- Analyzing Likert data: Items vs scale scores
Distinguish an individual ordered response from a supported multi-item score before selecting summaries or statistical tests.
- Appraising evidence without getting lost
Separate study design, methodological quality, and local fit when deciding what evidence can inform your DNP project.
- Building and reading a run chart
Plot comparable observations over time, keep a clear reference median, and recognize patterns without overstating causation.
- Can your outcome change in time?
Match the outcome to the intervention pathway, follow-up window, and time needed for complete data to become available.
- Choosing a statistical approach for your DNP project
Choose an approach by starting with the question, observation unit, comparison structure, and kind of outcome.
- Did the intervention actually happen? Measuring fidelity
Document reach, dose, and delivery so that you can interpret outcomes in light of the intervention participants actually received.
- DNP project types explained
Compare project purposes, recognize overlapping designs, and identify what your program expects you to implement and evaluate.
- Do you have baseline data? Testing your problem statement
Use local evidence to establish who has the problem, how often it occurs, and what comparison makes the gap meaningful.
- Finding a validated instrument and getting permission
Evaluate an instrument for your purpose, population, scoring rules, burden, and conditions of use before building the survey.
- Outcome, process, and balancing measures
Separate the result you want, the work needed to achieve it, and the possible costs of making the change.
- Paired t-test or Wilcoxon? Analyzing pre-post data
Understand what paired tests examine and why the distribution of individual changes matters more than a simple sample-size rule.
- Planning for dropout and missing data
Prevent avoidable missingness, distinguish why observations are absent, and report who contributes to each analysis.
- Pre-post surveys: Same instrument, linked responses
Plan consistent measurement and reliable response linkage before collecting a before and after survey.
- QI or research? How project review decisions work
Understand why project purpose, activities, data, and institutional review matter more than the label on your proposal.
- Sample size and power for small DNP projects
Understand what a fixed participant pool means for power, precision, feasibility, and the claims your project can support.
- Setting up your project data
Create a consistent dataset with a clear observation unit, documented codes, visible missingness, and approved privacy safeguards.
- Statistical vs clinical significance
Interpret the size, uncertainty, and practical meaning of a result rather than treating a p value as the conclusion.
- Sustaining the change after your project ends
Plan ownership, routine work, monitoring, and adaptation so that continuation does not depend on the student remaining at the site.
- The proxy trap: When easy data measures the wrong thing
Recognize when an available indicator stands in for a different concept and keep your conclusions aligned with what you measured.
- Valid, feasible, relevant: Choosing outcome measures
Choose measures that represent the right concept, fit your setting, and can support a useful decision.