I am currently a third year Ph.D candidate in the Quantitative Psychology program at the University of Virginia. I am working in the Mathematical Psychology Lab, where Timo von Oertzen is my advisor. I also spend time working in the Social Development Lab in the Curry School of Education with my secondary advisor, Sara Rimm-Kaufman, and in the Implicit Social Cognition Lab with Brian Nosek. I earned bachelor degrees in Applied Mathematics and Psychology from the University of Rhode Island, and plan to develop these interests further in graduate school.

The Meta-Science of Data Analysis in Behavioral Research

A big research interest of mine involves the meta-science of statistical analysis, or in other words, how data analysis is being used in present-day behavioral research. In a standard scientific analysis, one analyst or team presents a single analysis of a data set. However, there are often a variety of defensible analytic strategies that could be used on the same data. Raphael Silberzahn, Eric Uhlmann, Brian Nosek, and I are currently pursuing a project examining this potential variability in more detail. If you are interested in finding out more information, please see our project website on the Open Science Framework.

Exploratory Data Analysis for Longitudinal Data

The incorporation of exploratory longitudinal analysis techniques, namely identifying hetereogeneous sub-populations based on longitudinal trajectories, can provide new avenues to answer theoretically interesting research questions that often remain unanswered after traditional confirmatory measures have already been exhausted. While many techniques to accomplish this task exist, a common method used in psychology is the growth mixture model. Recent simulations have found that this analytic method shows a decline in performance for smaller sample sizes commonly found in psychological research. This raises the question: are there better methods available for smaller sample sizes? Using Monte Carlo simulations, I compared growth mixture models with other clustering methods, ranging on a spectrum from not-informed to very-informed, across different simulation conditions. Results indicated that despite this decreased performance for smaller sample sizes, growth mixture models still outperform simpler, more general clustering methods.

Student Engagement in Elementary School Mathematics

My experiences as a mathematics tutor and teaching assistant at the University of Rhode Island has made me interested in developing better teaching methods for statistics/math as well as the understanding the current state of STEM education in schools. To gain such substantive experience, I collaborate with Dr. Sara Rimm-Kaufman's Social Development Lab in the Curry School of Education to assess how student engagement in math is related to both internal psychological constructs (such as math self-efficacy) and student-teacher interactions.

Research Methods & Data Analysis II Lab, Spring 2013

Department of Psychology, University of Virginia
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Research Methods & Data Analysis I Lab, Fall 2012
Department of Psychology, University of Virginia
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Honors Quantitative Methods in Psychology, Spring 2011
Department of Psychology, University of Rhode Island

URI 101 Co-Instructor, Fall 2010
Office of Internships and Experiential Education, University of Rhode Island

Dan Martin

Contact


    102 Gilmer Hall
    University of Virginia
    P.O. Box 400400
    Charlottesville, VA 22904
    dm4zz at virginia dot edu

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