Elea McDonnell Feit

@eleafeit.bsky.social

496 Followers 309 Following

Professor of Marketing at Drexel. Philadelphian. Bayesian.

Image shows an excerpt from the chapter.
A warning and a plea - As fields start to use more advanced quantitative / "cause" methods, there is a desire to help consumers of the research (four journal reviewers, editor) to easily assess the study quality and validity (e.g. JAMA causality language) ... - We need to push against this - need ways to help people understand and assess the (inherently untestable) assumptions in many studies.
screenshot of cited paper
screenshot of cited paper
screenshot of cited paper
Screenshot of a guide to identifying errors and misunderstandings in the data. It begins with "When you encounter a new dataset, assume from the start that it contains errors. These errors may stem from how the data was collected, processed, or stored. Additionally, you might misunderstand what the data represents. Such misunderstandings and errors can occur at any stage—from data collection to data delivery to data processing that you’ve done. Before jumping into analysis, your first and most critical task is to "get to know the data." This involves methodically exploring the dataset to uncover potential errors and gaps in understanding."
US flags displayed on the Drexel University campus in honor of current students who have served.