NGR 6991-83101
Statistical Interpretation for Advanced Practice
Fall 2008
Distance Learning

Instructor:  Peter Wludyka

Office:  14/2705                       Telephone:  620-1048

Office Hours: Tuesday & Thursday: 2:00-4:30, and by appointment

e-mail:   Use Blackboard mail to send in all assignments or to ask questions of the instructor.  An alternative is pwludyka@unf.edu , but you cannot rely on assignments or questions reaching me with this email address. 

Web page:   www.unf.edu/~pwludyka

Course prerequisite:  Permission of instructor or program director

Course description:  This course covers the use and interpretation of statistical methods commonly used in health care studies.  The advantages and disadvantages of specific techniques will be considered along with various examples of computer based statistical packages.  Particular attention is paid to inferential methods used to evaluate the strength of evidence claimed to support particular interventions.

Course objectives: At the completion of this course the learner will be able to:

  • identify which statistical methods are appropriate for a given data set;
  • evaluate the effectiveness of statistical support claims in health publications
  • employ statistical methods that will support strength claims for evidence..

Text:

How to Use SPSS, Brian Cronk, Pyrczak Publsihing, 5th Edition

Biostatistics: A Methodology for the Health Sciences, Gerald van Belle, et all, Wiley, 2nd Edition

SPSS 16: Student Version (There is a somewhat more expensive Graduate Student version which is more powerful and would be useful with later research; this more powerful version is optional).

A Cross Section of Nursing Research: Journal Articles for Discussion and Evaluation, Roberta Peteva, Pyrczak Publsihing, 4th Edition.

Software:   SPSS, EXCEL, WORD, PowerPoint, G*Power 3, and Blackboard media.   Students are not expected to have previous experience with SPSS, but will be expected to acquire a certain level of expertise.   Students will download G*Power 3 (freeware).  SAS output appears in some of the PowerPoint presentations.  Students are not expected to use SAS; however, they should be able to interpret the SAS output that appears in the presentations.

Attendance and Other Policies: Assignments will all be turned in electronically.  Hence they will be time stamped. Late assignments that have not been pre-approved will be “docked points”.  No homework more than 24 hours late will be accepted.  The late penalty (without prior approval) is 10/100 points per 24 hours.

Graded activities:

  • 7 homework assignments.  Lowest grade dropped. These will primarily consists of sets of short, clearly defined activities, often involving SPSS and written comments on SPSS output.  Each homework assignment will be graded; however, in some cases only a subset of the problems will be marked.
  • 4 Short Reports.  This may involve some teaming.  For example, an assignment may include writing a short report and evaluating another student’s short report (which will include a written critique).  Some of this may be transparent to other students via web based or blackboard media.
  • 7 Five Slide PowerPoint Reports.  These will based on research papers appearing in Peteva, in which the student summarizes or selects interesting aspects of the paper focusing on statistical methods and their role in the paper.  These reports will be posted on a blog or discussion board.
  • Group/Team Projects (team sizes or whether a particular project will be a team activity will be decided by the instructor at a later date).
    • Midterm
    • Final
  • Participation in class activities (primarily through Backboard media).

 

Grading:

Activity

         Percent

           Grade

     Percent/Average

Short Reports

Midterm Project Report

Final Project Report

Homework

Participation in Class

Five Slide PPT Reports

20

20

20

20

5

15

              A

              B+

              B

              B-

              C

             

           90-100

            87-89

            84-86

            80-83

            75-79

           

Topics:
Descriptive statistics, inference (estimation and hypothesis tests, including ANOVA and categorical data methods), modeling (including multiple logistic and standard multiple regression), longitudinal data, and repeated measures with the overall goal of using statistical methods to evaluate evidence in nursing practice.  The course will cover these topics, but is not limited to these topics.  Depth and extent of treatment will be determined by the instructor and will be consistent with the overall objectives of the course.

 

 Learning assistance and communication:

Email via Blackboard is the best way to get help.

FAQs will be available on Blackboard

Calling my office via telephone during posted office hours is a reliable way to reach to instructor

Blackboard media in which students share experiences and information will be made available and is expected to be a valuable resource