PSYC 3032 M:
Intermediate Statistics Laboratory II
Course Outline
Course Description
This is an applied and interactive course on statistics and data analysis in psychological contexts. Statistics is a crucial topic for students interested in psychology, research, and beyond because it is the primary way of knowing used in psychological research. This course provides students with the opportunity to apply, consolidate, and extend their statistical analysis skills to realistic psychological data.
An important component of the course is the use of the statistical software package R, which will be used throughout the course for analyses, data visualization, and results communication. This course is designed to provide students with an in-depth understanding of how the general linear model (GLM) is used to analyze data in psychological research; topics will include correlation, simple linear regression, multiple linear regression with categorical and continuous variables, regression diagnostics, interactions, and—time permitting—one or more advanced topics such as logistic regression, mediation, cross-validation, Bayesian statistics, etc.
By the end of the course, students will develop the skills to explore, visualize, and analyze psychological data using statistical software, deriving meaningful conclusions, with an emphasis on effect sizes and uncertainty for both academic and industry settings.
Target Audience
This course is meant for 3rd or 4th year undergraduate students in psychology. Students are assumed to be comfortable with basic descriptive statistics, graphics, R/RStudio, as well as principles of statistical inference (e.g., significance testing).
Course Prerequisites
Prerequisites are strictly enforced:
- HH/PSYC 1010 6.0 (Introduction to Psychology)
- HH/PSYC 2020 6.0 (Statistical Methods I and II) or substitute (e.g., HH/PSYC 2021 3.0 & HH/PSYC 2022 3.0)
- HH/PSYC 2030 3.0 (Introduction to Research Methods)
- HH/PSYC 3031 3.0 (Intermediate Statistics I)
- Completed at least 54 earned credits
Course Credit Exclusions: Please refer to York Courses Website for a listing of any course credit exclusions.
Lab Instructor
Name: Stephen Perkovic
Email: perkovic@yorku.ca
Office Hours: By appointment
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.
Course Time and Location
Day and Time: Tuesdays, 11:30AM - 2:30PM
Location: Ross Building (South), Room 103
Course Website
All course materials will be available on the course eClass site, unless otherwise indicated by the instructor. The site will be your central access point for course materials.
Software
We will be using R and RStudio exclusively.
Course Learning Outcomes
Students will be able to identify and apply appropriate statistical models and analyses to address a given research question in upper-level undergraduate psychology contexts
Students will be able to explain the distinction between explanation and prediction, and select the appropriate approach for a given research scenario.
Students will be able to discuss the limitations of the GLM and the use of significance testing
Given a dataset, students will be able to use R/RStudio to:
Explore the data and understand its nuances
Visualize the data for better comprehension and effective communication
Analyze the data using suitable statistical methods
Evaluate relevant statistical assumptions and make necessary adjustments or remedies
Interpret and communicate statistical results effectively, using APA-style reports, with attention to and interpretation of effect sizes, uncertainty, precision, and practical significance
Students will be able to troubleshoot software issues independently or locate external resources to find viable solutions
Course Format
This course includes one in-person meeting of roughly 3 hours each week. You are expected to come prepared—having read or listened to the assigned reading or podcast episode—and ready to engage in discussions and work with R. Each class will include approximately 2 hours of lecture (with a break midway, of course!) and 1 hour of a lab component. Lectures will cover conceptual material, including discussions on the readings, along with concrete examples. The lab component will offer hands-on experience in conducting statistical analyses using R.
Attendance and active participation are crucial to your success in this course. However, if you are unwell or have a justified reason for missing class, please stay home. Refer to the Participation and Missed Evaluation sections for detailed instructions on what to do in these situations.
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 Schedule
NOTE: This schedule and the deadlines are tentative and may change depending on pace or need
Student Evaluation
You will be evaluated by two data analysis assignments, lab exercise submissions, participation in class through class activities (e.g., iClicker, peer-presentation evaluation), and a final group presentation.
