025. Handling Missing Data in Longitudinal Models
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

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026. Handling Missing Data in Longitudinal Models - MCAR, NMAR, and Likelihood Techniques

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RLS 2021 - Introduction to Missing Data in Clinical Research

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028. Recap of Longitudinal Methods

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Longitudinal Data Analysis Using R: An Introduction to Panel Data with Stephen Vaisey

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Tech talk: A practical introduction to Bayesian hierarchical modelling

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Mixed Effects Models Part 1: What is a Mixed Effects Model?

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Longitudinal Multilevel Modeling in R Studio (PART 1)

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Missing Data Assumptions (MCAR, MAR, MNAR)

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Handling Missing Data and Missing Values in R Programming | NA Values, Imputation, naniar Package

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Dealing with MISSING Data! Data Imputation in R (Mean, Median, MICE!)

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Longitudinal Data Analysis

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Latent growth models (LGM) and Measurement Invariance with R in lavaan

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ACRM 2022 IC17: Longitudinal Data Analysis Using R: Part I Introductory Topics

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Dealing with Missing Data in R

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Handling missing values in Stata using Multiple imputation in Panel Data#Part2_2023

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Dealing With Missing Data - Multiple Imputation

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Every Machine Learning Model Explained in 15 minutes

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3 Things You Should NOT Do with Missing Data

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Item Response Models Using Stata

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