| Table 1: Grading Components | ||
| Type | Points | |
|---|---|---|
| Charrette 1 (Bootcamp) | Team | 10 |
| Charrette 2 (KCRW) | Team | 10 |
| Charrette 3 (Qualitative) | Team | 10 |
| Charrette 4 (Experimental) | Team | 10 |
| Learning Checks (2 of 3) | Individual | 25 |
| Challenges (3) | Individual | 10 |
| IA1: Qualitative Analysis | Individual | 5 |
| IA2: Crew’s Cup Analysis | Individual | 5 |
| Participation | Individual | 15 |
| Total | — | 100 |
MKT 512
Customer Insights & Analysis
Syllabus
Course Description
This course will introduce you to various approaches to understand customers and their contribution through data. It includes qualitative and quantitative methods to help you analyze data and develop insights that lead to more profitable and equitable business outcomes. These approaches are essential for developing a successful career in marketing or in any management discipline that relies upon modeling and predicting customer behavior.
Learning Objectives
At the conclusion of this course, you will be able to:
- Design primary research studies, including surveys, qualitative interviews, and experiments—that address specific business questions about customer behavior and preferences.
- Construct survey instruments that minimize response bias and capture meaningful variation in customer attitudes and behaviors.
- Conduct qualitative research interviews using structured discussion guides and analyze resulting data through systematic coding techniques.
- Apply statistical methods, including cluster analysis, regression, and experimental analysis—to identify patterns and test causal hypotheses in customer data.
- Evaluate research designs and findings for validity, reliability, and actionable implications.
- Communicate research insights through written reports and oral presentations tailored to managerial decision-makers.
Materials
Required Texts
Course Reader for assigned articles and cases available on the Harvard Business School Publishing website. The assignments are posted on Brightspace. The custom cases I authored are provided for download on Brightspace.
Optional Texts
Marketing Research by David Aaker is the gold standard for textbooks on marketing research. It includes numerous chapters that delve deep into qualitative methods, survey research, and emerging analytical approaches. If you wish to further explore the material we cover in the class, this is a book that will age well on your bookshelf. It is not required for the course. It is entirely optional.
Slides
I upload most of the slides that I present in class to Brightspace. I have also compiled an easy to reference repository for the class at mkt512.profvincent.com. Some slides that I present in class may be omitted due to confidentiality or for intellectual property rights purposes. I use slides as visual exhibits to illustrate specific points. They are not a complete record of my lectures or our discussions, and they are not a substitute for notes you should take on your own.
All of my slides are created in Quarto, an open-source scientific and technical publishing system that makes it easy for data scientists to create beautiful documents. Quarto slide presentations are actually mini-websites created with reveal.js, an open source HTML presentation framework. Quarto reveal.js presentations are best viewed on a Chrome browser. Instructions for how to make PDFs of these slides (and print, if you like) can be found here
Though not a requirement of the course, you may want to explore Quarto while you’re here. It is becoming a reference standard for reproducible research and has been growing in popularity and usage in big companies like Netflix and Airbnb, as well as many consulting firms. Knowing how to use it may give you an advantage when seeking internships or jobs in data-driven roles. The beauty of Quarto is that it is language agnostic. You can use it with R, Python and many other data science-based programming languages. You analyze the data once, then enjoy the power to output your findings in multiple formats without having to reformat.
Required Resources
You will need two categories of tools to complete this course: survey software and statistical analysis software.
Survey Software
All students are required to use Qualtrics for survey research in this course. Qualtrics is available free to the Marshall community—you can register using your USC email account. We will cover Qualtrics functionality in class, and the platform offers extensive documentation and tutorials. Standardizing on a single platform allows us to troubleshoot issues efficiently and ensures consistency across team projects.
Statistical Analysis Software
You will need software to perform the statistical analyses covered in the course. Most analyses can be completed in Excel with add-ins, and you are free to use whatever platform you are most comfortable with.
That said, I encourage you to experiment with advanced statistical languages such as R or Python. These open-source platforms are proliferating in the marketing analytics world, and marketers who can leverage these tools are attractive to employers. I conduct all of my analyses in R and will share code throughout the semester. The learning curve for R has also flattened considerably—AI assistants like ChatGPT and Claude can help you write and debug code, making it far easier to get started than it was even a few years ago. (I find Claude to be superior for writing code.)
