Federal judges are appointed by the president and confirmed by the Senate. The president is likely to appoint judges that share his or her political ideologies. Since federal judges serve on the bench for life, their appointments have a lasting impact on the composition of the courts and judicial outcomes. Therefore, it is important to consider how judges make decisions that affect law and public policy. While judges are supposed to remain impartial in their decision making, some evidence suggests that their political ideologies influence how they interpret the Constitution and make decisions about cases. To prepare for this Discussion:
- Review the article, â€œFederal Judge Selection: Process and Participants.â€ Think about how politics influence judge selection. .
- Review the article, â€œStudents Fourth Amendment Rights and the Federal Judgeship: Examining the Link between Political Appointments and Case Outcomes.â€ Consider how judgesâ€™ political leanings or party affiliations might impact how they interpret the Constitution and decide on cases.
- Consider how politics might influence the selection process of federal judges.
- Think about how federal judgesâ€™ political ideologies might influence judicial outcomes.
With these thoughts in mind:
Day 4 a brief explanation of how politics might influence the selection process of federal judges. Then explain how federal judgesâ€™ political ideologies might influence judicial outcomes. Be specific and use examples to illustrate your explanation.
Be sure to support your postings and responses with specific references to the Learning Resources.
- Course Text: Harrington, C. B., & Carter, L. H. (2015). Administrative law and politics: Cases and comments(5th ed.). Washington, DC: CQ Press
- Chapter 10, “Judicial Review”
- Course Text: Rulemaking: How Government Agencies Write Law and Make Policy
- Chapter 6, “Oversight of Rulemaking” (pp. 247-268)
- Article: Congressional Digest. (2005). Federal judge selection. Congressional Digest, 84(5), 140-160.
- Article: Jaquette, I. (2007). Boston University School of Law Merck KGaA v Integra Lifesciences I, Ltd: Implications of the Supreme Court’s decision for the people who matter mostâ€¦ the consumer. American Journal of Law & Medicine, 33(1), 97â€“117.
- Article: Torres, M., Jr., & Stefkovich, J. (2007). Students’ fourth amendment rights and the federal judgeship: Examining the link between political appointments and case outcomes. Brigham Young University Education & Law Journal, 2007(2), 257â€“280.
- Web Site: Caperton et al. v A. T. Massey Coal Co., Inc., et al.http://www.supremecourt.gov/
- Web Site: Methods of Judicial Selection
- Web Site: Our Governmentâ€“Judicial Branch
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 â€œperfectâ€ approach to regression analysis. Rather, you must select the â€œmost appropriateâ€ 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 United Way to help answer your research question.
Post by Day 4 an explanation of whether OLS regression or binary logistic regression is more appropriate to evaluate the program, problem, or policy you selected for United Way. Explain how you would use the selected analysis, and justify why this type of regression analysis is most appropriate.
- Johnson, G. (2014). Research methods for public administrators (3rd ed.). Armonk, NY: M. E. Sharpe.
- Chapter 15, â€œData Analysis: Regressionâ€ (pp. 216â€“229)
- Garson, G. D. (2009, April 4). Logistic regression. Retrieved from http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm
- Laureate Education (Producer). (2013a). Correlation and introduction to regression. [Multimedia file]Baltimore, MD: Author. “Correlation and introduction to regression” Transcript (PDF)
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