reasonable and necessary, arrangements will be made for an alternative due date and time.
- Each student is responsible for making sure that he or she has completed the written work prior to submission.
- Late work will be accepted with consequences as outlined per course syllabi.
The APA Publication Manual 7th edition is required for use in all nursing school programs.
Students are expected to be above reproach in all scholastic activities. Students who engage in scholastic dishonesty are subject to disciplinary penalties, including the possibility of failure in the course and dismissal from the university. "Scholastic dishonesty includes but is not limited to cheating, plagiarism, collusion, and submission for credit of any work or materials that are attributable in whole or in part to another person, taking an examination for another person, any act designed to give unfair advantage to a student or the attempt to commit such acts." Regents Rules and Regulations, Part One, Chapter VI, Section 3, Subsection 3.2, Subdivision 3.22.
PROFESSIONAL CODE OF CONDUCT
Students who are nurses or are preparing to enter the profession of nursing are expected to treat others with respect and compassion. “The principle of respect for persons extends to all individuals with whom the nurse interacts. The nurse maintains compassionate and caring relationships with colleagues and others with a commitment to the fair treatment of individuals, to integrity-preserving compromise and to resolving conflict. This standard of conduct precludes any and all prejudicial actions, any form of harassment or threatening behavior, or disregard for the effects of one’s actions on others” (American Nurses Association Code for Nurses, Interpretive Statement 1.5).
The students, faculty, Department Chairs, Associate Deans, and the Dean of the School of Nursing of the University Texas Health Science Center San Antonio subscribe to the highest standards of conduct. Our aim is professional behavior beyond reproach. Failure to abide by the signed code of professional conduct may lead to suspension and/or permanent dismissal from the UTHSCSA SON. In particular, we subscribe to the provisions of the Code of Ethics for Nurses (http://bit.ly/1mtD5p2) and the following points of conduct.
Any student seeking reasonable accommodations through the Americans with Disabilities Act (ADA) should contact either the Associate Dean for Admissions and Student Services within the first week of the semester or schedule a meeting with the UTHSCSA ADA Compliance Office so that appropriate accommodations may be arranged. A request for accommodations (Form ADA-100: http://uthscsa.edu/eeo/form100-Faculty-student-resident.pdf) must be completed and submitted to the Executive Director of the ADA Compliance Office before accommodations can be provided. Additional information can be provided in the Student Success Center, Room 1.118 or through the ADA Compliance Office website: http://uthscsa.edu/eeo/request.asp.
REQUIRED TEXT / REFERENCE
Field, A (2017) Discovering Statistics Using IBM SPSS Statistics (5th edition), Sage Publications, Thousand Oaks, CA ISBN-13: 978-1526436566
You may also use the 4th edition of this text
RECOMMENDED (OPTIONAL) TEXT / REFERENCE
Keith, T. Z. (2015). Multiple regression and beyond (2nd edition). Pearson Education, Inc. ISBN: 978-11388-11959
Supplements will also be provided to students at start of class and subsequent class sessions.
- Correlation and simple linear regression
- Principles of regression estimation / basic regression terminology
- Use of regression software (SPSS) / multiple linear regression analysis with 2 or more predictor variables
- Hypothesis testing and prediction / choosing the regression method
- Regression diagnostics and analysis of effects
- Continuous and categorical predictors: Comparing regression equations
- Categorical predictors: Dummy, effect, and orthogonal coding
- Logistic regression
- Comparing multiple regression to other multivariate analyses
CALENDAR - 1st Day Only
Please check the schedule for recent updates on Class Dates & Room.
CALENDAR - First Week Only
Please check the Fall 2021 schedule for recent updates.
|Date||Topic / Assignment Due|
August 23, 2021
11:00 - 1:50
SON NS 1.208
CANVAS Conference Session recordings will be available for all course sessions
|Topic: Overview of Correlation and Regression|
Readings: Field, Discovering Statistics, 5th edition: Read Chapters 8 (Correlation) + 9 (The Linear Model: Regression)
Field, Discovering Statistics, 4th Edition: Read Chapters 7 (Correlation) + 8 (Regression)
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