I am currently a Senior Data Scientist with Organizational Solutions at McKinsey & Company. In August 2015 I received my Ph.D from the Quantitative Psychology program at the University of Virginia, where I worked with Brian Nosek, Sara Rimm-Kaufman, and Timo von Oertzen on projects focusing on quantitative challenges found in applied educational research. I also participated in the July 2015 Insight Health Data Science program in Boston. My interests include statistics, data mining/machine learning, education, and open science. If you look around, you will find some blog posts, publications, and relevant code. You can also see a copy of my resume here.
mleda: Contains exploratory plotting and model validation functions to assist applied educational researchers in the analysis of two-level multilevel models.
IAT: Contains a dplyr implementation of the D-Score algorithm for the Implicit Association Test, as well as plotting functions to visualize the raw data from these tests.
*Martin, D. P. & von Oertzen, T. (2015). Growth mixture models outperform simpler clustering algorithms when detecting longitudinal heterogeneity, even with small sample sizes. Structural Equation Modeling, 22, 264 - 275.
Martin, D. P., & Rimm-Kaufman, S. E. (2015). Do student self-efficacy and teacher-student interaction quality contribute to emotional and social engagement in fifth grade math? Journal of School Psychology, 53, 359 - 376.
Martin, D. P. (dissertation). Efficiently exploring multilevel data with recursive partitioning.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, 943
Ebersole, C. R. et al. (2016). Many Labs 3: Evaluating participant pool quality across the academic semester via replication. Journal of Experimental Social Psychology.
* Fun fact: This publication gave me an Erdös number of 4:
Daniel P. Martin → Timo von Oertzen → Gustav Nordh → Pavol Hell → Paul Erdös