Part 1 of a case study in three parts, illustrating how we work with longitudinal student-level records.
Goals. Introducing the study.
Data. Transforming the data to yield the observations of interest.
Results. Summary statistics, metric, chart, and table.
Definitions
- student-level data
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Data at the “student-level” refers to information about individual students including, for example, demographics, programs, academic standing, courses, grades, and degrees. Also called Student Unit Records (SURs). In MIDFIELD, student-level data are compiled by an institution and anonymized and curated by the MIDFIELD data steward.
- stickiness
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Stickiness \((S)\) is the ratio of the number of graduates of a program \((N_g)\) to the number ever enrolled in the program \((N_e)\).
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Stickiness is a more-inclusive alternative to graduation rate as a measure of a program’s success in attracting, keeping, and graduating their undergraduates. Stickiness includes many students excluded by graduation rate such as part-time students, transfers, students admitted in any term, and migrators (Ohland et al. 2012).
Goals
The technical goal of the case study is to compare the program stickiness of Civil, Electrical, Industrial, and Mechanical Engineering programs with students grouped by program, race/ethnicity, and sex.
The meta-goal of the case study is to illustrate a typical procedure for working with student-level data.
What the case study does. We focus on our procedures and underlying rationale for working with student-level data. For reproducibility, we provide all the necessary code.
What the case study does not do. To render the case study complete but brief, we omit the details of package syntax or functions, leaving such discussions to later articles. One can always use the R help system to read more about a data set or function.