Psychology 2811A 200 FW24
Statistics for Psychology I
If there is a discrepancy between the outline posted below and the outline posted on the OWL course website, the latter shall prevail.
WESTERN UNIVERSITY
LONDON CANADA
Department of Psychology
Fall 2024
Psychology 2811A - 200
Statistics for Psychology I
1 CALENDAR DESCRIPTION
This course introduces students to the basics of data analysis for psychological research. Topics include probability, sampling, estimation, data visualization, and the conduct and interpretation of basic statistical analyses. Throughout the term, students will gain experience in computer-based data analytic methods.
Antirequisite(s): Biology 2244A/B, Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B, MOS 2242A/B, the former Psychology 2810, the former Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Statistical Sciences 2244A/B, Statistical Sciences 2858A/B.
Antirequisites are courses that overlap sufficiently in content that only one can be taken for credit. If you take a course that is an antirequisite to a course previously taken, you will lose credit for the earlier course, regardless of the grade achieved in the most recent course.
Prerequisite(s): Prerequisite(s): At least 60% in 1.0 credits of Psychology at the 1000 level; a passing grade (i.e., at least 50%) in Data Science 1000A/B and a passing grade (i.e., at least 50%) in 0.5 credit of Year 1 Math from among the following courses: Calculus 1000A/B, Calculus 1301A/B, Calculus 1500A/B, Calculus 1501A/B, Mathematics 1225A/B, Mathematics 1228A/B, Mathematics 1229A/B, Mathematics 1600A/B, or Applied Mathematics 1201A/B. Students enrolled in Year 2 of an Honours Specialization in Neuroscience may enrol with 0.5 credit of Applied Mathematics 1201A/B and 0.5 credit of Computer Science 1026A/B. Students who have completed Statistical Sciences 1024A/B (or other introductory statistics course, in addition to 0.5 credit of Year 1 Math) may enrol after completing an introductory programming class from the following list: Computer Science 1025A/B, Computer Science 1026A/B, Computer Science 2120A/B, Data Science 1200A/B, Digital Humanities 2220A/B, or Engineering Science 1036A/B. Data Science 2000A/B may be substituted for Data Science 1000A/B for students entering the program with 1.0 Year 1 Math courses.
Unless you have either the prerequisites for this course or written special permission from your Dean to enrol in it, you may be removed from this course and it will be deleted from your record. This decision may not be appealed. You will receive no adjustment to your fees in the event that you are dropped from a course for failing to have the necessary prerequisites.
2 lecture hours and 2 laboratory hours, 0.5 course
2 COURSE INFORMATION:
Lecture (in person): see Student Centre Time Table
Lab (Online/Asynchronous): New labs will be posted at 9am Monday every second week (see course schedule below)
COURSE STAFF:
Instructor: Dr. Erin Heerey
Phone: 519-661-2111 ext. 86917
Email: eheerey@uwo.ca
Office Hours: TBD; Zoom (see link on OWL; passcode: 2811a)
TAs: See information on OWL for names, email addresses and office hours.
For courses that include an online component, students must have a reliable internet connection and computer that are compatible with online learning system requirements.
3 COURSE MATERIALS
There is no specific textbook for this course. Instead, readings will be drawn from a number of sources – mainly online textbooks but sometimes blog posts and other resources. All of these sources are freely available online. The links for each reading appear in the course reading list.
4 COURSE OBJECTIVES
The aim of this course is to develop students’ basic data literacy skills by learning to use a data-driven approach to think critically about data. Students will develop statistical knowledge via sampling data from real and simulated datasets, visualizing their results, testing for relationships in their data, and interpreting the patterns they see. The class will extend basic data science training by teaching students to code their own statistical tests and visualizations in Python.
STUDENT LEARNING OUTCOMES
|
Learning Outcome |
Learning Activity |
Assessment |
|
Depth and Breadth of Knowledge. Demonstrate basic knowledge of probability as it applies to sampling.