Due to time constraints, I am unable to provide in-depth tutoring for R beginners. For those interested in learning, I have included a list of books and resources in Appendix I. Some students prefer Radiant, a menu-driven application built on R that reduces the need to write code.
Collaboration with EMBA and KCRW
Module II of this course features a unique collaboration with KCRW, the renowned Los Angeles public radio station, and USC Marshall’s Executive MBA program (EMBA). This is the first time the MS MKT and EMBA programs have partnered on a research initiative. EMBA teams are developing membership strategy recommendations for KCRW, and your research will directly inform their work. You will receive briefs from the EMBA teams, conduct primary research to address their strategic questions, and present your findings to the EMBA students. This is not a simulation. Your insights might shape real decisions for a cultural institution that reaches millions of listeners. All research conducted on behalf of EMBA and KCRW will be shared with the KCRW client.
Course Format
This course is hands-on and application-oriented. Rather than a single semester-long project, you will complete four intensive research cycles called charrettes—a term borrowed from design fields where teams work under compressed deadlines to produce a deliverable. Each charrette builds on the previous one, and together they give you repeated practice with the full research process: design, field work, analysis, and presentation.
The course is organized into four modules:
| Module | Focus | Charrette |
|---|---|---|
| I: Research Bootcamp | Survey fundamentals, research design | C1: Survey Practice |
| II: Applied Research | Descriptive analysis, clustering | C2: KCRW Partnership |
| III: Qualitative Methods | Interviews, coding, thematic analysis | C3: Qualitative Study |
| IV: Experimental Design | Causal inference, hypothesis testing | C4: Experiment |
Each module integrates several components:
Instruction—Lectures and discussions introduce methods and frameworks. Cases anchor many of our discussions, providing real-world scenarios to explore research challenges from a managerial perspective.
Charrettes—Team-based research projects that follow a three-week cycle: design your study, collect data, analyze and present findings. Each charrette produces two deliverables: a presentation deck for decision-makers and a technical document with your analytical work.
Learning Checks—Three short assessments in multiple-choice format, each designed to be completed in about 30 minutes. Your lowest score is dropped; only your two best count toward your grade.
Challenges—Three very brief assignments, one each for Modules I, III, and IV, for you to solve individually. Challenges are graded pass/no-pass: you earn credit by submitting before the deadline and making a good-faith effort. They are intended to help you apply techniques from class, warm up for discussions, and give me visibility into how you are progressing so I can offer guidance. Submit your work as a PDF via Brightspace before the start of class when due.
Individual Assignments—Two short analysis memos: one tied to qualitative data analysis (IA1) and one tied to the Crew’s Cup case (IA2). These are graded on a simple rubric and assess your ability to independently apply analytical techniques covered in class.
Participation—Active engagement in discussions, case analyses, and in-class activities. Quality matters more than quantity.
Asynchronous Content-I create and post videos that dive deeper on concepts covered in class. This material is optional, but most students find it helpful, especially when preparing for Learning Checks or when deep in analysis on a Charrette. Links are posted on Brightspace. You can watch at your own pace.
Grading
This course uses ranked grading to comply with USC Marshall guidelines for graduate core courses. Here’s how it works:
Step 1: Earn Points
You accumulate points throughout the semester by completing the assessments listed below. A maximum of 100 points is possible.
Step 2: Rank
At the end of the semester, I rank students from highest to lowest based on point totals.
Step 3: Assign Letter Grades
Marshall guidelines specify that no more than 45% of students in a graduate core course may receive a grade of A- or better. I apply this threshold to the ranked list: the top 45% receive A- or higher. The remaining grade breaks (B+, B, B-, etc.) are determined by natural clusters in the distribution and my professional judgment based on the cohort’s overall performance.
Why Ranked Grading?
This approach ensures consistency with Marshall standards and fairness across cohorts. It also reflects how performance is typically evaluated in the workplace—particularly in roles where compensation, bonuses, and promotions are tied to how you perform relative to peers rather than against a fixed benchmark. Learning to excel in this environment is part of your professional preparation.
That said, 40% of your grade comes from team-based work. In my experience, students who focus narrowly on outperforming classmates often underperform students who invest in their teammates and learn from their peers. The best outcomes (in this course and in your career) come from balancing individual excellence with genuine collaboration.
I will provide regular updates on your point accumulation throughout the semester on Brightspace. If you have questions about where you stand, please ask.
Attendance
You are expected to attend and be prepared for all class sessions in-person unless you are experiencing an illness. There is no Zoom option for this class.
