Analyze, interpret, and report results of a logistic regression analysis.Last week, as you examined linear regression, you may have noticed some limitations or shortcomings of this method of statistical analysis.
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LOGISTICS REGRESSION.
Quantitative Methods: Logistic Regression
Last week, as you examined linear regression, you may have noticed some limitations or shortcomings of this method of statistical analysis. Linear regression assumes that the relationships between variables are linear and that the variables themselves are continuous in nature. Linear regression is therefore not useful to examine variables that are binary or dichotomous (i.e., variables that only have two possible outcomes, such as gender).
This week builds on the information presented in Week 7 on linear regression and provides an overview of the concepts and applications of logistic regression, especially as it pertains to the health care field and evidence-based practice.
Objectives:
By the end of this week, you will be able to:
• Assess the application of logistic regression in nursing research and practice
• Analyze, interpret, and report results of a logistic regression analysis
Please proceed to the Learning Resources.
Required Resources
This page contains the Learning Resources for this week. Be sure to scroll down the page to see all of this week’s assigned Learning Resources. To view this week’s media resources, please use the streaming media player below.
Media
• Course Media: Research Methods for Evidence-Based Practice
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o “Logistic Regression”
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Readings
• Course Text: Statistics and Data Analysis for Nursing Research
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o Chapter 12, “Logistic Regression”
This chapter provides an overview of logistic regression, which is a form of statistical analysis frequently used in nursing research.
• Article: Hoerster, K. D., Mayer, J. A., Gabbard, S., Kronick, R. G., Roesch, S. C., Malcarne, V. L., & Zuniga, M. L. (2011). Impact of individual-, environmental-, and policy-level factors on health care utilization among US farmworkers. American Journal of Public Health, 101(4), 685–692. doi:10.2105/AJPH.2009.190892
Retrieved from the Walden Library databases.
This article discusses the results of a study of how many U.S. farmworkers accessed U.S. health care. The study considered this question on several levels—individual, environmental, and policy—a nd used logistic regression to analyze the multivariate data gathered.
• Article: Tritica-Majnaric, L., Zekic-Susac, M., Sarlija, N., & Vitale, B. (2010). Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of Biomedical Informatics, 43(5), 774–781. doi:10.1016/j.jbi.2010.04.011
Retrieved from the Walden Library databases.
This article describes the methods and results of a neural network study on the effectiveness of the influenza vaccine using historical data in three neural network algorithms. The article also provides a discussion of logistic regression in comparison to the neural network algorithms used.
• Article: Xiao, Y., Griffin, M. P., Lake, D. E., & Moo
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