Psychology 2811A 001
Statistics for Psychology 1
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
2022-2023
Revised Nov/22
Psychology 2811A - 001
Statistics for Psychology I
1.0 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, Psychology 2855F/G, Psychology 2856F/G, 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): At least 60% in 1.0 credits of Psychology at the 1000 level; Data Science 1000A/B and 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, or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Mathematics 1228A/B is recommended. In addition to completion of 1.0 Psychology 1000-level course, 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
2.0 COURSE INFORMATION:
Lecture (in person): Wednesdays, 11:30AM to 1:30PM
AHB-1R40
Lab (in person): Varies (see OWL)
(Lab sessions begin Wednesday 14 Sept, after the first lecture)
COURSE STAFF:
Instructor: Dr. Erin Heerey
Office: SSC 6322 (519-661-2111 ext. 86917)
Email: eheerey@uwo.ca
Office Hours: Wednesdays 2:00PM-3:00PM (Zoom; See OWL for link)
TAs: See OWL for details.
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. You may also contact Accessible Education at aew@uwo.ca or 519-661-2147.
3.0 TEXTBOOK
Cote, L. R., Gordon, R., Randell, C. E., Schmitt, J., & Marvin, H. (2021). Introduction to Statistics in the Psychological Sciences. Open Educational Resources Collection. 25. Available at: https://umsystem.pressbooks.pub/isps/
4.0 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.
4.1 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 |
Weekly homework; Exams
Weekly homework; Exams |
|
Application of Knowledge. Produce appropriate statistics to describe sample data.
Plot sampling distributions and graphs that show the relationships between continuous and categorical data. |
Lab activities
Lab activities |
Weekly homework; Exams
Weekly 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 |
Weekly homework; Statistics in the News Project; Exams
Weekly homework; Exams |
|
Application of Methodologies. Produce code to accurately calculate statistical tests and data visualizations.
|
Lectures; readings; lab activities |
Weekly homework; Exams
|
|
Demonstrate basic data wrangling skills including outlier exclusion, data cleaning and transformation. |
Lab activities
|
Weekly homework; Exams |
|
Awareness of Limits of Knowledge. Explain the strengths and weaknesses of null hypothesis significance testing. |
Lectures; readings |
Weekly homework; Statistics in the News Project; Exams |
5.0 EVALUATION
Weekly Homework 18%
Statistics in the News Project 15%
Midterm Exam 30%
Final Exam 37%
All elements of the coursework (including exams) are necessary for meeting the core learning outcomes of the course.
Weekly Homework (18%): Each week, you will complete a set of homework problems in a Jupyter Notebook. These will be based on the lecture material for the week. The Jupyter Notebook with the assignment will be released on OWL after lecture each Wednesday. It will be due 9 days later, on Friday at 5pm. You must upload the Notebook to the homework portal on OWL. Note that you may not submit the Notebook over email – the UWO email server will reject it because it is an executable file. There are a total of 10 homeworks that you will complete over the course of the term. I will drop your lowest homework score, which means that you can skip the homework once without penalty. Each of the remaining 9 homeworks will count toward 2% of your grade. The solution to the homework will be released on Monday at noon. If your homework has not been submitted before the solution is posted, you will receive a grade of 0.
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 be true. The statistic should also have a clear source citation (e.g., a research article, published in a scientific journal; a report from StatsCanada). You should then critically evaluate the claim, as well as the original source article, and interpret the news report. Write a 280-character Tweet that states your conclusion. Additional requirements and rubric are available in the assignment guidance on OWL.
Exams (67%): There will be two in-person, proctored exams in the course. The midterm will cover the course material from weeks 1-5. The final will be cumulative (weeks 1-13). Both exams will be open book/open note. You will complete the midterm exam on your laptop in the classroom. You will complete the Final Exam on pencil and paper. This means that you will NOT have access to OWL or the internet during the Final Exam (although you will for the midterm). The exams will include a set of multiple-choice/matching/fill in the blank questions, along with several short answer data visualization and interpretation questions. These will be available to you in a Jupyter Notebook (midterm only) or via pencil and paper (final exam only).
