Syllabus

PREREQUISITE

Math 1070, minimum C grade.

COURSE DESCRIPTION

This course focuses on quantitative research methods applicable to the study of public policy. Students will be introduced to the use of descriptive statistics as well as to the development and testing of empirical hypotheses using basic inferential statistical methods.

This is a rigorous course, requiring 4-6 hours of work per week outside of class, on average. You are expected to read the lecture notes and any other materials provided by the instructor each week before coming to class. Remember, the key to learning is persistence.

LEARNING OBJECTIVES

At the end of the course, students should be able to:

• Understand the value of data analysis and formulate questions that can be answered using data
• Understand the structure and organization of data
• Understand data collection methods and their implications for the scope of inference
• Manipulate data and run basic statistical analyses using R software
• Conduct basic exploratory data analysis
• Calculate descriptive statistics
• Make basic data visualizations
• Use scatterplots and correlation coefficients to show the direction and strength of relationships between interval-level variables
• Construct and Interpret contingency tables
• Interpret regression coefficients in both bivariate and multiple regressions
• Justify claims based on confidence intervals and hypothesis testing
• Document and share data analyses with others

TEXTS, TOOLS, AND OTHER RESOURCES

1. iCollege iCollege will be used for communication and to disseminate course content, lecture notes, PowerPoint slides, datasets, and more. Students are expected to use this resource on a regular basis for course materials and announcements.

2. Course Website For your convenience, materials relevant to this course, including examples of analyses and reports, will be available at the course website https://yuriygdv.github.io/pmap4041spring2020

3. Required Textbooks

   • OpenIntro Statistics, 4th Edition , available at https://leanpub.com/openintro-statistics

   • Roger D. Peng (2016), Exploratory Data Analysis with R, available at https://leanpub.com/exdata

4. Recommended Textbooks Advanced High School Statistics, Second Edition, available at https://leanpub.com/ahss

   • Moore, David S., Notz, William, I., & Fligner, Michael, A. (2011). The Basic Practice of Statistics (with Student CD). 6th Edition. W.H. Freeman. (or any newer edition)

5. For those of you who require more detailed treatment of the statistics topics, the following textbooks are recommended as additional resources (earlier editions are also fine):

   • Meier, Kenneth, Jeffrey Brudney, and John Bohte. Applied statistics for public and nonprofit administration. Cengage Learning, 2011.

   • Healey, Joseph F. (2013). The Essentials of Statistics: A Tool for Social Research. 3rd Edition. Wadsworth Cengage Learning.

   • Weiers, R. M. “Introduction to Business Statistics. 2005.” Thomson Brooks/Cole, Belmont, CA, USA.

6. R Software: You are required to use R software for all homework assignments. R is a free software environment for statistical computing and graphics available at www.r-project.org . In this course, we will use RStudio - an open-source integrated development environment for R available at https://rstudio.com/products/rstudio/ and RStudio Cloud at https://rstudio.cloud/ .

7. R Books & Resources:

   • R for Data Science by Garrett Grolemund and Hadley Wickham, at https://r4ds.had.co.nz/

   • An Introduction to R by Longhow Lam (PDF, 2010-10-28, 212 pages), at https://cran.r-project.org/doc/contrib/Lam-IntroductionToR_LHL.pdf

   • Cookbook for R at http://www.cookbook-r.com/

   • R Programming Tutorial on YouTube - Learn the Basics of Statistical Computing https://www.youtube.com/watch?v=_V8eKsto3Ug

8. Computers: For the purposes of this course, having a personal laptop with all the required software (R and RStudio) is recommended but not required. All the course assignments can be completed in RStudio Cloud using the library computers.

INTERACTIVE VIDEO TECHNOLOGY TRAINING LinkedIn Learning (formerly Lynda.com) offers an online training library on a variety of topics, including software tools for data analysis. Service is free to Georgia State University’s students. You can access online video tutorials with your CampusID and password using the following link: https://technology.gsu.edu/technology-services/it-services/training-and-learning-resources/linkedin-learning/

For the purposes of this course, the following training can be very helpful: Learning R by Barton Poulson (2h 51m Beginner + Intermediate Released: Aug. 29, 2019) https://www.linkedin.com/learning/learning-r-2/r-for-data-science?u=76216298

R Statistics Essential Training by Barton Poulson (5h 59m, Beginner + Intermediate, Released: Sep. 26, 2013) https://www.linkedin.com/learning/r-statistics-essential-training/next-steps?u=76216298

Download the Syllabus here

---