Course Requirements and Assessment:
Assessment | Weighting |
---|---|
Data Analysis Assignments (2x) | 50% |
Lab Exercises (5x) | 15% |
Group Presentation | 25% |
Participation | 10% |
Total | 100% |
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:
Replicating the data analysis examples from a lecture module
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).”
Key to an Excellent Presentation
The most effective presentations will take complex or difficult concepts and make them palatable and easy to understand. Using examples, metaphors, analogies, or demonstrations can help clarify challenging ideas and enhance engagement. Strive to teach your peers in a way that is relatable and clear, using creative techniques to “simplify” the material.
This part of your evaluation is designed to foster collaboration and independent learning, allowing you to explore a new subject beyond the core course content. Groups must submit their chosen topic or article for approval by the date indicated in the course schedule (unless otherwise announced). Topic selection will operate on a first-come, first-served basis, although different aspects of the same topic may be covered by different groups.
While accuracy and detail are important, the primary goal is to make the presentation interesting and explain why the topic is relevant. Creativity and out-of-the-box thinking are encouraged, and you are welcome to explore diverse and unexpected topics.
Recording Options
Groups may meet in person to record the presentation in one session. Alternatively, students can record separate sections individually and edit them together using tools such as Canva, or collaborate in real-time using similar platforms that support remote, multi-person presentation recording.
Submission and Interaction
Once uploaded to eClass, the presentation will be available for your classmates to watch. They will be expected to engage with your presentation by providing feedback, posting comments, and/or asking questions, creating a collaborative learning environment. Be sure to present your topic clearly and concisely to facilitate this interaction.
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.
Text and Readings
There is no required textbook. Material from the in-class lectures, assigned (free) reading/podcast episodes, and lab exercises will be sufficient for successful performance on all assignments. But, to solidify your knowledge and to have references for future work, I recommend the following (free!!!) texts:
Policy on Missed or Late Evaluation
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:
Specify which assignment or lab was missed or submitted late.
Provide the reason for missing the assignment or lab.
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.
Grading
As per Senate Policy, the grading scheme for the course conforms to the 9-point grading system used in undergraduate programs at York (e.g., A+ = 9, A = 8, B+ = 7, C+ = 5, etc.).
Assignments will bear either a letter grade designation or a corresponding number grade (e.g. A+ = 90 to 100, A = 80 to 89, B+ = 75 to 79, etc.). For a full description of York grading system see the York University Undergraduate Calendar – Grading Scheme for 2024-25
Add/Drop Deadlines
For a list of all important dates please refer to Undergraduate Fall/Winter 2024-2025 Important Dates
Event | Deadline |
---|---|
Last date to add a course without permission of instructor (also see Financial Deadlines) | January 20 |
Last date to add a course with permission of instructor (also see Financial Deadlines) | January 31 |
Drop deadline: Last date to drop a course without receiving a grade (also see Financial Deadlines) | March 14 |
Course Withdrawal Period (withdraw from a course and receive a grade of ‘W’ on transcript – see note below) | March 15 - April 4 |
There are deadlines for adding and dropping courses, both academic and financial.
Since, for the most part, the dates are different, be sure to read the information carefully so that you understand the differences between the sessional dates below and the Refund Tables.
You are strongly advised to pay close attention to the “Last date to enroll without permission of course instructor” deadlines. These deadlines represent the last date students have unrestricted access to the registration and enrollment system. After that date, you must contact the professor/department offering the course to arrange permission.
You can drop courses using the registration and enrollment system up until the last date to drop a course without receiving a grade (drop deadline).
You may withdraw from a course using the registration and enrollment system after the drop deadline until the last day of class for the term associated with the course. When you withdraw from a course, the course remains on your transcript without a grade and is notated as ‘W’. The withdrawal will not affect your grade point average or count towards the credits required for your degree.
Academic Accommodations
While all individuals are expected to satisfy the requirements of their program of study and to aspire to do so at a level of excellence, the university recognizes that persons with disabilities may require reasonable accommodation to enable them to do so. The university encourages students with disabilities to register with Student Accessibility Services (SAS) to discuss their accommodation needs as early as possible in the term to establish the recommended academic accommodations that will be communicated to course directors as necessary. Please let me know as early as possible in the term if you anticipate requiring academic accommodation so that we can discuss how to consider your accommodation needs within the context of this course. https://accessibility.students.yorku.ca/
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.
