My Research
What I Study
Broadly, I have a very statistics-focused field of study. I received my B.S. in Statistics and am currently obtaining my master’s degree in Educational Psychology, with a specialization in Quantitative Methodology. For a snappier name, I study psychometrics.
The word psychometrics breaks down to essentially mean psychological measurement, but my focus is in an educational setting. To specify further, I study statistical models used to develop certain types of educational assessments and analyze their examinee responses.
The specific family of models I study are called diagnostic classification models, or DCMs. These models are fairly new in the educational measurement world, and a lot of research needs to be done on the subject, but I see a lot of potential in these types of models and strongly desire to contribute to the current literature surrounding DCMs.
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My Thesis
My master’s thesis consists of two simulations that I programmed in a language called R. These simulations examine the effects of a process called retrofitting. In psychometrics, this refers to using a diagnostic classification model to analyze the responses to a non-diagnostic test. This process has not been thoroughly researched yet, and while it is not recommended in practice, researchers should be as prepared as possible in case of a situation where they may need or wish to retrofit.
National Council on Measurement in Education
Chicago, IL 2023
I was able to present about these two simulations at the annual conference for the National Council on Measurement in Education in Chicago in April 2023. It was an incredible trip and a wonderful learning experience, and I was honored to have been invited to speak about my research.
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PUBLICATIONS AND PRESENTATIONS
Moore, A. & Madison, M.J. (2023, April). Examining the effects of retrofitting diagnostic models to item response theory data. Paper presented at the annual meeting of the National Council on Measurement in Education.
Madison, M. J., Jeon, M., Moore, A., Zor, S., Fager, M., Maas, L., Haab, S., & Cotterell, M. E. (2023). The effects of measurement and structural model misspecifications in longitudinal diagnostic classification models. Manuscript under review.
Madison, M. J., Fager, M., & Moore, A. (2021, June). Weighing parsimony and flexibility in diagnostic classification model selection. Paper virtually presented at the annual meeting of the National Council on Measurement in Education.