Describe the logic and basic elements of null hypothesis significance testing. |
Lectures; readings; lab activities
Lectures; readings; lab activities |
Homework; Exams
Homework; Exams |
|
Application of Knowledge. Produce appropriate statistics to describe sample data.
Plot sampling distributions and graphs that show the relationships between different types of variables. |
Lab activities
Lab activities |
Homework; Exams
Homework; Exams
|
|
Interpret both graphical and statistical evidence to make conclusions about data.
Recognize from data and/or study design descriptions which statistical tests should be used. |
Lectures; readings; lab activities
Lectures; readings; lab activities |
Homework; Statistics in the News Project; Exams
Homework; Exams |
|
Application of Methodologies. Produce code in Jupyter Notebook to calculate statistical tests and data visualizations.
|
Lectures; readings; lab activities |
Homework; Exams
|
|
Demonstrate basic data wrangling skills including outlier exclusion, data cleaning and transformation. |
Lab activities
|
Homework; Exams |
|
Awareness of Limits of Knowledge. Explain the strengths and weaknesses of null hypothesis significance testing. |
Lectures; readings |
Homework; Statistics in the News Project; Exams |
5 EVALUATION
Lab/Homework Assignments 15%
Statistics in the News Project 15%
Midterm Exam 32%
Final Exam 38%
The evaluation and testing formats for this course were created to assess the learning objectives as listed in section 4 and are necessary for meeting these learning objectives.
Bi-weekly Lab/Homework Assignments (15%):
*** This assessment has flexible deadlines. It is exempt from the academic considerations policy. ***
For each lesson, you will complete a set of lab and homework problems in a Jupyter Notebook. The lab elements will be guided by video tutorial. The homework problems you will do on your own. The homework problems will be based on the lecture material for the lesson and will also relate to the corresponding lab material. The Jupyter Notebook with the lab/homework assignment will be released on OWL on the same day as the video tutorial it corresponds with (Mondays of the release week at 9am). It will be due 12 days later, on Friday at 5:00pm. You must upload the Notebook (‘.ipynb’ extension) to the assignment portal on Gradescope. You are responsible for uploading the correct file, in the correct format to the correct portal on Gradescope. If you upload the file incorrectly, you will receive a mark of 0. There are a total of 6 assignments that you will complete over the course of the term. I will drop your lowest score, which means that you can skip one assignment without penalty. Each of the remaining 5 assignments will count toward 3% of your grade. The solution to the assignment will be released the Monday after the assignment is due at noon. If your assignment has not been submitted before the solution is posted, you will receive a grade of 0. There will be absolutely no exceptions to this policy.
Statistics in the News Project (15%):
We frequently see statistics reported in the news. But are they noteworthy? Or not worthy of the space they take up? The goal of this assignment is to critically evaluate a statistical claim reported in a media outlet. You should select a statistic that is interesting to you but that sounds a bit too good/weird/unusual/outlandish be true. The statistic should also have a clear source citation (e.g., a research article published in a scientific journal, upon which the news story is based). You should then critically evaluate the claim, as well as the original source article, using evidence from both sources. Write a 280-character “Tweet” style report that states your conclusions about the news article, relative to your evaluation of the source article. Additional details and rubric are available in the resources section on OWL.
Exams (70%): There will be two proctored exams in the course. These exams will take place in person. The midterm will cover the course material from weeks 1-5. The final will be cumulative (weeks 1-12).
*** Final Exams are always exempt from the academic considerations policy. ***
*** The Midterm Exam is the designated course component that is exempt from the academic considerations policy. ***
Both exams will be closed book/closed note. No calculators or other devices will be allowed. You will be allowed one “cheat sheet” of notes for the midterm and two cheat sheets for the final exam. Your cheat-sheet(s) may include any course material that you think will help you on the relevant exam and you may use both the front and back sides of each paper. Each cheat sheet may not include more than a single piece of “letter” sized paper (no stapled, glued, or taped elements are allowed) and all your notes must be entirely handwritten (no photocopies, pictures, or printouts will be allowed). Your cheat sheet(s) will be checked by the proctors. If they are found to be in violation of the requirements the proctors will confiscate them during the exam. The exams will include multiple-choice/select all that apply/matching/fill in the blank questions, along with several short answer, and graph/code interpretation questions.
