I graduated from the University of Minnesota with a PhD in Quantitative Psychology/Psychometric Methods, an MA in Psychometric Methods, and an MS in Statistics. My bachelor’s degree is in Mathematics and Psychology from Syracuse University. Most of my research and expertise involves Computerized Adaptive Testing (CAT) based on Item Response Theory (IRT). IRT proposes underlying trait(s) that account for responses to test/questionnaire items, and CAT administers those items in real-time based on responses to previous items. That is to say, IRT provides a foundation for measurement, and CAT determines the algorithm for efficiency in test administration. My dissertation applied CAT and IRT to multiple-trait classification tasks with a well-defined passing score. I am also interested in simplifying procedures and analyses using the R programming language.
I am currently employed as a Psychometrician at Pearson VUE in Bloomington, MN. Before coming to Pearson, I interned at ACT and the ARRT. At ACT, I applied my classification testing knowledge to determine whether an exam could be best administered as a classification CAT, and at the ARRT, I improved exam and process design for various psychometric domains, including standard settings, task analyses, and item types. I have also taught undergraduate and graduate courses in Statistics, Data Analysis, Psychological Measurement, and R Programming.
Aside from IRT/CAT, my academic interests include estimation algorithms, model-based cluster analysis (also known as mixture modeling), matrix calculus, factor analysis, philosophy of statistics, philosophy of science, history of statistics, and statistical pedagogy.