The midterm will be available during the regular lecture time. You will have 1 hour and 50 minutes to complete it. The final exam (date/time TBD) will be 3 hours long. You MUST take the exams in person (if you are an accommodated student, you must take the exam with the accommodated exams office). Attendance will be taken. If the course staff do not have a record of your presence in the exam room and you submit an exam that you have taken elsewhere, you will receive a grade of 0. The answers on the exam must be your own work and you must complete the exam independently. If there is evidence that you worked with another student on the exam or that the work is not your own, you will receive a score of 0 on the exam.
Extra Credit (OPTIONAL; up to 2% of the final grade): 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 sign up system and you may participate in any 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 2.0 SONA credits. 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 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. The maximum number of bonus credits you may earn is 2.0. For each credit you earn, you will receive an additional 1% in the gradebook. SONA credits must be completed by midnight on 7 December 2022 to count toward your grade.
5.1 POLICY ON MISSING COURSEWORK
Weekly Homework: Homework is due at 5pm on Friday evening each week (starting in week 3). The solution to the homework will be released on Monday at noon. For each 24-hour period (or portion thereof) that your homework is late until Monday at noon, it will incur a penalty of 0.5% (out of 2%). 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. The homework mark will automatically reflect the late penalty. If your homework has not been completed by the time the answer key is released, it will receive a score of 0. Because the homeworks are worth only 2% each and the lowest score is dropped, I will not accept any excuses for missed homeworks. If you miss a series of four or more homeworks in a row, due to a long-term illness or other issue of concern, please contact academic counselling in your home faculty with appropriate documentation to request relief. If academic counselling approves your request, the missed homework marks will be added to the weighting of final exam mark. This will make the final exam worth a larger proportion of the total mark.
Statistics in the News Project: The project will be due at 5pm on Wednesday 7 Dec. For each 24-hour period (or portion thereof) that your project is late, it will incur a penalty of 1.5% (out of 15%) up to a maximum of 36 hours. There is no need to email the course staff about your late project, as the submission portal will remain available for 36 hours after the project is due. After that point, the project will be assigned a score of 0, unless academic counselling in your home faculty approves a request for late submission. In the event that academic counselling approves a late submission request, please contact the course instructor to discuss a revised due date.
Exams: If you need to miss an exam due to illness or other issue, you MUST request relief from academic counselling. Without an approved accommodation from academic counselling, you will not be allowed to take the make-up exam. There will be one opportunity to make up each missed exam. The make-up exam for the MIDTERM will be SATURDAY, 19 November at 10:30am (room TBD). The make-up exam for the FINAL will be held on SATURDAY, 14 January 2023 at 10:30am (room TBD). Note that the make-up exams will include new test questions and datasets. They will assess content similar to the exams they replace.
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
Note that 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. To maximize your grade, do your best on each and every assessment within the course.
6.0 ASSESSMENT/EVALUATION SCHEDULE
Weekly Homework
Just after lecture each week, a new homework will be released on OWL, starting in Week 2. The Homework will be related to that week’s lecture and will be due on Friday at 5pm of the following week, starting in Week 3. You will upload your homework to the OWL portal.
Statistics in the News Project Wednesday, 7 December at 5pm
Midterm Exam Wednesday, 26 October (in class)
Final Exam December Exam Period (21 Dec 2022; 7pm-10pm; FEB GYM)
7.0 CLASS SCHEDULE
|
Class |
Lecture Topic |
Lab Topic |
Reading |
|
1 14 Sept |
Course introduction and descriptive statistics |
Introduction to Jupyter / Python; Descriptive statistics |
Chapters 1 - 6 |
|
2 21 Sept |
Sampling distributions Homework 1 Assigned |
Distributions and sampling |
Chapters 1 - 6 |
|
3 28 Sept |
Probability Homework 2 Assigned |
Probability Homework 1 Due |
Chapters 1 - 6 |
|
4 5 Oct |
Estimation, effect size and precision Homework 3 Assigned |
Estimating differences Homework 2 Due |
Chapters 1 - 6 |
|
5 12 Oct |
Null hypothesis significance testing (NHST) Homework 4 Assigned |
NHST basics and limitations Homework 3 Due |
Chapters 7 |
|
6 19 Oct |
Tests of association: Continuous data & Categorical data Homework 5 Assigned |
Simple Correlation & Chi-squared tests Homework 4 Due |
Chapters 12 & 14 |
|
7 26 Oct |
Midterm Exam (bring your laptop) Exam tests content from weeks 1-5 Homework 6 Assigned |
