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Introduction
In nursing research and evidence-based practice, descriptive statistics are essential tools that enable Doctor of Nursing Practice (DNP) students to comprehend, analyze, and utilize data effectively. This provides a crucial foundation for understanding what the data are saying. When examining literature or conducting a capstone project, it is absolutely essential to have a solid grasp of the nature, trends, and overall picture that the data present, along with the empirical evidence that forms the foundation for any decisions that might be made.
Descriptive statistics provide three main types of information: averages (mean, median, mode), the dispersion of the data (range, standard deviation, interquartile range), and the shape of the data distribution (percentiles, quartiles, z-scores) (Field, 2018). These tools help synthesize large volumes of raw data into understandable formats. As Hallett (1997) explains, these statistics allow for systematic description of phenomena in nursing, such as symptom burden, patient demographics, or the frequency of follow-up appointments.
One of the most basic and useful is an understanding of a dataset's central tendency. Measures of central tendency—like the mean, median, and mode—help DNP students get a grasp on the average or most common values within a given dataset.
For example, in my project on oncology survivorship, a study may report the mean age of breast cancer survivors, the standard deviation of their anxiety scores, or the percentage of patients experiencing fatigue. These statistics offer a snapshot of the sample characteristics and can help determine whether findings are relevant to the population that I am working with.
Alongside measures of central tendency, measures of variability are another aspect of descriptive statistics. Statistics books on the subject often dedicate a few pages to this topic, even if they do so in an elementary fashion. In tandem with understanding the mean, median, and mode, a student of statistics should also understand the range, variance, and standard deviation. DNP students especially need to grasp these concepts because they are fundamental building blocks for moving into more complex investigations of phenomena.
Furthermore, positional statistics like percentiles and quartiles shed light on the relative positioning of specific data points in a distribution (The Doctoral Journey, 2021). Take, for example, a DNP student examining the body mass index (BMI) of a specific cohort of patients. Knowing that one of these patients falls in the 75th percentile allows the student to make a much clearer and more insightful judgment about the patient's weight status than if she were simply considering the BMI number in isolation.
In my DNP project, which focuses on a survivorship program for oncologic patients, descriptive statistics will be essential for both pre- and post-implementation evaluations. In my capstone projects involving structured survivorship care, I will be able to collect descriptive data on various variables, including patient satisfaction scores, the number of unmet needs identified during visits, and referral rates to supportive services. For instance, a pre-implementation survey might reveal that 65% of oncology survivors did not receive a care plan after treatment. Post-implementation data might show that 90% now receive one, with a corresponding increase in satisfaction from a median score of 3.1 to 4.5 on a 5-point scale. These examples illustrate measures of frequency and central tendency, demonstrating program effectiveness. The use of visual aids—such as frequency tables, pie charts, and histograms—enhances comprehension, especially for stakeholders unfamiliar with statistical jargon (The Doctoral Journey, 2022). These visuals can help convey data trends to healthcare teams and administrative leaders, facilitating data-driven decisions.
Conclusion
Descriptive statistics are essential for DNP students, as they play a crucial role in both literature appraisal and the implementation of capstone projects. These students significantly benefit from using descriptive statistics throughout their exploration of existing literature and during their own capstone projects. They not only guide initial assessments but also support outcome evaluation and program refinement—particularly in projects like mine aiming to improve survivorship care in oncology. It allows me to summarize and provide a clear picture of the nature of the data I am working with.
References
Hallett C. (1997). The use of descriptive statistics in nursing research. Nurse Researcher, 4(4), 4–16.
Fulk, G. (2023). Descriptive Statistics, An Important First Step. Journal of Neurologic Physical Therapy, 47(2), 63. https://doi.org/10.1097/NPT.0000000000000434
The Doctoral Journey. (2022, February 23). Descriptive statistics, part 1 [Video]. YouTube.
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