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The Supplemental Material includes a sample dataset, R code to model the building and analysis presented in the paper, and an HTML output from the R code. To continue developing reliable and generalizable knowledge, PER should use hierarchical models when analyzing hierarchical datasets. There is no post hoc fix, however, if researchers use inappropriate single-level models to analyze multilevel datasets. Research can leverage multi-institutional datasets to improve the field’s understanding of how to support student success in physics. We then present analysis of a dataset from 112 introductory physics courses using both multiple linear regression and hierarchical linear modeling to illustrate the potential impact of using an inappropriate analytical method on PER findings and implications. In this publication, we outline the theoretical differences between how single-level and multilevel models handle hierarchical datasets. Hierarchical models (also known as multilevel models) account for this hierarchical nested structure in the data. Presents first of two-part editorial (second part to appear in next journal issue) proposing guidelines for developing useful tables to report multiple regression outcomes. The improper use of single-level models to analyze hierarchical datasets can lead to biased findings. However, education datasets can have hierarchical structures, such as students nested within courses, that single-level models fail to account for. Physics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression).