Improving DNP Student Statistical Competence: A Faculty Quality Improvement Initiative : Nurse Educator

Journal Logo

Teaching Strategies

Improving DNP Student Statistical Competence

A Faculty Quality Improvement Initiative

Johnston, Heidi DNP, RN, CNE; Chung, Catie PhD, RN, CNE; Astrella, Julie DNP, RN, CNE; Grimm, Jessica DNP, APRN, ACNP-BC, CNE

Author Information
Nurse Educator ():10.1097/NNE.0000000000001360, January 19, 2023. | DOI: 10.1097/NNE.0000000000001360
  • Open
  • PAP

Although there is limited literature related to doctor of nursing practice (DNP) student statistical competence, what literature is available suggests statistical competence can be challenging for DNP students.1,2 Additionally, a study conducted by Roush and Tesoro2 found that project design and data analysis were 2 areas with questionable findings in relation to DNP projects and a review conducted by Bradley et al1 found mistakes in both the design and evaluation sections of DNP projects. The design and evaluation aspects were also areas noted by this school of nursing's DNP faculty where students struggled the most, thus initiating a quality improvement (QI) process to ensure students' learning needs are being met.


Recognizing that DNP project quality requires valid methodology, DNP faculty decided to use a QI approach to assess their program's recent statistical and project-related curricular updates. The faculty decided to use the PDSA (Plan-Do-Study-Act) cycle to determine whether curricular changes were facilitating the students' knowledge, skills, and attitudes (KSAs) in the areas of statistical design and evaluation in their DNP projects as they were intended. The DNP program is committed to rapid cycle QI as the program operates continuously with 3 cohorts being admitted and graduating per calendar year.

This school of nursing's DNP project process is conducted over 3 semester-length courses. The courses scaffold the project process beginning with topic selection and literature review in the first course, developing the project methodology and implementation plan in the second course, and performing the implementation and evaluation of the project in the third course.

The faculty implemented a 2-pronged approach to support students' statistics and methodology KSAs: development of a statistical methods algorithm and revised curriculum in a research methods course to better align with the statistical methods in the algorithm. To develop the statistical methods algorithm, the DNP faculty collaborated with the University's Department of Research.3 The algorithm was developed by 3 experienced nursing faculty who had overseen numerous DNP projects, had experience teaching statistical methods, and were ranked as either an assistant or associate professor in the school of nursing. The faculty compiled commonly used univariate statistical tests with descriptions, examples, and considerations for each statistical test. To guide the students in appropriate data collection and analysis, examples of chart review tools were provided as well. The draft of the statistical methods algorithm was sent to the University's Department of Research for final review and approval by a PhD-prepared statistician. The goals of the statistical methods algorithm are to (1) improve student statistical competence applicable to the DNP project, (2) provide direction when selecting appropriate statistical methods for data analysis for the DNP project, and (3) help decrease student stress and frustration regarding statistical methods.3

Students take a research methods didactic course that coincides with the project course when students are designing their project methods. The second prong of our 2-pronged approach was to revise the research methods course to ensure students worked with the statistical tests identified in the statistical methods algorithm to further increase the student's familiarity with those univariate tests. Using IBM SPSS, students learn to run, analyze, and present the different statistical tests and their interpretation of the results.3

The statistical methods algorithm was first provided to students during the design phase of the second DNP project course and is called the DNP Project Statistical Worksheet. Students are responsible for determining which statistical methods to use based on their individual project design and objectives. The options students were given included the paired-samples t test, Wilcoxon signed rank test, χ2 test, Fisher's exact test, and descriptive statistics.3 Students then submit the DNP Project Statistical Worksheet identifying project objectives, planned data collection approach, and the appropriate statistical method(s) based on the statistical methods algorithm. The student's project faculty member and a statistician in the research department review the statistical worksheet submitted by the student to ensure appropriate methods are chosen. If the statistical method(s) are inappropriate, then a recommendation is made for a more appropriate method to the student.


Faculty sought feedback from the first cohort of students who had the revised research methods course and used the statistical methods algorithm. An email was sent to students containing a link to a short anonymous online survey seeking feedback about the process of determining the statistical analysis plan for their project. Using a 5-point Likert scale with scores ranging from 1 (lowest/strongly disagree) to 5 (highest/strongly agree), students were asked questions to determine whether the statistical methods algorithm provided direction when choosing statistical methods, and whether the statistical methods algorithm helped to decrease their stress around their project methodology. There was also an opportunity for students to give a free-text response of anything else they wanted faculty to know about project methodology development. This survey was administered to expert DNP faculty prior to sending to students to obtain face validity and ensure conciseness and clarity of questions. Since this was part of a QI project, institutional review board submission was not required by the university.

Admittedly, the evaluation data are based on student self-report of their attitudes toward the statistical methods in their DNP project, which is a limited measure. However, since this is a continuous QI initiative, faculty wanted to give the students an opportunity to give anonymous feedback, and this was the best available mechanism.

Of the 11-student cohort, 8 responded to the survey, a 73% response rate. One item was: The DNP project statistical worksheet provided me direction when I chose statistical methods for my project's data analysis plan. Seventy-five percent of the respondents chose agree or strongly agree. Another item was: The DNP project statistical worksheet helped decrease my stress while I was choosing statistical methods for my project. For that item, 62.5% of respondents chose agree or strongly agree. There were no responses to the free-text response option. Additionally, DNP faculty are assessing and supporting students' statistical competence and confidence as their projects are implemented and results are gathered and analyzed. As this is an ongoing QI process, our next steps will be continuing data collection for each cohort of students to determine whether the revised course curriculum and statistical methods algorithm are meeting the students' learning needs. Faculty will also begin a review of past student projects for errors in design or evaluation as a baseline measure to compare current and future student performance.


Univariate statistical competence is of vital importance for the DNP graduate. Statistical competence promotes appropriate DNP project design methods and ensures outcomes are evaluated and interpreted correctly, further preparing DNP students to translate evidence and guide improvements in practice and outcomes of care upon graduation.


1. Bradley C, Boykin A, Kilmer M. Enhancing DNP project success: a statistical collaboration approach. Nurse Educ. 2023;48(1):37–42. doi:10.1097/NNE.0000000000001264
2. Roush K, Tesoro M. An examination of the rigor and value of final scholarly projects completed by DNP nursing students. J Prof Nurs. 2018;34(6):437–443. doi:10.1016/j.profnurs.2018.03.003
3. Johnston H, Astrella J, Grimm J, Chung C. DNP project statistical methods algorithm. Nurse Educ. Published online October 28, 2022. doi:10.1097/NNE.0000000000001319
© 2023 Wolters Kluwer Health, Inc. All rights reserved.