Summer 2020

NURS 7382-001 Structural Equation Models for 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

See 1st Day Calendar to prepare for our first class on May 14, 2020.

Supplementary Materials will be provided throughout the semester.

FACULTY CONTACT INFORMATION

Andrea E. Berndt, PhD, Associate Professor and Statistician 
E-mail: berndt@uthscsa.edu
Office Phone: (210) 567-5839
Office Fax: (210) 567-5822
Office Room: NS 2.216
Office Hours: By appointment

UT Health San Antonio, School of Nursing

COURSE DESCRIPTION

This course presents structural equation modeling (SEM) for nursing science. The course will begin with a review of regression from an SEM perspective. The first major topic of the course will be path analysis, including model specification, methods of estimation, recursive and non-recursive models, direct, indirect, and total effects, methods of estimation, single and multi-group analyses, moderators and mediators, and the assessment of causality. The second major topic will be psychometrics from an SEM perspective, including congeneric test theory, reliability and stability, convergent and discriminant validity, and confirmatory factor analysis. The third major topic will combine the first two into structural equations, including model specification and identification, methods of estimation, second-order factor analysis, and the assessment of causal structure.

CREDIT AND TIME ALLOCATION

Credit Hour Allocation: 3 Semester Credit Hours
Clock Hour Allocation: 3 Clock Hours Class (45 hours class)

PREREQUISITES

Intermediate Statistics

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, components, and estimation methods of SEM.
  2. Formulate regression, psychometrics, factor analysis, and other special cases of SEM.
  3. Create and specify SEMs.
  4. Fit SEMs to data using SEM software.
  5. Evaluate goodness-of-fit of SEMs
  6. Interpret SEM software output.
  7. Explain the principles of causal structure and inference.

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

Grade Percentages:
50% - Article Reviews (2 @ 25% each)
50% - Data Analysis (2 @ 25% each)
100% - Total

Competencies include: Read, interpret and critique articles using path analysis, confirmatory factor analysis, and structural equation model analyses. Formulate, interpret, and present appropriate path, confirmatory factor, and structural equation model analyses.

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/

School of Nursing Netiquette Guidelines for Online Interaction

Netiquette guidelines provide information for behaving properly online, when using email, tweets or texts so that you may successfully communicate your thoughts in a manner that is respectful and avoids misunderstandings with others.

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

Blunch, N. J. (2014). Introduction to structural equation modeling: Using IBM SPSS Statistics and AMOS. 2nd Ed. Thousand Oaks, CA: Sage Publishing. (Paperback) ISBN: 978-1-4462-4900-0

RECOMMENDED (OPTIONAL) TEXT / REFERENCE

Schumacker, R. E. & Lomax, R. G. (2015). A beginner's guide to structural equation modeling. 4th Edition. Routledge Academic. ISBN: 978-1138811935 

CONTENT OUTLINE

1. Exploratory factor analysis
2. Confirmatory factor analysis
3. Second order factor analysis
4. Path analysis
5. From regression to structural equation modeling
6. Moderators, mediators, and interactions
7. Recursive and non-recursive models
8. Model specification and identification
9. Estimation methods
10. Direct, indirect, and total effects
11. Assessment of causal structure
12. Hypothesis testing and model revisions
13. Goodness-of-fit indices
14. Single and multiple group analyses

CALENDAR - 1st Day Only

Please check the schedule for recent updates on Class Dates & Room.

Date: Thursday, May 14, 2020      Time: 9:00 am to 11:50 am
CANVAS Conference Sessions

Topic / Assignment Due: Read these three articles prior to 1st class

Clayton, M. F., & Pett, M. A. (2011). Modeling relationships in clinical research with path analysis Part 2: Evaluating the model. Journal for Specialists in Pediatric Nursing, 16, 75-79.

Pett, M. A., & Clayton, M. F. (2010). Modeling relationships in clinical research with path analysis Part 1: An overview of the process. Journal for Specialists in Pediatric Nursing, 15 (4), 329-332.

Stage, F. K., Carter, H. C., & Nora, A. (2004). Path analysis: An introduction and analysis of a decade of research. The Journal of Educational Research, 98 (1), 5-12.

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