WHO SHOULD TAKE THIS COURSE?
This course is designed for students who are interested in developing skills for working with data and using statistical tools to analyze them. No prior experience with data or statistics is required.
WHAT ELSE SHOULD YOU KNOW?
The approach is “statistics in the service of questions”. As such, the research question that you choose (from data sets made available to you) is of paramount importance to your learning experience. It must interest you enough that you will be willing to spend many hours reading about it, thinking about it and analyzing data having to do with it.
The course will offer a focused hands-on experience in the research process. You will develop skills in 1) generating testable hypotheses; 2) conducting a literature review; 3) understanding large data sets; 4) formatting and managing data; 5) conducting descriptive and inferential statistical tests; and 6) reporting and interpreting results.
COURSE REQUIREMENTS
Class Sessions: Class sessions include instructor, peer mentor and guest support aimed at helping you to make consistent and meaningful progress on your research project and mini-assignments.
Lessons: Rather than a traditional textbook, this course provides a series of “lessons” aimed at preparing students conceptually and technically for the various steps taken in completing their research project. Lessons are presented in video with corresponding text and content/demonstrations. All assigned lessons should be completed prior to each class session.
Component Assignments: Students will submit project components through Moodle. The purpose is to encourage you to reflect on the research process. So please feel free to ask questions, reflect, or extend beyond what is asked of you.
Research Poster/Oral Presentation: Assignments will build to the completion of an individual project that will be presented at the end of class as a research poster and oral presentation. The poster session and presentations are scheduled for Thursday June 23 scheduled.
Exams: Exams will be given in multiple choice format. Students are permitted to reference one page of notes during each exam. Exams are timed and you are given 60 minutes to complete each exam. Exam 1, Exam 2, and the first part of the Final Exam do not require any coding or syntax. The 2nd part of the final exam does require you to code and answer questions based on your own statistical output.
Teaching Assistants:
Evan Wacks and Amy Zang
Commitment to the Course: Students are expected to make marked progress each day and to come to class sessions prepared with questions and planned next steps. It is important to note that to really learn the material and skills presented in this course, students will need to devote a substantial amount of time outside the regular class meetings.
Scientific Integrity: The rules of science should be carefully upheld in everything that you do. The following behavior is absolutely unacceptable: Data fabrication, selective reporting, omission, suppression or distortion. Please be mindful that there is no such thing as a “little scientific misdemeanor”.
AI Statement The use of AI tools (e.g., ChatGPT, Bing, Elicit, etc.) in completing course assignments is not allowed and constitutes a violation of our honor code.
Accommodations: It is the policy of the University to provide reasonable accommodations to students with documented disabilities. Students are responsible for registering with Disabilities Services, in addition to making requests known to their instructor.
Grades: Course grades will be based on
- Quizzes (5%)
- Mini-Assignments (15%)
- Project Components (10%)
- Research Poster/Oral presentation (30%)
- Exam 1 (10%)
- Exam 2 (10%)
- Final Exam – 2 parts (20%)
Passing Letter Grades/Percentages: A 96-100%; A- 91-95.9%; B+ 88-90.9%; B 85-87.9%; B- 81-84.9%; C+ 78-80.9%; C 75-77.9%; C- 71-74.9%; D+ 68-70.9%; D 65-67.9%; D- 60-64.9%