Spring 2026 - IAT 802 G100

Quantitative Research Methods and Design (3)

Class Number: 6423

Delivery Method: In Person

Overview

Course Times + Location:

Jan 5 – Apr 10, 2026: Wed, 9:30 a.m.–12:20 p.m.
Surrey

Instructor:
Bernhard Riecke
ber1@sfu.ca

Prerequisites:
Graduate student status.

Introduction to the research enterprise from a quantitative perspective. It covers structures of research that are prevalent across fields, introduces research methodologies and tools, teaches methods for interdisciplinary work and fosters a critical discourse around differences among research in different areas.

COURSE DETAILS:

Course Description & Motivation

Your education, research, and thesis work at SIAT (and beyond) requires you to engage in various forms of doing and communicating research. Moreover, your success at SIAT and beyond will in part be evaluated based on the quality of your own research and communication thereof. The overarching goal of this course is to help you develop the knowledge & skills essential for designing and conducting proper scientific and quantitative research, as well as critically analyzing, discussing, and communicating it. In sum, IAT 802 is an introduction to experimental design and research methodologies where quantitative approaches are appropriate. There will be particular focus on research design for HCI and the sciences.

COURSE-LEVEL EDUCATIONAL GOALS:

Course Objectives, Learning Goals & Outcomes

The course structure and teaching/learning activities are designed around the following questions. That is, by actively participating in this course, you should be prepared to effectively argue for and critically discuss the following questions and perform the respective tasks:

What is science, the “scientific method” and quantitative research? How do you think and argue like a good scientist?
Why do science? What is scientific & quantitative research useful for
1. Why could you be excited about science? What drives and excites a researcher?
2. What are advantages and disadvantages of quantitative & scientific research methods (as compared to other methods)? That is, what are they appropriate and useful for? Where/when/why might mixed methods be more suitable?
What to research? Why research something?
1. How to devise effective research questions and hypotheses?
2. How to effectively motivate research questions?
How to use quantitative & scientific methods properly, carefully & effectively?
1. Experimental design: How to design an effective experiment, and argue why it is (not)effective?
2. Descriptive statistics: How to present data effectively, and argue what is or is not be an effective way to present data? What does effective even mean?
3. Inferential statistics: What can you conclude from quantitative data? Why? What are your chances of being wrong? How do you decide and argue which statistical methods to use? How to apply them properly? How to do this in a given statistical analysis software?
How to communicate all that effectively and scholarly? How can you enhance (or damage) your ethos as a researcher?
How to critically evaluate and discuss the quality of quantitative / scientific research (of yourself and others)?

Practically speaking, engaging in this course will (hopefully) empower you to

no longer fear statistics (in case you ever might have ;-), but instead appreciate and enjoy the beauty and craft of rigorous scientific research
design, conduct, analyze, write up, present, and discuss your own quantitative (or mixed-methods) research projects, and analyse other’s research
tackle your own thesis research projects successfully, by having the most powerful scientific research toolbox at your fingertips
use AI in a sensible and ethical way to support your learning and research
have enough skills to continue learning and applying scientific/quantitative research methods properly. (Note: the goal of the course is to give you a solid basis in the basic research methods and the skills to keep learning and successfully applying more advanced ones)

Grading

Research project (incl presentations & report) (based on several iterations and feed-back/reviewing) 50%
Tests, quizzes, and other assignments 40%
Community grade: This community grade is based on how well each person and the whole class supported and helped each other to learn better, provide useful feedback, create better research pro-jects, and created a supportive and inclusive community. 10%
NOTE: Regular attendance and active, supportive participation in class and team activities is necessary to pass; else could result in point reduction/no-pass. In particular, failure to contribute sufficiently to in-class activities, individual and team assignments, failure to responsibly do your part of the teamwork, or failure to reliably attend and contribute in team meetings can result in additional point reductions beyond the participation & peer evaluation. NOTE: Any kind of plagiarism or other forms of academic dishonesty will automatically result in zero points for that assignment, and potentially in more serious consequences, including course failure. null%

NOTES:

Teaching/learning activities may include but are not limited to:

Interactive lecturing and demonstrations
Flipped-classroom (reading and video tutorials before class incentivized by assignments)
Group discussions (in-class and online chat- and discussing forums)
Short in-class writing activities
Weekly reading, writing and/or revision/reviewing assignments
Weekly short written reflection papers (JiTTs) that provide the basis for in-class discussions and activities
In-class mini-quizzes
Online and in-class tutorials on experimental design, probability, and statistics
Group research projects (early in semester) and final individual research projects where students get a chance to work with actual data
Group/individual feedback
Peer-reviewing (formal & informal)
Student presentations (including elevator pitch presentations and final public project presentation)
Ungrading/coaching sessions
Teams of 2-4 students each will be used for focused teamwork both in- and out-of-class

Several items provided in this course and through Canvas or other means have been copied of the Copyright Act as enumerated in SFU Appendix R30.04A - Application of Fair Dealing under Policy R30.04. You may not distribute, e-mail or otherwise communicate these materials to any other person.

REQUIREMENTS:

There are no formal pre-requisites, although IAT804 will be quite useful. If you have never taken any quantitative research methods courses before, make sure to reserve enough time for reading especially during the first half of the course.

Materials

MATERIALS + SUPPLIES:

Software needed: JMP & Microsoft Office (download from http://www.sfu.ca/itservices/technical/software.html). Additional software used (e.g., python, JupyterLab/Hub or https://pingouin-stats.org/) should be available for free.
Please always bring a laptop (and power supply) to class.

REQUIRED READING:

"How to Design & Report Experiments" (2003) by Andy Field, Graham J. Hole; 1st Edition; Sage Publications. this is our main textbook, please make sure you have a copy (phyical or electronic) before the first class.
ISBN: 9780761973836

TBD: Delete Open Learning Initiative Statistics courses (online learning modules such as https://oli.cmu.edu/courses/statistical-reasoning-copy/ or https://oli.cmu.edu/courses/causal-and-statistical-reasoning-open-free/, potentially with a $25.00 fee)

additional materials provided through Canvas/online

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit:

SFU’s Academic Integrity Policy: S10-01 Policy
SFU’s Academic Integrity website, which includes helpful videos and tips in plain language: Academic Integrity at SFU

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.

Search for a new outline

Simon Fraser University
Engaging the World

Course Outlines