Psychology 3801G 001 FW24

Statistics for Psychology III

Western University

London                   Canada

 

Department of Psychology

Winter 2024

 

Psychology 3801G    Section 001

 

Statistics for Psychology III

 

 

1     Calendar Description

 

This course extends beyond traditional single-sample datasets. Students work with data on a larger scale by examining population data and implementing basic meta-analyses using a modern coding language. In addition, students extend their knowledge of statistical decision-making by learning to apply basic Bayesian models of statistical decision-making. Antirequisite(s): the former Psychology 3800F/G. Extra Information: 2 lecture hours and 2 laboratory hours.

 

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.

 

RESTRICTED TO MAIN CAMPUS HONS SPECIALIZATION IN PSYCH, DEVELOPMENTAL COGNITIVE NEUROSCIENCE, AND ANIMAL BEHAVIOUR.

 

WAIT LIST OPTION AVAILABLE.

 

REQUISITES: Prerequisite(s): At least 75% in Psychology 2802F/G and Psychology 2812A/B, plus registration in third or fourth year Honours Specialization in Psychology or Honours Specialization in Developmental Cognitive Neuroscience, or Honours Specialization in Animal Behaviour.

 

 

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     Course Information

 

Instructor: J Bruce Morton                

Office: see Brightspace                                       

Office Hours: By appointment                        

Email: bmorton3@uwo.ca 

 

Teaching Assistant 1: Niki Sinha     

Office: see Brightspace

Office Hours: By appointment         

Email:  nsinha7@uwo.ca      

 

Teaching Assistant 2: Ramkumar Jagadeesan

Office Hours: By appointment         

Email:  rjagadee@uwo.ca

 

 

Teaching Assistant 3: David Mekhaiel        

Office Hours: By appointment         

Email:  dmekhaie@uwo.ca

  

 

Time and Location of Classes and Tutorials: See Student Centre or Brightspace  

 

 

3     Course Materials

 

Lecture slides for PSY3801 are available in the Resources folder of the OWL Brightspace website for the course. 

 

Required readings for PSY3801 are available in the Resources folder of the OWL Brightspace website for the course.

 

Software for PSY3801 is R. Required packages are available on the internet for free and can be installed within R-studio.

 

Assignments will be posted and submitted on Brightspace.

 

Quizzes will be posted and submitted on Brightspace.

 

 

4     Course Objectives and Learning Outcomes

 

The primary goal of PSY3801 is to provide students with logical and statistical skills required to critically evaluate data and research literature. It will achieve this goal by providing students with an in-depth introduction to two advanced statistical procedures: Bayesian analysis and meta-analysis. Through lectures, tutorials, readings, and homework assignments, students will learn the underlying rationale for both procedures, work with sample data sets in R to acquire a basic mastery of core techniques, learn basic diagnostic procedures, and learn to report the results of their analyses in written text and basic tables. Upon completing the course, students will: understand the difference between null hypothesis significance testing (NHST) and Bayesian inference; be capable of critically evaluating research literature by identifying uncertainties surrounding prevailing psychological theories and detecting biases in the reporting of evidence; and be capable of interpreting and producing written reports of Bayesian and meta-analytical statistical analyses. These skills will help students recognize the limits of scientific knowledge.

 

After successfully completing this course, students should be able to:  

 

Learning Outcome

Learning Activity

Assessment

Depth and Breadth of Knowledge.

  • Understand the difference between null hypothesis significance testing and Bayesian inference
  • Identify and evaluate discrepant findings in research literature

Lectures

 

Readings

 

Assignments

 

Quizzes

 

Exams

Knowledge of Methodologies.

  • Read and use R scripts for implementing basic Bayesian procedures
  • Read and use R scripts for implementing basic meta-analytic procedures

 

Lectures

Readings

Coding and analysis assignments

Assignments

 

Quizzes

 

Exams

Application of Knowledge.

  • Apply Bayesian analysis to sample data sets
  • Apply meta-analysis to sample data sets

Coding and analysis assignments

Assignments

 

Quizzes

 

Exams

Communication Skills.

  • Read and write a report of a Bayesian analysis
  • Read and write a report of a meta-analysis

Readings

Coding and analysis assignments

Assignments

 

Quizzes

 

Exams

Awareness of Limits of Knowledge.

  • Understand the Bayesian interpretation of probability as an index of credibility
  • Recognize uncertainty surrounding prevailing psychological theories

 

Lectures

Readings

 

Assignments

 

Quizzes

 

Exams

Autonomy and Professional Capacity.

  • Apply knowledge responsibly

Lectures

 

 

 

5     Evaluation & Policy on Missing Coursework 

 

The evaluation and testing formats for this course were created to assess the learning objectives as listed in section 4.0 and are considered necessary for meeting these learning objectives. The evaluation components of the course consist of the following: 

 

Assignments: 4 x 1.25 = 5% of final mark

Quizzes: 3 x 10 = 30% of final mark

Midterm = 32.5% of final mark

Final exam = 32.5% of final mark

 

Assignments help students learn lecture materials through practicing sample problems. Therefore, due dates for and grading of the Assignments are flexible. Students have 9 days to complete each Assignment but can take up to an additional week to submit their work. As well, students receive 1.25 marks for each submitted assignment regardless of the correctness of their work. Please note that because the submission deadline for Assignments already includes flexibility in the form of a 1-week grace period, the instructor reserves the right to deny academic consideration for assignments which are submitted following the end of the period of flexibility. Late Assignments without academic consideration following the period designated above will be subject to a late penalty of 5%/day.  

