| Week | Date | Weekly Module |
|---|---|---|
| 1 | T: January 21, 2025 | W1: Getting started + Statistical Thinking |
| 1 | Th: January 23, 2025 | W1 continued… |
| 1 | Su: January 26, 2025 | Week 1 Quiz due |
| 2 | T: January 28, 2025 | W2: Summarizing & Fitting models to data |
| 2 | Th: January 30, 2025 | W2 continued… |
| 2 | Su: February 2, 2025 | Week 2 Quiz due |
| 3 | M: February 3, 2025 | PS1 due / Opt-out Deadline 1 |
| 3 | T: February 4, 2025 | W3: Variability & z-scores |
| 3 | Th: February 6, 2025 | W3 continued… |
| 3 | Su: February 9, 2025 | Week 3 Quiz due |
| 4 | M: February 10, 2025 | PS2 + PS1 revision due |
| 4 | T: February 11, 2025 | W4: Correlation & Regression | No Class! |
| 4 | Th: February 13, 2025 | W4 continued… |
| 5 | M: February 17, 2025 | PS2 revision due |
| 5 | T: February 18, 2025 | W5: More Correlation & Regression |
| 5 | Th: February 20, 2025 | W6 continued… |
| 5 | Su: February 23, 2025 | Week 5 Quiz due |
| 5 | M: February 24, 2025 | PS3 due |
| 6 | T: February 25, 2025 | W6: Loose Ends / Exam 1 review |
| 6 | Th: February 27, 2025 | Exam (Midterm) 1 |
| 7 | M: March 3, 2025 | PS3 revision due |
| 7 | T: March 4, 2025 | W7: Sampling and Hypothesis Testing |
| 7 | Th: March 6, 2025 | W7 continued… |
| 7 | Su: March 9, 2025 | Week 7 Quiz due |
| 8 | T: March 11, 2025 | Spring Break! |
| 8 | Th: March 13, 2025 | Spring Break! |
| 9 | T: March 18, 2025 | Spring Break! |
| 9 | Th: March 20, 2025 | Spring Break! |
| 10 | M: March 24, 2025 | PS4 due / Opt-out Deadline 2 |
| 10 | T: March 25, 2025 | W10: Modeling Relationships I |
| 10 | Th: March 27, 2025 | W10 continued… |
| 10 | Su: March 30, 2025 | Week 10 Quiz due |
| 11 | M: March 31, 2025 | PS5 + PS4 revision due |
| 11 | T: April 1, 2025 | W11: Modeling Relationships II |
| 11 | Th: April 3, 2025 | W11 continued… |
| 12 | M: April 7, 2025 | PS6 + PS5 revision due |
| 12 | T: April 8, 2025 | W12: Loose Ends / Exam 2 review |
| 12 | Th: April 10, 2025 | Exam (Midterm) 2 |
| 13 | T: April 15, 2025 | W13: Additional Predictors |
| 13 | Th: April 17, 2025 | W13 continued… |
| 13 | Su: April 20, 2025 | Week 13 Quiz due |
| 14 | M: April 21, 2025 | PS6 revision due |
| 14 | T: April 22, 2025 | W14: Repeated Measures |
| 14 | Th: April 24, 2025 | W14 continued… |
| 14 | Su: April 27, 2025 | Week 14 Quiz due |
| 15 | M: April 28, 2025 | PS7 due / Opt-out Deadline 3 |
| 15 | T: April 29, 2025 | W15: Miscellaneous Data |
| 15 | Th: May 1, 2025 | W15 continued… |
| 16 | M: May 5, 2025 | PS7 revision due |
| 16 | T: May 6, 2025 | W16: Last Class / Final Exam review |
| 17 | Finals week (TBD) | Final Exam |
Syllabus: Data Analysis
Basic Course Information
Course number: PSYC 2520
Semester: Spring 2025
When: Tuesdays & Thursdays, 1.15 PM - 2.40 PM
Where: Visual Arts Center (VAC) South Website: You are here!
