Descriptive statistics are a fundamental component of examining literature and conducting research, especially for Doctor of Nursing Practice (DNP) students. They provide a concise summary of a dataset’s main features, allowing for a clear understanding of the characteristics of a sample or population.

Types of Information Descriptive Statistics Provide to DNP Students When Examining Literature:

When DNP students examine existing literature, descriptive statistics offer crucial insights into the studies, enabling them to assess the generalizability and applicability of findings to their own practice or Capstone project. Here’s the type of information they provide:

1. Characteristics of the Sample/Participants:
   • Demographics: Age, gender, ethnicity, educational level, socioeconomic status of the study participants. This helps the DNP student determine if the study population is similar to their own patient population or target group, which is vital for evidence-based practice.
   • Clinical Characteristics: Information on diagnoses, comorbidities, disease severity, duration of illness, or specific health behaviors. This helps understand the clinical context of the study and its relevance.
   • Baseline Data: Initial measurements of variables before an intervention, allowing the DNP student to understand the starting point of the study participants.
2. Description of Variables:
   • Distribution of Data: How data points are spread across a range of values. This helps identify outliers or skewed data, which can influence the interpretation of results.
   • Central Tendency: The “typical” or average value of a variable. This provides a quick understanding of the most common responses or measurements.
   • Variability/Spread: How much the data points differ from each other. This indicates the homogeneity or heterogeneity of the sample and the consistency of the measurements.
3. Context for Inferential Statistics:
   • While descriptive statistics don’t make inferences about a larger population, they provide the necessary context for understanding and interpreting inferential statistics presented in the literature. They help the DNP student assess if the assumptions for inferential tests were met and if the results are plausible.
4. Identification of Trends and Patterns:
   • Descriptive statistics can highlight prevalent conditions, common patient profiles, or typical responses to interventions within the studied group. This can inform the DNP student’s understanding of a health phenomenon or the effectiveness of a particular approach in a specific context.

Examples of Descriptive Statistics Presented in the Literature:

You will commonly encounter these descriptive statistics in nursing and healthcare literature:

• Measures of Central Tendency:
  • Mean (xˉ): The average. Example: “The mean age of participants was 65.2 years (SD = 7.8).”
  • Median (Mdn): The middle value in a sorted dataset. Example: “The median length of hospital stay was 4 days (IQR = 2-7 days).” This is often used when data is skewed (e.g., income, hospital stay).
  • Mode: The most frequently occurring value. Example: “The mode for pain intensity on a 0-10 scale was 7.” (Useful for categorical data).
• Measures of Variability (Spread):
  • Standard Deviation (SD): The average distance of data points from the mean. Example: “Patients’ systolic blood pressure averaged 130 mmHg (SD = 12).” A smaller SD indicates data points are closer to the mean.
  • Range: The difference between the highest and lowest values. Example: “Participant ages ranged from 45 to 88 years.”
  • Interquartile Range (IQR): The range of the middle 50% of the data. Example: “The interquartile range for patient satisfaction scores was 75-90.” This is less affected by outliers than the full range.
• Frequency and Distribution:
  • Frequencies (Counts) and Percentages (%): Used to describe categorical data. Examples: “60% of the sample were female,” “25 patients reported mild pain, 15 moderate, and 5 severe pain.” These are often presented in tables (e.g., demographic tables) or bar charts.
  • Histograms or Bar Charts: Visual representations of frequency distributions.
  • Skewness and Kurtosis: Describe the shape of the data distribution (e.g., whether it’s symmetrical or peaked/flat). While less commonly reported numerically in nursing articles, visual inspection of histograms can give a sense of these.

Examples of Descriptive Statistics You Can Collect When Conducting Your Capstone Project:

When conducting your DNP Capstone project, you will gather data and use descriptive statistics to characterize your sample and variables before moving on to any inferential analysis (if applicable).

• For Patient/Participant Demographics:
  • Age: Mean, median, standard deviation, range.
  • Gender: Frequencies and percentages (e.g., “Number of male participants: 30 (40%), female: 45 (60%)”).
  • Ethnicity/Race: Frequencies and percentages (e.g., “Caucasian: 70%, African American: 15%, Asian: 10%, Other: 5%”).
  • Education Level: Frequencies and percentages (e.g., “High school diploma: 20%, Bachelor’s degree: 50%, Master’s/Doctorate: 30%”).
  • Insurance Status: Frequencies and percentages.
  • Primary Language: Frequencies and percentages.
• For Clinical Variables (related to your intervention or outcome):
  • Baseline Measurements:
    • Mean, Median, SD, Range, IQR for continuous variables like:
      • Blood pressure (systolic and diastolic)
      • HbA1c levels
      • Body Mass Index (BMI)
      • Pain scores
      • Quality of life scores (from a validated instrument)
      • Pre-intervention knowledge scores
    • Frequencies and Percentages for categorical variables like:
      • Presence/absence of a specific comorbidity
      • Type of medication currently prescribed
      • Stage of disease
      • Frequency of hospital readmissions (counts)
  • Intervention Fidelity/Process Measures:
    • Percentages of adherence to a new protocol.
    • Mean time spent on a new care activity.
    • Frequencies of specific actions performed by nurses.
• For Outcome Measures (after your intervention):
  • Mean, Median, SD, Range, IQR for continuous outcomes:
    • Post-intervention pain scores
    • Patient satisfaction scores
    • Knowledge scores
    • Functional status scores
  • Frequencies and Percentages for categorical outcomes:
    • Reduction in fall rates (e.g., “Falls decreased by 25%”).
    • Number of patients achieving a specific target (e.g., “80% of patients achieved target blood pressure”).
    • Number of readmissions (counts).

In summary, descriptive statistics are essential for DNP students as they provide the foundational understanding of any dataset, whether encountered in existing literature or generated through their Capstone project. They paint a clear picture of the study population and variables, allowing for informed interpretation and application of evidence to nursing practice.