 

 

Quizzes are designed to help students track their mastery of concepts and procedures and to familiarize them with the testing procedures used in the course. Because workload demands can vary through the term, students are not expected to write or be fully prepared for all 4 quizzes. Therefore, the final quiz mark will be based on the top 3 of the 4 quiz marks. Please note, because not all elements of this assessment (i.e., the final Quiz mark is based on the top 3 of 4 administered quizzes) are required in the calculation of the final course grade, the instructor reserves the right to deny academic consideration for these missed elements.

 

The Midterm will consist of SA questions based on readings and lectures pertaining to Bayesian statistics. For the Midterm exam, students will be allowed to use R and scripts that have been distributed in class. Additional notes that you have added to your scripts as comments are allowable but must be limited to text-based comments in the R-script we use together in class and tutorials. Any additional annotations to R-scripts, or additional scripts you have written independently, are NOT permitted for use during the Midterm exam. Laptop computers cannot be connected to the internet during the exam. Please note that this assessment is central to the learning objectives for this course. Accordingly, students seeking academic consideration for this assessment will be required to provide formal supporting documentation. Students who are granted academic consideration for this assessment will be provided one opportunity write a make-up exam no more than 1 week following the scheduled Midterm exam. 

 

The Final exam will consist of SA questions based on readings and lectures pertaining to meta-analysis. For the Final exam, students will be allowed to use R and scripts that have been distributed in class. Additional notes that you have added to your scripts as comments are allowable but must be limited to text-based comments in the R-script we use together in class and tutorials. Any additional annotations to R-scripts, or additional scripts you have written independently, are NOT permitted for use during the Final exam. Laptop computers cannot be connected to the internet during the exam. Please note that this assessment will be scheduled by the Office of the Registrar and that supporting documentation is always required for academic consideration requests for examinations scheduled by the office of the registrar. Students who are granted academic consideration for this assessment will be provided one opportunity write a make-up exam no more than 1 week following the scheduled Final exam. 

  

This course is exempt from the Senate requirement that students receive assessment of their work accounting for at least 15% of their final grade at least three full days before the date of the deadline for withdrawal from a course without academic penalty.

 

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     Assessment/Evaluation Schedule

 

Assignment due dates: January 24; February 23; March 14; March 29

 

Quizzes: In tutorials January 23/24; February 6/7; March 13/14; March 27/28

 

MIDTERM exam: In class on February 23

 

FINAL: to be scheduled by the Registrar’s Office

 

7     Class Schedule

 

 

LECTURE #/DATE

TOPIC

QUIZZES/ASSIGNMENTS

READINGS

L1: Week of Jan 7

Introduction

 

 

L2: Week of Jan 14

Introduction to Bayes

ASSIGNMENT 1 assigned

 

Navarro, D. J. (2018). Learning statistics with R: A tutorial for psychology students and other beginners. Ch 17.5

 

 

McClave & Sincich, Ch 3.5

L3: Week of Jan 21

Bayes Theorem

ASSIGNMENT 1 due Jan 24

 

QUIZ 1 (during tutorial)

McClave & Sincich, Ch 4.2, 4.4, Ch5 (optional)

 

Coghlan, A. (2017) “A little book of R…”

L4: Week of Jan 28

Bayesian estimation

ASSIGNMENT 2 assigned

 

L5: Week of Feb 4

Bayesian model comparison I

 

 

QUIZ 2 (during tutorial)

Navarro, D. J. (2018). Learning statistics with R: A tutorial for psychology students and other beginners. Ch 17.2; 17.7

 

L6: Week of Feb 11

Bayesian model comparison II

 

Navarro, D. J. (2018). Learning statistics with R: A tutorial for psychology students and other beginners. Ch 17.8

 

Week of Feb 17

 

READING WEEK

 

Week of Feb 23

 

ASSIGNMENT 2 due Feb 23

 

MIDTERM

 

L7: Week of March 4

Meta-analysis: Intro

ASSIGNMENT 3 assigned

Lowe et al., (2021). The bilingual advantage in children’s executive functioning is not related to language status. Psych Sci

L8: Week of March 11

Meta-analysis: modelling effect sizes

ASSIGNMENT 3 due March 14

 

QUIZ 3 (during tutorial)

 

L9: Week of March 18

Meta-analysis: effect size heterogeneity and moderation analysis

ASSIGNMENT 4 assigned

 

L10: Week of March 25

Meta-analysis: publication bias

ASSIGNMENT 4 due March 29

 

 

QUIZ 4 (during tutorial)

 

 

Week of April 1

 

REVIEW

 

 

 

 

 

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

 

For both the Midterm and Final exams, students will be allowed to use R and scripts that have been distributed in class. Additional notes that you have added to your scripts as comments are allowable but must be limited to text-based comments in the R-script we use together in class and tutorials. Any additional annotations to R-scripts, or additional scripts you have written independently, are NOT permitted for use during quizzes and exams. Laptop computers cannot be connected to the internet during any exam.

 

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.

 

 

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.

 

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

 

 

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.