Decision-Based Learning in Multiple Regression and Structural Equation Modeling Courses

Abstract

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

Citation

Howland, S.J. and Larsen, R.A.A. (2021), "Decision-Based Learning in Multiple Regression and Structural Equation Modeling Courses", Wentworth, N., Plummer, K.J. and Swan, R.H. (Ed.) Decision-Based Learning: An Innovative Pedagogy that Unpacks Expert Knowledge for the Novice Learner, Emerald Publishing Limited, Leeds, pp. 79-92. https://doi.org/10.1108/978-1-80043-202-420211008