Selected Publications

This graphic shows the probability of providing a correct response to an item in a multidimensional item response theory (MIRT) model. In the 1PL, 2PL, and 3PL MIRT models, the choice of compensatory parameterizations can greatly affect the probability of a correct response.

We use a factor analytic approach to assess the dimensionality of the Interpersonal Reactivity Index (IRI). Specifically, we examine the validity of structures that have been commonly used in empathy research, but have yet to be psychometrically validated.

Recent Publications

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  • Construct Irrelevant Variance


  • Creating an R package for a Reproducible Workflow in Educational Assessment.

    Details Dynamic Learning Maps

  • Visualizing different levels of compensation in multidimensional item response theory models

    Details PNG

  • Evaluating an initialization tool for student placement into a map-based assessment.


  • Assessing cognitive and affective empathy through the interpersonal reactivity index: An argument against a two-factor model

    Details PDF

Recent & Upcoming Talks

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Recent Posts

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I recently converted my website from Jekyll to Hugo with blogdown. If you haven’t tried out blogdown yet, Yihui Xie just hosted a webinar that does a great job of introducing the package. This post won’t focus on how to use blogdown to create a website, but rather how to host that website on GitHub pages and use Travis-CI to automatically update the website. For this post, I’m assuming that you’re making a user or organization site.


If you haven’t seen it yet, there’s a great example of why it’s always important to visualize your data making its way around the Twitter-verse. A great demonstration of why we need to plot the data and never trust statistics tables! — Taha Yasseri (@TahaYasseri) May 1, 2017 Despite looking very different, all of these datasets have the same summary statistics to two decimal places. You can download the datasets, get details about the project, and read the whole paper by Justin Matejka and George Fitzmaurice here.


March Madness officially tips off tomorrow with the First Four games in Dayton before the round of 64 begins on Thursday. In this post, we’ll look at each team’s chance of advancing and winning the national title. We’ll also look at who was help and hurt most by how the committee seeded the tournament. As always, the code and data for this post are available on my Github page. The Ratings The team ratings come from my sports analytics website, Hawklytics.



Other useful R-related blogs and resources:


Soccer predictions using Bayesian mixed effects models

Models developed for the prediction of individual games, European domestic leagues, and the UEFA Champions League.


Sports analytics website for the analysis of college football and men’s college basketball.