STRESS AND PERFORMANCEIN NAVY SELECTION AND CLASSIFICATION

DR. STEPHEN E. WATSON

DIRECTOR, NAVY SELECTION AND CLASSIFICATION

09FEB16

FEDERAL EXECUTIVE INSTITUTE, CHARLOTTESVILLE VIRGINIA

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.

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

Yerkes-Dodson Law

4

5RIDE Ability Model: Efficient Resource Allocation

0%

100%

TEST SCORE

SCH

OO

L SU

CCES

S

1000

85%

85

QS-FPPS Correlation

Example Graph of Qual-Score Against FPPS (AM/M FY08-FY11 : Population 2045)

6

190 200 210 220 230 240 250 260 27070%

75%

80%

85%

90%

95%

100%

AM/Male (n=2045)

Qualification Score (AR+AS+MK+VE)

FPPS

RIDE 7

FIRS

T PA

SS P

IPEL

INE

SUCC

ESS

CUTSCORE

POINT OF DIMINISHED RETURN

CUTSCORE COMPOSITE

QS-FPPS Correlation

8

190 200 210 220 230 240 250 260 27070%

75%

80%

85%

90%

95%

100%

AM/Male (n=2045)

Qualification Score (AR+AS+MK+VE)

FPPS

Cut-score

PDR

9YERKES-DODSON

Activation / Stress

PER

FOR

MA

NC

E

hi low

10RIDE Ability Function

Scho

ol S

ucce

ss(F

irst P

ass P

ipel

ine

Succ

ess)

CUTSCORE

POINT OF DIMINISHED RETURN

CUTSCORE COMPOSITE

AFQTOptimal challenge level

ASVAB

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

11

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

12

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

13

RIDEMeasures of

Effectiveness

05/02/2023 14

First Pass Pipeline Success (A-School)FPPS vs RIDE Rank (binned) (n=125k)

80%

82%

84%

86%

88%

90%

92%

94%

1-5

11-1

5

21-2

5

31-3

5

41-4

5

51-5

5

61-6

5

71-7

5

81-8

5

91-9

5

101-

105

111-

115

121-

125

131-

135

141-

145

151-

225

RIDE Rank (bin)

FPP

S

05/02/2023 15

Advancement

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

1-5

6-10

11-1

5

16-2

0

21-2

5

26-3

0

31-3

5

36-4

0

41-4

5

46-5

0

51-5

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56-6

0

61-6

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66-7

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71-7

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81-8

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86-9

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91-9

5

96-1

00

101-

105

106-

110

111-

115

116-

120

121-

125

% R

ecru

its n

ow a

t >=

E6

RIDE Rank (bin)

Recruits Attaining E-6 vs RIDE Rank (binned) (n=105k)

05/02/2023 16

Retention

1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81+40%

45%

50%

55%

60%

65%

70%

75%

80%First Term Re-enlistment

RIDE rank (binned)

% S

ailo

rs R

e-en

listin

g

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

Questions?