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Title: A Primer on Working with Longitudinal Data
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.  more » « less
Award ID(s):
2142087
PAR ID:
10488308
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ASEE
Date Published:
Journal Name:
ASEE Annual Conference proceedings
ISSN:
1524-4644
Format(s):
Medium: X
Location:
Baltimore, MD
Sponsoring Org:
National Science Foundation
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