Email Etiquette

When contacting the any of the teaching staff, please include the course code and section (i.e., PSYC3032M) in the subject line. Additionally, include your full name and student number in the email body.

Please be aware that the instructor may take up to 3 business days to respond. Emails sent over the weekend may not be addressed until the following week (Monday – Friday) unless specifically stated or if it’s an urgent matter.

Electronic Device Policy

It is expected that students will have a laptop or other device during in-person sessions to complete class activities using statistical software. If you do not have your own device you can pair up with another student to facilitate your learning.

The use of electronic devices to share information in any form (e.g., screenshots) about personal feedback received on submitted work or work related to course assessments will be considered a violation of the electronic policy. Unauthorized sharing of these details and/or other course materials in any way (e.g., WhatsApp group, Reddit, Discord, etc.) is strictly prohibited.

Course Requirements and Assessment:

Data Analysis Assignments

Each assignment will require you to analyze data and summarize your findings—with a particular focus on discussing and interpreting effect sizes, uncertainty, and practical significance—emphasizing how statistical results relate to the underlying conceptual research problem. Aim for concise yet insightful explanations.

In your write-ups, do not include computer input or raw output. Present your results and interpretations in a style similar to that of a journal article’s Results and Discussion section (but much shorter). Instead of pasting raw output, create APA-style tables to present your findings clearly and professionally.

Lab Exercises

Throughout the term, you will complete several “computer lab” exercises involving data analysis using R. Each lab will typically have two main parts:

1. Replicating the data analysis examples from a lecture module

2. Performing similar analyses on new datasets and answering interpretation questions

The goal of these exercises is to help you practice the types of analyses and conclusions you will need for your assignments. To support your learning, a solution will be provided for each lab, including R code and answers to the interpretational questions. Note that the style of these solutions will be less formal than what is expected in your assignment submissions.

There will be roughly one lab exercise per lecture module. Since some modules span more than one week (e.g., Module 1 may cover the first two weeks of the term), we may continue working on the same lab over multiple weeks. Each lab exercise will be due one week (by Tuesday at 11:30) after we finish the corresponding lecture module. Lab exercises will be graded on a pass/fail basis, focusing on hands-on practice, effort, exploration, and understanding rather than accuracy. As long as you attempt all parts of the lab and demonstrate a satisfactory effort and understanding (using #comments in your code will help you express your reasoning and justification), you will receive the full marks allocated for the lab exercise.

Unlike the formal assignments, you are required to submit R code, output, and answers to interpretation questions for each lab exercise. Use R Markdown to generate an HTML, PDF, or Word document for your submission, which should be uploaded to eClass. Here is a video showing how to do this, and I will post other resources on eClass as well.

Group Presentation

Students will work in groups of 2 or 3 to create and record a 10-minute presentation, which will be uploaded to eClass. Your presentation should explore a topic or article that complements the material discussed in class. This can be based on one of the assigned readings or a new topic/article that your group finds interesting.

The main purpose of the presentation is to introduce your classmates to the topic and teach them what you have learned. This includes explaining key concepts, providing context, and highlighting why the topic is relevant and interesting. You may also discuss any implications or connections to other areas covered in the course. While accuracy and depth are important, your focus should be on making the topic engaging and accessible to your peers. A strong presentation will give your classmates a clear understanding of the topic—enough to grasp the “big idea(s).”

Participation

Participation will be evaluated through different activities, either during class or between classes, attendance, and contributions to in-class discussions (e.g., discussions on course materials, readings, and podcast episodes). Activities will involve various forms of ungraded participation, such as responding to iClicker/eClass questionnaires and quizzes, completing reflection surveys, providing peer-presentation feedback, and engaging with the Additional Topic survey.

If you need to miss a class, please inform the course director in advance to ensure your absence does not affect your participation grade. Each student is allowed one missed class without affecting their participation grade (no justification required). For additional absences, a justified reason must be provided, similar to the requirements for missing any evaluated component of the course.

Submission Policy

• Assignments and labs must be submitted before the beginning of the specified lecture (i.e., Tuesday at 11:30 AM).

• Late submissions will not be accepted (unless approved in advance by the course director or student used their Time Dilation Deadline Delay prior to the deadline), and any missed assignment without valid justification will receive a grade of zero.

• Reasons such as “my code produced errors” or “my computer crashed” are not considered valid excuses for missing a deadline. To prevent data loss, ensure you regularly back up your work using cloud storage services like Dropbox, OneDrive, Google Drive, or iCloud. Additionally, allow sufficient time ahead of the deadline to address any technical issues or code errors that may arise. Remember, no error is unsolvable! You can always (and no doubt will) need to consult online resources (e.g., Google/Stackoverflow), your peers, and/or the instructor.

