In MIDFIELDR/midfieldr: Tools and Methods for Working with MIDFIELD Data in 'R'

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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

: $$ \small S = \frac{N_g}{N_e} = \frac{\mathrm{number\ of\ graduates\ of\ a\ program}}{\mathrm{number\ ever\ enrolled\ in\ the\ program}} $$

: 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+Orr+others:2012].

Goals

Task

: Compute and compare the stickiness of Civil, Electrical, Industrial, and Mechanical Engineering programs with students grouped by race/ethnicity and sex.

Purpose

: The case study illustrates how we work with student-level data. Starting with the curated data and concluding with a chart of the metric, we focus throughout on our process and the underlying rationale.

Constraint

: While we provide all the necessary code, we limit our discussion of the code (functions, arguments, syntax, etc.) to meet the constraint of providing a brief, yet complete, case study. Such discussions are left to later articles. One can always use the R help system to read more about a data set or function.