Statistics in the News Project Q&A Homework 5 Due |
No new reading |
|
8 2 Nov |
Reading week |
No Lab |
|
|
9 9 Nov |
Single sample tests Homework 7 Assigned |
Z-test, Logic of t-tests Homework 6 Due |
Chapters 4 & 8 |
|
10 16 Nov |
Two-sample tests Homework 8 Assigned |
Simple group comparisons Homework 7 Due |
Chapter 10 |
|
11 23 Nov |
One-way ANOVA Homework 9 Assigned |
Comparing multiple groups Homework 8 Due |
Chapter 11 |
|
12 30 Nov |
Correlated samples t-tests Homework 10 Assigned |
Non-independent data and paired samples t-test Homework 9 Due |
Chapter 9 |
|
13 7 Dec |
Exam review and open Q&A |
Review Homework 10 Due |
No new reading |
|
Wednesday 21 Dec
|
Final Exam (pencil and paper format) Exam tests content from weeks 1-12 |
Time: 7pm to 10pm Location: FEB GYM |
|
8.0 LAND ACKNOWLEDGEMENT
We acknowledge that Western University is located on the traditional lands of the Anishinaabek, Haudenosaunee, Lūnaapéewak and Attawandaron peoples, on lands connected with the London Township and Sombra Treaties of 1796 and the Dish with One Spoon Covenant Wampum.
With this, we respect the longstanding relationships that Indigenous Nations have to this land, as they are the original caretakers. We acknowledge historical and ongoing injustices that Indigenous Peoples (e.g., First Nations, Métis and Inuit) endure in Canada, and we accept responsibility as a public institution to contribute toward revealing and correcting miseducation, as well as renewing respectful relationships with Indigenous communities through our teaching, research and community service.
9.0 STATEMENT ON ACADEMIC OFFENCES
Students are responsible for understanding the nature and avoiding the occurrence of plagiarism and other scholastic offences. Plagiarism and cheating are considered very serious offences because they undermine the integrity of research and education. Actions constituting a scholastic offence are described at the following link: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/scholastic_discipline_undergrad.pdf
As of Sept. 1, 2009, the Department of Psychology will take the following steps to detect scholastic offences. All multiple-choice tests and exams will be checked for similarities in the pattern of responses using reliable software, and records will be made of student seating locations in all tests and exams. All written assignments will be submitted to TurnItIn, a service designed to detect and deter plagiarism by comparing written material to over 5 billion pages of content located on the Internet or in TurnItIn’s databases. 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 (http://www.turnitin.com).
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.
In classes that involve the use of a personal response system (PRS), data collected using the PRS will only be used in a manner consistent to that described in this outline. It is the instructor’s responsibility to make every effort to ensure that data remain confidential. However, students should be aware that as with all forms of electronic communication, privacy is not guaranteed. Your PRS login credentials are for your sole use only. Students attempting to use another student’s credentials to submit data through the PRS may be subject to academic misconduct proceedings.
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.
10.0 POLICY ON THE USE OF EXAM PROCTORING SOFTWARE
If a remote proctoring service is used, the service will require you to provide personal information (including some biometric data). The session will be recorded. In the event that in-person exams are unexpectedly canceled, you may only be given notice of the use of a proctoring service a short time in advance. More information about remote proctoring is available in the Online Proctoring Guidelines. Please ensure you are familiar with any proctoring service’s technical requirements before the exam. Additional guidance is available at the following link: https://www.uwo.ca/univsec/pdf/onlineproctorguidelines.pdf
* Please note that Zoom servers are located outside Canada. If you would prefer to use only your first name or a nickname to login to Zoom, please provide this information to the instructor in advance of the test or examination. See this link for technical requirements: https://support.zoom.us/hc/en-us
11.0 POLICY ON ACCOMMODATION FOR ILLNESS OR OTHER ABSENCES
Western’s policy on Accommodation for Medical Illness can be found at:
https://www.westerncalendar.uwo.ca/PolicyPages.cfm?PolicyCategoryID=1&Command=showCategory&SelectedCalendar=Live&ArchiveID=#Page_12
If you experience an extenuating circumstance (e.g., illness, injury) sufficiently significant to temporarily make you unable to meet academic requirements, you may request accommodation through the following routes:
- For medical absences, submitting a Student Medical Certificate (SMC) signed by a licensed medical or mental health practitioner in order to be eligible for Academic Consideration;
- For non-medical absences, submitting appropriate documentation (e.g., obituary, police report, accident report, court order, etc.) to Academic Counselling in their Faculty of registration in order to be eligible for academic consideration. Students are encouraged to contact their Academic Counselling unit to clarify what documentation is appropriate.