The midterm will be completed during class time on Wednesday, 30 October (11:30am to 1:30pm). You will have the class period to complete it. The final exam will be scheduled by the registrar during the December exam period. It will be 3 hours long. If you are an accommodated student, your time will be adjusted according to the time listed in your official accommodation if and only if you request an exam via the accessible education office. You MUST take the exams independently. The answers on the exam must be entirely your own work. If there is evidence that you worked with another student on the exam or that the work is not entirely your own, you will receive a score of 0.
Extra Credit (OPTIONAL; up to +3%): Statistics is a discipline that relies on the analysis of empirical data. You have the chance to participate in this process by helping to generate research data. To take part, you will be given access to the SONA system, and you may participate in any “for credit” studies that you wish. You will receive bonus credits added to your overall course grade for each SONA credit you earn, to a maximum of 3.0 SONA credits (50% of these credits must be earned in in-person, rather than online studies). However, the bonus will only be added if you have achieved a passing course grade without any bonus credit – in other words before bonus credits are added you must get at least 50% on the regular coursework/exams. Note that if you sign up for a study and then fail to attend, you will receive a penalty equal to the number of study-credits the original study was worth. This penalty will count against your earned credits until it is made up.
The SONA system will track the studies you complete, and I will be given this information at the end of the term. No grade adjustments will appear until after the final grade has been calculated. This is an opportunity to earn extra credits and is not required as part of your normal grade, you will not lose any marks if you do not participate in research studies. If you wish to earn extra credit but do not wish to participate in studies, more information about an alternative assignment will be available on OWL. The maximum number of bonus credits you may earn is 3.0. For each credit you earn, you will receive an additional 1% in the gradebook. All extra credits must be completed by 11:55AM (just before noon) on the last day of term to count toward your grade. Because this item is extra credit and will never count against you, there will be absolutely no exceptions to this deadline. *** Because this is not an official assessment and is not required, it is exempt from the academic considerations policy. ***
POLICY ON MISSING COURSEWORK
Weekly Lab/Homework Assignments: Assignments are due at 5:00pm on Friday evening of the week they are due (see schedule below). The solution to the assignment will be released the following Monday at noon. The grace period for assignment submission is until Monday at noon. Please ensure that you give yourself enough time to complete your submission by this time. There is no need to email the course staff about late homework, as the submission portal will remain available until the answer key is released (Monday of the relevant week at noon). If your assignment has not been completed and correctly uploaded by the time the answer key is released, it will receive a score of 0. Because the assignments are worth only 3% each, there is a grace period between the due date and assignment submission portal closure, and the lowest score is dropped, I will not accept any excuses for missed assignments. If you have a long-term illness or other issue of concern, please contact academic counselling in your home faculty with appropriate documentation to request relief.
Statistics in the News Project: The project will be due at 11:55pm on Wednesday 4 December. If you wish to attest to an illness or other extenuating circumstance, you may do so using the appropriate reporting portal. If you attest to an illness or other short-term extenuating circumstance, your assignment will be due 24 hours after the deadline to make an attestation. With a signed attestation, the project will be due on Saturday, 7 December at 11:55pm. You must upload the completed assignment to the assignment portal before that deadline or you will receive a score of 0.
Exams: If you need to miss an exam due to illness or other issue, you MUST request relief from academic counselling. Without an approved consideration from academic counselling, you will receive a score of 0 the exam. There will be one opportunity to make up the final exam. The make-up final exam will be held on a date and time in January that will be announced in class. Note that the make-up exam may include new test questions. The practical component of the make-up exam will include a Jupyter notebook that you will complete on your own computer. Note that if you miss the make-up exam, your next opportunity to take the final exam will be during the finals period the next time the course is offered. You must have the approval of the Associate Dean (Undergraduate) to use this option.
