1. STRESS AND PERFORMANCE
IN NAVY SELECTION AND
CLASSIFICATION
DR. STEPHEN E. WATSON
DIRECTOR, NAVY SELECTION AND CLASSIFICATION
09FEB16
FEDERAL EXECUTIVE INSTITUTE, CHARLOTTESVILLE VIRGINIA
2. Loosely Based on…
Praxis: Bold as Love
-OR-
Testing, Validating and Employing an Empirical Model of Human
Performance in a High Performing Organization. In, Human Performance
Enhancement: Insights, Developments and Future Directions from Military
Research. O’Connor and Cohn (Eds.) 2010.
3. Navy Selection & Classification -
Characteristics
Problem Characteristics
– Recruits arrive for classification one at a time
– No way of knowing whether a ‘better person for the job’ will turn up tomorrow
– Not all recruits are qualified for all available jobs
– Quotas on each job
No exact optimization exists for this problem
– Putting each recruit into the job which is individually best for them will probably not lead to the best
overall outcome
– Putting a recruit in a job for which he/she is “over-qualified” leads to …
– fewer such jobs available for later recruits
– possible that no jobs are suitable for last arrivals
– waste of ‘talent’
– Putting a recruit in a job for which he/she is “under-qualified” leads to …
– higher likelihood of failure at the job (at Initial Skills Training)
– later arrivals of high ability are likely to be ‘wasted’
… Balance is the key
3
10. RIDE Ability Function 10
SchoolSuccess
(FirstPassPipelineSuccess)
CUTSCORE
POINT OF DIMINISHED RETURN
CUTSCORE COMPOSITE
AFQTOptimal challenge level
ASVAB
11. Rating Identification Engine (RIDE)
Model: Efficient Resource Allocation
Considers first pass pipeline success (FPPS) as the training
success measure
– FPPS: pass entire training pipeline, no setbacks
Reduces exaggerated “best” test score
– Developed plateau relationship between training success and cut score,
vice simple linear relationship
– Modified utility score by a factor reflecting the degree of difficulty of a job
Penalizes for “over-qualification” of applicant
– AFQT based for a given program/rating, to minimize resource “wastage”
Increases number of jobs applicant “optimally” qualified for
– Increases number of ratings “tied” for the top of the list
– Increases opportunity for interest based vocational guidance
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12. RIDE Score
For an individual Sailor i, the score for a given job r is found by:
RCS = 0.5 * Hr * Ŝir + 0.5 * Qir
where:
Qir = is the AFQT penalty function,
= 1 if the Sailor-AFQT < AFQT-μr + 0.5 * AFQT-σr
= 0 if the Sailor-AFQT > AFQT-μr + 3.5 * AFQT-σr
= linear interpolation if Sailor-AFQT between these values
Ŝir = is the school success function
= 1 if the Sailor-QSir > PDR r
= 0 if the Sailor-QSir < Cut-score r
= linear interpolation if Sailor-QS between these values
Hr = job ‘hardness’ factor – a normalized function of the rating PDR
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13. RIDE Web Services Interfaces
PRIDE MOD
– To classify Navy applicants
– Provides classifier/applicant with a job ranking (recommendation)
Fleet RIDE
– Whenever a Recruit or Trainee is re-classified
– Whenever an Apprentice Sailor applies for Rating Entry
– Whenever a Fleet Sailor is ‘qualified’ for conversion
– Whenever a Sailor transitions from Active to Reserve or vice versa
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18. References
Watson, S. (2010) Testing, Validating and Employing an Empirical Model of
Human Performance in a High Performing Organization. In, Human
Performance Enhancement: Insights, Developments and Future Directions from
Military Research. O’Connor and Cohn (Eds.)
Yerkes, R. M. & Dodson, J. D. (1908). The Relation of Strength of Stimulus to
Rapidity of Habit-Formation, Journal of Comparative Neurology and Psychology,
18, 459-482.
Clark, D. (1999). Yerkes-Dodson law – Arousal. Retrieved May 23, 2004 from:
http://www.nwlink.com/~donclark/hrd/history/
arousal.html
“Fleet-RIDE: Enabling Technology for Sailor Continuous Career Counseling”,
Watson, S. E., & Blanco, T.A., Interservice/Industry Training, Simulation, and
Education Conference (I/ITSEC), 2004
