Interpreting Descriptive Statistics:

Here’s a breakdown of how to interpret descriptive statistics for each variable in your data (vitals, pain scores, medications):

1. Vitals:

• Look for measures like:
  • Mean: Average value of a specific vital sign (e.g., average heart rate, average blood pressure).
  • Median: The middle value when data is ordered from lowest to highest.
  • Standard Deviation (SD): Indicates how spread out the data is from the mean. A high SD suggests greater variability in vital signs among patients.
  • Range: Difference between the highest and lowest values for a vital sign.
• Interpretation:
  • Compare the mean values to established normal ranges for each vital sign.
  • A high SD indicates some patients deviate significantly from the average, suggesting potential health concerns.

2. Pain Scores:

• You might encounter:
  • Mean: Average pain score reported by patients (e.g., average on a 0-10 pain scale).
  • Median: The middle pain score reported by patients.
  • Mode: The most frequently reported pain score.
  • Frequency distribution: Shows how many patients reported each pain score level.
• Interpretation:
  • Analyze the mean and median to understand the overall level of pain experienced by patients.
  • Consider the mode to see if there’s a common pain level reported by many patients.
  • The frequency distribution reveals the spread of pain scores across the population.

3. Medications:

• Analyze:
  • Frequency distribution: Shows how many patients are taking each medication.
  • Cross-tabulation (if available): Might reveal associations between specific medications and patient characteristics (e.g., age, diagnosis).
• Interpretation:
  • Identify the most commonly prescribed medications for this patient population.
  • If cross-tabulation is available, explore potential relationships between medications and patient characteristics.

Moving Beyond Raw Data:

Descriptive statistics help us understand the central tendencies (averages and medians) and variability (standard deviation) of the data. They also allow us to see the distribution of values (frequency distributions) for each variable. This summarized data provides a clearer picture than looking at raw scores alone.

Additional Considerations:

• Look for missing data flags in the SPSS output. Missing data can impact the interpretation of your results.
• Consider the research question you are trying to answer when interpreting the descriptive statistics.

By analyzing these descriptive statistics, you can gaina valuable insights into your patient population, identify trends, and potentially formulate research questions for further analysis.