025. Handling Missing Data in Longitudinal Models

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In this video we briefly discuss missingness in longitudinal data, introducing the concept of missingness, the ways of categorizing it, and provide a high-level overview for mechanisms to handle it. The specifics are not worked through in too much detail, as they are not the focus of this course, but more information is available on the course website if desired.

Video Timeline
00:00 - Introduction
02:54 - Missing Longitudinal Data
05:47 - Classification of Missing Data Mechanisms
16:24 - Impacts of Missingness
21:31 - General Techniques for Handling Missingness
25:11 - Weighting Techniques
35:22 - Imputation Techniques
  1. Dr. Dylan Spicker
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Комментарии
Автор

Hi~ your lecture is really helpful. And i want to ask how to test missing mechanism(MCAR, MAR or NAR) with longitudinal data?

ayu
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Can someone explain a little bit more about the difference between 'complete case analysis' and 'available data analysis'? Many thanks

yaningfeng
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Thank you for this lesson.
Please point me in the direction for text that speaks about this kinda and type of missingness.

Am trying to understand MAR and unbalanced and irregular data this grammer confused me.

etiniakpayang
Автор

thank you for this great lecture. really helpful to have a theoretical understanding of handling missing values. contents in the slides were a bit difficult to comprehend for me, nevertheless, it was good. If you can do an application of this lecture using software like (state, R or python) it would be helpful a lot.

junaidkp