Contribution: This work provides evidence of various approaches to studying longitudinal student unit record data in undergraduate education in the USA and the outcomes that can be realized using a large multi-institutional longitudinal dataset, Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD). Background: Cross-sectional studies introduce a variety of sources of error in estimating student pathways and outcomes. Longitudinal outcomes that ignore pathways also miss important information, and some populations are systematically excluded (such as transfer students). Intended Outcomes: By providing examples of how longitudinal student unit-record data can be analyzed and the results that can be expected, this work aims to deepen the research toolbox in engineering education. Findings: MIDFIELD is being used to support studies of demographic and financial trends among universities in the southeastern USA, required math and science course grades and disciplinary cultures, time to find graduation major, educational data mining, and applications of selected advanced models.