Frequently Asked Questions
What is the focus of the PhD program?
The program is designed to prepare nurse scientists who may assume a variety of roles in education, research, leadership and health policy.
How long does it take to complete the program?
MSN to PhD full-time students complete coursework in two calendar years and then focus on completion of their dissertation. For part-time students, generally, three years are needed to complete coursework. After the coursework, students focus on completion of their dissertation.
Can you be a part-time student?
Yes. Please review residency information below.
Is there a requirement for full-time residency?
To assure that students have the opportunity to utilize the educational facilities fully and to participate in the intellectual life and research atmosphere of the University, at least two years of study must be spent at the University of Louisville and at least one must be spent in full-time residency. To be considered in full-time residency for one year, a student must be registered for 18 hours in a 12 month period.
Can I transfer in hours?
Yes. The maximum number of semester hours transferable, upon request , is six. Up to six additional hours may be requested and considered for special approval. Credits which have been applied toward an earned degree may not be applied toward the doctoral nursing degree.
Are there any courses required prior to starting the program?
Applicants accepted into the PhD program are strongly advised to have completed a three-credit master’s level statistics course (applied descriptive and inferential statistics) with a grade of B (3.0) or higher within five years before the date of enrollment. Applicants who do not meet this requirement will be advised of available statistics courses prior to beginning fall PhD coursework in statistics. In order to do a self-assessment of knowledge in statistics, the following represents anticipated basic competencies from previous statistics coursework:
- Design of research
- Frequency distributions
- Central tendency and variability
- Probability theory
- Normal distributions
- Simple linear regression
- Statistical inference
- Decision, error, and power
- One and two-way ANOVAs
- Nonparametric tests