A short overview of survival analysis and how it can be used in HR or workforce analytics to better predict employee turnover.

1. Survival Analysis and the
Proportional Hazards Model for
Predicting Employee Turnover
Primary source:
Hom, P. W., & Griffeth, R. W. (1995). Employee turnover.
Cincinnati, OH: Southwestern College Publishing.
Tom Briggs
tbriggs@gmu.edu
November 2014

2. AUDIENCE SURVEY
TBRIGGS@GMU.EDU [ 2 ] NOVEMBER 2014

3. “Our new Constitution is now
established, and has an appearance
that promises permanency; but in
this world nothing can be said to be
certain, except death and taxes.”
--Benjamin Franklin (1789)
TBRIGGS@GMU.EDU [ 3 ] NOVEMBER 2014

4. “In this world nothing can be said to
be certain, except death, taxes, and
employee turnover.”
--George Mason Student (2014)
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5. ROAD MAP
BACKGROUND
WHY
Survival
Analysis
Survival
Analysis
RESULTS
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6. BACKGROUND
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7. FIRST PIONEERS
Peters,
L.
H.,
&
Sheridan,
J.
E.
(1988).
Turnover
research
methodology:
A
criCque
of
tradiConal
designs
and
a
suggested
survival
model
alternaCve.
Research
in
personnel
and
human
resources
management,
6,
231-­‐262.
Morita,
J.
G.,
Lee,
T.
W.,
&
Mowday,
R.
T.
(1989).
Introducing
survival
analysis
to
organizaConal
researchers:
A
selected
applicaCon
to
turnover
research.
Journal
of
Applied
Psychology,
74(2),
280–292.
Singer,
J.
D.,
&
Wille/,
J.
B.
(1991).
Modeling
the
days
of
our
lives:
using
survival
analysis
when
designing
and
analyzing
longitudinal
studies
of
duraCon
and
the
Cming
of
events.
Psychological
Bulle/n,
110(2),
268.
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8. WHO IS THIS MAN?
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9. SIR DAVID COX
#9 on the George Mason Department of Statistics list of
“Great Statisticians” – just below Tukey and William Sealy Gosset.
Known for the Cox proportional hazards model, an application of
survival analysis.
And yes…he rocks this look pretty much all the time.
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10. BY ANY OTHER NAME
StaCsCcs
• Survival
analysis
• Reliability
theory
Engineering
• Reliability
analysis
• DuraCon
analysis
Economics
• DuraCon
modeling
Sociology
• Event
history
analysis
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11. WHY SURVIVAL ANALYSIS
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12. WHAT SIZE IS THE HERD?
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13. WHAT SIZE IS THE HERD?
A. 39 SHEEP
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14. WHAT SIZE IS THE HERD?
B. 40 SHEEP
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15. WHAT SIZE IS THE HERD?
C. DON’T KNOW
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16. WHAT SIZE IS THE HERD?
A. 39 SHEEP
B. 40 SHEEP
C. DON’T KNOW
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17. WHAT SIZE IS THE HERD?
C. DON’T KNOW - CORRECT!
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18. VOCABULARY: CENSORING
CENSORING is a missing data problem
common to survival analysis
(and cross-sectional studies…)
In the herd example, our cross-sectional
“view” was censored in two
respects: what came before and what
is yet to come!
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19. HOM & GRIFFETH ON WHY
• Cross-sectional study start and end dates
are usually arbitrary
• Short measurement periods weaken
correlations – fewer employees leave –
smaller numbers of “quitters” shrink
turnover variance
• Cross-sectional approach distorts results by
arbitrarily dictating which participant is a
stayer and which is a leaver
• Cross-sectional approach neglects tenure –
10 days or 10 years treated the same
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20. NOT WHETHER, BUT WHEN
Death, taxes, and employee turnover:
All employees will ultimately turn over, so the
question is not whether, but when?
And a related question: what effects do
potential predictor variables have on
turnover probability?
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21. VISUAL: CENSORING
leZ
stayed
Right-censoring most common in turnover research;
an employee could quit the day after the study ends!
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22. SURVIVAL ANALYSIS
RESULTS
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23. SURVIVAL ANALYSIS RESULTS
• Generates conditional probabilities – the
“hazard rate” – that employees will quit
during a given time interval.
• Generates graphs of the survival function –
the cumulative probability of staying.
• Allows for subgroup comparison based on
predictor variables.
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24. SURVIVAL RATES
1.05
1.00
Survival Rates for New Staff Accountants
Cumulative Survival Rate Tenure (in months)
0.95
0.90
0.85
0.80
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
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25. SURVIVAL PREDICTORS
1.05
1.00
0.95
0.90
0.85
0.80
Survival Rates for New Staff Accountants as Functions of
RJPs and Job Tenure
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Cumulative Survival Rate
Tenure (in months)
Traditional Job Preview Realistic Job Preview
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26. PROPORTIONAL HAZARD
• Profile comparisons “ill-suited for estimating
the temporal effects of continuous predictors
and of several predictors simultaneously.”
• Uses regression-like models – the dependent
variable is the (log of) entire hazard function
• Assumes a predictor shifts hazard profile up
(RJP = 0) or down (RJP = 1) depending on
predictor scores and that each subject’s
hazard function is some constant multiple of
the baseline hazard function
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27. PROPORTIONAL HAZARD
BENEFITS
• Can examine multiple predictors (continuous
or categorical) and estimate unique
contribution of each while statistically
controlling other predictors
• Estimated βs interpreted as regression
weights, or transformed into probability
metrics by antilogging
• RJP example: RJP subjects have 0.61 times
the risk of quitting than control subjects (or
hazard decreased by 39 percent)
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28. HAZARDS OF
PROPORTIONAL HAZARD
• Assumes different predictors all have same
log-hazard shape – Singer and Willett (1991)
found many examples of violations
• Assumes different predictors are constant
over time (parallel hazard profiles)
Investigators should test assumptions of shape
and parallelism (see Singer and Willett, 1991)
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29. CONCLUSION
Survival analysis and the proportional
hazard model can offer a compelling
alternative to cross-sectional
methodology for investigating
dynamic relations between turnover
and antecedents.
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30. Contact:
Tom Briggs
tbriggs@gmu.edu
Twitter @twbriggs