Pre-requisites: Two of:
- PSYC 1101 or Placement in above PSYC 1101
- Either BIOL 1102 or BIOL 1109 or Placement in BIOL 2000 level or PSYC 2510
Who is your instructor?
Abhilasha Kumar: Hear my name!
Pronouns: she/her
About me: I am a cognitive scientist who is fascinated by how humans think, learn, and communicate. My work involves conducting psychological experiments to understand different aspects of human behavior such as how we learn the meaning of words, how we search for information, and how we cooperate with one another. When I am not working, I enjoy playing board games, learning new recipes, and playing tennis (badly)!
Email: a.kumar@bowdoin.edu
Office: Kanbar Hall, Room 217
Who are your learning assistants?
Sydney Morrison
Pronouns: she/her
About me: I am a junior Psychology and Government double major with a minor in music! I am from Dedham, MA, and am just getting back from my semester abroad in Athens, Greece. In my free time, I love to sing with my a cappella group (Missy!) and to explore the outdoors! I was nervous before taking Data Analysis last spring, but I ended up really enjoying the course and learning so much. I am excited to be a resource for you all and please feel free to contact me with any questions!
Email: smorrison@bowdoin.edu
Jon Sides
Pronouns: he/him
About me: I am a senior from Minnesota/Connecticut majoring in psychology and math. I am interested in clinical neuroscience and statistics, and I did language comprehension research in Prof. Kumar’s lab. I was also a barista for five years. Outside school, my interests include hockey, coffee, makeup, Star Wars, and musicals. Last fall, I studied abroad in Copenhagen.
Email: jsides@bowdoin.edu
Student/Office Hours
You are strongly encouraged to drop-in during student/office hours - this is time specifically dedicated to you and any thoughts, questions, or concerns you may wish to share with us. If the designated hours don’t work for you, please email us to find a different time.
Prof. Kumar’s office hours (Kanbar 217):
- Tuesdays & Thursdays, 4.15 PM - 5.30 PM
- Fridays, 11 AM - 12.45 PM (with some exceptions)
Sydney’s office hours (Kanbar 101)
- Mondays: 6-8 pm
Jon’s office hours (Kanbar 107)
- Sundays: 7-8 pm
What is this course about?
We live in what has been called the “golden age of data” (Poldrack, 2019), where professionals from all walks of life have access to and are sharing data freely, including but not limited to government officials, policymakers, scientists, and engineers. We see charts, numbers, and scientific “facts” all around us, and it is imperative that we are equipped with the skill set to interpret this information in a logical and useful manner. Statistics is a toolkit that enables us to understand and describe the complex world we live in, while also empowering us with skills that allow us to test questions and claims about the world.
This course is an introduction to the statistical procedures commonly used by psychologists to describe, analyze, and interpret data. You will acquire skills to empirically test questions and claims about human behavior and mental processes. You will learn the types of inferences and conclusions that can (and cannot) be drawn from various scientific research procedures. As such, this course provides a conceptual and mathematical understanding of common statistical procedures.
This course counts towards Bowdoin’s MCSR distribution requirement.
Why take this course? a.k.a. learning goals
My hope as an instructor is to empower you with an analytical toolkit that will not only prepare you for other psychology courses you may take in your academic career, but also apply to other areas of your life. At the end of this course, you will be able to:
- Describe the conceptual principles behind statistical thinking and uncertainty [Department Goal #1]
- Apply a computational and statistical toolkit to test specific claims and questions [Department Goal #5]
- Communicate effectively through numbers, graphs, and scientific writing [Department Goal #7]
Course textbook
- Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences (10th ed.). Belmont, CA: Thomson Wadsworth.
Please make sure you obtain the 10th edition as the problem sets come from the textbook and they vary from one edition to another.
All other course materials (e.g., slides, data, etc.) will be available in a timely fashion on this course website and/or posted on Canvas.
Weekly module structure
There are 15.5 total weeks in this course.This is an in-person class.
