Syllabus
Course
| JOURN 8016: Adv Quant | Tue 3:30 - 6:00pm | Lee Hills Hall 214 |
Instructor
| Dr. Michael W. Kearney | Thu 11am - 2:00pm | Lee Hills Hall 314 |
Texts
| R for Data Science | O'Reilly Media | Wickham & Grolemund | |
| Applied Regression Analysis & Generalized Linear Models (3rd ed.) | CRC Press | Hadley Wickham |
Course description
This course provides an introduction to statistical methods used in journalism/media-related social science research–analytical techniques include simple group comparisons, correlations, factor analysis, and linear models–and previews of several advanced statistical models and tools from data science. An introduction to R, an open source [and free] statistical computing environment used in the examples and assignments in this course, will also be provided.
Grades
| 20% | Participation |
| 30% | Homework |
| 25% | Exam 1 |
| 25% | Exam 2 |
Assignments
- Late work policy for homework assignments:
- late, but within 24 hours of due date/time: -20%
- any later: no credit
Exams
There will be a two take-home [or non-timed online] exams that you are expected to complete individually. Each exam will ask you to complete a number of statistical and/or analytical tasks related to the material presented in the class. The exams will be written to take between 2-5 hours. The exact structure and content of the exams will be discussed in more detail before they are assigned.
Excused absences
Students who miss a class due to a school-related event, religious holiday, or short-term illness should notify me one-week prior (or, in the case of illness, as soon as possible). Note that these excused absences do not excuse you from assigned homework. It is your responsibility to make alternative arrangements to turn in any assignments in a timely fashion. Those with a personal emergency or bereavement should speak with your director of graduate studies or your academic dean.
Academic integrity
Academic integrity is fundamental to the activities and principles of a university. All members of the academic community must be confident that each person's work has been responsibly and honorably acquired, developed, and presented. Any effort to gain an advantage not given to all students is dishonest whether or not the effort is successful. The academic community regards breaches of the academic integrity rules as extremely serious matters. Sanctions for such a breach may include academic sanctions from the instructor, including failing the course for any violation, to disciplinary sanctions ranging from probation to expulsion. When in doubt about plagiarism, paraphrasing, quoting, collaboration, or any other form of cheating, consult the course instructor.
Software
The statistical software we will learn and use in this class is R, an open-source statistical computing environment. We will interface with R using Rstudio, a fully featured interactive development environment (IDE) designed specifically for working in the R environment. Click here for instructions on how to install R and Rstudio.