Syllabus
PSYC 575 Multilevel Modeling
Units: 4
Term–Day–Time: Fall 2022–Tues & Thurs–10:00-11:50 am
Location: THH B10
Instructor: Hok Chio (Mark) Lai
Office Hours: Tues 12:00–1:00 pm, and by appointment.
Contact Info: (Email) hokchiol@usc.edu, (Slack) https://usc.enterprise.slack.com/.
Timeline for replying to emails: within 48 hours.
IT Help: ITS, Blackboard
Contact Info:
ITS (Email, Monday – Friday, 8:00 A.M. – 6:00 P.M.) consult@usc.edu, (Phone, 24/7/365) 213-740-5555, (Online) ServiceNow Portal
Blackboard (Email, 24/7/365) blackboard@usc.edu, (Online Help) Blackboard Help for Students
Course Description
This is a graduate-level class in statistical methods on multilevel modeling, a popular technique in behavioral and social science research. The course covers topics in multilevel modeling, including two- and three-level hierarchical linear models (HLM), random intercepts and slopes, longitudinal models and growth curve models, and some recent development in multilevel modeling.
The course begins with a brief overview of the ubiquity of multilevel data and the problems of using conventional methods to handle such data. It then transitions to the conceptual and statistical foundations of two-level multilevel models. Students will learn from different real data examples, and perform analyses using data of their own or provided by the instructor. Later material covers the use of multilevel modeling as a general framework for longitudinal data analysis, and other modeling considerations such as categorical data, non-hierarchical (e.g., cross-classified) data structure, and study designs. Students are also encouraged to provide input in suggesting topics to be covered for this course.
Learning Objectives
After the successful completion of this course, students will be able to . . .
- Explain the problems of analyzing clustered data with multiple regression/ANOVA;
- Identify the types of multilevel data structure in different research scenarios;
- Describe the statistical and conceptual foundations of multilevel modeling;
- Independently analyze real data using statistical software for multilevel modeling;
- Evaluate published research that uses multilevel modeling;
- Apply multilevel modeling in a research project, and effectively communicate findings/products in an oral research presentation or a written research report.
Prerequisite(s): PSYC 503: Regression and the General Linear Model (or a similar regression class)
Co-Requisite(s): None
Concurrent Enrollment: None
Recommended Preparation: Experience with statistical software (preferably R)
Course Notes
This class will be in-person and will follow a flipped course design. The benefit of a flipped course model is that the lecturer can spend more time with students to go through applications of concepts and hands-on exercises of data analyses.
The lecture videos and course materials will be available at https://psyc575-2022fall.netlify.app by 9:00 am of each Monday, and students are expected to review these materials and the assigned readings on their own. Please note that the lecture slides only serve to guide class discussions and cannot replace the assigned readings.
Students are expected to have reviewed the lecture videos before attending the Tuesday meetings. The Tuesday meetings are structured in two halves: (a) software demonstration by the instructor and (b) Q&A sessions. During the Thursday meetings, students will complete quizzes and/or in-class exercises, which are part of their participation grades.
Before attending the class sessions, students are expected to have
- Reviewed the posted videos and materials.
- Identified questions that come up in their learning.
- Started working on the homework problems.
Communication
To promote independence and critical thinking, students are encouraged to work through the following process for obtaining answers to course-related questions before contacting the instructor:
- consult the course syllabus;
- consult a classmate;
- post your questions on the Q&A channel on Slack;
- meet with the instructor during office hours or Q&A sessions on Tuesdays;
- for personal questions, email the instructor at hokchiol@usc.edu
Technological Proficiency and Hardware/Software Required
- R and RStudio are needed to complete the course assignments. It is highly recommended that students update to the latest versions of both software (R 4.2.1, RStudio 2022.07.1, or above). We will discuss how to set up R and RStudio in Week 1.
- Stable internet connection (for reviewing lecture videos)
USC Computing Center Laptop Loaner Program
If you need a laptop to successfully participate in this class, you may be eligible for the USC Computing Center Laptop Loaner Program.
USC Technology Support Links
Blackboard help for students
Slack information for students
Software available to USC Campus
Required Readings and Supplementary Materials
Recommended
- Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: Sage.
- (Alternative text) Hox, J. J., Moerbeek, M., & van de Schoot, R. (2018). Multilevel analysis: Techniques and Applications (3rd ed.). New York, NY: Routledge.
- Other required readings will be posted on Slack
References
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
- Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge, UK: Cambridge University Press.
- Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford, UK: Oxford University Press. [For longitudinal data analysis]
- West B. T., Welch, K. B., & Gałecki, A. T. (2014). Linear mixed models: A practical guide using statistical software (2nd ed.). Boca Raton, FL: CRC. [A reference for using different software]
- Gałecki, A. T., & Burzykowski, T. (2013). Linear mixed-effects models using R: A step-by-step approach. Springer.
- Luke, D. A. (2020). Multilevel modeling (2nd ed.). Sage.
- Heck, R. H., Thomas, S. L., & Tabata, L. N. (2014). Multilevel and longitudinal modeling with IBM SPSS (2nd ed.). New York, NY: Routledge. [A reference for SPSS users]
Description and Assessment of Assignments
In-class exercises (12%). During the Thursday sessions, students will participate in group exercises. If students miss a session for any reason, they can complete the exercise posted on Blackboard within 36 hours (i.e., Friday by end of day, Pacific Time) to get credits.
Homework problems (60%). There will be 10 homework assignments for students to apply the concepts and techniques discussed in class to analytic problems. The assignments typically involve performing data analyses using data sets of your own or provided by the instructor, and interpreting the results with some guided questions.
