Course:FRE518/Syllabus

COURSE INFORMATION

Session and term: 2023W2 Class location: MCML154

Class times: M/W 12:30pm to 2pm Lab times: 4 labs, Fridays from 1-2:30 pm

Course duration: Jan 8th - Feb 16th Credits: 1.5

COURSE DESCRIPTION

Focus groups and surveys are the two most common data collection methods used in industry. This course will teach students industry best-practices for conducting applied market research using focus groups and surveys. The course will give students hands-on experience with focus group and survey research so that they leave the course with the ability to design, implement and analyze data collected via focus groups and surveys. Topics include the market research process, defining research objectives, focus group design and implementation, survey design and implementation, sample selection, data analysis, and presenting research findings. Classes will be discussion-based, interactive, and will present real-world examples of how the various research methods are used in industry. This course will be of interest to anyone who wants the ability to collect original data and insights using focus groups and surveys.

INSTRUCTOR

Instructor: Dr. Tim Silk

Phone: 604-822-8362

Office: Henry Angus 569

Email: tim.silk@sauder.ubc.ca

Office hours: T/Th 12-1 pm via Zoom link on Canvas

LEARNING OUTCOMES

By the end of this course, students will be able to:

Write a well-defined research question that acts as a guide in the market research process.
Identify and apply the appropriate data collection techniques to address the research question.
Design and implement effective exploratory interviews that identify new insights about the problem domain.
Design and implement effective surveys that provide confirmatory data about the problem domain.
Analyze focus group and survey data using a variety of qualitative and quantitative techniques including narrative theme coding, descriptive analysis, crosstab analysis, t-tests, chi-square analysis, and cluster analysis.
Present research findings in a captivating manner that clearly articulates key findings and insights.

ASSESSMENTS

Class Participation (Individual)	10
Project Part 1: Exploratory Interview Script (teams)	15
Project Part 2: Qualitative Coding Results (teams)	15
Project Part 3: Survey Design (teams)	20
Project Part 4: Final Presentation of Findings (teams)	20
Cluster Analysis Exercise (Individual)	20
TOTAL	100

READINGS

All readings are on the course page on Canvas (no textbook to purchase). Estimated cost of required materials: $0. Students will use the Qualtrics Survey Tool - Free license via UBC. See link on Canvas to set up your account.

COURSE SCHEDULE

Class	CLASS TOPICS	READINGS	WHAT’S DUE
Week 1	Week 1 Jan 8-12: Teams choose problem domain for project.
1. Mon Jan 8	Defining Research Objectives	Backward Market Research	Be prepared to discuss article.
2. Wed Jan 10	Exploratory Methods: Focus Groups & Narrative Interviews	An Anthropologist Walks Into A Bar… Know Your Customers Jobs to be Done Video: Exploratory Narrative Interviews	Be prepared to discuss articles & videos.
Week 2	Week 2 Jan 15-19: Teams meet with TA to review script for exploratory narrative interview.
3. Mon Jan 15	Focus Group Design & Implementation	Guidelines for Conducting Focus Groups Focus Group Moderator Guide	Be prepared to discuss articles.
4. Wed Jan 17	Qualitative Data Analysis: Thematic Coding	Qualitative Data Analysis: Thematic Coding	Be prepared to discuss articles.
Lab #1 Jan 19	Lab #1: Qualitative Data Analysis: Thematic Coding. Interview Script due 8pm Sunday Jan 21st
Week 3	Week 3 Jan 22-26: Teams conduct 3 exploratory narrative interviews before Friday.
5.Mon Jan 22	Questionnaire Design: Approaches to Asking Questions	Tactics for Asking Questions	Be prepared to discuss articles.
6. Wed Jan 24	Questionnaire Design: Drafting the Questionnaire	Drafting & Crafting the Questionnaire	Be prepared to discuss articles.
Lab #2 Jan 26	Lab #2: Thematic coding of data from exploratory narrative interviews. Coding results due 8pm Sunday Jan 28th
Week 4	Week 4 Jan 29-Feb 2: Teams meet with TA to review survey questions & survey design.
7. Mon Jan 29	Survey Data Analysis Part 1: Descriptive Statistics.	Survey Data Analysis	Be prepared to discuss articles.
8. Wed Jan 31	Survey Data Analysis Part 2: Chi-Square, T-tests	Survey Data Analysis	Be prepared to discuss articles.
Lab #3 Feb 2	Lab #3: Data Analysis of Colombia Survey Data. Qualtrics Survey due 8pm Sunday Feb 4th
Week 5	Week 5 Feb 5-9: Teams collect survey data (members of the class respond to surveys before Friday)
9. Mon Feb 5	Presenting Insights from Data	Best Practices for Presenting Research Findings	Be prepared to discuss articles.
10. Wed Feb 7	Cluster Analysis	Cluster Analysis	Be prepared to discuss articles
Lab #4 Feb 9	Lab #4: Cluster Analysis Exercise
Week 6	Week 6 Feb 12-16: Teams prepare final presentations
11. Mon Feb 12	Work Period for team presentations
12. Wed Feb 14	Team presentations in class (8 minutes per team)
Fri Feb 16	Upload Cluster Analysis Exercise to Canvas by 5pm Friday Feb 16th

