What did you find after analyzing the data? Explain updated 2023

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Will properly labeling a patient’s chart and teaching nursing staff in a skilled nursing facility how to navigate the patient’s chart in relation to their code status

reduce the time it takes to access a specific patient’s code status by at least 10% over the course of a three-week period?

Included a change theorist and theory. Role of the RN regarding the issue? Target population. Sample size. 15-point questionnaire included (mandatory). Nursing interventions used.


Tool used (teaching/brochure/new policies etc. Barriers and enablers to the change. Detailed explanation on how the change intervention was implemented. Checklist to help you to evaluate your project. Analysis of the data.

I have included the 15-point questionnaire that I created and implemented at the end of the paper, please refer to it for information

How many needs were identified? What were they?

Was there a main problem?

What were the demeanor/reaction of the clinical preceptor, staff /and/or participants about the need(s) and the change?

Was a clear need for change identified?

What did the literature say about the effect of the problem on the participants?

LVN, CNAs, and RN staff were the ones who had the most use out of our project as they are the primary caretakers at the skilled nursing facility where this project was implemented.
What did you find after analyzing the data?

Explain. Limitations to the study- ex. Sample size, studies, age, race, gender, etc. Any new ideas about how would conduct a similar future study?

Nursing Implications (mandatory).
One limitation would be not much time at facility as it was only about 2-3 weeks of clinical time.

What is Data Analysis? Research, Types & Example
What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

In this Data Science Tutorial, you will learn:

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

  • Text Analysis
  • Statistical Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis
Text Analysis

Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools. It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

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1. Gentleman, R., & Temple Lang, D. (2004). Statistical analyses and reproducible research. Journal of Computational and Graphical Statistics,

2. Wilke, C. O. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. O’Reilly Media

3. Press, I. S., Sacks, S., Diggle, P. J., & Ridout, M. S. (2018). Applied statistics for agriculture, veterinary, fishery, dairy and allied fields. Routledge





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