...
The model for providing ongoing enrollment analytics data/insights should be:
flexible: can integrate new requirements rapidly without requiring wholesale changes
robust: can withstand uncertainty, constant changes, and missing data without compromising overall integrity or validity
comprehensive: all schools, programs, centers and units
with programs or courses that ultimately generate a transcriptare includedinclusive: ensuring our core data reflects as accurately as possible the multiple disparate identities that every single prospect, applicant, and student brings with them
Needs to accommodate all kinds of schools and programs, most especially “non-traditional cycle” graduate programs
multiple annual entry terms
spring or summer start dates
alternate application stages like interviewing/traffic rules
small application populations
Needs to accommodate variety of dataset sizes; not all programs will have statistically significant numbers or numbers that make sense
Points of ambiguity:
What counts as a “cycle”?
Application changes within cycle (MPH change concentration or start term from spring to fall)
Clearly define when admissions stops being the “system of record” (i.e., dropping during add/drop, etc.)
SIS quick admit
SIS history of admit decline? Program Action codes?
What counts as a complete application?
Deferrals
Withdrawals
Deny vs. Do Not Interview
our policies vs. external reporting organization requirements/policies (i.e., what APTA considers complete vs. what we consider complete.
Race/Ethnicity – different prompts in different systems, how do classify consistently (East Asian vs. West Asian, Hispanic Spanish, etc.)
...