Policy Implementation
Course Code
PBPL 22300 10
Course Description
Good public policy has the potential to advance justice in society. However, once a policy or program is established, there is the challenge of getting it carried out in ways intended by the policy makers or program designers.
This course explores some of the common obstacles, dilemmas, and opportunities that emerge when governments and non-governmental actors attempt to put a policy into effect.
Focusing on the United States, we will draw on organizational theory and case studies in public education and transportation policy to delve into the black box of policy implementation.
Course Criteria
This course fulfills a Public Policy major requirement.
This course is primarily comprised of undergraduate students. A select number of places are reserved for advanced high school students.
Instructor(s)
Karlyn Gorski
Other Courses to Consider
These courses might also be of interest.
Introductory Statistical Methods and Applications for the Social SciencesThis course introduces and applies fundamental statistical concepts, principles, and procedures to the analysis of data in the social and behavioral sciences. Students will learn computation, interpretation, and application of commonly used descriptive and inferential statistical procedures as they relate to social and behavioral research. These include z-test, t-test, bivariate correlation and simple linear regression with an introduction to analysis of variance and multiple regression.
The course emphasizes understanding normal distributions, sampling distribution, hypothesis testing, and the relationship among the various techniques covered, and will integrate the use of R as a software tool for these techniques.
By the end of the course, the student will be able to (1) differentiate, utilize and apply statistical description and inference to applied research in behavioral sciences or other disciplines, (2) understand and be able to utilize various forms of charts and plots useful for statistical description, (3) understand and utilize the concept of statistical error and sampling distribution, (4) use a statistical program for data analysis, (5) select statistical procedures appropriate for the type of data collected and the research questions hypothesized, (6) distinguish between Type I and Type II errors in statistical hypothesis testing, (7) understand the concepts of statistical power and the influence of sample size on inference, and (8) summarize and write up the results.
Remote