Title: Quantitative Small-N Methodologies Speaker: Assistant Professor Aaron Warren Mathematics/Statistics/Physics Department Purdue University North Central Abstract: Although many quantitative techniques exist in education research, there is a lack of methods available for measuring the dynamical behavior of students, groups, and classes at both small timescales (second-to-second) and larger timescales (day-to-day). Also, established methods generally require large-N statistical power, leaving instructors in small-N environments with few options for quantitative course assessment. This talk presents an introduction to two techniques that can address such shortcomings: network analysis and ARIMA analysis. Network analysis employs statistics and graph theory to quantitatively characterize behavior in student groups, identify patterns in group behavior, and test for correlations of observed behavior with independent factors. ARIMA (autoregressive integrated moving average) analysis is a general approach to modeling time-series data, capable of identifying trends underlying noisy data, as well as changes in trends and the impacts of transient exogenous factors. The emphasis of this talk is the ongoing development of methodologies for utilizing these tools, with a view toward how they may provide new information in physics education research.