9/4/2011 Update: granovaGG is now available directly from CRAN.
Just over one year ago, I wrote about creating Dependent Sample Assessment Plots (DSAP) Using granova and R. Since then, Brian Danielak has been developing a new, ggplot2-based version of granova named granovaGG, which is almost ready for release on CRAN. This article updates my earlier granova-based version, but leaves much of the article text unchanged.
Dependent Sample Assessment Plots (DSAP) constitute a way of visualizing data in the context of two dependent sample analyses. One (of at least four ways) to think about this would be to think of pre-intervention and post-intervention response data scores, when studying the effects of intervention.
Suppose you’re an educator and you administer an assessment to students at the beginning of a unit asking about their level of confidence or understanding of a topic. You then teach a lesson that spans some period of time. At the end you collect responses to the same questions again. You now have a dependent sample: two responses that related to the same individual for some number of individuals.
Like many educators, I worry about the level of effort that my students commit to their studies (the process) and the quality of their work (the product). We call the process many things: engagement, time on task, passion… But we mean to describe that self-driven, motivated commitment to learning for the sake of learning that we value.
Unfortunately, in many educational environments, the standard proxy for effort is the course grade. Grades are a poor proxy, but are so ingrained in educational practice (in some of the institutions where I teach) and in students’ minds that it may be useful to consider a way to structure grade rewards to encourage the genuine engagement from students that we desire.