While attendance is not directly graded, consistent participation in class sessions is essential for your success in this course.
There are no makeup sessions or alternative assignments for missed classes. Out of fairness to all students, and in adherence with university policy, I do not offer extra credit assignments for missed work or to accumulate participation points due to absence.
Preparation and Participation
Participation enriches the quality of the classroom and the student learning experience. It also constitutes 15% of your course grade. The primary way you earn participation points is through active engagement in class discussions, though I may provide other opportunities to participate during the semester.
Come prepared. Do the work. Marketing research is both an art and a science, and the way it is practiced varies greatly across companies, industries, and contexts. Our discussions—whether anchored by cases, charrette presentations, or methodological challenges—are where you develop judgment. They provide a space to apply critical concepts, learn from peers, and prepare for real-world decision-making.
Sometimes I cold call on students, particularly when volunteers are scarce. It is never punitive. My goal is to encourage active participation and to gain multiple perspectives. This creates a richer learning experience and makes the grading of participation more fair.
Quality participation means asking questions about key concepts, sharing points of view on issues and decisions, relating relevant personal experience, contributing to class debates, offering constructive feedback during Crit Sessions, or building upon points raised by others. The best rule of thumb is to act like you are a stakeholder in the problem we’re discussing. Imagine you are sitting in a conference room at company headquarters. How would you engage if these decisions affected your livelihood? That’s the right level of engagement for the classroom.
Individual Assignments
You will complete two individual assignments during the semester. Each is a one-page memo summarizing the conclusions from your analysis of a provided dataset. A brief with specific instructions will be distributed before each assignment is due.
- IA1: Qualitative Analysis — You will analyze qualitative data and summarize your findings in a concise memo.
- IA2: Crew’s Cup Analysis — You will analyze data from the Crew’s Cup case and summarize your findings in a concise memo.
These assignments assess your ability to independently apply analytical techniques covered in class and communicate insights clearly. Evidence is central to everything we do in this course. Your conclusions are only as credible as the data that supports them. The one-page constraint forces you to be highly selective. Choose the single table, chart, or quotation that most powerfully supports your argument. Your job is to present the evidence and interpret what it means. This is the core skill of marketing research: showing decision-makers what the data reveals and shaping it into actionable insight. Submit your work as a single PDF via Brightspace by the deadline. Late submissions will not be accepted.
Learning Checks
There will be three learning checks—short assessments in a multiple-choice format—over the course of the semester. Each is designed to be completed in about 30 minutes. Your lowest score will be dropped; only your two best scores count toward 25% of your final grade.1.
1 For the final course grade, only your two highest learning check scores will be used. If you miss a learning check, the other two scores will be used.
Charrettes
A charrette2 is an intensive, time-boxed design exercise—a term borrowed from architecture and design fields where teams work under tight deadlines to produce a deliverable. In this course, you will complete four charrettes, one in each module. Each charrette follows an approximately three-week cycle: design your study, collect data, analyze your findings, and present your conclusions.
2 The term charrette comes from 19th-century Paris, where students frantically worked on designs collected by a “charrette” (cart).
Teams
During the first week of class, you will be assigned to one of ten teams. These teams will remain constant throughout the semester. Some charrettes allow your team to choose a research topic; others—such as the KCRW charrette—will be assigned based on briefs received from the Executive MBA program.
Briefings
Before each charrette begins, you will receive a briefing that outlines the research objectives, constraints, and deliverable requirements. Treat these briefings as you would a client kick-off meeting: ask clarifying questions, take notes, and make sure your team understands the scope before you begin.
Deliverables
Each charrette requires two deliverables:
1. Presentation Deck
This is the document you would present to a client or senior decision-maker. It should tell a clear story about what you set out to learn, what you found, and what it means. Assume your audience has minimal statistical training. Your job is to translate analytical findings into plain language and actionable insight.
Each module includes Crit Sessions—class periods dedicated to team presentations and feedback. While not every team will present in every Crit Session, every team needs to be prepared to present or share their findings. Each and every team will present at least once during the semester.
2. Technical Document
This is your opportunity to share the analytical work behind your presentation with me. It does not need to be polished—think of it as an appendix. The Technical Document should include:
- Statistical output (regression tables, test results, cluster diagnostics, etc.)
