Psychology 2812B 001 FW24

 Statistics for Psychology II

Western University

London                   Canada

Department of Psychology

Winter 2024

 

Psychology 2812B – Section 001

Statistics for Psychology II

 

1.0     Calendar Description

 

In this course, students learn advanced data analytic techniques for psychological research. Topics include advanced analyses within the general linear model (GLM), e.g., multiple and logistic regression, as well as special applications of the GLM such as ANOVA. Students continue to gain experience in computer-based analytic methods and coding techniques.

 

Antirequisites: the former Psychology 2810; the former Psychology 2820E.

 

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.

 

Prerequisites: Prerequisite(s): At least 70% in Psychology 2811A/B; at least 60% in Data Science 1000A/B and at least 60% 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, or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Math 1228A/B is recommended. Students who have completed Statistics 1024A/B (or other Year 1 introductory statistics course in addition to 0.5 credit of Year 1 Math) instead of Data Science 1000A/B 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 credits of Year 1 Math courses.

 

 

2 lecture hours; 2 laboratory hours; Course Weight: 0.5

 

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.

 

2.0      Course Information

 

Instructor:                                          Dr. Tyler Pattenden (he/they)

Office & Phone:                                  tba

Office Hours:                                      tba

Email:                                                  tpattend@uwo.ca

 

Teaching Assistant:                           tba (see Brightspace course site)

 

Time and Location of Classes:          Lectures:        (see Student Centre website)

                                                            Labs:               varies (see Brightspace course site)

 

Students who are in emotional/mental distress should refer to Health and Wellness @Western https://www.uwo.ca/health/ for 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 arrangements to make this course more accessible to you.  You may also contact Accessible Education at aew@uwo.ca or 519-661-2147.

 

 

2.1 Online Learning Notice

Please note: For courses delivered in an online format, include an online component, or are required to pivot online, students must have a reliable internet connection and computer that are compatible with online learning system requirements. Some courses may also require the use of a remote proctoring platform to ensure assessments are taken fairly in accordance with Western’s policy on Scholastic Discipline for Undergraduate Students and Scholastic Discipline for Graduate Students. Please refer to the course syllabus for further information.

 

3.0      Course Materials

 

We will be using freely available web-based resources. Please check Brightspace course site for a complete list.

 

4.0      Course Objectives and Learning Outcomes

 

The aim of this course is to advance students’ statistical acumen by focusing on a family of analytic techniques that rely on the General Linear Model (GLM). Students will develop statistical knowledge by coding a variety of statistical tests, interpreting test output, and visualizing their results. They will enhance their data science skills by learning to use advanced data visualization toolkits and code in R and RStudio, a free software environment for statistical computing and graphics.

 

Learning Outcome

Learning Activity

Assessment

Depth and Breadth of Knowledge

·         Demonstrate knowledge of various uses of the GLM in hypothesis testing

Lectures; readings; lab activities

Weekly homework; exams

Knowledge of Methodologies

 

·         Produce code to accurately calculate statistical tests and generate data visualisations

·         Understand the implications of statistical assumptions in hypothesis testing

·         Demonstrate advanced data wrangling skills by compiling complex data sets

Lectures; readings; lab activities

 

 

Lectures; lab activities

 

 

Lab activities

Weekly homework; exams

 

 

Weekly homework; exams

 

Weekly homework; exams

Application of Knowledge

 

·         Interpret both graphical and statistical evidence to understand data and make conclusions

·         Recognize from data and/or study design descriptions which statistical tests should be used

Lectures; readings; lab activities

 

 

Lectures; readings; lab activities

 

Weekly homework; exams

 

 

Weekly homework; exams

Awareness of Limits of Knowledge

 

·         Explain the strengths and limitations of the GLM in statistical decision-making

Lectures; readings

Weekly homework; exams

5.0      Evaluation

 

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

 

The course requirements, along with relative weightings in the determination of final grades, are:

 

            iClicker Participation (each class)                                          5%

Weekly Homework (10 equally weighted)                          20%

            Midterm Exam (in-class; February 26th)                              35%

            Final Exam (scheduled by registrar)                                    40%

 

Comments on evaluations:

 

iClicker Participation (5%):  Each class will end with a short multiple-choice question in regards to the content presented in-class that day.  You will be graded on your participation in the question, rather than correctness. However, it is recommended that you try to get the correct answer.  These questions will be used to gauge both attendance and understanding.  To earn the full 5%, you must participate in at least 80% of the iClicker questions (meaning, you can miss a few classes without penalty). If you do not participate in at least 80% of the questions, then your score will be calculated by the equation
, where  is the proportion of questions you did participate in.

