傳播數據分析
Communication Data Analysis
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 傳播數據分析 HK206（六家） 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course is an introduction to mediation and moderation. It aims to prepare students with statistical thinking and practical skills for tackling academic and real-world problems and serves as a foundation for advanced statistical courses offered throughout the university. Emphasis will be on conceptual understanding and problem solving rather than mathematical computation. This course is also a practical course and requires a good deal of hand-on work in which students practice their learning to achieve sustainable mastery of core data analytics techniques.
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The Taiwan Communication Survey will be used for the secondary data analysis paper.
R demonstration 50% Secondary data analysis paper - Research question 5% - Annotated bibliography 10% - Data analysis plan 5% - Paper draft 5% - Peer review 5% - Paper presentation 5% - Final paper 15%
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction & overview of course |
| 第 2 週 | Fundamentals of linear regression analysis |
| 第 3 週 | The simple mediation model |
| 第 4 週 | Causal steps, scaling, confounding, and causal order |
| 第 5 週 | More than one mediator |
| 第 6 週 | Mediation analysis with a multicategorical antecedent |
| 第 7 週 | Fundamentals of moderation analysis |
| 第 8 週 | Extending the fundamental principles of moderation analysis |
| 第 9 週 | Some myths and additional extensions of moderation analysis |
| 第 10 週 | Multicategorical focal antecedents and moderators |
| 第 11 週 | Fundamentals of conditional process analysis |
| 第 12 週 | Further examples of conditional process analysis |
| 第 13 週 | Conditional process analysis with a multicategorical antecedent |
| 第 14 週 | Individual advising on research papers |
| 第 15 週 | Advanced topic: With-subjects design and mediation |
| 第 16 週 | Paper presentations |
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model (2nd ed.). O'Reilly Media. (https://r4ds.hadley.nz/)
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