KPC 8237-0

Brief

This is a course on multilevel modeling (MLM), aka hierarchical linear modeling (HLM), random-effect, or mixed-effect modeling. We’ll focus on its uses in psychology, starting from the basics and covering some of the most used techniques in MLM. Master’s students are welcome to come to the first class and then enroll manually given permission. 加簽期間，開放碩班同學線上加簽。

Description

Course keywords: multilevel modeling, MLM, hierarchical linear modeling, HLM, mixed effect (Ver. 2024/2/9) 一、課程說明 (Course Description) This is a course on multilevel modeling (MLM), aka hierarchical linear modeling (HLM), random-effect, or mixed-effect modeling. We’ll focus on its uses in psychology, starting from the basics and covering some of the most used techniques in MLM. What does “multilevel” mean, you might ask. If your data are individuals in groups, and maybe those groups further belong to larger units like companies, then this whole set of data is very likely multilevel/hierarchical/nested data. And MLM is what you need to adequately analyze them. 二、指定用書 (Text Books) No. 三、參考書籍 (References) No. All texts are fine. Find the one that fits you if needed. There are useful online learning resources too, such as these: http://www.bristol.ac.uk/cmm/research/gallery/ https://stats.idre.ucla.edu/other/mult-pkg/seminars/#SPSS 四、教學方式 (Teaching Method) Course format: The course will be me lecturing, you practicing in class and presenting your own analyses using MLM in the end of the semester. If you have some data you’ve got and been wanting to analyze but don’t know how, then great, this is your chance. If you don’t have data, it’s fine, too. I’ll tell you where to find free data for practices. There will be no exam. Lastly, I plan to have 10-15 presentations in the end, so you may need to team up with someone in class, depending on the size of the class. -- We’ll have “playtimes” in class. This is individual work. -- If there are fewer than 10 people, everyone will be their own group for the final project. If there are more people, you will be grouped into 10 groups, then do the same thing. We’ll do the grouping in the first class. -- Except for your final group/individual projects, I expect all exercises to be done in class. So I can really monitor everyone’s progress and help everyone in person. -- The final group/individual project will be an everything-included research paper, albeit with a smaller scope, using MLM. You can use your own data or free open data. See below for more info. Course website: The course website is located on eLearn. Announcements and course materials will be posted there. You will also find supplementary reading, if any, on eLearn. Attendance: I don’t take attendance, although it is strongly recommended. Otherwise, why bother taking the course? The only benefit you’ll get from the class is to come to the class. Communication: Everyone is welcome to send me messages “on eLearn,” to request for an appointment, ask questions, share your thoughts and concerns about the course. It’s the best way for me not to miss your messages, as I miss regular emails all the time but don’t want to miss yours. I will try my best to respond to you within two business days. Office hours: TBD -- I encourage you to take advantage of office hours at least once during this semester. Office hours are a great opportunity to clarify course material and ask questions regarding assignments. -- You’re welcome to just walk in during the designated time. I however would ask you for a favor: Whenever possible, message me before you come, telling me what I can do for you, what questions/suggestions you have etc. Just so I can be better prepared and make our meetings as efficient and meaningful as possible. Also, that prevents you from bumping into others, for their and your own privacy. Expectations: -- Please be respectful of your fellow students and silence your phone. -- Please be responsible for your learning by budgeting your time, being on time and prepared, and seeking assistance when needed. -- You are responsible for all information presented in class, even if you have an excused absence for a particular day. If you are going to miss a class, plan ahead to get the material from classmates or me, before or after the class. AI policies: -- I do love to see people, students included of course, to start harvesting the power of AI. So try it whenever you see fit. -- However, you sign up for the course, or generally, enroll in the program for a reason: to get the training I, as well as the program, may offer. So, at the same time, whenever you’re thinking about using AI to assist you, ask yourself, are you losing any training because of AI? It’s not me who don’t wanna see AI covering you. It’s you who don’t wanna see AI taking away your opportunity to learn. -- Indeed, ask around, then you’ll know you don’t need to do this much – I mean, using AI – just to get ahead of others in this class. Everyone putting in fair work, that is, pretty much literally everyone, gets an A in the end. So whether to call your AI in really is about you yourself, not you in comparison to others. 