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

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  • Ben O. Smith
  • Jamie Wagner

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.”

Suggested Citation

  • 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, Taylor & Francis Journals, vol. 49(4), pages 307-323, October.
    • Handle: RePEc:taf:jeduce:v:49:y:2018:i:4:p:307-323
    DOI: 10.1080/00220485.2018.1500959
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    Cited by:

    1. Emerson, Tisha L.N. & McGoldrick, KimMarie & Wagner, Jamie, 2023. "Decomposing a pre- post-test outcome to measure the effect of cooperative learning on student achievement," International Review of Economics Education, Elsevier, vol. 44(C).

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