Day 1: Introduction to multi-level data problems

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Course logistics and overview ◮ Purpose of the course ◮ introductory ◮ basic mathematical understanding of MLMs ◮ applied, emphasis on Stata ◮ Day 5 covers a few non-linear models
Course logistics and overview ◮ Purpose of the course ◮ introductory ◮ basic mathematical understanding of MLMs ◮ applied, emphasis on Stata ◮ Day 5 covers a few non-linear models ◮ What we will not do ◮ work with really complicated multi-level structures ◮ deal with estimation issues ◮ use Bayesian methods
Page 1: Day 1: Introduction to multi-level
Page 5 and 6: Course logistics and overview ◮ P
Page 7 and 8: What is multilevel data? ◮ Multil
Page 15 and 16: The structure of multilevel data 5
Page 17 and 18: The structure of multilevel data 5
Page 19 and 20: Why would special models be needed
Page 21 and 22: The ecological fallacy ◮ The ecol
Page 23 and 24: The ecological fallacy ◮ The ecol
Page 25 and 26: The atomistic fallacy ◮ The atomi
Page 27 and 28: Stata and “robust” clustered st
Page 29 and 30: Stata and “robust” clustered st
Page 31 and 32: The organization of multilevel data
Page 33 and 34: The organization of multilevel data
Page 35 and 36: Zen and the art of reshapeing ◮ s
Page 37 and 38: Zen and the art of reshapeing ◮ s
Page 39 and 40: Zen and the art of reshapeing conti
Page 41 and 42: Benoit and Marsh (2008) example con
Page 43 and 44: Long v. wide data format: PPMD exam
Page 51 and 52: Reshape example with expert survey
Page 53 and 54:
Reshape example with expert survey
Page 55 and 56:
Introducing variance decomposition
Page 57 and 58:
Introducing variance decomposition
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Day 1: Introduction to multi-level data problems

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