Adjusting for Guessing and Applying a Statistical Test to the Disaggregation of Value-Added Learning Scores
Ben O. Smith & Jamie Wagner (2018) Adjusting for guessing and applying a statistical test to the disaggregation of value-added learning scores, The Journal of Economic Education, DOI: 10.1080/00220485.2018.1500959
Posted: 27 Mar 2017 Last revised: 19 Oct 2018
Date Written: October 15, 2018
In 2016, Walstad and Wagner developed a procedure to split pre- and post-test responses into four learning types: positive, negative, retained, and zero learning. This disaggregation is not only useful in academic studies, it also provides valuable insight to the practitioner: an instructor would take different mitigating actions in response to zero versus negative learning. However, the original disaggregation is sensitive to student guessing. This paper extends the original work by accounting for guessing and provides adjusted estimators using the existing disaggregated values. Further, a statistical test is provided for the adjusted learning type estimates. Using this test, an instructor can determine if a difference in positive (or negative) learning is the result of a true change in learning or 'white noise.'
Keywords: Difference score, learning disaggregation, guessing, simulation
JEL Classification: A20, A22, A23, C63
Suggested Citation: Suggested Citation