2023 ASEE Annual Conference & Exposition

A Primer on Working with Longitudinal Student Unit Records

Presented at Faculty Development and Research Programs (NEE)

Longitudinal, student-level data are a rich resource for characterizing how students navigate the terrain of higher education. Learning to work effectively with such data, however, can be a challenge. In this paper, we share some of our experiences over years of conducting research with the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). MIDFIELD contains individual student-level records for all undergraduate students at 19 US institutions with over 1.7 million unique students. This paper focuses on our lessons learned about processing longitudinal data to prepare it for analysis. We describe and define the steps that we take to process the data including filtering for data sufficiency, degree-seeking, and program (major), then classifying by completion status and demographics. We use the examples of calculation of graduation rate and stickiness to show the details of how the processed data is used in analysis. We hope this paper will help introduce the landscape of longitudinal research to a wider audience and provide tips for working with this valuable resource.

Authors
  1. Richard A. Layton Layton Data Display [biography]
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