Type of Credit: Elective
Credit(s)
Number of Students
Multilevel modeling (MLM), sometimes referred to as hierarchical modeling, allows researchers to account for data collected at multiple levels and variation between different groups. The course begins with an introduction to multilevel linear models and discuss the following aspects: model fit and adequacy, causal inference, and power. Then the course proceeds on to cross-classification models, multilevel generalized linear models, and multilevel longitudinal models.
能力項目說明
Multilevel Modeling provides an introduction to statistical models with explicitly defined hierarchies. The course aims at helping students to understand the core concepts behind multilevel models, to apply them to substantive problems, and to learn how to effectively present and communicate results to others. In this course, students will learn to develop statistical models with hierarchical structures for their own purpose and to specify, fit, and check multilevel models in the R environment and other software.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
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週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|---|---|---|---|
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1 |
Overview |
OMB cha1 |
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2 |
Introduction to MLM |
OMB cha2 |
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3 |
Model Fit and Adequacy |
OMB cha3 |
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4 |
Causal Inference |
OMB cha4 |
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5 |
Power |
OMB cha5 |
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6 |
Brainstorming Session on Term Papers |
No assigned readings |
Students need to come with a research proposal (2 pages) that specifies the research question(s), a brief review of the literature, and the contributions of this project. Students will take turns sharing their thoughts and receiving feedback from the professor and their peers. |
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7 |
Cross-classification |
OMB cha6 |
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8 |
Generalized Models |
OMB cha7 |
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9 |
Count Models |
OMB cha8 |
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10 |
Longitudinal Data |
OMB cha9 |
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11 |
Workshop on Data and Methods |
No assigned readings |
Students need to come to class with a proposal that discusses the data and methods they will use in their projects. After their presentation (about 10 minutes), they will have a QA session where comments and questions will be provided. |
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12 |
Reporting Results |
OMB cha17 |
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13 |
Large-scale sample datasets |
OMB cha13 |
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14 |
Measurement |
OMB cha14 |
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15 |
Missing Data |
OMB cha15 |
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16 |
Multilevel Mediation Analysis |
OMB cha16 |
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17 |
Term paper oral report |
Please circulate your paper by the designated deadline |
Please read other students’ papers and be prepared to offer comments in class |
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18 |
Term paper oral report |
Please circulate your paper by the designated deadline |
Please read other students’ papers and be prepared to offer comments in class Term paper due at the end of class |
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Attendance and Participation (20%) Homework (40%) Final Paper (30%) Final Paper Presentation (10%) Attendance and Participation (20%): Your preparation, presence, and participation are crucial. Please complete the required readings, be on time for each class, bring all relevant readings, and contribute energetically to the class. Your class participation grade will be assessed based on attendance and your contributions to discussions and group activities. Please note that unexcused absences will count heavily against your grade. An absence will be excused only with documentation of medical necessity or with prior approval from the professor. Homework (40%): Students will complete five problem sets designed to test the comprehension of the material covered in class. You may consult with your classmates. However, each student must write up and turn in their own work/assignment. Assignments deemed too similar to another student’s assignment will receive a score of 0. Working (struggling) on the homework is the only sure way to master the material. All homework assignments are due at the beginning of the class. Final Paper (30%) and Paper Presentation (10%): At the end of the semester, students need to write a research paper that uses one of the methods that we have discussed in class. The paper should include: research questions, literature review, hypotheses, data and methods, results, implications and conclusions. The papers should be about 20-25 pages, double-space. Students will present their findings in Weeks 17 and 18. Policy on using Generative AI tools: It is totally fine for students to consult with Generative AI tools for learning purposes. However, for completing their assignments and the data analysis project, all the input should be the students' own original work. Violations of this policy will be considered academic misconduct. |
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指定書目
參考書目
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