Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Introduction
00:00 • 2min
The Importance of Missing Data
01:52 • 2min
The Problem With Missing Data
03:49 • 2min
How to Deal With Missing Data
05:20 • 2min
How to Avoid Missing Data in Long-Term Studies
07:42 • 5min
The Language of Missing Data Mechanisms
13:05 • 3min
The Paradox of Missing at Random
16:04 • 4min
The Beauty of Tautological Cycles
20:01 • 4min
The Importance of External Validity in Modeling
23:46 • 2min
Multiple Imputation and Full Information Maximum Probability
26:12 • 4min
The Pros and Cons of Multiple Imputation
30:04 • 4min
How to Fit a Model to Different Subsets of Data
34:29 • 4min
The Importance of Maximum Probability in FIML
38:48 • 2min
The Importance of Auxiliary Variables in a Model
40:52 • 3min
Auxiliary Variables in a Longitudinal Model
43:33 • 3min
The Importance of Describe the Nature of the Missing Data
46:40 • 3min
How to Estimate Conditional Likelihood in a Multi-Level Model
49:33 • 3min
How to Avoid Missing Data in a Grant Proposal
52:57 • 4min
The Future of Missing Data
57:02 • 3min
The Five Way Interaction
59:55 • 2min