Graduate Curriculum Committee Course Proposal Form
for Courses Numbered 6000 and Higher
Note: Before completing this form, please carefully read the accompanying instructions.
Submission guidelines are posted to the GCC Web site: http://www.ecu.edu/cs-acad/gcc/index.cfm
1. Course prefix and number:
NURS 8267
2. Date:
09/06/12
3. Requested action:
New Course: Design and Statistical Methods for Advanced
x Nursing Practice
Revision of Active Course
Revision & Unbanking of a Banked Course
Renumbering of an Existing Course from
from
to
#
x
Required
#
Elective
4. Method(s) of delivery (check all boxes that apply for both current/proposed and expected
future delivery methods within the next three years):
Current or
Proposed Delivery
Method(s):
Expected
Future Delivery
Method(s):
On-campus (face to face)
Distance Course (face to face off campus)
x
Online (delivery of 50% or more of the instruction is offered online)
x
5. Justification (must cite accreditation and/or assessment by the graduate faculty) for new course
or course revision or course renumbering:
The Graduate Faculty of the College of Nursing, in response to the 2004 endorsement by
the American Association of Colleges of Nursing (AACN) to move the entry level
preparation for all advanced practice nursing from the master’s level to the doctorate
level by 2015, determined that the Doctor of Nursing Practice (DNP) will be offered. The
DNP degree more accurately reflects competencies required for the complexities of the
evolving healthcare environment, prepares graduates for leadership in practice, and
translates research into evidence-based practice that promotes high quality and safe
patient care. This course proposal supports this endeavor and is approved by the graduate
faculty at the College of Nursing.
6. Course description exactly as it should appear in the next catalog:
8267. Design and Statistical Methods for Advanced Nursing Practice (3) P: Admission to
the DNP program or consent of the DNP program director; C: NURS 8266. Provide
basis to research, retrieve, and manipulate statistical data. Focuses on quantitative
methodologies, research design, and data analysis, providing essential knowledge for the
evaluation of research to guide evidence-based advanced nursing practice.
7. If this is a course revision, briefly describe the requested change:
8. Course credit:
Lecture Hours
3
3
Weekly
OR
Per Term
Credit Hours
Lab
Weekly
OR
Per Term
Credit Hours
s.h.
Studio
Weekly
OR
Per Term
Credit Hours
s.h.
Practicum
Weekly
OR
Per Term
Credit Hours
s.h.
Internship
Weekly
OR
Per Term
Credit Hours
s.h.
Other (e.g., independent study) Please explain.
s.h.
3
Total Credit Hours
25
9. Anticipated annual student enrollment:
10. Changes in degree hours of your programs:
Degree(s)/Program(s)
Changes in Degree Hours
DNP CON
N/A
11. Affected degrees or academic programs, other than your programs:
Degree(s)/Program(s)
Changes in Degree Hours
None
s.h.
N/A
12. Overlapping or duplication with affected units or programs:
x Not applicable
Documentation of notification to the affected academic degree programs is
attached.
13. Council for Teacher Education (CTE) approval (for courses affecting teacher education):
x Not applicable
Applicable and CTE has given their approval.
14. University Service-Learning Committee (USLC) approval:
x Not applicable
Applicable and USLC has given their approval.
s.h.
15. Statements of support:
a. Staff
x Current staff is adequate
Additional staff is needed (describe needs in the box below):
b. Facilities
x Current facilities are adequate
Additional facilities are needed (describe needs in the box below):
c. Library
x
Initial library resources are adequate
Initial resources are needed (in the box below, give a brief explanation and an
estimate for the cost of acquisition of required initial resources):
d. Unit computer resources
x
Unit computer resources are adequate
Additional unit computer resources are needed (in the box below, give a brief
explanation and an estimate for the cost of acquisition):
e. ITCS resources
x
ITCS resources are not needed
The following ITCS resources are needed (put a check beside each need):
Mainframe computer system
Statistical services
Network connections
Computer lab for students
Software
Approval from the Director of ITCS attached
16. Course information (see: Graduate Curriculum and Program Development Manual for
instructions):
a. Textbook(s) and/or readings: author(s), name, publication date, publisher, and
city/state/country. Include ISBN (when applicable).
1. APA (2009). Publication manual of the American Psychological Association
(6th ed., 2nd printing). Washington, D.C.: American Psychological Association.
ISBN-13: 978-1433805615
2. DiCenso, A., Guyatt, G., & Ciliska, D. (2005). Evidence-based nursing: A
guide to clinical practice. St. Louis: Elsevier Mosby. ISBN-13: 9780323025911.
3. Polit, D. F., & Beck, C. T. (2011). Nursing research: Generating and assessing
evidence for nursing practice (9th ed.). Philadelphia: Lippincott Williams &
Wilkins. ISBN-13: 978-1605477084
4. Houser, J. (2012). Nursing Research: Reading, using, and creating evidence (2nd
Ed.). Sudbury, MA: Jones & Bartlett Learning. ISBN: 978-1-4496-3173-4.
