Logistic Regression in Nursing Practice

Logistic regression is used to analyze a wide variety of variables that may surround a singular outcome. For example, logistic regression could be used to identify the likelihood of a patient having a heart attack or stroke based on a variety of factors including age, sex, genetic characteristics, weight, and any preexisting health conditions.

The biological systems and issues with which the health care field is concerned represent the kinds of applications for which logistic regression is especially useful.

Logistic regression is used in the health care field for many purposes, including diagnoses, predictions, and forecasting. The three articles in this week's Learning Resources illustrate the many uses of logistic regression in the health care field.

This Discussion allows you to explore the different uses of logistic regression and cultivate a deeper understanding of the application of logistic regression in evidence-based practice.

To prepare:

Review the three articles in this week's Learning Resources and evaluate their use of logistic regression. Select one article that interests you to examine more closely in this Discussion

Critically analyze the article you selected considering the following questions:

What are the goals and purposes of the research study the article describes?

How is logistic regression used in the study? What are the results of its use?

What other quantitative and statistical methods could be used to address the research issue discussed in the article?

What are the strengths and weaknesses of the study?

How could the weaknesses of the study be remedied?

How could findings from this study contribute to evidence-based practice, the nursing profession, or society?

Write a minimum of 550 words essay in APA format.

Article : Characteristics of people with disabilities receiving assistive technology services in vocational rehabilitation: A logistic regression analysis By I-Chun Huang,Gladys Cheing, Philip Rumrill , Kevin Bengtson, Fong Chand, Jana Telzlaf and Mikael Snitker.