- Your dataset, cleaned and formatted so I can inspect the data you used
- For survey-based charrettes: your questionnaire
- For qualitative charrettes: your discussion guide and codebook (transcripts are optional)
For survey-based charrettes, you must also add me as a collaborator on your Qualtrics project. You can do this when the project is finished, but I encourage you to add me sooner—if you do, I can use that access to help troubleshoot issues while you are preparing for fieldwork or collecting responses. Unless you ask for my input, I will not inspect your survey until after you have submitted your deliverables.
If you use a code-based platform, you are welcome to submit your Technical Document as a computational notebook (Quarto, Jupyter, R Markdown, etc.), but this is not required. The goal is transparency: I want to see how you arrived at your conclusions.
A Note on “Pre-Flighting” Research Work
I do not pre-flight3 questionnaires or discussion guides. Reviewing every team’s instruments before fieldwork would be unsustainable, and it would also short-circuit the learning process. What I will do is help you troubleshoot specific issues: how to phrase a sensitive question, how to strengthen an experimental design, how to present a tricky finding. Schedule time with me during office hours for these conversations. Please plan ahead as my calendar fills quickly, especially near deadlines. I have been doing client-driven research for over 30 years and I know urgent situations happen that require a mentor to offer guidance. I will do my best to accommodate requests to meet outside normally scheduled hours, but I cannot guarantee availability if you wait until the last minute.
3 Pre-flighting is when a student team sends me a questionnaire or discussion guide to review and approve before they begin their fieldwork.
Final Exam / Charrette 4 Presentations (Summative Experience)
In lieu of a traditional final exam, all ten teams will present their Charrette 4 findings during the final exam period. This session will be held remotely via Zoom on Tuesday, May 12 from 11:00am–1:00pm. I’ve scheduled this as a remote session because I know many of you travel home before the semester officially ends.
Each team will have approximately 10 minutes to present, followed by brief Q&A and feedback. Attendance is mandatory—this is your final exam. If you do not attend, a 3-point deduction will be applied to your final course grade. I suggest taking this into consideration when making any travel arrangements.
Details on presentation order and Zoom logistics will be shared in the final weeks of the semester on Brightspace.
Grading
Each charrette is worth 10% of your final grade (40% total across all four). Charrettes are evaluated on the quality of your research design, the rigor of your analysis, the clarity of your presentation, and the soundness of your conclusions. A detailed rubric will be provided with each briefing.
Peer Evaluations
At the end of the semester, you and every member of your team will also be required to submit a peer evaluation. The link to the evaluation form is provided on Brightspace. I use peer evaluations to ensure that the grade the group achieves for the project is fairly distributed to the individuals based on their individual performance.
Course Evaluations
Towards the end of the course, you will be asked to complete a course evaluation. Your evaluations are extremely valuable to me. I am continuously revising and developing this course based on our classroom experiences together and your feedback. Please take the time to complete the course evaluation. Additionally, I may ask for periodic feedback to help guide the pace and flow of content during the semester. Your timely responses are greatly appreciated.
Course Policies
Emergency Preparedness
In case of a declared emergency if travel to campus is not feasible, the USC Emergency Information website will provide safety an other information, including electronic means by which instructors will conduct class using a combination of USC’s Brightspace learning management system, teleconferencing, and other technologies.
Use of Recordings
Pursuant to the USC Student Handbook (pages 13 and 27), students may not record a university class without the express permission of the instructor and announcement to the class. In addition, students may not distribute or use notes, recordings, exams, or other intellectual property based on USC classes or lectures without the express permission of the instructor for purposes other than personal or class-related group study by students registered for the class. This restriction on unauthorized use applies to all information that is distributed or displayed for use in relationship to the class. Distributing course material without the instructor’s permission will be presumed to be an intentional act to facilitate or enable academic dishonesty and is strictly prohibited. Violation of this policy may subject an individual or entity to university discipline and/or legal proceedings.
All class sessions are recorded and the recordings are posted on Brightspace.
Lecture Notes and Distributed Materials
Notes made by students based on a university class or lecture may only be made for purposes of individual or group study, or for other usual non-commercial purposes that reasonably arise from the student’s membership in the class or attendance at the university. This restriction also applies to any information distributed, disseminated or in any way displayed for use in relationship to the class, whether obtained in class, via email or otherwise on the internet, or via any other medium. Actions in violation of this policy constitute a violation of the Student Conduct Code and may subject an individual or entity to university discipline and/or legal proceedings.