Academic Honesty
The following is an excerpt from York University’s Senate-approved Academic Conduct Policy and Procedures, code 6.2:
“6.2 It is the responsibility of students to:
- read and become familiar with this Policy and to comply with the principles and practices of good academic conduct set out herein;
- become familiar with related educational resources including, but not limited to those offered through the office of the Vice-Provost academic; York University libraries; York University Writing Centre, and at the Faculty level.
- follow their instructors’ expectations for using text-, image-, code-, or video-generating artificial intelligence (AI); referencing sources; group work and collaboration, and be proactive in pursuit of clarification and resources to support these expectations;
- take necessary precautions to prevent their work from being used by other students; * use course and exam software in a manner that is consistent with this policy; and act in accordance with this policy and/or the Policy on Responsible Conduct of Research when conducting and reporting research.”
For more information about academic honesty and other policies and procedures, please access the Undergraduate Academic Calendar: https://calendars.students.yorku.ca/
Academic honesty and integrity references, resources and workshops: * Academic Conduct Policy and Procedures
- Academic Integrity website
- Faculty of Health Academic Honesty website
- Faculty of Health Academic Honesty “Do you realize you may be risking your career?”
- SPARK Academic Integrity
- York University Libraries Academic Integrity website
- York University Libraries Academic Integrity workshop “Learn to Stop Worrying about it”
Students are strongly recommended to complete the following: * Academic Integrity Tutorial * York University Libraries Academic Integrity workshop “Learn to Stop Worrying about it” * Academic Integrity Quiz
Academic Integrity for Students
York University takes academic integrity very seriously; please familiarize yourself with the Information about the Senate Policy on Academic Honesty.
It is recommended that you review Academic Integrity by completing the Academic Integrity Tutorial and Academic Honesty Quiz
Code of Student Rights and Responsibilities
Policy on plagiarism and Academic Honesty
Use of Generative AI Tools in This Course
In this course, generative AI (GenAI) tools such as ChatGPT, Bard, Claude, Gemini, Perplexity, or similar platforms may be used under specific guidelines to support your learning process, but not as a substitute for your own work. The following policies apply to ensure that GenAI tools are used ethically and to foster deeper learning:
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:
- The tool(s) used
- The specific purpose for which it was used (e.g., debugging, asking for a concept explanation)
- 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
Retention of Drafts
- Students are required to retain all drafts and intermediate work for assignments. In the event of academic misconduct investigations, drafts may be requested as evidence of original work.
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.
Course Materials Copyright Information
These course materials are designed for use as part of the PSYC3032 course at York University and are the property of the instructor unless otherwise stated.
Third-party copyrighted materials (such as book chapters, journal articles, music, videos, etc.) have either been licensed for use in this course or fall under an exception or limitation in Canadian Copyright law.
Copying this material for distribution (e.g. uploading material to a commercial third-party website) may lead to a violation of Copyright law, see Intellectual Property Rights Statement.
Helpful Resources
APA Formatting and Style Guide (7th Edition): Purdue University
Presenting Your Findings by Nicol & Pexman (2010). A practical guide for creating tables. Washington, DC: American Psychological Association.
Best Practices in Data Cleaning by Osborne, J. W. (2012). Best practices in data cleaning. Los Angeles, CA: Sage Publications Inc.
Choosing Appropriate Plots & Example R Code: Data to Viz
A Pep Talk
I promise you can succeed in this class!
Learning R and statistics is difficult—Hadley Wickham, the chief data scientist at RStudio and the author of great R packages like ggplot2 (which you’ll use often), once shared some valuable advice:
“It’s easy when you start out programming to get really frustrated and think, ‘Oh it’s me, I’m really stupid,’ or, ‘I’m not made out to program.’ But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later.”
Even seasoned programmers struggle with seemingly impossible errors. If you’re stuck for too long, take a break, reach out to classmates, or feel free to email me for help.🙂