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2023 APA citation ad reference Please respond to this post Oncology study involves

2023 Nursing NSG 6101

APA citation ad reference Please respond to this post Oncology study involves 2023

APA , citation ad reference. Please respond to this post 

Oncology study involves the attempt to investigate how cancer can be diagnosed and treated through an evaluation of early symptoms in patients. The data obtained in such research is inferential on most occasions. Inferential statistical procedures can be categorized into two; parametric and non-parametric. When conducting researches, a majority of researchers can decide to use either parametric or non-parametric statistical analyses or test. However, the choice depends on the level of the data, such as nominal, ordinal, or continuous, that the researcher plans to examine. In the literal meaning, parametric tests involve making assumptions regarding the parameters of the population from which the researcher’s data is drawn. In contrary, non-parametric tests do not make such assumptions.

Parametric tests highly rely on the assumptions that the data that the researcher is testing resembles a particular distribution. For instance, a research on investigating the early signs and symptoms of cancer cannot be deemed to be nominal. This is because the symptoms vary from one patient to another. On the other hand, non-parametric tests are commonly referred to as distribution-free tests. This is because they do not involve limited assumptions to check in with regards to the distribution of the data. Additionally, parametric tests are highly selected when the dependent variable is being evaluated on a continuous scale. The non-parametric tests suit well when the dependent variable’s level of measurement is nominal or ordinal.

A good example of a parametric test is the t-tests and the analysis of variance. The investigator has to ensure that the underlying study population is normally distributed. Further, they must assume that the measures are deriving from an equal interval scale (Sullivan & Artino, 2013). For instance, the symptoms obtained during an oncology investigation can be analyzed using analysis of variance (ANOVA) to assess the difference between the early warnings or signs of cancer diseases. Through this approach, individuals and the health personnel will be able to mitigate the effects early enough before implication of the patient’s overall health. A Pearson correlation (r), which is a parametric test can be used to evaluate the relationship between unhealthy nutrition and cancer symptoms.

The non-parametric do not follow the assumptions made by an investigator while using parametric tests (Dergiades, Martinopoulos & Tsoulfidis, 2013). On some occasions, the non-parametric tests can be utilized as alternatives to parametric tests. For instance, t-test and analysis of variance (ANOVA) have non-parametric tests Mann-Whitney U test and the Kruskal-Wallis test respectively. The Spearman correlation (p) is an alternative to Pearson correlation and it is appropriate for application when at least one of the variables in a study is measured on an ordinal scale (Garson, 2014).  

Finally, there are reasons behind a selection of either parametric or non-parametric test. The parametric tests have high performance when the spread of a sample data is different and when the investigator wishes to obtain high statistical power. Contrary, the selection of non-parametric tests can be as a result of a good representation of the investigator’s data by the median, a small sample size, and the data is ordinal or ranked. It is clear that making a decision on choosing between parametric and non-parametric tests is challenging. For instance, the oncology study might be involving both small sample size and non-normal data. According to various researchers, the representation of the center of distribution and sample size of the investigator’s data can dictate the choice of a statistical test.
 

                                                                References

Dergiades, T., Martinopoulos, G., & Tsoulfidis, L. (2013). Energy consumption and economic      growth: Parametric and non-parametric causality testing for the case of Greece. Energy             Economics, 36, 686-697.

Garson, G. D. (2014). Testing statistical assumptions. Asheboro, NC: Statistical Associates            Publishing.

Sullivan, G. M., & Artino Jr, A. R. (2013). Analyzing and interpreting data from Likert-type        scales. Journal of graduate medical education, 5(4), 541-542. 

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2023 A T score and Z score are used in hypothesis testing When to use a T Score

2023 Nursing comment from post 2

A T score and Z score are used in hypothesis testing When to use a T Score 2023

A T score and Z score are used in hypothesis testing. 

When to use a T Score:

  • The sample size is below 30
  • The population standard deviation is unknown (estimated from your sample data).
  • the variation of scores in the two groups is not reliably different.

You must know the standard deviation of the population and your sample size has to be above 30 in order to use the Z score. If it doesn’t meet these criteria then use the T score. 

T-scores are used when the conversion is made without knowledge of the population standard deviation and mean

When to use, a Z score: 

  • The standard deviation must be known and the sample size must be above 30. 

A Z-score tells you how many standard deviations from the mean your result is. The standard deviation must be known and the sample size must be above 30. 

 

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2023 600 words excluding the first and reference page essay discussing the specific health issues that affect men erectile dysfunction prostate

2023 Nursing Men’s health issues

600 words excluding the first and reference page essay discussing the specific health issues that affect men erectile dysfunction prostate 2023

 600 words (excluding the first and reference page) essay discussing the specific health issues that affect men (erectile dysfunction, prostate cancer, testicular cancer, lung cancer, HIV/AIDS) and how as a community health nurse you help to prevent them applying the levels of prevention (primary, secondary, tertiary) give  examples.

The essay must be presented in an APA format word document

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2023 For Catherine Owens only Due by 8p m Employee Health Insurance Plans Part I Consider the four health plans

2023 Nursing For Catherine Owens only Employee Health Insurance Plans

For Catherine Owens only Due by 8p m Employee Health Insurance Plans Part I Consider the four health plans 2023

For Catherine Owens only

Due by 8p.m.

Employee Health Insurance Plans

Part I

Consider the four health plans below with an eye to choosing one to offer to the company’s employees. Assume that the health plans and their annual per employee premiums are as follows:

Health Plan

Premium, Individual

Premium, Family

Aetna Health

$4,555

$11,428

MetroPlus

$4,267

$10,540

Empire

$4,217

$10,767

Oxford

$6,029

$13,417

The employer will pay 80% of the premium for individual coverage, and the employee will pay the remaining 20% as well as the entire additional premium for family coverage. (The premiums listed above, while realistic in magnitude, are hypothetical and computed solely for the purpose of this project.) All of the plans are managed care plans. Assume that the benefit package is the same across all plans, so there is no difference between them in what services are covered.

In addition to the above data, click here to access the latest report for ‘Health Plan Comparison in New York State’. Review the online report and incorporate relevant information into your evaluation.

You can view the various categories of measures on which health plans are rated (e.g., Access to Care, Adult Living with Illness, etc.). Click each link for a summary chart that presents the performance (usually as a percentage score) of each plan in the group on the relevant measures and how each plan compares to regional and statewide scores.

Submit a 2-page analysis in a Microsoft Word document that outlines the plan you selected and why. Generally, you would select the plan with the highest score, but if you chose a plan with a lower score, explain why. Include the following elements in your analysis:

  • Explain which factors (e.g., price and/or performance measures) were most important to your choice of plan and how you derived the weights for each factor you used.
  • Indicate, on a scale of 1 to 10, how comfortable or confident you are that you made the right choice, with 10 being most confident.

Part II

You need to use the multiattribute utility (MAU) technique to respond to the following questions. Although the technique can be performed with pencil and paper, it is recommended you use a Microsoft Excel to do the various calculations involved.

Click the following links to access the information on using the MAU technique:

  • MAU Model
  • MAU Example
  • Compare your level of confidence at the time you completed Part I to your confidence level for Part II, when you used this decision aid.
  • Was it helpful? What were its advantages and disadvantages?

Did it make the decision harder to make or easier to justify?

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