Time Dilation Deadline Delay™ (TD³) Policy:

In this course, there are no tests or exams. However, if you ever feel overwhelmed or have scheduling conflicts, you can use TD³ (Time Dilation Deadline Delays)—stretching time just when you need it! Each student has two TD³s available, which provide a 24-hour extension on any data analysis assignment or lab, no questions asked and no documentation required.

For example, if an assignment is due on Tuesday at 11:29 AM, using one TD³ will extend your deadline to Wednesday at 11:29 AM. You can also use both TD3s at once, extending the deadline to Thursday at 11:29 AM.

To redeem a TD³, you must email the instructor before the original assignment deadline, specifying which assignment or lab you’re using the TD3 on, and how many you’re applying (i.e., 1 or 2). The only rule: TD³s must be redeemed before the assignment’s deadline and via email!

Missed Assignments/Labs

If you miss an assignment or lab deadline, you must contact the course instructor via email within 48 hours. In your email, please:

1. Specify which assignment or lab was missed or submitted late.

2. Provide the reason for missing the assignment or lab.

3. Confirm that you have relevant documentation (e.g., medical or other supporting documents).

Include your full name, student number, and course section in the subject line of your email for clarity. Upon request, you must be prepared to submit the relevant documentation within one week of the missed assignment or lab.

You should also complete the following online form which will be received and reviewed in the Psychology undergraduate office: HH PSYC: Missed Tests/Exams Form. Failure to complete the form within 48 hours of the original deadline will result in a grade of zero for the missed assignment or lab.

If you have any concerns or are unsure about a situation, please contact the course instructor.

Senate Policy on Academic Accommodation for Students with Disabilities

Pursuant to its commitment to sustaining an inclusive, equitable community in which all members are treated with respect and dignity, and consistent with applicable accessibility legislation, York University shall make reasonable and appropriate accommodations in order to promote the ability of students with disabilities to fulfill the academic requirements of their programs. This policy aims to eliminate systemic barriers to participation in academic activities by students with disabilities.

All students are expected to satisfy the essential learning outcomes of courses. Accommodations shall be consistent with, support and preserve the academic integrity of the curriculum and the academic standards of courses and programs. For further information please refer to York University Academic Accommodation for Students with Disabilities Policy.

Religious Accommodation

Appropriate accommodation will be made in accordance with the policies of the university. Please see the instructor at your earliest opportunity if you discover issues relating to your religious practices and the expectations of the course.

Permitted Uses of GenAI

• Debugging R Code: You may use GenAI tools to identify and resolve errors in yout R code or to clarify coding concepts
• Content-Related Questions: You are encouraged to use GenAI to explore additional, extra-curricular concepts that are related to, but not covered in this course or clarify topics discussed in class

Prohibited Uses of GenAI

• Data Analysis: You are NOT allowed to upload datasets to any GenAI platform for analysis due to concerns about data security, privacy, and ethical considerations

• Assignment Writing: You must NOT use GenAI to write your assignments or any part of them, including outlines, summaries, APA-style write-ups, or drafts. Assignments must represent your own original thinking and effort. Additionally, many data analysis assignments in this course involve unique “discoveries” and require specific decision-making processes that are taught in class. These steps are often context-specific and cannot typically be replicated accurately by GenAI platforms

Requirements for Transparency

If you use GenAI tools for permitted purposes, you must include a brief appendix with your submission detailing:

1. The tool(s) used
2. The specific purpose for which it was used (e.g., debugging, asking for a concept explanation)
3. What you learned from using the tool and how it supported your understanding of the course material

For more information about citing AI tools, please refer to the Library’s page on citing GenAI tools

Ethical and Thoughtful Use

• All GenAI output must be critically evaluated and fact-checked. Relying on AI-generated content without verification is not acceptable

• The use of these tools must aim to enhance your understanding of course material, not to bypass the effort of learning

Rationale for This Policy

This policy is designed to promote ethical use of technology in your learning journey. While GenAI tools offer valuable opportunities for exploration and clarification, misusing these tools can undermine your learning and violate academic integrity principles. By adhering to these guidelines, you can develop critical thinking and technical skills while maintaining the integrity of your work.

Additional Notes

• Expectations for the use of GenAI in this course may differ from those in other courses. It is your responsibility to understand and follow these course-specific guidelines.
• If you have concerns about these policies or the use of GenAI tools, please contact me to discuss an opt-out option.