Weekly learning modules include three components to encourage engagement with the data and statistics in different ways.
PREP: Most modules have some assigned material before class (a reading from the textbook or a video to watch). Student are expected to complete the assigned material before classes where the material is discussed.
TRY: At the end of each week, there will be a short online quiz where students will be expected to answer conceptual questions about the content covered during the week. These quizzes are meant to help you keep track of your progress and will count towards your final grade.
APPLY: Every week, you will also have a problem set due. These problem sets will help you build on the computational skills you learn in class and through watching the videos, and apply them to new data. There is some flexibility with how these problem sets can be completed that are discussed in detail below.
Course Schedule
Grading
The grading scale for this class is as follows:
| Letter grade | Points |
|---|---|
| A | 95 - 100+ |
| A- | 90 - 94.99 |
| B+ | 87 - 89.99 |
| B | 83 - 86.99 |
| B- | 80 - 82.99 |
| C+ | 77 - 79.99 |
| C | 73 - 77.99 |
| C- | 70 - 72.99 |
| D | 60 - 69.99 |
| F | fewer than 60% |
Grades will be determined based on the following rubric, which is based on emphasizing our three learning goals.
There are multiple ways for students to engage with class and course materials and achieve their desired grade. Course assessments that occur throughout the semester are designed to help students set and achieve their own goals for engaging in course content.
Points
| Component | Points |
|---|---|
| Weekly quizzes | 10 |
| Problem sets | 30 |
| Exam: Midterm 1 | 15 |
| Exam: Midterm 2 | 15 |
| Exam: Final | 20 |
| Class participation | 10 |
| Extra credit | 5 |
| Total | 105 |
Please note that all assignments must be submitted before the due date each week (details on Canvas) to count towards the final grade - late submissions will NOT be accepted (unless you are using a flex day, see late work policy).
Weekly Quizzes
At the end of each weekly module, there will be a Canvas quiz that covers the content discussed in class through multiple-choice or true-false questions. Quizzes will test your conceptual understanding of the material each week. All quizzes will be open-book but not open-person. That is, you can use our course material, but you cannot collaborate with other students or use generative AI.
Once you open the quiz you will have 30 minutes to complete it—this should be more than enough time if you are prepared. You will have at most 2 attempts for each quiz and your average score will be recorded, i.e., if you do really poorly on the first attempt and get a perfect score on the second attempt, you will still get an overall low score. Therefore, you are encouraged to study the week’s content carefully before the first attempt. Also, ALL attempts must be completed BEFORE the Sunday deadline.
Overall, these quizzes contribute 10 TOTAL points towards your final grade, but each quiz will be individually graded for 10 points and then all quizzes will be scaled to total up to 10 points at the end of the semester.
Problem Sets
Problem sets are meant to help you practice actually conducting data analysis on new datasets. Most problem sets will come from the course textbook and will involve solving problems that will resemble the kinds of questions you may see on the exams in the course. These problem sets will test your ability to work with datasets and conduct simple statistical analyses via Google Sheets. You are encouraged to look over the videos on the course website (inside each module) so that you get comfortable with using Google Sheets and using simple formulas within that platform.
Each problem set will be graded on 5 points (2.5 points per attempt) and there will be 7 problem sets throughout the semester. Your lowest scoring (attempted) problem set will be dropped. Problem sets will be due roughly every week and will contain questions that will require you to do basic calculations and/or analyses. They may also contain some short answer questions. Please refer to Canvas for exact deadlines - they will usually be due the Monday midnight before class.
You are strongly encouraged to work regularly on the problem sets, i.e., try to attempt problems after each class so that the work does not pile up right before the deadline. You will find that you are able to grasp the concepts and analyses better this way.
All submissions must be made through Canvas. Please make sure to carefully review the instructions on the submission format for these problem sets. The document with all problem sets is also available on Canvas.
