Course:FRE518/Syllabus
COURSE INFORMATION
Session and term: 2024W2 Class location: MCML154
Class times: M/W 12:30pm to 2pm Lab times: 4 labs, Fridays from 1-2:30 pm
Course duration: Jan 6th - Feb 14th 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: Teams choose problem domain for project. | ||
1. Mon
Jan 6 |
Defining Research Objectives | Backward Market Research | Be prepared to discuss article. |
2. Wed
Jan 18 |
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: Teams meet with TA to review script for exploratory narrative interview. | ||
3. Mon
Jan 13 |
Focus Group Design &
Implementation |
Guidelines for Conducting Focus Groups
Focus Group Moderator Guide |
Be prepared to discuss articles. |
4. Wed
Jan 15 |
Qualitative Data Analysis:
Thematic Coding |
Qualitative Data Analysis: Thematic Coding | Be prepared to discuss articles. |
Lab #1
Jan 17 |
Lab #1: Qualitative Data Analysis: Thematic Coding. Interview Script due | ||
Week 3 | Week 3: Teams conduct 3 exploratory narrative interviews before Friday. | ||
5.Mon
Jan 20 |
Questionnaire Design:
Approaches to Asking Questions |
Tactics for Asking Questions | Be prepared to discuss articles. |
6. Wed
Jan 22 |
Questionnaire Design: Drafting
the Questionnaire |
Drafting & Crafting the Questionnaire | Be prepared to discuss articles. |
Lab #2
Jan 24 |
Lab #2: Thematic coding of data from exploratory narrative interviews. Coding results due | ||
Week 4 | Week 4: Teams meet with TA to review survey questions & survey design. | ||
7. Mon
Jan 27 |
Survey Data Analysis Part 1:
Descriptive Statistics. |
Survey Data Analysis | Be prepared to discuss articles. |
8. Wed
Jan 29 |
Survey Data Analysis Part 2:
Chi-Square, T-tests |
Survey Data Analysis | Be prepared to discuss articles. |
Lab #3
Jan 31 |
Lab #3: Data Analysis of Colombia Survey Data. Qualtrics Survey due | ||
Week 5 | Week 5: Teams collect survey data (members of the class respond to surveys before Friday) | ||
9. Mon
Feb 3 |
Presenting Insights from Data | Best Practices for Presenting Research
Findings |
Be prepared to discuss articles. |
10. Wed
Feb 5 |
Cluster Analysis | Cluster Analysis | Be prepared to discuss articles |
Lab #4
Feb 7 |
Lab #4: Cluster Analysis Exercise | ||
Week 6 | Week 6: Teams prepare final presentations | ||
11. Mon
Feb 10 |
Work Period for team presentations | ||
12. Wed
Feb 12 |
Team presentations in class (8 minutes per team) | ||
Fri Feb
14 |
Upload Cluster Analysis Exercise to Canvas |
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.