五、教學進度 (Syllabus) (subject to change as we go) 1) 2/23 Course overview & data search (short class ends at 12 pm) Fundamentals 2) 3/01 Data presentations (every group) & modeling basics 3) 3/08 Modeling basics 4) 3/15 MLM basics 5) 3/22 MLM basics (short class ends at 12 pm) Common techniques 6) 3/29 Between- & within-group effects 7) 4/05 No class (holiday) 8) 4/12 Random slopes 9) 4/19 Multilevel ANOVA (short class ends at 12 pm) 10) 4/26 Multilevel ANOVA Advanced techniques 11) 5/03 Model assessment (Project outline due by class time) 12) 5/10 3-level & cross-classified models 13) 5/17 Repeated measures & dyadic data (short class ends at 12 pm) 14) 5/24 Repeated measures & dyadic data 15) 5/31 Discrete DVs Term projects 16) 6/07 Presentations x 4 (PhD folks first, otherwise randomly ordered) 17) 6/14 Presentations x 4 (PhD folks first, otherwise randomly ordered) 18) 6/21 No class (Final proposal due by class time) 六、成績考核 (Evaluation) In-class exercises: We’ll have a lot of “playtimes” in class, in which you’ll apply what you’ll have just learned to your data and then share your findings with the class. You’ll NOT be graded on your findings or whether you succeed in running any practice analysis. Your job is simply to help each other learn, by presenting your work, your confusion, and asking questions about others’ findings. Your grade here will only be based on your level of participation and investment. -- An important thing to notice is that you’re responsible for finding your own practice data, which should of course be multilevel. You’ll use the data in class for exercises. If you already have that kind of data, great, you can and are encouraged to use them, so the course can actually contribute to your research work. If you don’t have the data yet, no worries. Survey Research Data Archive (https://srda.sinica.edu.tw/index.php) has lots of open data, for example, the Taiwan Youth Project (http://www.typ.sinica.edu.tw/intro). You can do a little search and pick a dataset that interests you. -- To motive you to find the data and help others understand what you’ll be dealing with—we’re a team, learning together and helping one another—every group/individual will do a quick 5-min presentation (with or without slides; really doesn’t matter) to introduce the class to the topic, the structure, and some of the representative variables of your chosen dataset. The presentation is worth 5 points of your total semester grade, and I’ll NOT judge the data by the content. All fair presentation will get full points. Term project: There will be no exam for this course, but when all classes end, as the embodiment of your learning, you’ll turn in a research paper on something, anything that is analyzed using MLM. That said, the paper will be an empirical paper, including all common sections you see in a psychological research paper, although with a smaller scope. To help you make sure you’re on track, there will be a few “checkpoints” in the semester. -- First, you’ll turn in an outline of what you’re going to write for the paper. This is for you to get my feedback early on. Please describe your research question, data source, data structure (VERY IMPORTANT!), planned analyses and, if possible, the significance of the work to the existing literature. There’s no format requirement for this assignment except that it should be about 400 to 450 words long. Submit through eLearn. -- Secondly, toward the end of the semester, when you’ve almost done analyzing the data, you’ll give a 20-min presentation on your work followed by a 10-min Q&A, like in a conference. You can, as many will, use slides, but this is not required. Most importantly, take the chance to get feedback from your peers to really polish your final product. I’ll prepare snacks and drinks. I mean, it’s a conference. -- Finally, the written research report. As mentioned, the work should be an empirical paper, including all common sections you see in a published psychological research article. Your paper should be at least 2000 words long (excluding bibliography), and formatted. You’re welcome to follow the APA style; that’s the go-to, but not required. The goal of formatting is merely to help you organize information and to help readers (i.e., me) see the organization. I won’t check your format like a bored librarian. Course grade: -- Practice-data presentation for 5% course grade -- Course participation for 25% course grade -- Term project outline for 10% course grade -- Term project presentation for 20% course grade -- Term project report for 40% course grade 七、可連結之網頁位址 https://elearn.nthu.edu.tw/course/view.php?id=30808

Timetable

Scores

Average GPA 4

Std. Deviation 0.15

Remarks

此為16週課程。

Restrictions

限心諮系博士班

Compulsory

Elective

Cross Discipline

First Specialization

Second Specialization