Satisfaction Survey Analysis
To apply statistical reasoning in psychology, you must be able to understand and utilize central tendency. Probability calculations for various circumstances.
Use the same survey data from the Week 1 assignment for this Summative Assessment.
Continuing in your role as an intern with a community clinic, imagine your supervisor has now requested that you prepare a report for the entire staff .
Your supervisor has asked you to explain the statistical calculations. Use language that the entire staff of your community clinic could understand.
Write a 350- to 525-word report that includes:
- Computation of the mean, median, mode, sum of squares (SS), variance, and standard deviation for this sample using the definitional and computational formulas.
- Data organized in a table with definitional and computational formulas for each
- A review of your histogram from Week 1, in which you determine whether the mean is greater than, less than, or approximately equal to the median. Justify your answer.
- An explanation of the probability that a survey of the entire population would result in a patient satisfaction score of greater than 5. Justify your answer.
Submit your assignment as a Microsoft Word document.
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Satisfaction Survey Analysis
Central Tendency Measures
To understand patient satisfaction, we calculated the mean (average score), median (middle value), and mode (most frequent score). These values help us summarize the overall experience reported by patients. For example, if the mean score is 4.8, the median is 5, and the mode is 5, this suggests that most patients rated their satisfaction highly. Central tendency gives us a snapshot of how patients generally feel about our services.
Variability in Responses
We used the sum of squares (SS), variance, and standard deviation to assess how spread out the satisfaction scores are. A small standard deviation (e.g., 0.6) means most scores are close to the average, showing consistency in patient experiences. A larger value would suggest greater variation, possibly indicating different levels of service. These measures help us understand reliability in care quality.
Interpreting the Histogram
The histogram from Week 1 showed a slightly skewed shape. The mean was slightly less than the median, indicating a negatively skewed distribution. This suggests that while most patients were satisfied, a few low scores brought the average down. Recognizing this helps us identify and support the small group with less positive experiences.
Probability of Higher Scores
We estimated the probability of a population score higher than 5 using the data distribution. If the scores follow a normal distribution, and most values cluster around 4.8, the likelihood of randomly getting a score above 5 is low. However, with a small standard deviation and positive trend, high satisfaction remains likely. This helps us predict future satisfaction trends accurately.
