Adjusting for guessing and applying a statistical test to the disaggregation of value-added learning scores

Authors

Ben O. Smith, University of Nebraska at OmahaFollow
Jamie Wagner, University of Nebraska at Omaha

Author ORCID Identifier

Ben O. Smith

Document Type

Article

Publication Date

10-15-2018

Publication Title

The Journal of Economic Education

Abstract

In 2016, Walstad and Wagner developed a procedure to split pre-test and post-test responses into four learning types: positive, negative, retained, and zero learning. This disaggregation is not only useful in academic studies; but 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 article extends the original work by accounting for guessing and provides adjusted estimators using the existing disaggregated values. Further, Monte Carlo simulations of the adjusted learning type estimates are provided. Under certain assumptions, an instructor can determine if a difference in positive (or negative) learning is the result of a true change in learning or “white noise.”

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in THE JOURNAL OF ECONOMIC EDUCATION on 15 October 2018, available online: https://doi.org/10.1080/00220485.2018.1500959

Recommended Citation

Smith, Ben O. and Wagner, Jamie, "Adjusting for guessing and applying a statistical test to the disaggregation of value-added learning scores" (2018). Economics Faculty Publications. 40.
https://digitalcommons.unomaha.edu/econrealestatefacpub/40