  Across the field of education research there has been an increased focus on
the development, critique, and evaluation of statistical methods and data usage
due to recently created, very large data sets and machine learning techniques.
In physics education research (PER), this increased focus has recently been
shown through the 2019 Physical Review PER Focused Collection examining
quantitative methods in PER. Quantitative PER has provided strong arguments for
reforming courses by including interactive engagement, demonstrated that
students often move away from scientist-like views due to science education,
and has injected robust assessment into the physics classroom via concept
inventories. The work presented here examines the impact that machine learning
may have on physics education research, presents a framework for the entire
process including data management, model evaluation, and results communication,
and demonstrates the utility of this framework through the analysis of two
types of survey data.