5. The IBM SPSS Statistics Standard GradPack 19 software package will be
required for this course. Each homework assignment involving SPSS will have
detailed instructions for completing the required assignment.
6.
Selected readings from current periodicals and URLs.
b. Course objectives for the course (student – centered, behavioral focus)
Upon completion of this course, students will be able to:
1. Investigate analytical methods to critically appraise existing literature and other
evidence to determine and implement the best evidence for practice.
2. Explain the major purpose, assumptions, strengths, and weaknesses of selected
statistical techniques.
3. Plan an appropriate statistical methodology for a research question based on the
study hypothesis, design, and measures using the five step protocol (stating a
research hypothesis, selecting the level of significance, calculating the test
statistic, formulating a decision rule, and interpreting the result).
4. Synthesize the use of research methods for the collection of appropriate and
accurate data to inform and guide evidence for practice.
5. Develop statistical foundation to function as a practice specialist/consultant in
collaborative knowledge-generating research.
6.
Devise effective plans to disseminate findings from evidence-based practice and
research to improve healthcare outcomes.
c. Course topic outline
1. Research Concepts
a. Intro to clinical nursing research
b. Research paradigms and phases
c. Quantitative research concepts
d. Intro to quantitative research designs
e. Design and analysis core concepts
f. Data/information gap identification
2. Research Designs
a. Research designs
b. Threats to validity
c. Controlling for confounds
d. Randomization methods
e. Bias and random error –Part 1
3. Research Methods
a. Design and analysis
4.
5.
6.
7.
8.
b. Surrogate outcomes
c. Sample size and sampling methods
d. Intention-to-Treat Principle
e. Reliability and validity
Descriptive Statistics
a. Levels of measurement
b. Descriptive statistics
c. Standard deviation and standard error of the mean
d. Data transformation methods
e. Describing risk
f. Descriptive vs inferential statistics
g. Skewness, kurtosis, and plots
h. Assessment of normality
Inferential Statistics
a. Inferential statistics
b. Probability
c. Sampling distributions
d. Estimation and confidence intervals
e. Hypothesis testing
f. Type I and Type II Errors
g. Significance testing steps
h. Parametric and Non-Parametric tests
i. Bias and random error – Part 2
Correlation and Regression Analysis
a. Simple correlation
b. Correlation vs prediction
c. Bivariate regression
d. Scatterplots vs residual plots
e. Multiple regression
f. Multicollinearity
g. Variable selection methods
h. Regression overview
Evaluating the Quality of evidence
a. Synthesis of information
b. Significance testing steps
c. Critiquing quantitative studies
d. Selecting statistical methods
e. Ten common statistical errors
f. Statistical tests
Analysis of differences in proportions
a. Categorical outcomes
b. Prevalence and incidence rates
c. Risk indexes
d. Chi-Squares Tests
e. Fisher, McNemar, and CMH Tests
f. Logistic Regression
g. Overview of Chi-Square tests
h. Overview of NcNemar’s Test
i. Overview of Cochran-Mantel-Haenszel
j. Overview of Logistic Regression
k. Chi-Square: Reading SPSS Output
l. Logistic Regression: Reading SPSS Output
m. Thinking About Odds Ratios
9. Analysis of Differences in Means, t-tests
a. Introduction to T-Tests
b. Standard Error of the Mean
c. Independent and Dependent T-Tests
d. T-Test Nonparametric Alternatives
10. Analysis of Difference in Means, F tests
a. One-way ANOVA
b. Multiple Comparisons
c. Factorial ANOVA
d. Factorial ANOVA Interactions
e. Analysis of Covariance
f. ANOVA-RM
g. ANCOVA Overview
11. Analysis of Longitudinal Data
a. Change Over Time – Continuous Outcomes
b. Fixed and Random Effects
c. Change Over Time – Binary Outcomes
d. Time to Event Analysis
12. Magnitude of Effect
a. Effect Sizes - Continuous Outcomes
b. Effect Sizes – Binary Outcomes
c. NNT and NNH
d. Benefit to Risk Ratios
e. Interpreting Effect Sizes
13. Statistical Power
a. Statistical power
b. Interim and subgroup analyses
c. Moderators and mediators
d. Stance on subgroup analyses
d. List of course assignments, weighting of each assignment, and grading/evaluation system
for determining a grade
Grading Scale
93-100 = A
85-92 = B
77-84 = C
<76 = F
Evaluation Methods:
Online Discussion Board Participation
(5 discussions worth 3 points each)
Statistical Application Assignments
(10 assignments worth 5 points each
15%
based on the content of each module)
50%
Mid-Term Exam (25 multiple choice)
15 %
Final Exam (50 short answer and
multiple choice questions)
20%
100%