You will NOT have an opportunity to make up the midterm exam. Instead, if you have an approved consideration for the midterm, you will receive a midterm score based on the items on the final exam that cover the same content as the midterm. Your proportion correct on these items will be used to calculate a midterm score for you. Your final exam score will then be calculated based on the proportion of items you get correct that cover content from the second part of the course.
The expectation for course grades within the Psychology Department is that they will be distributed around the following averages:
70% 1000-level to 2099-level courses
72% 2100-2999-level courses
75% 3000-level courses
80% 4000-level courses
The Psychology Department follows Western’s grading guidelines, which are as follows (see: https://www.uwo.ca/univsec/pdf/academic_policies/general/grades_undergrad.pdf
A+ 90-100 One could scarcely expect better from a student at this level
A 80-89 Superior work that is clearly above average
B 70-79 Good work, meeting all requirements, and eminently satisfactory
C 60-69 Competent work, meeting requirements
D 50-59 Fair work, minimally acceptable
F below 50 Fail
In the event that course grades are significantly higher or lower than these averages, instructors may be required to make adjustments to course grades. Such adjustment might include the normalization of one or more course components and/or the re-weighting of various course components.
Policy on Grade Rounding
Please note that although course grades within the Psychology Department are rounded to the nearest whole number, no further grade rounding will be done. No additional assignments will be offered to enhance a final grade; nor will requests to change a grade because it is needed for a future program be considered.
6 ASSESSMENT/EVALUATION SCHEDULE
Bi-Weekly Lab/Homework Assignments
On Monday every other week, a new lesson, including a lab/homework assignment will be released on OWL, starting in Week 1. The assignment will be related to that week’s lesson and will be due 12 days later on Friday at 5:00pm. You will upload your homework to the Gradescope portal.
Statistics in the News Project Wednesday, 4 December at 11:55pm
Midterm Exam Wednesday, 30 October at 11:30am
Final Exam TBA (December Exam Period)
7 CLASS SCHEDULE
|
Class |
Lecture Topic |
Lab Topic |
|
1 11 Sept |
Course introduction and descriptive statistics |
Introduction to Jupyter / Python; Descriptive statistics Lab/Homework 1 Assigned (Monday) |
|
2 18 Sept |
Sampling distributions |
Lab/Homework 1 Due (Friday) |
|
3 25 Sept |
Probability |
Distributions and sampling; Probability Lab/Homework 2 Assigned (Monday) |
|
4 2 Oct |
Estimation, effect size and precision |
Lab/Homework 2 Due (Friday) |
|
5 9 Oct |
Null hypothesis significance testing (NHST) |
Estimating differences; NHST basics and limitations Lab/Homework 3 Assigned (Monday) |
|
6 16 Oct |
Reading week |
No Lab |
|
7 23 Oct |
Exam Review & Tests of association: Continuous data & Categorical data |
Review Stats in the News Assignment Guidelines Lab/Homework 3 Due (Friday) |
|
8 30 Oct |
Midterm (pencil and paper format) Exam tests content from weeks 1-5 |
Simple Correlation/Regression & Chi-squared tests Lab/Homework 4 Assigned (Monday) |
|
9 6 Nov |
Single sample tests |
Lab/Homework 4 Due (Friday) |
|
10 13 Nov |
Two-sample tests |
Z-tests, t-tests; Simple group comparisons Lab/Homework 5 Assigned (Monday) |
|
11 20 Nov |
One-way ANOVA |
Lab/Homework 5 Due (Friday) |
|
12 27 Nov |
Correlated samples tests |
Comparing multiple groups; Non-independent data Lab/Homework 6 Assigned (Monday) |
|
13 4 Dec |
Exam review and open Q&A |
Lab/Homework 6 Due (Friday) |
|
Exam Period |
Final Exam (pencil and paper format) Exam tests content from weeks 1-12 |
Time: TBA Location: TBA |
8 Academic Integrity
Scholastic offences are taken seriously, and students are directed to read the appropriate policy, specifically, the definition of what constitutes a Scholastic Offence, at the following Web site: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/scholastic_discipline_undergrad.pdf.
