Course Syllabus
Evidence-Based Decision-Making
- Course Specific Information
- O'Neill & IU Policies and Expectations
- Additional Information
Course Details - SPCN V548
Instructor: Dr. Jennifer Silva, Associate Professor
Office Hours and Contact Info: Office hours can be set up via email as needed: Jensilv@iu.edu
Course Description
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This course is designed to teach students about and provide them with practice with gathering information and evidence, and presenting the evidence compellingly and ethically in a way that informs decision-making.
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Learning Outcomes
Learning Objectives
- Conduct research for a specific purpose;
- Describe what constitutes credible evidence in support of a management decision or policy argument;
- Identify appropriate resources available (at IU, online, etc.) to aid in research;
- Describe the data environment;
- Assess the quality of a data source;
- Identify appropriate technology for analytical tasks;
- Discuss ethical considerations pertaining to the analysis performed;
- Effectively/professionally communicate findings to experts and non-experts, the general public, and policy-makers using appropriate models of written and spoken research communication, and using effective data and conceptual visualizations.
Learning outcomes will be assessed by a student’s participation and comments in class, written and presented work, the case competition, and the final discussion post.
Course Requirements
This course will involve several assignments, a case competition, and a final post. The grade breakdown is as follows.
- Discussion Posts 20%
- Homework assignments 30%
- Case competition report 19%
- Case competition presentation 19%
- Case competition effort 7%
- Final discussion post 5%
Discussion posts are graded on effort and quality of content. Homework assignments are graded on a letter-grade basis using the grading rubric.
The full case competition grade includes a report, a presentation and a participation grade. The first two are a group grade, based on the quality of the submitted assignment and graded according to a rubric. The participation grade is based on a self-evaluation, a peer-review, and my own evaluation.
Grade Conversion
Assignments will be graded through Canvas using a point system. The letter grades will be converted as follows:
A+ 98.0% B+ 87.0% C+ 77% D+ 67.0% F 00.0%
A 93.0% B 83.0% C 73% D 63%
A- 90.0% B- 80.0% C- 70% D- 60%
The letter grade conversion above also applies to course grades. The instructor may round up the final course grade ONLY if you are within .5% of the next letter grade.
Academic Integrity
You are expected to adhere to the Indiana University guidelines regarding academic standards and behavior. I expect that all work you turn in will be your own. If you synthesize or rely on other people’s ideas or words to make your argument, you must cite them. Please take a moment to review your responsibilities here: https://studentcode.iu.edu/.
Required Texts
This class mostly relies on articles and online readings or talks. In the last two class sessions, I assign readings in two books:
- Schwabish, J. 2021. Better data visualizations: A guide for scholars, researchers, and wonks. Columbia University Press: New York City.
- Schwabish, J. 2016. Better presentations: A guide for scholars, researchers, and wonks. Columbia University Press: New York City.
These books are available online through the IU library at no cost. You may elect to purchase these books—they offer excellent guides and examples for visuals and presentations—but it is not required or necessary for this course.
Course Policies
Please take a few minutes and review the Core Rules of Netiquette (Links to an external site.) and considerations for when you are attending class remotely (Links to an external site.) .
- Make-up Exams: There are no in-class exams for this course. In the event of missed presentations, students should speak with the instructor to determine the appropriate course of action.
- Late Assignments: If you face any problems completing your work (whether due to family emergencies, financial issues, technology failures, illness, etc.), please talk to the instructor and reasonable arrangements may be made. In the case of unexcused late work, 10% will be deducted each day from the total grade for the assignment.
- Technology Use: Technology will be permitted in the classroom for the express purpose of taking notes and other tasks directly related to the course. The instructor retains the right to ask students to put technology away in the case that it is found to distract students from the course materials or is otherwise used inappropriately.
- Late Withdrawal: The auto-W deadline is NO LONGER THE LAST DAY OF CLASS (as it was during and post-Covid). After the auto-W deadline, withdrawal will be significantly limited, and you will need permission to withdraw and must meet requirements established by O’Neill.
- Incompletes: The grade of Incomplete used on the final grade reports indicates that work is satisfactory as of the end of the semester but has not been completed. The grade of Incomplete may be given only when the completed portion of a student’s work in the course is of passing quality. Instructors may award the grade of Incomplete upon a showing of such hardship to a student as would render it unjust to hold the student to the time limits previously fixed for the completion of his/her work.
Possible analytical software applications used in the course
The course is “tool agnostic” and require students identify their own approach, with resources provided. Software programs that students may choose among include R, Tableau Desktop, Python, and Excel. This policy does not refer to use of AI/ChatGPT (see above).
Writing Resources
This course seeks to help students improve their writing skills. There are several assignments that require writing. If you need help with writing, IU offers writing tutorial services, which you can access here: http://wts.indiana.edu/.
Course Communication Policy
This is a core course in the MPA program and an elective for any other graduate program. Classes will be entirely asynchronous and all lectures are pre-recorded. We will have online discussions each week.
Assignments and Grades
The course is organized by weekly modules. There are discussion board posts as well as major assignments nearly every week. Students will submit all assignments through Canvas. Assignments will be given feedback through Canvas and graded within one week. Due date can also be found on the Assignments tab on this course site.
| Week | Dates | Major Assignment | Discussion |
|---|---|---|---|
| 1 | Aug. 24–30 | Draft Literature Review — due Sun. Aug. 30, 11:59 PM | Introduction Post + Research Question/Resources Discussion — due Sun. Aug. 30 |
| 2 | Aug. 31–Sept. 6 | Literature Review — due Sun. Sept. 6, 11:59 PM | Writing for Different Audiences + Data Discussion — due Sun. Sept. 6 |
| 3 | Sept. 7–13 | Validity Threat Memo — due Sun. Sept. 13, 11:59 PM | None |
| 4 | Sept. 14–20 | Workflow Assignment — due Sun. Sept. 20, 11:59 PM | Ethics in Research Discussion — due Sun. Sept. 20 |
| 5 | Sept. 21–27 | None | Primary Data/Survey Critique Discussion — due Sun. Sept. 27 |
| 6 | Sept. 28–Oct. 4 | Case Competition Report — due Sun. Oct. 4, 11:59 PM | None |
| 7 | Oct. 5–11 | Case Competition Presentation & Effort — due Sun. Oct. 11, 11:59 PM | Effective Presentations Discussion — due Sun. Oct. 11 |
| 8 | Oct. 12–16 | — | Final Discussion Post — due Fri. Oct. 16, 11:59 PM |