Missing Data

Visualizing Variance in Multilevel Models Using the Riverplot Package

Spurred on by Alex Shackman, I have been working to figure out a good way to visualize different sources of variation in momentary mood. The most common way of visually depicting variance decompositions from the sort of multilevel models we used to analyze our data is a stacked bar plot. So that seemed like a good place to start. Figure 1. Stacked Barplot of Model Variance Decomposition Now, choosing a color scheme that screams “HI I’M A COLOR!

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Bayesian Multilevel Model with Missing Data Complete Workflow (Part 2 of 3)

Overview: This is the second post in a three-part blog series I am putting together. If you have not read the first post in this series, you may want to go back and check it out. In this post, I will focus on running and evaluating the imputation model itself, having identified the appropriate covariates that help account for missingness in the first post. Data Brief Description: The data in question come from a study that involved a one-week ecological momentary assessment (EMA) protocol.

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Bayesian Multilevel Model with Missing Data: Complete Work Flow - Part 1 of 3

Overview: This is the first post in a three-part blog series I am putting together. The focus of this initial post is effective exploration of the reasons for missingness in a particular set of data. The second post in the series will focus on running and evaluating the imputation model itself after having identified the appropriate covariates that help account for missingness. The third and final post will be a walkthrough of the final models and their interpretation - including a comparison of the same models using listwise deletion (which is bad unless missingness is small or definitely, 100% completely at random).

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