Possible penalties for a scholastic offence include failure of the assignment/exam, failure of the course, suspension from the University, and expulsion from the University.
Statement on Use of Electronic Devices
Electronic devices are allowed during ordinary class periods. Electronic devices (all types) may not be used during examinations.
Plagiarism Detection Software
All required papers may be subject to submission for textual similarity review to the commercial plagiarism detection software under license to the University for the detection of plagiarism. All papers submitted for such checking will be included as source documents in the reference database for the purpose of detecting plagiarism of papers subsequently submitted to the system. Use of the service is subject to the licensing agreement, currently between Western and Turnitin.com.
Use of AI
The use of generative AI tools such as ChatGPT to produce written work is not permitted unless permission is granted by the instructor for specific circumstances. Any work submitted must be the work of the student in its entirety unless otherwise disclosed. When used, AI tools should be used ethically and responsibly, and students must cite or credit the tools used in line with the expectation to use AI as a tool to learn, not to produce content.
Multiple Choice Exams
Computer-marked multiple-choice tests and/or exams will be subject to submission for similarity review by software that will check for unusual coincidences in answer patterns that may indicate cheating.
9 Academic Accommodations and Accessible Education
View Western’s policy on academic accommodations for student with disabilities at this link.
Accessible Education provides supports and services to students with disabilities at Western.
If you think you may qualify for ongoing accommodation that will be recognized in all your courses, visit Accessible Education for more information. Email: aew@uwo.ca Phone: 519 661-2147
10 Absence & Academic Consideration
View Western’s policy on academic consideration for medical illnesses this link
Find your academic counsellor here: https://www.registrar.uwo.ca/faculty_academic_counselling.html
Students must see the Academic Counsellor and submit all required documentation in order to be approved for certain academic considerations. Students must communicate with their instructors no later than 24 hours after the end of the period covered SMC, or immediately upon their return following a documented absence.
Medical Absences
Submit a Student Medical Certificate (SMC) signed by a licensed medical or mental health practitioner to Academic Counselling in your Faculty of registration to be eligible for Academic Consideration.
Nonmedical Absences
Submit appropriate documentation (e.g., obituary, police report, accident report, court order, etc.) to Academic Counselling in your Faculty of registration to be eligible for academic consideration. Students are encouraged to contact their Academic Counselling unit to clarify what documentation is appropriate.
Religious Consideration
Students seeking accommodation for religious purposes are advised to contact Academic Counselling at least three weeks prior to the religious event and as soon as possible after the start of the term.
11 Other Information
- Office of the Registrar: https://registrar.uwo.ca
- Student Development Services: sdc.uwo.ca
- Psychology Undergraduate Program: https://www.psychology.uwo.ca/undergraduate/index.html
Students who are in emotional/mental distress should refer to Health and Wellness@Western https://www.uwo.ca/health/ for a complete list of options about how to obtain help.
Please contact the course instructor if you require material in an alternate format or if you require any other arrangements to make this course more accessible to you.
If you wish to appeal a grade, please read the policy documentation at: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/appealsundergrad.pdf. Please first contact the course instructor. If your issue is not resolved, you may make your appeal in writing to the Undergraduate Chair in Psychology (psyugrd@uwo.ca).
Copyright Statement
Lectures and course materials, including power point presentations, outlines, videos and similar materials, are protected by copyright. You may take notes and make copies of course materials for your own educational use. You may not record lectures, reproduce (or allow others to reproduce), post or distribute any course materials publicly and/or for commercial purposes without the instructor’s written consent.
12 Land Acknowledgement
We acknowledge that Western University is located on the traditional territories of the Anishinaabek, Haudenosaunee, Lūnaapéewak, and Chonnonton. Nations, on lands connected with the London Township and Sombra Treaties of 1796 and the Dish with One Spoon Covenant Wampum. This land continues to be home to diverse Indigenous Peoples (First Nations, Métis and Inuit) whom we recognize as contemporary stewards of the land and vital contributors of our society.
