多階層模式建構與應用
Applied Multilevel Data Analysis
| 節 | 週四 |
|---|---|
5 13:20–14:10 | 多階層模式建構與應用 HA216 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
In this course, the methods for analyzing multilevel data will be introduced with the emphasis on practical, hands-on analysis and interpretation of hierarchical linear models. The following topics will be covered in this course: conceptual development of multilevel models, estimation, model evaluation, centering and heterogeneity treatment, power, longitudinal data, and an introduction to multilevel models with non-normal outcomes (e.g., dichotomous and ordinal data).
Students taking this course should have a strong background in multiple regression as well as ANOVA models (at a level equivalent HSIE30047), and be comfortable analyzing data in R. In this course we will learn and mainly use R for running the MLM analysis, as well as the Optimal Design software for power.
•In this course, we will mainly use R and RStudio. Please bring your own laptop and have R and RStudio installed. If you find yourself in need of a computer, please reach out to me. •In addition, please download SPSS. •Late submission of assignments or lab projects will result in score deduction.
Assessment a) Participation/Attendance (Attendance and participation for course discussion is expected.) b) Article summary and presentation (10%; 5% for summary and 5% for presentation) c) Lab projects (20%): Students will be working on guided lab projects (hands-on exercise) throughout the course. d) Assignments (10%): Students will be working on 2 problem sets (HW1-2). e) Two comprehensive projects (30%) with guided data analysis and write-up (Project 1-2). f) Final in-class presentation (30%): Use existing public-available data or your own data (if you are using the data from your advisor or others, please obtain permission to do so) to build MLM models to answer unanswered research questions and prepare a presentation to share the findings. Every student (or every team) will have 15 minutes to present the project with 5 minutes Q&A and the PowerPoint slides should be uploaded to E3.
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction and course overview |
| 第 2 週 | Brief review of regression and software options (R review) & Clustering and ICC |
| 第 3 週 | Two-level models: Part I (random-intercepts model, means-as-outcomes model) |
| 第 4 週 | Two-level models: Part II Decomposing between- and within-group effects centering options and centering predictor variables |
| 第 5 週 | Two-level models: Part III Intercepts- and slopes-as-outcomes model Modeling cross-level Interactions |
| 第 6 週 | Estimation (REML and FEML) and model fit evaluation |
| 第 7 週 | Midterm exam |
| 第 8 週 | Variance explained, R square and effect sizes MLM Assumptions and troubleshooting |
| 第 9 週 | MLM with longitudinal data I |
| 第 10 週 | Article presentation (Individual) |
| 第 11 週 | Project Discussion with Instructor |
| 第 12 週 | MLM with longitudinal data II |
| 第 13 週 | MLM with longitudinal data III: Compared to latent growth curve modeling in SEM, missing data treatment |
| 第 14 週 | Final exam and review. |
| 第 15 週 | Project Presentation |
| 第 16 週 | Project Presentation |
Raudenbush, S.W., and Bryk, A.S. (2002). Hierarchical Linear Models (2nd edition). Newbury Park: Sage. O'Connell, A.A. & McCoach, D.B. (2008). Multilevel Modeling of Educational Data. Charlotte, NC: Information Age Publishing
- 地點
- Office: HA324
- 時間
- Office Hour: Mondays 10:00am-12:00pm by appointment
- 聯絡方式
- Email: mtlo@nycu.edu.tw Email is the best way to reach me outside of class for a prompt response. When you email, please include your name and class name in the title so that I can respond to your emails more efficiently.