Statistical tools such as growth curve modeling and time-series analyses are techniques that are useful to examine learning over time, and could potentially be used to assess the desired attributes of detecting variability from week to week and differentiating between good and poor work.  However not all components of the OPT total score are changing uniformly, with many reaching their maximum possible contribution on the first or second worksheet evaluated.  Further the mixture of limited-category ordinal variables from different measurement scales presents challenges for these continuous data techniques, and alternate strategies are presented.

Explorations of the individual component contributions via factor analysis to assist in the refinement of the OPT model in this clinical setting is also presented.  Again variable limitations make application of this and many other common multivariable methods problematic.These issues coupled with the lack of a common standard for each week’s assessment make comparisons between students using the tool a challenge.  However descriptive and less-complicated analyses have proven useful.