 

Weekly Homework (20%): Each week you will complete a set of homework problems in an RStudio notebook. These will be based on the lecture material for the week. The RStudio notebook with the assignment will be released on OWL after lecture each Wednesday. It will be due 9 days later, on Friday at 5pm. The final homework will be due April 5, 2024. April 4, 2025 You must upload the notebook file (.Rmd file) to the homework portal on OWL. There are a total of 10 homework assignments in the course (2% each). The solution to the homework assignments will be posted on Mondays at 12:00 pm. If your homework has not been submitted before the solution is posted, you will receive a grade of zero.

 

Exams (75% total): There will be two in-person, proctored exams in the course. The midterm will cover the course material in weeks 1–5. The final exam will be cumulative based on all course material from weeks 1-13. The exams will include multiple-choice/matching/fill-in-the-blank questions, and short answer questions assessing your understanding of key concepts, as well as your ability to perform data visualization and statistical tests and interpret them. These will be similar to the weekly homework assignments.

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 to be announced) 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 zero. 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 grade of zero on the exam.

You will be allowed one “cheat sheet” of notes for the midterm and one cheat sheet for the final exam. Your cheat-sheet 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. Your cheat sheet may not include more than a single piece of letter-sized paper and all your notes must be entirely handwritten. Your cheat sheet 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.  No other resources may be used in the exams. Calculators are not required nor permitted.

 

5.1      Policy on Missing Coursework

 

iClicker Participation: Due to the flexibility of the marking scheme, there are no make-up participation questions. Accommodation is already considered in the marking scheme.

 

Weekly Homework: Homework is due at 5:00 pm on Friday evening each week (starting in week 2). The final homework will be due April 5, 2024. April 4, 2025 The solution to the homework will be released the following Monday at 12:00 pm. For each 24-hour period (or portion thereof) that your homework is late until Monday at 12:00 pm, 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. If you miss several homework assignments 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 the final exam. This will make the final exam worth a larger proportion of the total mark.

 

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 after the Registrar scheduled April examination period – date and time to be announced. Note that the make-up exam may include new test questions and may be in a different format from the original exam. 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 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 Psychology Department follows Western’s grading guidelines:  https://www.uwo.ca/univsec/pdf/academic_policies/general/grades_undergrad.pdf

 

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

 

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.

 

6.0      Assessment/Evaluation Schedule

 

iClicker:                         During each lecture time one (1) participation question will be presented.

 

Weekly Homework:    After lecture each week a new homework will be released on Brightspace. The homework will be related to that week’s lecture and will be due on Friday at 6pm of the following week. The final homework will be due April 4th. You will upload your homework to the Brightspace course site.

 

Midterm Exam:             February 26th, 2025 (in class)

 

Final Exam:                   April exam period (scheduled by Registrar)

                                        

 

7.0      Class Schedule

 

Week

Date

Topic

Homework (due)

1

Jan 8th

RStudio Intro & Data Visualisation with ggplot2

HW 1 (Jan 17th)

2

Jan 15th

Data wrangling with dplyr

HW 2 (Jan 24th)

3

Jan 22nd

Correlation review & Bivariate Linear Regression

HW 3 (Jan 31st)

4

Jan 29th

Multiple Regression

HW 4 (Feb 7th)

5

Feb 5th

Logistic Regression & Non-Linear Regression

HW 5 (Feb 14th)

6

Feb 12th

One-Way ANOVA & The GLM

HW 6 (Feb 28th)

7

Feb 19th

Reading Week (no class)

--

8

Feb 26th

Midterm Exam (in-class)

--

9

Mar 5th

One-Way ANOVA: follow-up tests and statistical power

HW 7 (Mar 14th)

10

Mar 12th

ANCOVA

HW 8 (Mar 21st)

11

Mar 19th

Factorial ANOVA: main effects and interactions

HW 9 (Mar 28th)

12

Mar 26th

Factorial ANOVA: follow-up tests

HW 10 (Apr 4th)

13

Apr 3rd

Repeated Measures ANOVA

--

--

TBA

Final Exam (scheduled by Registrar)

--

 

            Note that this schedule is subject to change with sufficient notice by the instructor.

 

 

 

 

 

 

 

 

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      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.

 

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.

 

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

 

Students are encouraged to bring their personal computers to all class activities (lectures and labs).  No electronic devices will be permitted 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.

 

Exam Proctoring Software

 

Tests and examinations for online courses may be conducted using a remote proctoring service. More information about this remote proctoring service, including technical requirements, is available on Western’s Remote

Proctoring website at: https://remoteproctoring.uwo.ca.

 

Personal Response Systems (“Clickers”)

 

In classes that involve the use of a personal response system, data collected 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.

 

10.0   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

 

11.0   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

 

 

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.

 

 

 

 

VERSION: DEC 11, 2024