Some students find it useful to consistently practice problem solving throughout the semester, whereas others are more comfortable with sporadic assessments. You will have the option to choose between two possibilities:
- Option 1: You may complete (and self-grade) all problem sets. If you choose this option:
- You must submit an initial attempt by the deadline for the specific problem set. This initial attempt is worth 2.5 points and will be graded as whether or not you have attempted at least 75% of the problems. An “attempt” only counts if you have genuinely tried to solve the problem and demonstrated your work. To calculate 75%, add up each individual item (e.g., 1a, b, c, d counts as 4 problems) and then take 75% of that total.
- Once the solution set has been released and you receive your initial attempt back (by Wednesday), to gain the remaining 2.5 points, you must grade your initial attempt and re-attempt the problems by the resubmission deadline. If you submit a corrected version (that includes ALL corrected problems), you will receive the additional 2.5 points for that problem set. Please follow the template described in this video to submit corrections and explanations.
- Option 2: You may choose to forego the problem sets and add these points to your exams. Specifically, there will be three deadlines to drop out of doing the problem sets:
- Deadline 1: At the beginning of the course
- Deadline 2: After the first midterm
- Deadline 3: After the second midterm
If you choose to opt out of problem sets, those points will be re-distributed to the exam points for that portion of the semester (e.g., if you opt out at the beginning of the semester, the 15 points for the first three problem sets are re-allocated to Midterm 1, meaning your midterm will be worth 30 points instead of 15 points). You must opt out of Option 1 (or back into Option 1) by indicating so on Canvas, so that we both have a record of your decision. Due dates for opting in and out are on the course schedule and Canvas. These are hard deadlines. You are strongly encouraged to opt for Option 1 at least through the first midterm. At that point, you may consider opting out. I am happy to meet with anyone who wants to talk about which approach would be best for them.
Exams
The two midterm exams are not cumulative. The final is cumulative. Each exam is divided into two parts: a conceptual in-person part and a computational take-home part.
The conceptual in-person part includes multiple choice questions, matching items, and short answer questions. You may use a Help Sheet. The Help Sheet includes both sides of a single, 8 1/2 X 11 sheet of paper.
The computational take-home part contains computational (problem-set like) questions; you will analyze various sets of data using the statistical techniques you have learned in the course. All take-homes are open-book but not open-person. That is, you can use our course materials, but you cannot collaborate with other students or ask anyone other than me (not even LAs) for help.
Class participation
We all learn better by actively participating in and outside of class. To encourage you to critically engage with the class content, your participation will be assessed through the following mechanisms:
In-class activities[2.5]: You will work on several exercises by yourself and with your peers during class. You will submit responses to these exercises via Canvas and your participation will be recorded. You will get full credit if you participate in at least 90% of the class exercises. Beyond that, you will be assigned partial credit based on the number of activities/exercises you miss, i.e., 0.5 point will be deducted for each 10% drop in participation (e.g., if you participate in 80% of the activities, you will earn 2 points, if you attend 70% of the classes, you will earn 1.5 points etc.).
Videos [2.5]: Each week, there will be videos that will describe how to conduct statistical analyses via Google Sheets. Watching these videos will count towards class participation. If you watch 80% of these videos, you will be awarded the full 2.5 points. Beyond that, you will be assigned partial credit based on the number of videos you watch.
Practice assessments [5]: Before each exam, practice exams will be made available to you to help with your preparation. Submitting these practice exams and getting at least 50% on them will count towards class participation. Practice exams for midterms are worth 1.5 point each and the practice exam for the final will be worth 2 points.
Extra credit
There will be some opportunities to earn extra credit during the semester. These opportunities are described below:
Complete class surveys (2 points) : There will be 3 surveys during the semester to gather your reflections and suggestions to improve the course. You will be able to earn 1 point for completing the first survey, and 0.5 for the other two surveys. With the exception of the pre-class survey, the two other surveys will be anonymous.