You must submit your work electronically to Blackboard by the assigned due date. See policy on late work.Final project (28%: 5% prospectus, 3% peer review, 20% presentation/final paper). You will complete a research project related to multilevel modeling, typically a research report of an empirical study using real data or a theoretical/methodological paper about certain aspects of multilevel modeling. Students interested in other project ideas (e.g., software package development) should discuss their ideas with the instructor. Each student can choose to work on their own or in a group of up to three people. Each student/group will schedule an appointment with the instructor to talk about their project during weeks 9 and 10 (October 17–28).
There are three grading components for your final project:
Prospectus (5%)
A prospectus about your project should be submitted by Monday, October 17. The prospectus should contain a concise description of what you (or your group) plan to do for your project, including a preliminary plan for statistical analysis. The prospectus should be limited to 1 single-spaced page (excluding tables, figures, references, and other supplemental materials).Peer Review (3%)
After the individual meeting with the instructor, each individual/group will refine their research questions and post a summary of their research questions and preliminary analyses by Friday, November 4. Each student will then give specific comments to the analyses of other students/groups by Monday, November 14. More information on what feedback to give will be included in the grading rubric.Final Presentation/Paper (20%)
If you choose to do a presentation, on November 29 or December 1, you or your group will give an 18-minute presentation (including Q&A) on your project. The final presentation should include the following four sections: introduction, method, results, and discussion; for methodological or theoretical work, students should follow organizations that are typical in previous papers in their areas of research. More emphasis should be put on describing the technical details of the analysis and the interpretations of the results. You will also need to submit your slides to Blackboard for grading on the day of your presentation, which should include a link to the reproducible codes for your analyses. A grading rubric on the research presentation will be posted on Blackboard.
If you choose to do a final paper, your paper will be due Tuesday, December 13, at 10:00 am Pacific Time (the assigned final exam time for the class). The final paper should include four sections: introduction, method, results, and discussion, or comparable sections; however, more emphasis should be put on describing the technical details of the analysis and the interpretations of the results. There should also be a link to the reproducible codes for your analyses. The final paper should be 8-15 double-spaced pages of text (i.e., excluding the title page, abstract, references, tables, figures, and appendices).
Participation
Participation accounts for 12% of the course grade. To earn full credit for participation, students should complete and turn in all in-class exercises.
Grading Breakdown
| Assignment | % of Grade |
|---|---|
| In-class exercises | 12 |
| Homework 1-10 | 60 |
| Prospectus | 5 |
| Peer review | 3 |
| Final presentation/paper | 20 |
| TOTAL | 100 |
Grading Scale
Course final grades will be determined using the following scale
| A | 93-100 |
| A- | 89-92 |
| B+ | 85-88 |
| B | 81-84 |
| B- | 77-80 |
| C+ | 73-76 |
| C | 70-72 |
| C- | Below 70 (failing) |
Course-specific Policies
Assignment Submission
The assignments should be submitted through Blackboard by Fridays. If needed, students can submit the homework before Sunday at 5:00 pm, without any penalty.
Grading Timeline
Generally, all graded work will be returned no later than one week from the submission deadline. However, given the high number of students in the class, the instructor may only grade selected questions in each assignment. Solutions will be posted so that students can check their work.
Late work
Late work will be penalized by a 10% deduction in the assignment grade every 24 hours late unless due to an emergency excused by the instructor. Email the instructor as soon as possible to discuss alternate arrangements due to an emergency.
Technology in the classroom
Phones
Your phone should be turned off or in silent mode (not on vibrate), and should not be used in the classroom.
Tablets and Laptops
During lecture time in the classroom, students can use tablets and laptops only for purposes of viewing course materials and taking notes. During the in-class exercises, students should use their laptops to complete the assignments.
Attendance
Students are expected to attend all Thursday class sessions on time. If they miss a session, they should complete the class exercises and turn in their work within the timeframe specified in Description and Assessment of Assignments.
Classroom Norms
From USC’s COVID-19 safety and other key reminders on August 2,
Monitor your symptoms before coming to campus. If you experience symptoms consistent with COVID-19, stay home and do not go to class, work or any other location. Arrange to be tested for COVID-19.
We strongly recommend wearing medical-grade masks or respirator masks (including surgical, N95, KN95, KF94) when indoors around others to reduce transmission and risk of infection.
The following applies to both in-person and online communications (e.g., Slack discussions and email communications)
- Respect each other’s views.
- In written communication messages, make sure they are something you could say to someone to their face.
- Recognize and/or remember that we have different backgrounds.
- Criticize ideas, not individuals or groups.
- Either support statements with evidence, or speak from personal experience.
Course Evaluation
Student feedback is essential for the instructor and the Department to keep improving this course. Students are encouraged to share their feedback and suggestions in an early-term feedback survey around week 4 to 5, and respond to the standard USC course evaluation survey at the end of the semester.
(Tentative) Course Schedule: A Weekly Breakdown
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| Week 1 Aug 23 & 25 |
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| Week 2 Aug 30 & Sep 1 |
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| Week 3 Sep 6 & 8 |
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| Week 4 Sep 13 & 15 |
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| Week 5 Sep 20 & 22 |
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| Week 6 Sep 27 & 29 |
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| Week 7 Oct 4 & 6 |
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| Week 8 Oct 11 |
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| Week 9 Oct 18 & 20 |
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| Week 10 Oct 25 & 27 |
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| Week 11 Nov 1 & Nov 3 |
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| Week 12 Nov 8 & 10 |
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| Week 13 Nov 15 & 17 |
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| Week 14 Nov 22 |
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| Week 15 Nov 29 & Dec 1 |
Final Presentation | Upload slides (on presentation day) | |
| FINAL | Final paper (due Dec 13, 10:00 am) |
SB = Snijders & Bosker (2012)
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