assignment

Suppose you were involved in a research study examining the effect of drinking soda on a child’s weight. You performed a study over the course of several months on a sample of fifth graders, allowing one group to drink two cans of soda per day, another group one can per day, and the control group no soda at all. After you gathered your data, you would need to analyze the results for each of the three groups to determine whether to accept either the null or alternative hypothesis in your study. A useful method of analysis for this particular study is known as linear regression.

As you examine linear regression, you may notice 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 continues your exploration of correlation and relationships between variables in quantitative research studies, focusing on the concepts of linear regression. This week also provides an overview of the concepts and applications of logistic regression, especially as it pertains to the health care field and evidence-based practice. Last week you examined the uses and methods of simple linear regression as a basis for this type of analysis. This week, you expand on those basic concepts and explore multiple regression, which can be used to show relationships between more than two variables.

Learning Objectives

Students will:

Analyze, interpret, and report results of a linear regression analysis

Analyze, interpret, and report results of a logistic regression analysis

Assess the application of logistic regression in nursing research and practice

Learning Resources

Required Media

Rocchi, M. (2014, September 8). Tutorial: Multiple regression [Video File]. Retrieved from https://www.youtube.com/watch?v=4ERLK7F9nWc

TheRMUoHP Biostatistics Resource Channel (2012, October 2). How to use SPSS: Logistic regression [Video File]. Retrieved from https://www.youtube.com/watch?v=zj15KUXtC7M

Required Readings

Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.

Chapter 24, “Using Statistics to Predict”

This chapter asserts that predictive analyses are based on probability theory instead of decision theory. It also analyzes how variation plays a critical role in simple linear regression and multiple regression.

Statistics and Data Analysis for Nursing Research

Chapter 9, “Correlation and Simple Regression” (pp. 208–222)

This section of Chapter 9 discusses the simple regression equation and outlines major components of regression, including errors of prediction, residuals, OLS regression, and ordinary least-square regression.

Chapter 10, “Multiple Regression”

Chapter 10 focuses on multiple regression as a statistical procedure and explains multivariate statistics and their relationship to multiple regression concepts, equations, and tests.

Chapter 12, “Logistic Regression”

This chapter provides an overview of logistic regression, which is a form of statistical analysis frequently used in nursing research.

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

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—and used logistic regression to analyze the multivariate data gathered.

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

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.

Xiao, Y., Griffin, M. P., Lake, D. E., & Moorman, J. R. (2010). Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Medical Decision Making, 30(2), 258-266. doi:10.1177/0272989X09337791

This article outlines the procedures and findings of a study on the use of two methods of neonatal sepsis diagnosis: nearest-neighbor analysis and logistic regression analysis. The results indicated that each method generates unique information useful to diagnosis, and therefore both methods should be used simultaneously for improved accuracy of diagnoses.

Optional Resources

Walden University. (n.d.). Linear regression. Retrieved August 1, 2011, from http://streaming.waldenu.edu/hdp/researchtutorials/educ8106_player/educ8106_linear_regression.html

Assignment 5: Linear Regression

Review the Statistics and Data Analysis for Nursing Research chapters that you read as a part of the Week 7 Learning Resources. As you do so, pay close attention to the examples presented—they provide information that will be useful for you to recall when completing the software exercises. You may also wish to review the Research Methods for Evidence-Based Practice video resources.

Refer to the Week 7 Linear Regression Exercises page and follow the directions to calculate linear regression information using the Polit2SetA.sav data set.

Compare your data output against the tables presented on the Week 7 Linear Regression Exercises SPSS Output document.

Formulate an initial interpretation of the meaning or implication of your calculations.

To complete:

Complete the “Simple Regression” and “Multiple Regression” steps and Assignments as outlined in the Week 7 Linear Regression Exercises page.