Win Conceptual Czar (1 point): To incentivize timely preparation and encourage you to master the class content, you will have the opportunity to submit multiple-choice and/or true/false questions based on the course content covered each week. Submitting questions for 8 of the 11 content weeks will earn you this extra credit point. Additionally, if your question is selected to be on any of the exams, you will earn an additional extra credit point.
Win Analysis Ace (1 point): To incentivize timely preparation and encourage you to master data analysis, the two students who score the highest on the computational exams throughout the semester will earn 1 extra credit point each.
Win Memer of the Semester (1 point) : Each week, you will have the opportunity to submit a meme via Canvas, that reflects your experience with the course content of that week. Memes should be original, i.e., they should be course-specific and something you have created yourself and not simply found on the internet, although you are allowed to use common images/tropes from popular memes as a starting point. All memes will be gathered and sent to the class anonymously at the end of the semester for a survey, and the student(s) with the average highest score and the best scoring meme will both receive 1 additional point. Note: A student can only receive a maximum of 1 point through this mechanism, even if the same student has the highest average score in the context and the best scoring meme.
Course Policies
Academic honesty and plagiarism
We, as a class, will abide by the Bowdoin College Academic Honor Code. While you are encouraged to discuss ideas and thoughts with your classmates, plagiarism in any form will be subject to grade reductions and disciplinary action. Specifically, you are permitted to make use of online resources and/or search platforms. However, directly copying or adapting written material and/or your classmates’ answers or ideas will be considered plagiarism. This policy applies to both individual and group assignments.
Please refer to this page for a list of resources related to plagiarism and other academic integrity issues. Here is another helpful infographic about plagiarism that you are encouraged to go over.
Use of generative artificial intelligence tools
Acknowledgement: This policy about generative AI was generated using the Generative AI Syllabus Statement Tool provided by Seaver College
Generative artificial intelligence tools — software that creates new text, images, computer code, audio, video, and other content—have become widely available. Well-known examples include ChatGPT/Bard for text and DALL-E for images. This policy governs all such tools, including those released during our semester together. You may use generative AI tools for work in this course to help with idea generation, understanding code, literature review, drafting, and other such academic work. If you do use generative AI tools on assignments in this class, you must properly document and credit the tools themselves. Cite the tool you used, following the pattern for computer software provided by the American Psychological Association (APA) guide. If you choose to use generative AI tools, please remember that they are typically trained on limited datasets that may be out of date. Additionally, generative AI datasets are trained on pre-existing material, including copyrighted material; therefore, relying on a generative AI tool may result in plagiarism or copyright violations. Finally, keep in mind that the goal of generative AI tools is to produce content that seems to have been produced by a human, not to produce accurate or reliable content; therefore, relying on a generative AI tool may result in your submission of inaccurate content. It is your responsibility as a scholar — NOT the tool’s — to assure the quality, integrity, and accuracy of work you submit in any college course. Although you have wide latitude to determine how you use generative AI tools in this course, you must be wary of unintentional plagiarism or fabrication of data. Please act with integrity, for the sake of both your personal character and your academic record.
Electronic devices
Most of our class time will be spent in active learning and in-person/online activities that require the use of technology. Therefore, please bring a Macbook and iPad to class. Some students find it helpful to examine data on their Mac and use their iPad for looking at slides, so please consider bringing both devices to class.
Please make sure that your devices are fully charged when you arrive to class. In order to minimize distractions, please close or exit out of all other programs except those needed during class, and put your Mac/iPad on “work” mode. All class time should be devoted to working on in-class activities and group projects.
Attendance: How many classes can you miss?
It not only benefits your learning, but benefits all of our learning to be present together in the same space. Class time is designed to take advantage of our presence together.
While attendance will not count directly count towards your grade, participating in in-class activities does count towards class participation. (see here).
If you miss class, you cannot make submissions for activities that are for class participation. Additionally, it is entirely your responsibility to make sure you get notes and keep track of the material. I will be happy to meet to answer questions about the content, but will not redo/review the material outside of class time. You may also make use of LA’s office hours to ask questions about the material, but they are not expected to redo/review the material with you.
