Spring 2018 - IAT 802 G100
Quantitative Research Methods and Design (3)
Class Number: 7078
Delivery Method: In Person
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 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 a particular focus on research design for HCI and the sciences.
COURSE-LEVEL EDUCATIONAL GOALS:
Course Objectives, Learning Goals & OutcomesThe course structure and teaching/learning activities are designed around the following questions. That is, by actively participating in this course, student should be able to effectively address the following questions and perform the respective tasks:
1) What is science, the “scientific method” and quantitative research? How do you think and argue like a good scientist?
2) Why do science? What is scientific & quantitative research useful for?
a) Why could you be excited about science? What drives and excites a researcher?
b) What are advantages and disadvantages of quantitative & scientific research methods (as compared to other methods)? That is, what are they appropriate and useful for?
3) What to research? Why research something?
a) How to devise effective research questions and hypotheses?
b) How to effectively motivate research questions?
4) How to use quantitative & scientific methods properly, carefully & effectively?
a) Experimental design: How to design an effective experiment? What does effective mean?
b) Descriptive statistics: How to present data effectively? What does effective mean?
c) Inferential statistics: What can you conclude from quantitative data? Why? What are your chances of being wrong? How do you decide which statistical methods to use? How to apply them properly? How to do this in a given statistical analysis software?
5) How to communicate all that effectively and scholarly?
6) How to critically evaluate and discuss the quality of quantitative / scientific research (of yourself and others)?
- JiTT/Reflection papers, short in-class quizzes & presentations, participation & peer evaluations/reviews 10%
- Iron Researcher test(s): Analyze provided data & write up in scholarly manner 25%
- Research project pitch: short written proposal + presentation 8%
- Final project presentation in SIAT research colloquium 12%
- Final written project report 45%
- 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: To be eligible for full marks in the major assignments, you must complete the corresponding weekly in-class activities. Although these may not be formally marked, completion of these activities is a prerequisite for the corresponding major assignments, and failure to complete them appropriately could result in overall point reduction. Any kind of plagiarism or other forms of academic dishonesty will automatically result in 0 points for that assignment, and potentially in more serious consequences including course failure
Teaching/learning activities may include
• Interactive lecturing and demonstrations
• 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
• Roughly bi-weekly in-class mini-quizzes (adapted from Team-Based Learning concepts)
• 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)
• Teams of 2-4 students each will be used for focused teamwork both in- and out-of-class
Student presentations from previous course offering can be found at iSpaceLab.com/Riecke/Teaching/#802
There are no formal pre-requisites apart from a graduate student status. 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 + SUPPLIES:
JMP & Microsoft Word (download from http://www.sfu.ca/itservices/technical/software.html)
If possible, bring a laptop to class every time.
"How to Design & Report Experiments" (2003) by Andy Field, Graham J. Hole; 1st Edition; Sage Publications [this is the main textbook we’ll use – make sure to have your own copy by the first week of the semester!]
Open Learning Initiative Statistics (online learning modules, potentially with a $25.00 fee. Registration infos will be provided in class / by email)
"Discovering Statistics Using R" (2012) by Andy Field, Jeremy Miles, Zoe Field; 1st Edition; Sage Publications Ltd
"Experimental Design: From User Studies to Psychophysics" (2011) by Douglas Cunningham, Christian Wallraven; 1st Edition; A. K. Peters/CRC Press
“Applying Contemporary Statistical Techniques” (2002) by Rand R. Wilcox; 1st Edition; Academic Press
"Methods in Psychological Research" (2013) by Annabel Evans, Bryan Rooney; 3rd Edition; Sage Publications
"Discovering Statistics Using IBM SPSS Statistics" (2013) by Andy Field; 4th Edition; Sage Publications Ltd
"A First Course in Design & Analysis of Experiments" (2000) by Gary W. Oehlert; W. H. Freeman (.pdf of book & data sets available online); this book is out of print
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.
SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS