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Statistical Analysis of Stress Levels and Quality of Care in Healthcare

May 11, 2023
🇬🇧 United Kingdom
Statistical Analysis
With a master's degree in Statistics from the University of Quantitative Sciences, over 7 years of experience, she excels at statistical analysis assignments at statisticsassignmenthelp.com.
Key Topics
  • Problem Description
    • Descriptive Statistics:
    • Correlation Analysis:

In our comprehensive statistical analysis, we delve into the intricate relationship between stress levels and the quality of care within the healthcare domain. Through descriptive statistics and a paired samples t-test, we explore the essential variables of age, experience, and subjective care ratings. Our findings shed light on the effectiveness of interventions in alleviating stress and reveal correlations within specific healthcare wards. This study opens new avenues for further research, emphasizing the significance of diverse populations and objective measures in understanding and improving the well-being of both patients and healthcare professionals. Explore the data, draw insights, and pave the way for evidence-based healthcare decisions.

Problem Description

This statistical analysis assignment presents the results of an analysis of data collected from a sample of 250 individuals. The data includes various variables, such as Age, Years of Experience, Quality of Care, and Stress levels (both pretest and post-test). The primary objective is to provide a comprehensive overview of the descriptive statistics for each variable and assess the impact of an intervention or event on stress levels through a paired samples t-test.

Descriptive Statistics:

1. Age:

  • Minimum:20 years
  • Maximum: 68 years
  • Mean: 34.14 years
  • Standard Deviation:10.806

Description:The age variable represents the age of the individuals in the sample. The minimum recorded age was 20 years, while the maximum age was 68 years. On average, the individuals in the sample had an age of 34.14 years, with a standard deviation of 10.806 indicating variability around the mean.

2. Years of Experience:

  • Minimum: 0 years
  • Maximum:38 years
  • Mean:10.90 years
  • Standard Deviation:11.406

Description:The years of experience variable represents the professional experience of the individuals. The minimum experience recorded was 0 years, and the maximum was 38 years. On average, individuals had approximately 10.90 years of experience, with a standard deviation of 11.406 showing variability.

3. Quality of Care:

  • Minimum:1
  • Maximum:7
  • Mean: 4.31
  • Standard Deviation:2.018

Description:This variable reflects the subjective rating of care quality. The ratings ranged from 1 to 7, with a mean rating of 4.31, indicating a moderate assessment. The standard deviation of 2.018 suggests some variation in perceived quality.

4. Stress (Pretest):

  • Minimum:20
  • Maximum: 66
  • Mean:36.20
  • Standard Deviation: 12.736

Description: Stress levels before an event or intervention ranged from 20 to 66. On average, individuals reported a stress level of 36.20, with a standard deviation of 12.736 signifying considerable variability.

5. Stress (Posttest):

  • Minimum:10
  • Maximum:40
  • Mean:20.37
  • Standard Deviation:6.959

Description: Stress levels after the event ranged from 10 to 40. On average, individuals reported a stress level of 20.37, with a standard deviation of 6.959 indicating relatively low variability.

Conclusion: In conclusion, this report provides descriptive statistics for Age, Years of Experience, Quality of Care, and Stress levels. These statistics offer insights into the range, central tendency, and variability of these variables within the sample of 250 individuals, enabling informed decision-making based on the collected data.

Correlation Analysis:

1. Ward = ICU:

  • Stress_pretest ranged from 20 to 61, with a mean of 32.85 and a standard deviation of 10.737.
  • Quality_care ratings ranged from 1 to 7, with a mean of 3.98 and a standard deviation of 2.076.
  • The correlation between Stress_pretest and Quality_care in the ICU ward is -0.173, but not statistically significant (p = 0.113, two-tailed).

2. Ward = ED:

  • Stress_pretest ranged from 20 to 66, with a mean of 37.93 and a standard deviation of 13.355.
  • Quality_care ratings ranged from 1 to 7, with a mean of 4.48 and a standard deviation of 1.971.
  • The correlation between Stress_pretest and Quality_care in the ED ward is -0.165 and statistically significant (p = 0.034, two-tailed).

In the ICU ward, a weak negative correlation between stress and care quality was observed but was not statistically significant. In the ED ward, a similar weak negative correlation was observed, and it reached statistical significance. These findings suggest a potential relationship in the ED, but further investigation is needed.

Paired Samples T-Test: A paired samples t-test was conducted to evaluate the difference between Stress_pretest and Stress_posttest scores. The mean score for Stress_pretest was 36.20, with a standard deviation of 12.736, while Stress_posttest had a mean of 20.37 and a standard deviation of 6.959.

The paired samples correlations indicate a positive correlation of 0.241 between Stress_pretest and Stress_posttest, which is statistically significant (p < 0.001).

T-Test Results:The paired samples t-test showed a significant difference (t = 31.946, df = 249, p < 0.001). Stress_pretest (M = 36.20, SD = 12.736) was significantly higher than Stress_posttest (M = 20.37, SD = 6.959).

Discussion:The results indicate a significant decrease in stress levels from pretest to posttest, suggesting the intervention had an impact. The positive correlation further supports this finding. However, these results apply to the sample, and generalizations should be made with caution. Further analysis and replication are recommended.

Recommendations for Future Research:

  1. Longitudinal Studies:Investigate long-term intervention effects.
  2. Diverse Populations:Include various demographics to broaden understanding.
  3. Intervention Types:Compare different intervention approaches.
  4. Objective Measures:Incorporate physiological and neurobiological indicators for a more accurate assessment of stress levels.

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