Note: If you are sick, please stay home.
Late Work Policy
Sometimes, life doesn’t go as planned and you have way too much going on to turn things in on time. That is OKAY! This course has the following policies for late work:
- Each student has 3 “flex” days that they can use at their discretion throughout the semester. You can use both days at once for a single assignment and turn in one assignment 3 days late (with no questions asked), OR you can spread the love across different assignments.
- Flex days can ONLY be used for quizzes and problem sets unless otherwise stated in class.
- If you need to turn in work late and do not have any flex days left, I will consider extensions based on legitimate reasons, which ONLY include verified illnesses and/or family emergencies. In these cases, you are encouraged to reach out to me at least 24 hours in advance of the due date.
- Work that is handed in late beyond the flex days or without an approved extension will automatically be graded on 90% of the original points if it is 12 hours late, 70% of the original points if it is between 12-24 hours late, and 50% of the original points if it is over 24 hours late.
- Using ONE flex day means you get a 24-hour extension. Please note that this is a strict extension and any work that is handed in beyond 24 hours after a flex day was requested will either need to use an additional flex day or will be graded based on the policy specified in Point 4 above.
- To request a flex day, you can send me an email within the 24-hour extension window, or simply leave a comment on your submitted assignment on Canvas.
Inclusion
I will do my very best to ensure that students from all backgrounds and perspectives receive equitable access and opportunity in this course, that students’ learning needs be addressed both in and out of class, and that the diversity students bring to this class is viewed as a resource, strength, and benefit. I hope to employ materials and engage in activities and dialogue that are respectful of different aspects of your identity.
Religious Holidays
No student is required to take an examination or fulfill other scheduled course requirements on recognized religious holidays. Students are expected to declare their intention to observe these holidays at the beginning of the semester.
Accommodations
Students with documented Bowdoin-granted accommodations have a right to have these met. I encourage you to see me in the first 2 weeks of class to discuss how your accommodations may support your learning process in this course. I highly encourage all students to meet with me in the first few weeks of class to discuss your learning preferences, challenges you may face learning this semester, and how we can create an effective learning experience for you. If you are interested in learning more about accommodations please see Lesley Levy in the Office of Student Accessibility or visit Bowdoin’s website on accessibility-related policies and resources.
Counseling Resources
As a student, you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may lead to diminished academic performance or reduced ability to participate in daily activities. Bowdoin College is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. You can learn more about the broad range of confidential mental health services available on campus at this link. The Dean of Students Office is also a resource for students facing personal and academic challenges. I encourage you to reach out to the amazing people in the dean’s office for a meeting anytime.
Learning Resources
The Baldwin Center for Learning and Teaching offers peer-to-peer resources including mentors, Q-Tutors, and Writing Assistants. If you or your family are multilingual, you may also take advantage of Lisa Flanagan to work on writing and speaking assignments and projects. Tina Chong is available as an Academic Coach to work with you on goal setting, managing time, study habits and other strategies to support academic success. You are encouraged to make an appointment and learn more about how the Center can support your learning.
Other resources
If you are on a budget or would benefit from financial assistance of any kind at any point in the semester, I encourage you to contact your Dean and explore the Supplemental and Emergency Funding website. The Office of the Dean of Students has grant and loan funds available to remove financial barriers so that students can more fully access the Bowdoin experience or to assist students with unexpected financial needs.
Mandated Reporter
As a faculty member, I am considered a Responsible Employee, per the Student Sexual Misconduct and Gender Based Violence Policy. As a Responsible Employee I am required to report disclosures of sexual misconduct, dating violence, stalking, and/or sexual and gender-based harassment to Bowdoin’s Title IX Coordinator, Kate O’Grady. My reporting does NOT mean that any actions will be taken beyond Kate reaching out to you and trying to schedule a time to talk to see what assistance you might need to be successful as a student here at Bowdoin. For more information please check out Bowdoin’s Title IX page.