Abstract
The purpose of the present paper is to encourage textbook authors, quantitative instructors, curriculum writers, and software developers to move away from the use of isolated apparently disconnected analyses and instead move towards the use of the general linear model as a foundational framework for graduate level statistics training. It is argued that an understanding of modeling, simple linear equations, and commonly used analogous statistical terms will facilitate students understanding of frequently used parametric analyses. Additionally, this holistic approach will equip students with the necessary preparatory skills to understand newer analytical approaches. Three heuristic examples are provided.DOI:10.2458/azu_jmmss_v6i2_skidmore
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