But more generally, a hierarchical model not only treats the dependent variable observations as random, but also treats model parameters (e.g., regression coefficients, error variance parameter) as random variables, that follow some distribution (e.g., normal, or gamma, etc.). Sometimes, they are referred to as random-effects models. This course introduces students to Hierarchical Linear Models, which are mixture models that are widely applied in education, psychology, medicine, and other fields. Or otherwise, may watch the class (lecture) recordings which will be made available in BlackBoard around Tuesday 9pm. Students may either attend these lectures live, while asking any questions A new video lecture is presented and recorded through BlackBoard CollaborateĮvery Tuesday 5:00-8:00pm during the semester. ![]() **Classroom : Online asynchronous course.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |