Psychology 3991G-001 (Online for 2020-21)

Special Topics in Psychology

"Data Science in Psychology"

If there is a discrepancy between the outline posted below and the outline posted on the OWL course website, the latter shall prevail.

1.0    CALENDAR DESCRIPTION

This course is an introduction to data science for psychologists and behavioural scientists. It concerns how to prepare, shape, explore, filter, summarize and visualize data; essentially all the steps up to, but not including, statistical analysis. By the end of the course you will be able to take unformatted raw data and organize, filter and visualize it, using computer programs like R, RStudio and Excel. You will also learn open science best practices for data curation and data sharing. Key concepts covered include: learning how computers store different types of data; different ways of formatting and reformatting data; plotting different data types; data summarization and visualization; how to filter and transform data; and best practices to avoid data loss, manipulation and misuse. No computer programming experience is expected. This course is highly recommended for students intending to pursue post-graduate research.

Prerequisites: At least 0.5 Psychology course in Research Methods at the 2000 level or above, and registration in 3rd or 4th year of Honours Specialization or Honours Double Major in Psychology, or permission of the Department.

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.

3 lecture hours; 0.5 course.

2.0    COURSE INFORMATION

This course is an introduction to data science for psychologists and behavioural scientists. It concerns how to prepare, shape, explore, filter, summarize and visualize data; essentially all the steps up to, but not including, statistical analysis. By the end of the course you will be able to take unformatted raw data and organize, filter and visualize it, using computer programs like R, RStudio and Excel. You will also learn open science best practices for data curation and data sharing. Key concepts covered include: learning how computers store different types of data; different ways of formatting and reformatting data; plotting different data types; data summarization and visualization; how to filter and transform data; and best practices to avoid data loss, manipulation and misuse. No computer programming experience is expected. This course is highly recommended for students intending to pursue post-graduate research. 

 

Instructor:Marc Joanisse 

Office Hours:Wednesdays 10-11 am  

(see Owl for zoom link and passcode) 

Email:marcj@uwo.ca 

 

Teaching Assistant:Rebekka Lagace Cusiac 

Office Hours:Thursdays 1-2 pm 

(see Owl for zoom link and passcode) 

Email:rlagacec@uwo.ca 

 

Time and Location of Classes:Synchronous online lectures 
Mondays 9:30-12:30 

(see Owl for zoom link and passcode) 

 

                                                                              

Students who are in emotional/mental distress should refer to Mental Health@Western

http://www.uwo.ca/uwocom/mentalhealth/ 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 wish to contact Student Accessibility Services (formerly known as Services for Students with Disabilities) at 519-661-2147.

3.0  TEXTBOOK

The course textbook is available free online: 

Neth, H (2020) Data Science for Psychologists 

https://bookdown.org/hneth/ds4psy 

 

Other readings may be assigned as needed, and will be provided online 

 

4.0    COURSE OBJECTIVES

This course introduces students to data analysis methodologies using computers. Students learn to prepare, explore and visualize data in a way that is error-resistant and replicable. Most instruction is completed using R, a free open-source software package, with some diversions into closed-source tools commonly used in Psychology including Excel and Qualtrics. 

The lectures present a theoretical background and practical method by which raw data is turned into human-readable format, up to but not including statistical analyses.  

Self-guided assignments provide students with the opportunity to take what is learned during lectures and apply it to new data and situations. During class time and office hours, students will be able to work on their assignments with consultation with the instructor and Teaching Assistant. Most elements of the course use freely available software that operate across all computer types, and the use of publicly accessible data repositories. 


   4.1    STUDENT LEARNING OUTCOMES 

Learning Outcome  

Learning Activity  

Assessment 

Depth and Breadth of Knowledge.  

Recommend and justify an appropriate procedure for importing, processing, visualizing and summarizing large datasets.  

 

 

Weekly lectures and practical activities demonstrate practical knowledge 

 

Assignments contain additional self-guided activities 

 

Bi-weekly assignments test incremental learning of each stage of data curation 

 

Final Project requires the application of all aspects of the course to a novel dataset 

Knowledge of Methodologies.  

Understanding how computers store data and how to use appropriate tools to tabulate, filter, summarize and visualize data 

The importance and best practices of open science methodologies  

 

Weekly lectures and assignments provide didactic and self-guided activities 

 

Bi-weekly assignments test incremental learning of each stage of data curation 

 

Final Project requires the application of all aspects of the course to a novel dataset 

Application of Knowledge.  

Importing, pre-processing, summarizing, interrogating and visualizing a large dataset 

 

Final Project 

 

Students use what was learned in this course to produce a report that answers non-trivial questions about a self-selected large dataset 

Communication Skills.  

Students learn to translate raw data into visualizations and verbal explanations of patterns and trends in the data.  

R-markdown is used as an interface between the computer code used to analyze data, and human-readable PDF/HTML files  

 

Weekly lectures and practical activities demonstrate practical knowledge 

 

Assignments contain additional self-guided activities 

 

Final project preparation 

 

Bi-weekly assignments test incremental learning of each stage of data curation, including uploading results to github  

 

Part of the grade in the Final Project is the quality of the write-up generated from the R-markdown process 

Awareness of Limits of Knowledge. 