Academic Integrity
The University of Southern California is foremost a learning community committed to fostering successful scholars and researchers dedicated to the pursuit of knowledge and the transmission of ideas. Academic misconduct is contrary to this fundamental mission and includes any act of dishonesty in the submission of academic work (either in draft or final form), as well as cheating, plagiarism, fabrication (e.g., falsifying data), knowingly assisting others in acts of academic dishonesty, and any act that gains or is intended to gain an unfair academic advantage. Students are expected to uphold the highest standards of academic integrity in all coursework.
This course follows the expectations for academic integrity as stated in the USC Student Handbook. All students are expected to submit assignments that are original work and prepared specifically for the course/section in this academic term. Students may not submit work written by others or “recycle” work prepared for other courses without obtaining written permission from the instructor(s). Students suspected of academic misconduct will be reported to the Office of Academic Integrity.
Academic dishonesty has a far-reaching impact and is considered a serious offense against the university. Violations will result in a grade penalty, such as a failing grade on the assignment or in the course, and disciplinary action from the university, such as suspension or expulsion.
For more information about academic integrity see the Student Handbook, the Office of Academic Integrity’s website, and university policies on Research and Scholarship Misconduct.
Please ask your instructor if you are unsure what constitutes unauthorized assistance on an exam or assignment or what information requires citation and/or attribution.
Artificial Intelligence
I expect you to use AI (e.g., ChatGPT, Gemini, Anthropic’s Claude, Midjourney, Dall-E, etc.) in this class. Learning to use AI is an emerging skill, and I welcome the opportunity to explore with you over the semester. Keep the following in mind:
- AI tools are permitted to help you brainstorm topics or revise work you have already written. For example, some students have used AI to brainstorm survey questionnaires. The AI in programs like ChatGPT can write R and Python code that can be particularly helpful when conducting your analyses. Keep in mind that what AI produces tends to be generic and is rarely sufficient on its own.
- If you provide minimum-effort prompts, you will get low-quality results. You will need to refine your prompts to get good outcomes. This will take work.
- Proceed with caution when using AI tools and do not assume the information provided is accurate or trustworthy If it gives you a number or fact, assume it is incorrect unless you either know the correct answer or can verify its accuracy with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand. This is also true for code that it generates.
- Generative AI—especially large language models (LLMs)—is prone to cognitive biases such as confirmation bias and acquiescence bias. In practice, this means the model may present information that aligns with your assumptions or simply tells you what it “thinks” you want to hear. When applying AI to the design or analysis of marketing research, this can lead to false confidence in flawed ideas. Always verify AI-generated information with credible sources, and validate any theories or concepts before acting on them.
- AI is a permitted tool in this course, but its use must be acknowledged. When you use AI on an assignment, briefly explain how and why you used it, and describe the role it played in your work. A concise, high-level overview is sufficient—you do not need to submit every prompt or a full transcript. Failure to disclose AI use when required will be treated as a violation of academic integrity.
- As AI tools have proliferated, both industry and academia have observed a surge in workslop4. While AI can be a powerful aid, workslop represents its misuse. It is characterized by writing that may appear polished on the surface but lacks substance, relevance, or original thought. Some analysts argue that the rise of workslop is already eroding the productivity gains AI promised to deliver. In this class, students are encouraged to use AI tools to support and extend their thinking, but not to substitute for it. I do not reduce a grade simply because I suspect a student over-relied on AI. However, if submitted work reflects the hallmarks of workslop, I will grade accordingly, just as I would if a student had written it unaided. If AI helps you think better, use it. If it makes you think less, don’t expect it to carry the weight of your grade. If you choose to use AI, be sure it enhances your work rather than dilutes it.
4 Workslop is content generated by AI that is characterized by writing that may appear polished on the surface but lacks substance, relevance, or original thought.
5 “How Gen AI Is Transforming Market Research” in Harvard Business Review, 2025
Marketing research has recently been dubbed the managerial function that is “the one that’s most disrupted by generative AI.”5 During our time together we will frequently discuss its increasing role in the field–from coding surveys to serving as the source of public opinion. I encourage you to explore it often and with care.
Technology Policy
Because this is a technical course, laptops are occasionally allowed in class. It is acceptable for students to use laptops or other electronic devices to take notes, review material being presented in class or to conduct analysis and/or calculations. It is not permissible to use laptops or devices for unrelated purposes. During discussions, I may ask you to put laptops and devices away so that you can be fully present. Students who are distracted by devices during discussions may lose participation points or be asked to leave the classroom.