Students must see the Academic Counsellor and submit all required documentation in order to be approved for certain accommodation.
https://www.registrar.uwo.ca/faculty_academic_counselling.html
Students seeking academic consideration:
- are advised to consider carefully the implications of postponing tests or midterm exams or delaying handing in work;
- 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
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.
12.0 CONTINGENCY PLAN FOR RETURN TO LOCKDOWN: IN-PERSON & BLENDED CLASSES
In the event of a COVID-19 resurgence or any other event that necessitates the course delivery moving away from face-to-face interaction, all remaining course content will be delivered entirely online, either synchronously (i.e., at the times indicated in the timetable) or asynchronously (e.g., posted on OWL for students to view at their convenience). The grading scheme will not change. Any remaining assessments will also be conducted online, as determined by the course instructor.
13.0 STATEMENTS CONCERNING ONLINE ETIQUETTE
In courses involving online interactions, the Psychology Department expects students to honour the following rules of etiquette:
- please “arrive” to class on time
- please use your computer and/or laptop if possible (as opposed to a cell phone or tablet)
- please ensure that you are in a private location to protect the confidentiality of discussions in the event that a class discussion deals with sensitive or personal material
- to minimize background noise, kindly mute your microphone for the entire class until you are invited to speak, unless directed otherwise
- In classes larger than 30 participants please turn off your video camera for the entire class unless you are invited to speak
- In classes of 30 students or fewer, where video chat procedures are being used, please be prepared to turn your video camera off at the instructor’s request if the internet connection becomes unstable
- Unless invited by your instructor, do not share your screen in the meeting
The course instructor will act as moderator for the class and will deal with any questions from participants. To participate please consider the following:
- If you wish to speak, use the “raise hand” function and wait for the instructor to acknowledge you before beginning your comment or question.
- Please remember to unmute your microphone and turn on your video camera before speaking.
- Self-identify when speaking.
- Please remember to mute your mic and turn off your video camera after speaking (unless directed otherwise).
General considerations of “netiquette”:
- Keep in mind the different cultural and linguistic backgrounds of the students in the course.
- Be courteous toward the instructor, your colleagues, and authors whose work you are discussing.
- Be respectful of the diversity of viewpoints that you will encounter in the class and in your readings. The exchange of diverse ideas and opinions is part of the scholarly environment. “Flaming” is never appropriate.
- Be professional and scholarly in all online postings. Use proper grammar and spelling. Cite the ideas of others appropriately.
Note that disruptive behaviour of any type during online classes, including inappropriate use of the chat function, is unacceptable. Students found guilty of Zoom-bombing a class or of other serious online offenses may be subject to disciplinary measures under the Code of Student Conduct.
14.0 OTHER INFORMATION
Office of the Registrar: https://registrar.uwo.ca
Student Development Services: www.sdc.uwo.ca
Please see the Psychology Undergraduate web site/Current Student Information for information on the following:
- Policy on Cheating and Academic Misconduct
- Procedures for Appealing Academic Evaluations
- Policy on Attendance
- Policy Regarding Makeup Exams and Extensions of Deadlines
- Policy for Assignments
- Short Absences
- Extended Absences
- Documentation
- Academic Concerns
- Calendar References
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 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.
Policy on the Recording of Synchronous Sessions: Some or all of the remote learning sessions for this course (if scheduled) may be recorded. The data captured during these recordings may include your image, voice recordings, chat logs and personal identifiers (name displayed on the screen). The recordings will be used for educational purposes related to this course, including evaluations. The recordings may be disclosed to other individuals participating in the course for their private or group study purposes. Please contact the instructor if you have any concerns related to session recordings. Participants in this course are not permitted to privately record the sessions, except where recording is an approved accommodation, or the student has the prior written permission of the instructor.
