Analyzing Descriptive Statistics Essay

Analyzing Descriptive Statistics Essay

Descriptive statistics is the brief information summarizing a data set, which can be a representation of a whole population or a sample (Washington et al. 2020). A DNP-prepared nurse should be conversant with appraising statistical tests in quantitative research studies and applying them effectively to a practice change project (Cooksey &Cooksey, 20220). This discussion presents an analysis and appraisal of descriptive statistics in a diabetes self-management education program intervention and uses the analysis to describe the outcomes of the practice change project.

Based on the provided dataset, the following calculations were performed

1. Percentage of patients with Uncontrolled diabetes pre-implementation and Post Implementation

Pre-implementation 90%

Post-implementation 50%

2. Mean of the HbA1c pre-implementation and post-implementation

Pre-implementation 7.92

Post-implementation 7.5

3. Median Scores, Pre-implementation and Post-Implementation

Pre-implementation 7.65

Post-implementation 7

4. Pre-implementation and Post-Implementation Standard deviation levels

Pre-implementation 1.448

Post-implementation 1.424

5. Pre-Implementation and Post-Implementation Range

Pre-implementation 5.4

Post-implementation 4.9

Descriptive Analysis Statistics

The measures of central tendency before the intervention implementation were mean 7.92, median 7.65, standard deviation 1.448, and range 5.4. Post-implementation measures were Mean 7.5, Median o7, standard deviation 1.424, and Range 4.9. 90% of the diabetes patient involved in the intervention had uncontrolled diabetes HbA1c greater than 7%, but the percentage had reduced to 50% by the end of the implementation, showing a difference of 40%. The practice change project demonstrates an improvement in the HbA1C levels of the patients involved since the average HbA1c levels reduced from 7.92-7.5 after the implementation of the intervention. The measures of central tendency calculated above show that the variability in the data set is large. However, the abnormal range could have been caused by the outlier on patient #10.

Following the implementation of the intervention, the mean of the HbA1c levels dropped from 7.92 to 7.5, showing an improvement in the efficacy of better management of glucose levels, thus improving HbA1c control. Additionally, the data set shows that patient #10 HbA1c levels are an outlier. Mishra et al. (2020) note that an outlier in a data set influences the understanding of the data. Similarly, it has affected the understanding and deriving conclusions from the data since it has caused the average to rise and translates to a large range, which is not the actual population representation.

Conclusion

The descriptive statistics analysis has demonstrated an improvement in the control of glucose levels for the patients after the implementation, thus showing success. Based on the intervention results, I would recommend continuing the intervention since it has improved overall diabetes control. I would also recommend including more diabetes patients in the diabetes self-management education intervention to address a larger population.

References

Cooksey, R. W., & Cooksey, R. W. (2020). Descriptive statistics for summarising data. Illustrating Statistical Procedures: Finding Meaning In Quantitative Data, 61-139. https://doi.org/10.1007/978-981-15-2537-7_5

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103%2Faca.ACA_157_18

Washington, S., Karlaftis, M., Mannering, F., & Anastasopoulos, P. (2020). Statistical and econometric methods for transportation data analysis. Chapman and Hall/CRC. https://doi.org/10.1201/9780429244018

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Purpose
The purpose of this discussion is for you to demonstrate an understanding of the DNP-prepared nurse’s role in the work of both appraising statistical tests in quantitative research studies and application of statistical tests to a practice change project.

Instructions
Consider the following scenario:

As a DNP-prepared nurse working in a rural clinic, you have a large population of patients with type 2 diabetes whose HbA1c levels are greater than 7% and body mass index (BMI) is over 30. You design a 9-month practice change project to impact these values.

Based on an exhaustive search and appraisal of research studies, you select an evidence-based intervention—diabetic self-management education (DSME)—to translate to your local rural clinic.

The evidence-based intervention includes exercise, healthy eating, and understanding the importance of regular blood glucose monitoring.

Before implementing the intervention, you retrieve aggregate data from 3, 6, and 9 months from medical records prior to the intervention being implemented. Data included HbA1c levels, BMIs, and numbers of patients with uncontrolled HbA1c. You also collected demographic data.

You collect the same data at 3, 6, and 9 months after implementation of the evidence-based intervention (DSME).

Pre-implementation and post-implementation data include the following.

Patient

Column A

HbA1c Pre-implementation

Column B

HbA1c > 7

Column C

HbA1c Post-implementation

Column D

HbA1c > 7

1

7.4

Y

6.9

N

2

7.8

Y

7.1

Y

3

7.1

Y

6.7

N

4

6.8

N

6.4

N

5

7.4

Y

6.8

N

6

7.8

Y

7.7

Y

7

7.8

Y

7.4

Y

8

8.2

Y

8

Y

9

7.5

Y

6.7

N

10

11.8

Y

11.3

Y

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As a DNP-prepared nurse, you will analyze descriptive statistics, such as measures of central tendency and variability, to describe outcomes of a practice change project. Reflect upon this scenario and the data presented. What conclusions would you make at the end of this practice change project? What recommendations would you make to stakeholders for continuing the diabetes self-management education (DSME) program based on these results?

In order to respond to this discussion question, you will first need to complete the following calculations and consider responses to your analysis of the descriptive statistics.

Perform the following calculations:
Based on the data set provided, calculate the average percentage of patients with uncontrolled diabetes (HbA1c>7) both pre-implementation and post-implementation.
Next, calculate the mean pre-implementation and post-implementation HbA1c values for patients involved in this practice change project.
Now calculate the pre-implementation and post-implementation median score of HbA1c levels.
Next, calculate the pre-implementation and post-implementation standard deviation of HbA1c levels of patients involved in the practice change project. The standard deviation will determine the spread of increase or decrease in HbA1c levels.
Finally, calculate the pre-implementation and post-implementation range of HbA1c levels. If no outliers exist, the range will determine how close together HbA1c levels are in the patients involved.
Based on your analysis of the descriptive statistics, what determinations related to the mean HbA1c levels following implementation of the evidence-based intervention can be made?
As you reflect upon HbA1c levels, you observe that patient #10 HbA1c levels are an outlier. What does this do to your understanding of the data?
Please click on the following link to review the DNP Discussion Guidelines on the Student Resource Center program page:

Link (webpage): DNP Discussion Guidelines
Links to an external site.

Course Outcomes
This discussion enables the student to meet the following course outcome(s):

Evaluate selected statistical methods for the purposes of critiquing research to complement the critical appraisal of evidence. (PO 3, 5, 9)
Analyze research and non-research data for the purposes of critical appraisal and judgment of evidence for translation into practice. (PO 1, 3, 5, 7, 9)