Fall 2020

NURS 7375-001 Regression Models in Nursing Science

All students are responsible for checking their UTHSCSA Livemail account regularly (i.e., daily or several times every week) to obtain Official University Communication regarding their courses, program and student status.

COVID-19
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 WebCT icon
Please be sure to check the Current Computer Requirements

This course requires the use of IBM SPSS Standard Graduate Pack (versions 24.0 through 27.0 are ok; 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:
Hearne Software:
https://www.hearne.software/shoppingcart?returl=%2Fspss-selection
journeyEd:
https://www.journeyed.com/item/IBM+SPSS/IBM+SPSS+Statistics/1868566
On the Hub: 
https://estore.onthehub.com/WebStore/OfferingsOfMajorVersionList.aspx?pmv=0a915582-02b0-ea11-812c-000d3af41938
Student Discounts:
https://studentdiscounts.com/ibmspssstatisticsgradpack27basedownload-winmac-6mnth.aspx?gclid=CjwKCAjw1K75BRAEEiwAd41h1FPtcuXrTfish7-mvSyXLQRufMD2o6OyZ3PRFt39ww3jt8dybGo-HhoCh7UQAvD_BwE

FACULTY CONTACT INFORMATION

Andrea E. Berndt, PhD, Associate Professor/Statisticianexternal link
E-mail: berndt@uthscsa.edu
Phone: (210) 567-5839
Office:2.216
Office Hours: By appointment.

COURSE DESCRIPTION

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)

PREREQUISITES

Graduate Standing

PROGRAM OUTCOMES

Upon completion of the Doctor of Philosophy (PhD) in Nursing Program students will:

  1. Advance the discipline of nursing through the generation of new knowledge and theory.
  2. Demonstrate excellence as a clinical researcher in the health sciences in a focal area of nursing.
  3. Synthesize theories from natural and/or behavioral sciences for application to a specified area of nursing.
  4. Advance evidence-based clinical practice.
  5. Assume nurse scientist roles within academic health centers and other health centers and other interdisciplinary health sciences and educational institutions.
  6. Evaluate the value and knowlege components of philosophical and ethical dimensions of issues confronting healthcare and nursing.

COURSE OUTCOMES

  1. Explain the principles for regression analyses.
  2. Select the appropriate regression approach for different data types.
  3. Appraise the statistics associated with each regression model.
  4. Formulate and execute models in statistical software.
  5. Assess model adequacy using regression diagnostics.
  6. Interpret, organize, and present results of regression analysis.

CLINICAL OUTCOMES

N/A

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

20% - Class Project
60% - Data Assignments (3 @ 20% each)
20% - Article Reviews (2 @ 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

CLASSROOM ATTENDANCE

Attendance in class is an expectation of each student.

WRITTEN ASSIGNMENTS

  1. If written assignments are made in a course they are required.
  2. Students are expected to submit written work on the scheduled date and time.
  3. 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.
  4. If the excuse is accepted as reasonable and necessary, arrangements will be made for an alternative due date and time.
  5. Each student is responsible for making sure that he or she has completed the written work prior to submission.
  6. Late work will be accepted with consequences as outlined per course syllabi.

APA GUIDELINES

The APA Publication Manual 7th edition is required for use in all nursing school programs. 

SCHOLASTIC DISHONESTY

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.

http://catalog.uthscsa.edu/schoolofnursing/policiesandprocedures/

ADA ACCOMMODATIONS

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.

CONTENT OUTLINE

  1. Correlation and simple linear regression
  2. Principles of regression estimation / basic regression terminology
  3. Use of regression software (SPSS) / multiple linear regression analysis with 2 or more predictor variables
  4. Hypothesis testing and prediction / choosing the regression method
  5. Regression diagnostics and analysis of effects
  6. Continuous and categorical predictors: Comparing regression equations
  7. Categorical predictors: Dummy, effect, and orthogonal coding
  8. Logistic regression
  9. 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 2020 schedule for recent updates.

DateTopic / Assignment Due

Monday
August 24, 2020 

11:00 - 1:50
CANVAS Conference Session

Topic: Overview of Correlation and Regression


Assignment Due
: 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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