Open Expression and Respect for All
An important goal of the educational experience at USC Marshall is to be exposed to and discuss diverse, thought-provoking, and sometimes controversial ideas that challenge one’s beliefs. In this course we will support the values articulated in the USC Marshall “Open Expression Statement.”
Academic Conduct and Support Systems
Accommodations
USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at osasfrontdesk@usc.edu.
Support Systems
Counseling and Mental Health
(213) 740-9355 (24/7 on call)
health.usc.edu/counseling
Free and confidential mental health treatment for students, including short-term psychotherapy, group counseling, stress fitness workshops, and crisis intervention.
988 Suicide and Crisis Lifeline
988 (24/7 on call – phone or text)
The 988 Suicide and Crisis Lifeline (formerly known as the National Suicide Prevention Lifeline) provides free and confidential emotional support to people in suicidal crisis or emotional distress 24 hours a day, 7 days a week, across the United States. The Lifeline is comprised of a national network of over 200 local crisis centers, combining custom local care and resources with national standards and best practices. The new, shorter phone number makes it easier for people to remember and access mental health crisis services (though the previous 1 (800) 273-8255 number will continue to function indefinitely) and represents a continued commitment to those in crisis.
Relationship and Sexual Violence Prevention Services (RSVP)
(213) 740-9355(WELL) press “0” after hours (24/7 on call)
studenthealth.usc.edu/sexual-assault
Free and confidential therapy services, workshops, and training for situations related to gender- and power-based harm (including sexual assault, intimate partner violence, and stalking).
Office for Equity, Equal Opportunity, and Title IX (EEO-TIX)
(213) 740-5086
eeotix.usc.edu
Information about how to get help or help someone affected by harassment or discrimination, rights of protected classes, reporting options, and additional resources for students, faculty, staff, visitors, and applicants.
Reporting Incidents of Bias or Harassment
(213) 740-5086 or (213) 821-8298
usc-advocate.symplicity.com/care_report
Avenue to report incidents of bias, hate crimes, and microaggressions to the Office for Equity, Equal Opportunity, and Title for appropriate investigation, supportive measures, and response.
The Office of Student Accessibility Services (OSAS)
(213) 740-0776
osas.usc.edu
OSAS ensures equal access for students with disabilities through providing academic accommodations and auxiliary aids in accordance with federal laws and university policy.
USC Campus Support and Intervention
(213) 821-4710
campussupport.usc.edu
Assists students and families in resolving complex personal, financial, and academic issues adversely affecting their success as a student.
Diversity, Equity, and Inclusion
(213) 740-2101
diversity.usc.edu
Information on events, programs and training, the Provost’s Diversity and Inclusion Council, Diversity Liaisons for each academic school, chronology, participation, and various resources for students.
USC Emergency
(213) 740-4321 (UPC) 24/7 on call
(323) 442-1000 (HSC) 24/7 on call
dps.usc.edu, emergency.usc.edu
Emergency assistance and avenue to report a crime. Latest updates regarding safety, including ways in which instruction will be continued if an officially declared emergency makes travel to campus unfeasible.
USC Department of Public Safety
UPC: (213) 740-6000 24/7 on call)
HSC: (323) 442-1200 24/7 on call)
dps.usc.edu
Non-emergency assistance or information
Office of the Ombuds
(213) 821-9556 (UPC)
(323-442-0382 (HSC)
ombuds.usc.edu
A safe and confidential place to share your USC-related issues with a University Ombuds who will work with you to explore options or paths to manage your concern.
Occupational Therapy Faculty Practice
(323) 442-3340
otfp@med.usc.edu chan.usc.edu/otfp
Confidential Lifestyle Redesign services for USC students to support health promoting habits and routines that enhance quality of life and academic performance.