COURSE SPECIFIC POLICIES: LATE ASSIGNMENTS

To ensure fairness to all students, late assignments will not be accepted and will receive a grade of zero. There are no partial grades for late submissions, no makeup assignments, and no reallocation of grades, do-overs, or extra credit options.

PEER EVALUATION

The peer evaluation form at the end of this course outline will be used to determine individual grades for the team project. Each student will be evaluated anonymously by their team members on the criteria shown on the form at the end of the project. Peer assessments will result in downward grade adjustments in cases where a student receives a score of 1 (Problematic) or 2 (Insufficient) on any criterion from more than one team member. The final question of the peer evaluation asks: all things considered, what percentage of the team’s grade does the individual deserve? I will take the average peer score for each student and multiply it by the team’s grade to arrive at the student's grade. For example, if a team receives a grade of 80% (an A-) and a member of the team receives an average peer score of 75% from their team members, that team member’s individual grade will be 75% x 80% = 60% (a “C” rather than an “A-”).

MFRE PROGRAM - COURSE PROTOCOL POLICIES

Recordings

There is no required distribution of recordings of class. Recording will be provided based upon on the decision of the course instructor. Classes are designed as and are intended to be in‐person.

Copyright

All materials of this course (course handouts, lecture slides, assessments, course readings, etc.) are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

Missing Classes/Labs

Students are expected to attend all classes, labs, or workshops. If you cannot make it to a class, lab, or workshop due to a medical or personal emergency, please email your instructor, your course assistant, and Olivier Ntwali, MFRE Program Coordinator ahead of time to let them know.

Respectfulness in the Classroom

Students are expected to be respectful of their colleagues at all times, including faculty, staff and peers. This means being attentive and conscious of words and actions and their impact on others, listening to people with an open mind, treating all MFRE community members equally and understanding diversity.

Respect for Equity, Diversity, and Inclusion

The MFRE Program strives to promote an intellectual community that is enhanced by diversity along various dimensions including status as a First Nation, Métis, Inuit, or Indigenous person, race, ethnicity, gender identity, sexual orientation, religion, political beliefs, social class, and/or disability. It is expected that all students and members of our community conduct themselves with empathy and respect for others.

Centre for Accessibility

The Centre for Accessibility (CfA) facilitates disability‐related accommodations and programming initiatives designed to remove barriers for students with disabilities and ongoing medical conditions. If you are registered with the CfA and are eligible for exam accommodations, it is your responsibility to let Olivier Ntwali, Academic Program Coordinator, and each of your Course Instructors know. You should book your exam writing with the CFA using its exam reservation system: for midterm exams or quizzes, at least 7 days in advance; and final exams, 7 days before the start of the formal exam period.

MFRE PROGRAM - ACADEMIC HONESTY POLICIES

Plagiarism and Academic Dishonesty

Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.

Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:

Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.

Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non‐acceptable conduct for examinations and graded assignments as presented via MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.

Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.

Peer Evaluation Form

Each student will be evaluated anonymously by their team members on the criteria below at the end of the course project. Peer assessments will result in downward grade adjustments in cases where a student receives a score of 1 (Problematic) or 2 (Insufficient) on any criterion from more than one team member.