Describe, critique, and justify their research methodology especially in write-up reports of assignments and final project. 

  

Generate ways to improve upon their research methodology. 

 

Feedback from instructors on the assignments highlights any needed correction in the interpretation of results and limitations.  

 

Instructional materials and assignments include questions that specifically address the limits of specific approaches, the need to write robust code and test for errors, and potential pitfalls in different analytic strategies 

 

The assignments and final project assess the ability to present justified conclusions and require students to show the source code in order to identify places where errors may lead to incorrect conclusions 

 

 

Autonomy and Professional Capacity. 

Demonstrate the capacity to work independently and in an ethical manner by producing their own written work and meeting the timelines for the assignments and final report 

 

Students are reseopnsible for bi-weekly assignments and a final report 

 

Late reports are subject to grade deductions; material that is not original is subject to grade deductions and academic penalties 

 

5.0     EVALUATION

6 take-home assignments: 75% of final mark 

Final analysis project: 25% of final mark 

 

Missed coursework: assignments and projects handed in late will incur a 5% deduction (off the final course grade) per day, rounded up to the next 24-hour period 

Because this is an essay course, as per Senate Regulations, you must pass the essay component to pass the course. That is, the average mark for your written assignments must be at least 50%. 

 

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. 

Per department policy, grades that are “close” to the next level will not be rounded up (e.g., a grade of 79.4 will become 79% in Student Centre).  

 

Although the Psychology Department does not require instructors to adjust their course grades to conform to specific targets, the expectation is that course marks will be distributed around the following averages:

70%     1000-level and 2000-level courses
72%     2190-2990 level courses
75%     3000-level courses
80%     4000-level courses
   
The Psychology Department follows Western's grading guidelines, which are as follows (see http://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


6.0  ASSESSMENT/EVALUATION SCHEDULE

Assignments will be due on Fridays at 5 pm:  Jan 22, Feb 5, Feb 26, March 12, March 26, April 5  

Final project will be due April 12  

 

7.0   CLASS SCHEDULE

Week 

Topic 

Reading 

What’s Due? 

Jan 11 

Introduction/Practicalities: 
Excel, RStudio, Qualtrics, Github 

 

 

Jan 18 

Introducing R 

Chapter 1 

Assignment 1 

Jan 25 

Visualizing 

Chapter 2 

 

Feb 1 

Transforming 

Chapter 3 

Assignment 2 

Feb 8 

Exploring 

Chapter 4 

 

 

Reading Week 

 

 

Feb 22 

Wrangling with Tibbles 

Chapter 5 

Assignment 3 

March 1 

Importing/exporting datafiles 

Chapter 6 

 

March 8 

Using open science tools 

 

Assignment 4 

March 15 

Tidying, joining datasets 

Chapter 7-8 

 

March 22 

Dealing with time, dates and text  

Chapter 9-10 

Assignment 5 

March 29 

R programming  

Chapter 11-12 

 

April 5 

Review 

 

Assignment 6 

 

 


8.0  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: http://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.

 

9.0  POLICY ON ACCOMMODATION FOR ILLNESS OR OTHER ABSENCES

 

Western’s policy on Accommodation for Medical Illness can be found at:
http://www.westerncalendar.uwo.ca/PolicyPages.cfm?PolicyCategoryID=1&Command=showCategory&SelectedCalendar=Live&ArchiveID=#Page_12

 

The full policy for consideration for absences can be accessed at: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/Academic_Consideration_for_absences.pdf

 

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:

  1. Submitting a Self-Reported Absence form (for circumstances that are expected to resolve within 48 hours);
  2. 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. The self-reported absence form must be submitted before the exam/coursework deadline in order to be valid. It may NOT be used for absences longer than 48 hours; coursework/tests/exams/etc., worth more than 30% of the final grade; or exams scheduled in the December or April final-exam periods: http://counselling.ssc.uwo.ca/procedures/medical_accommodation.html

 

Students seeking academic consideration:

  • are advised to consider carefully the implications of postponing tests or midterm exams or delaying handing in work;  
  • are encouraged to make appropriate decisions based on their specific circumstances, recognizing that minor ailments (upset stomach) or upsets (argument with a friend) are not normally an appropriate basis for a self-reported absence;
  • must communicate with their instructors no later than 24 hoursafter the end of the period covered by either the self-reported absence or SMC, or immediately upon their return following a documented absence

 

10.0      Contingency Plan for Return to Lockdown

 

In the event of a COVID-19 resurgence during the course 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.

 

11.0      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.

 

12.0      OTHER INFORMATION

 

Office of the Registrar: http://registrar.uwo.ca 

 

Student Development Services: www.sdc.uwo.ca

 

Please see the Psychology Undergraduate web site for information on the following:

http://psychology.uwo.ca/undergraduate/student_responsibilities/index.html

 

- 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

- 2020-2021 Calendar References

 

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.