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Using the Scoring Feature to Segment Your Applicant Pool

Using the Scoring Feature to Segment Your Applicant Pool. Emil Chuck, Ph.D . Cleveland, Ohio. Identifying Candidates in WebAdmit. Are you really attracting/choosing the best students?. How are you measuring this?. Common measurements at the conclusion of every application cycle.

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Using the Scoring Feature to Segment Your Applicant Pool

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  1. Using the Scoring Featureto Segment Your Applicant Pool • Emil Chuck, Ph.D.Cleveland, Ohio • Identifying Candidates in WebAdmit

  2. Are you really attracting/choosing the best students? • How are you measuring this?

  3. Common measurements at the conclusion of every application cycle • Are you really attracting/choosing the best students? • Accreditation goals • Number of applications received (per enrolled spot) • Incoming class GPA (rolling average over 3 years) • Incoming standardized exam score • “Survey” results • Peer-perceived reputation • Endowment and donor loyalty • Institutional assessment goals • Retention • Time to graduation • Racial/ethnic diversity

  4. Admission Decision-Making Process Triage/Screening Extend Offers

  5. Metrics • Grade point average • Standardized test scores • Attributes • Socioeconomic status • Underrepresented minority status • Geographic considerations • Experiences • Personal journey/distance traveled • Preprofessional observation • Community/volunteer service • Research/creative scholarship • Are you attracting and selecting the best based on: • History of GPA? • Standardized score history? • Number of students fitting specific demographics (SES, URM, Geography)? • Collective leadership accomplishments among students? • Diverse Stories? • Are you really attracting/choosing the best students?

  6. Shrinking applicant pool, rising quality concerns • The undergraduate student admissions pool is decreasing. • Some schools are unable to maintain their enrollment goals. • In the 1980’s, the applicant pool for dentistry dramatically decreased. • Quality of applicants was dramatically lower than desired (GPA and DAT) • The ADEA AADSAS applicant pool is slowly decreasing after experiencing a plateau. • How can you assure yourself that there are quality applicants available to fill your class? • How would you adjust?

  7. Do you use a formula to segment your applicant pool?(Some applicants do.) • ARS Score = (Stats * 5) + (Research * 3) + (Clinical Experience [9, 5, -10]) + (Shadowing [6, -5]) + (Volunteering * 2) + (Leadership * 2) + (Miscellaneous * 3) + [ (Undergrad-1) * 3] + [ (URM/SED - 1) * 7] + [(Upward Trend – 1) * 4] • Student Doctor Network "WARS score" • (GPA * 10) + MCAT estimate • Student Doctor Network LizzyM System

  8. Developing a percentile table • Exporting data for statistical analysis

  9. Develop a percentile table (using Microsoft Excel functions) • Export all applicant data • WebAdmit > Reports & Exports > Export Manager • [Create a New Export] using All Applicants • Export [ Everyone ] to a [ Microsoft Excel (.xls) ] file named [ YOUR CHOICE ] … • Export desired fields: [ Total with +/- GPA ], [ Science with +/- GPA ], … • When opening the new Excel file with your data, you can calculate: • Midrange (25th percentile to 75th percentile) using QUARTILE function • Deciles (10th to 90th percentile) using PERCENTILE function • If you have a GPA cutoff of 3.0, how many applicants do you exclude? • PERCENTILERANK function in Excel

  10. Monitoring holistic admissions with data • What about cohorts of interest?

  11. Monitor the impact of decisions in your process Triage/Screening Extend Offers

  12. Applicant subgroups of interest • Female / Male • In-state residency / Out-of-state / International • Race/ethnicity, specifically URM / Asian • Socioeconomically disadvantaged • Utilization of ADEA AADSAS EO Score for parental employment/occupation • Non-traditional / Postbaccalaureate • How does your admissions process evaluate applicants? • Can you detect preferences/biases in your triage/selection decisions?

  13. Selection of admitted students • Percentiles show effects of screening criteria in selecting applicants. • In this example, undergraduate GPA  holds weight. • GPA standard may result in disparities in selection percentile threshold. • Holistic review should be reflected with smaller differences in percentile range between applicant pool, interviewed pool, and accepted pool. • For applicants with a graduate GPA, despite having a low overall GPA, interview candidates within this group are selected based on graduate GPA.

  14. Creating point tables (lookup tables) • Evaluating applicants by metric percentiles

  15. WebAdmit • Scoring models and point (lookup) tables • WebAdmit > Management > Scoring • Scoring Models rely on Point Tables

  16. WebAdmit • Available point table templates in WebAdmit • States and Territories • Text or string input • GPA breakdown tenths • Range of numbers • GRE section (any standardized exam results can work) • Range of numbers • Interview points • Specific number

  17. Title (name of point table) • Point table type • Description • [Submit to create] • States and territories: • Can attribute "points" for a scoring model that gives more weight for state abbreviations noted in the application. • State of legal residence • State of birth • Create a point table

  18. Create a point table (lookup estimated GPA percentile rank)

  19. Create a scoring model with point tables Note: US DAT values are in DAT Official scorable items, but Canadian DAT values are in a different field!

  20. Develop multiple scoring models

  21. Reviewers can view scores and percentile rank for each metric. • Scoring can be viewed on WebAdmit (after GPA verification)

  22. Summary scoring vs. Individual scoring • Scoring feature can help select candidates • Verify that all individual components have a score (percentile) reported. • Based on summary score: triage • Highly desirable (component average score is [85th percentile]) • Very desirable (67th percentile average) • Qualified (50th percentile average) • Traditional metrics (oGPA, sGPA, DAT AA, DAT PAT, last 60 oGPA, last 60 sGPA) • For 6 components:: High 400-500 total, Very 300-400 total • Postbac/graduate metrics only (cohort percentiles) • URM/SED metrics only (cohort percentiles)

  23. Using the percentile lookup (point) tables • Percentile lookup tables created using data set from previous applicant pool (presuming not much changed from year-to-year) • Any verified GPA • Any local GPA • Standardized exam results • Use percentiles to read-out how the applicant compares to others • Percentiles should not substitute for in-depth reading and consideration

  24. “Talent is always jagged”

  25. Key takeaways • Use percentilesto compare applicants rather than the raw values • “Desirable applicants should perform within the top [67th] percentile in their cohort.” • Analyze by desired cohorts separately • Use WebAdmit Scoring Models and Point Tables to assist in triage for the subsequent applicant cycle • You can create separate models and tables according to desired cohorts or different times/rounds for interview consideration. • Train your screeners on properly using scoring models. • Evaluate your screeners’ decisions (maybe a future talk at LUC? )

  26. Emil Chuck, Ph.D. etchuck@yahoo.comLinkedIn: etchuck The data slides that were presented during Dr. Chuck’s presentation have been redacted, but the substance will be published in a peer-reviewed article accepted by the Journal of Dental Education. Thank You.

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