Research And Data Analysis In Healthcare

Research And Data Analysis In Healthcare

Research And Data Analysis In Healthcare.

Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.

Step 2: Develop the research question and

Step 3:  Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.

The Report Structure:

Start with the

1.Cover page (1 page, including running head).

In the title page include:

Research And Data Analysis In Healthcare – Nurses Homework | Nurses Homework

· Title, this is the approved topic by your instructor.

· Student name

· Class name

· Instructor name

· Date

2.Introduction

Introduce the problem or topic being investigated. Include relevant background information, for example;

· Indicates why this is an issue or topic worth researching;

· Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and

· Specify how others have operationalized this concept and measured these phenomena

Note: Introduction should not be more than one or two paragraphs.

Literature Review

There is no need for a literature review in this assignment

3.Research Question or Research Hypothesis

What is the Research Question or Research Hypothesis?

***Just in time information: Here are a few points for Research Question or Research Hypothesis

There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.

Examples of non-testable questions are:

How do managers feel about the reorganization?

What do residents feel are the most important problems facing the community?

Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.

In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:

Is there a significant difference between …?

Is there a significant relationship between …? 

For example:

Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?

A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:

There is a significant relationship between the age of managers and their attitudes towards the reorganization.

It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:

There is no significant relationship between the age of managers

and their attitudes towards the reorganization.

There is no significant difference between white and minority residents

with respect to what they feel are the most important problems facing the community.

All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:

1) reject the null hypothesis, or

2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”

“Significance is a statistical term that tells how sure you are that a difference or relationship exists.  To say that a significant difference or relationship exists only tells half the story.  We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship?  After finding a significant relationship, it is important to evaluate its strength.  Significant relationships can be strong or weak.  Significant differences can be large or small.  It just depends on your sample size.

To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:

P-value:  The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.

The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).

Note: This is an example from the week1 exercise.

The p-value from above example, 0.0001, indicates that we’d expect to see a meaningless (random) ‘number of the employees on payer’ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):

CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):

The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].

As a summary:

“Statistically significant means a result is unlikely due to chance.

The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn’t a difference for all users.

Statistical significance doesn’t mean practical significance. Only by considering context can we determine whether a difference is practically significant; that is, whether it requires action.

The confidence interval around the difference also indicates statistical significance if the interval does not cross zero. It also provides likely boundaries for any improvement to aide in determining if a difference really is noteworthy.

With large sample sizes, you’re virtually certain to see statistically significant results, in such situations, it’s important to interpret the size of the difference”(“Measuring U”, 2019).

Small sample sizes often do not yield statistical significance; when they do, the differences themselves tend also to be practically significant; that is, meaningful enough to warrant action.

4.Research Method

Discuss the Research Methodology (in general). Describe the variable or variables that are being analyzed. Identify the statistical test you will select to analyze these data and explain why you chose this test.  Summarize your statistical alternative hypothesis. This section includes the following sub-sections:

a)Describe the Dataset

Example:  The primary source of data will be HOSPITAL COMPARE MEDICARE DATA (APA formatted in-text citation). This dataset provides information on hospital characteristics, such as: Number of staffed beds, ownership, system membership, staffing by nurses and non-clinical staff, teaching status, percentage of discharge for Medicare and Medicaid patients, and information regarding the availability of specialty and high-tech services, as well as Electronic Medical Record (EMR) use (Describe dataset in 2-3 lines, Google the dataset and find the related website to find more information about the data).

Also, describe the sample size; for example, “The writer is using Medicare data-2013, this data includes 3000 obs. for all of the hospitals in the US.”

b)Describe Variables

Next, review the database you selected and select a variable or variables that are a “best-fit.”  That is, choose a variable that quantitatively measures the concept or concepts articulated in your research question or hypothesis.