Course Outline and Assignments
| Module I: Research Bootcamp | |||
|---|---|---|---|
| 1 | Tue, Jan-13 | Course introduction |
|
| 2 | Thu, Jan-15 | Designing Primary Research |
|
| 3 | Tue, Jan-20 | Survey Research Fundamentals |
|
| 4 | Thu, Jan-22 | The Psychology of Response |
|
| 5 | Tue, Jan-27 | Analyzing Research Data | |
| 6 | Thu, Jan-29 | Data Storytelling |
|
| 7 | Tue, Feb-03 | Case: Hecuba (A) |
|
| 8 | Thu, Feb-05 | Crit Session: Charrette I |
|
| Module II: Applied Research Experience | |||
| 9 | Tue, Feb-10 | Sampling Frames and Strategies | |
| 10 | Thu, Feb-12 | Scales and Latent Variables |
|
| 11 | Tue, Feb-17 | Fieldwork Management | |
| 12 | Thu, Feb-19 | Analyzing Multimodal Data |
|
| 13 | Tue, Feb-24 | Exploring Relationships Within Data |
|
| 14 | Thu, Feb-26 | Crit Session: Charrette II |
|
| Module III: Qualitative Methods | |||
| 15 | Tue, Mar-03 | Introduction Qualitative Research |
|
| 16 | Thu, Mar-05 | Qualitative Methods and Modalities |
|
| 17 | Tue, Mar-10 | Coding, Analysis, and Presentation of Qualitative Data |
|
| 18 | Thu, Mar-12 | AI and Qualitative Research |
|
| Tue, Mar-17 | Spring Recess | ||
| Thu, Mar-19 | Spring Recess | ||
| 19 | Tue, Mar-24 | Guest Speaker: TBA |
|
| 20 | Thu, Mar-26 | Case: All Nutrition (A): Focus Group Research for Market Segmentation |
|
| 21 | Tue, Mar-31 | Crit Session: Charrette III |
|
| Module IV: Experimental Design | |||
| 22 | Thu, Apr-02 | Introduction to Experiments |
|
| 23 | Tue, Apr-07 | Experimental Design Workshop |
|
| 24 | Thu, Apr-09 | Analyzing Experimental Data |
|
| 25 | Tue, Apr-14 | Advanced Analytical Techniques |
|
| 26 | Thu, Apr-16 | Factor Analysis and Scale Validation |
|
| 27 | Tue, Apr-21 | AI, Synthetic Data, and Ethics in Research |
|
| 28 | Thu, Apr-23 | Guest Speaker: TBA | |
| 29 | Tue, Apr-28 | Case: Crew’s Cup Fitness |
|
| 30 | Thu, Apr-30 | Course Wrap-Up |
|
| Final Exam/Summative Experience | |||
| 31 | Tue, May-12 | Crit Session: Charrette IV | |
| Shaded areas are holidays or periods that do not have synchronous class meetings. | |||
Appendix I: R Resources
Knowledge of R is not required for this course. You are free to conduct your analysis on any software you choose, including Microsoft Excel. However, as I do all of my analysis in R, many students are curious to experiment with the platform during the course. If you are new to R, the resources below may be helpful.
Many students who have previously taken the course used Radiant for analysis and had very positive reviews. Radiant is built on R but it features a menu-driven windows interface that lessens the need to learn any R code. You can learn how to install it here. Unfortunately, I cannot help you install Radiant on your local computer. If you need assistance, you might check with our excellent IT support team. Some DSO courses also use Radiant, so you may find additional resources there.
If you are brand new to R, the best starter book is R for Data Science by Garrett Grolemund and Hadley Wickham. The book is available free online.
The Big Book of R is an compendium of many free online books about R and its many custom packages.
RStudio is the most popular IDE (Individual Development Environment) for coding in R. It’s open source and free. It’s parent company, Posit, just launched an innovative new IDE called Positron The company is also home to a brilliant engineering and data science team that produces many free R packages. The most famous of these is the Tidyverse which I use liberally in the code examples I post.
Our former dean, Gareth James, co-authored an excellent book on statistics and data science that uses R in all of its examples. The book is titled An Introduction to Statistical Learning with Applications in R. It is available at most sellers of technical books and textbooks, including Amazon. The code in this book is all in “base R”, rather than using the Tidyverse. I rely more on Tidyverse code when I teach because students tend to find it more user-friendly, but if you plan on doing more work in R then I think it’s good to know the core language, which this book uses entirely. A new version of this book has been published using Python, as well.
R for Marketing Research and Analytics is an excellent book by Chris Chapman and Elea McDonnell Feit. Chapman is a data scientist at Google and Heit was formerly at GM and at several analytics-based research companies. You can purchase the book from most online re-sellers, including Amazon.
R is prolific. It has exploded in popularity over the last decade. A simple Google search will reveal many online tutorials. There is also a very active community online that is friendly and happy to answer questions in forums like Stack Overflow. I think this guide on Medium has a good list of tutorials and learning resources. You may also wish to connect with students or faculty in the DSO department, as many use R in their work.