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文件名:  Paul Tremblay Lecture Notes Multilevel Modeling University of Western Ontario.rar
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Psychology 9542A/B. Multilevel Modeling

(Last offered January 2013; course outline)
This course serves as an introduction to multilevel modeling (also known as hierarchical linear modeling, mixed models). The course is designed as a continuation of the Psychology 9555 Structural Equation Modeling (SEM) focusing on Mplus as the main analytical software and including research and analytical methods that merge MLM with SEM. Students should therefore have a solid understanding of multiple regression and structural equation modeling and would benefit from previous knowledge of analysis of variance. Course topics include a review of traditional regression procedures, research design with multilevel structures, the basic two-level regression model (and extension to three-levels), methodological and statistical issues including power analyses, models with longitudinal data, models with dichotomous, categorical or count outcomes and structural equation models with multiple data levels. The objective of this course is to provide students with the necessary knowledge to apply MLM to research; the course will therefore involve hands-on projects in which students have the opportunity to analyze their own data or to conduct simulation studies (in Mplus or other packages such as HLM or SPSS Mixed Models). Prerequisite: must have taken Psychology 9540 (Research Design) and should have taken Psychology 9555 (SEM) or obtained the permission of the instructor.

Lecture Schedule, Slides and Readings
DateTopicReading
1.Overview, multiple regression issues, MplusMplus manual Chapter 9
2.Overview of MLM logic and designHox Ch 1, Kahn (2011), Nezlek (2008)
3.The basic two-level regression modelHox Ch 2, Peugh (2010)
4.The basic two-level regression modelHox Ch 2, 3, Peugh (2010)
5.Methodological and statistical issuesHox Ch 3, 4, Peugh (2010)
6.Analyzing longitudinal dataHox Ch 5
7.Analyzing longitudinal dataHox Ch 5, 16 (LGM)
8.Dichotomous, categorical, count dataHox Ch 6, 7 (skim)
9.Sample size, power, Monte CarloHox Ch 12
10.MLM meets SEM: Factor modelsHox Ch 14
11.MLM meets SEM: Path modelsHox Ch 15
12.Presentations
13.Presentations




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