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.
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.
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.
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.
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:
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.
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/
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
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.
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.🙂
