Because public and nonprofit administrators operate in a complex world, they often ask research questions that can be answered with multiple explanations. Multiple explanations for an outcome require multiple independent variables. As a reminder, independent variables are those that influence the outcome or dependent variable. For example, consider research designed to explain why some students earn good grades in Applied Research and Evaluation, while others do not. Perhaps one independent variable, such as time spent studying, explains good grades versus poor grades (dependent variable). However, it is more likely that many independent variables are involved. It could be that time spent studying, along with previous experience taking a research methods course, and the amount of time spent at a job, contributes to the explanation. Simple associational research, such as the research you considered in Week 9, overlooks the complexity of multiple variables because it focuses only on one independent variable. Explanations that involve multiple independent variables are better served by approaches such as OLS regression or binary logistic regression analysis. The choice often depends on the nature of the dependent variable. An interval dependent variable may lead you to choose OLS regression, while a nominal dependent variable may require binary logistic regression. OLS is a simple, yet limited approach to regression. You must assume that variables are related in a linear fashion, and the dependent variable must be somewhat interval in nature. Research does not always present interval-dependent variables. There are situations, however, in which dependent variables are more nominal in nature. Assume that you want to predict the likelihood of an individual leaving the organization in the next six months. The dependent variable in this example would be nominal (either he/she leaves or he/she does not). OLS regression would not work in this situation, so you would need another type of regression, known as binary logistic regression. There is no “perfect” approach to regression analysis. Rather, you must select the “most appropriate” type of analysis, depending on the purpose of your research and the types of variables you are examining. Review the Learning Resources for this week. Consider how regression analysis (OLS and binary logistic) could apply to your Final Evaluation Design (Final Project) to help answer your research question. Post an explanation of whether OLS regression or binary logistic regression is more appropriate to evaluate the program, problem, or policy you selected for your Evaluation Design. Explain how you would use the selected analysis, and justify why this type of regression analysis is most appropriate. READINGS Johnson, G. (2014). Research methods for public administrators (3rd ed.). Armonk, NY: M. E. Sharpe. Chapter 15, “Data Analysis: Regression” (pp. 216–229) Garson, G. D. (2009, April 4). Logistic regression. Retrieved from http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm MEDIA Laureate Education (Producer). (2013a). Correlation and introduction to regression. [Multimedia file]Baltimore, MD: Author. “Correlation and introduction to regression” Transcript (PDF) Optional Resources Laureate Education (Producer). (2013c). Virtual community. [Multimedia file]. Baltimore, MD: Author. “Virtual Community” Transcript (PDF)