NURS 7316-001 Statistical Analysis for 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
For all class sessions, students should bring their laptop pre-loaded with either SPSS Graduate Pack 24.0, 25.0, 26.0, or 27.0
NOTE: You DO NEED to rent/download the IBM SPSS Standard Graduate Pack Version 24, 25, 26, or 27 (Don't get the IBM SPSS Base or Premium Graduate Pack)
SPSS is available to students as a two-week free trial download, a 6 month rental, or a 12 month rental for MAC or Windows. The cost for the 6 month rental usually ranges between $50-$70, and the cost for the 12 month rental usually ranges between $90-$110. I don't recommend the 24 month rental as there is often difficulty renewing the license at the 12 month period The current version of IBM SPSS is Version 27
SPSS can be rented from several vendors such as:
Hearne Software [https://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/Editions];
On the Hub [https://www.onthehub.com/spss/];
Student Discounts [https://studentdiscounts.com/spss.aspx];
or Studica [https://www.studica.com/IBMSPSS/ibm-spss-statistics-standard-v26-student-grad-pack.html].
To download a two week trial version of SPSS 27.0 from IBM, follow the directions provided at:
Supplemental handouts will be provided during class sessions and will also be available for students to download from CANVAS.
FACULTY CONTACT INFORMATION
Andrea E. Berndt, PhD, Associate Professor/Statistician
Phone: (210) 567-5839
Office Hours: By appointment.
This is an introductory course in statistics and computing. This course will allow the student to summarize numerical data, gain a working vocabulary of important statistical methods, develop some functional computing skills, and improve confidence in dealing with numbers. By the end of the course, you will be knowledgeable about:
- understand how statistics can inform research
- recognize limitations of statistical information
- develop the skills needed to critique a typical quantitative journal article;
- be able to perform and interpret basic statistical tests.
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.
- Identify levels of measurement and describe their relationship to statistical analysis.
- Demonstrate skill in displaying data in a format that is clearly understood.
- Explain the importance of distinguishing samples and populations in hypothesis testing, and describe principles of inferential statistics.
- Summarize and demonstrate usual ways of using graphics to describe the central tendency and variability of samples.
- Describe and demonstrate proper use of contingency tables and the x2 test.
- Describe and demonstrate the proper use of various forms of the t-test.
- Describe and demonstrate proper use of various forms of the analysis of variance (ANOVA) through a 2-way model.
- Explain how correlation and regression operate.
- Demonstrate understanding of the building blocks of simple statistics: sum of squares
- Explain the various regression hypothesis tests that appear on a statistical printout.
- Describe the linkages among alpha, power, effect size, and sample size.
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
Letter grades are used for evaluation of student progress, and are based on:
70% - Data Assignments/Article Critiques (7 @ 10 pts each)
30% - Statistics Project/Presentation
100% - Total
CELL PHONE POLICY
- Respect for classroom and clinical communication processes are necessary for teaching and learning.
- Silence mobile devices / cell phones in classrooms and clinical settings.
- Remove Bluetooth devices prior to entering the classroom and when in ANY clinical setting.
- Failure to do so can / will / may (depending on the faculty) either affect your class participation, clinical or final course 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.
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.
- Be courteous about what you say to or about others in any electronic format. In electronic communication the golden rule is "Remember the Human." Remember there is a real person with real feelings on the receiving end of your email or post.
- Be respectful and open to opinions and ideas that differ from yours. The exchange of diverse thoughts, ideas and opinions are an important part of the scholarly environment. Keep in mind that the people in your classes may come from different backgrounds and have views that may vary significantly from your own.
- Flaming (defined as posting of messages that are deliberately hostile and insulting in an online social context) is never appropriate. While everyone (learners and instructors alike) is encouraged to share ideas and opinions openly, you should never use insults or resort to name-calling even if you disagree strongly with what someone else has written.
- When responding to messages or posts made by others, address the ideas, not the person.
- It’s often best to avoid using sarcasm and humor online. Without social cues, such as facial expressions and body language, a remark meant as humorous could come across hurtful or offensive. Keep in mind that ‘emoticons’ (such as J) may not convey your tone or intent.
- Capitalizing whole words is generally seen as SHOUTING and is difficult for most people to read. Use all capital letters sparingly, such as to highlight an important word or point.
- Think and reread what you’ve written before you post! Make sure that what you’ve written makes sense (is clear and to the point).
- Remember you are responsible for the content you communicate on CANVAS. What you write represents you, so use appropriate language. Remember that all writing should be professional, consisting of complete sentences, and free of grammatical and spelling errors.
- Be aware that distributing copyrighted materials, such as articles and images, is illegal. Most of the materials on the Internet are copyrighted. The only time it’s ok to distribute materials from the Internet is when you are sure those materials are "fair use." To be safe, if you want to share materials with classmates and/or your instructor, share the web link or URL only.
- To avoid plagiarism, make sure you properly cite all source materials. All materials should be cited unless you are the author of the content.
- Protect your privileges in online communication (avoid posting spam or emailing chain letters).
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
1) Fields, A. (2017) Discovering statistics using SPSS (5th edition). Thousand Oaks, CA: Sage Publications. ISBN-13: 978-1526436566.
2) Morgan, S.E., Reichert, T., & Harrison, T.R. (2016). From Numbers to Words. Routledge. ISBN-13: 978-1138638082.
RECOMMENDED (OPTIONAL) TEXT / REFERENCE
Hancock, G.R., & Mueller, R. O. (2010). The reviewer's guide to quantitative methods in the social sciences, 1st edition. Routledge.
- Introduction to this course, SPSS, measurement, and statistics
- Frequency displays in tables and in graphs
- Samples, populations, and hypothesis testing
- Describing one or more groups
- Comparing proportions
- One and two-group comparisons
- Matched group comparisons
- Using ANOVA to compare more than two groups at once
- More complicated ANOVA models
- Correlation and simple linear regression
CALENDAR - 1st Day Only
Please check the schedule for recent updates on Class Dates & Room.
Date: Thursday, August 27, 2020
Time: 1:00 – 3:50
All classes are virtual on CANVAS Conference Sessions
Topic / Assignment Due:
How to Be Successful in Statistics
Reviewing the Basics
Review/skim the following chapters before our 1st class:
Fields - Discovering statistics using SPSS, 5th ed, Chapters 1 and 3
For full calendar and details, please refer to supplemental materials provided at the first class session.
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