Selected Publications

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are introduced by the model fitting process. Some of these issues are well known and adjustments have been proposed. Working with LME models typically requires that the analyst keeps track of all the special circumstances that may arise. In this article, we illustrate a simpler but generally applicable approach to diagnosing LME models. We explain how to use new visual inference methods for these purposes. The approach provides a unified framework for diagnosing LME fits and for model selection. We illustrate the use of this approach on several commonly available datasets. A large-scale Amazon Turk study was used to validate the methods. R code is provided for the analyses. Supplementary materials for this article are available online.

Recent Publications

More Publications

  • Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners

    Details PDF Code Journal

  • Variations of Q-Q Plots: The Power of Our Eyes!

    Details PDF Journal

  • Are You Normal? The Problem of Confounded Residual Structures in Hierarchical Linear Models

    Details Code Journal

  • Upper Midwest Climate Variations: Farmer Responses to Excess Water Risks

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  • Understanding Corn Belt farmer perspectives on climate change to inform engagement strategies for adaptation and mitigation

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Talks & Workshops

Recent Posts

My first blog post.



Data Science for Statistics

Give brief description of the project…


I am currently teaching the following courses at Carleton:

  • Math 265: Probability


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