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Survival Analysis and the 
Proportional Hazards Model for 
Predicting Employee Turnover 
Primary source: 
Hom, P. W., & Gr...
AUDIENCE SURVEY 
TBRIGGS@GMU.EDU [ 2 ] NOVEMBER 2014
“Our new Constitution is now 
established, and has an appearance 
that promises permanency; but in 
this world nothing can...
“In this world nothing can be said to 
be certain, except death, taxes, and 
employee turnover.” 
--George Mason Student (...
ROAD MAP 
BACKGROUND 
WHY 
Survival 
Analysis 
Survival 
Analysis 
RESULTS 
TBRIGGS@GMU.EDU [ 5 ] NOVEMBER 2014
BACKGROUND 
TBRIGGS@GMU.EDU [ 6 ] NOVEMBER 2014
FIRST PIONEERS 
Peters, 
L. 
H., 
& 
Sheridan, 
J. 
E. 
(1988). 
Turnover 
research 
methodology: 
A 
criCque 
of 
tradiCo...
WHO IS THIS MAN? 
TBRIGGS@GMU.EDU [ 8 ] NOVEMBER 2014
SIR DAVID COX 
#9 on the George Mason Department of Statistics list of 
“Great Statisticians” – just below Tukey and Willi...
BY ANY OTHER NAME 
StaCsCcs 
• Survival 
analysis 
• Reliability 
theory 
Engineering 
• Reliability 
analysis 
• DuraCon ...
WHY SURVIVAL ANALYSIS 
TBRIGGS@GMU.EDU [ 11 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
TBRIGGS@GMU.EDU [ 12 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
A. 39 SHEEP 
TBRIGGS@GMU.EDU [ 13 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
B. 40 SHEEP 
TBRIGGS@GMU.EDU [ 14 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
C. DON’T KNOW 
TBRIGGS@GMU.EDU [ 15 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
A. 39 SHEEP 
B. 40 SHEEP 
C. DON’T KNOW 
TBRIGGS@GMU.EDU [ 16 ] NOVEMBER 2014
WHAT SIZE IS THE HERD? 
C. DON’T KNOW - CORRECT! 
TBRIGGS@GMU.EDU [ 17 ] NOVEMBER 2014
VOCABULARY: CENSORING 
CENSORING is a missing data problem 
common to survival analysis 
(and cross-sectional studies…) 
I...
HOM & GRIFFETH ON WHY 
• Cross-sectional study start and end dates 
are usually arbitrary 
• Short measurement periods wea...
NOT WHETHER, BUT WHEN 
Death, taxes, and employee turnover: 
All employees will ultimately turn over, so the 
question is ...
VISUAL: CENSORING 
leZ 
stayed 
Right-censoring most common in turnover research; 
an employee could quit the day after th...
SURVIVAL ANALYSIS 
RESULTS 
TBRIGGS@GMU.EDU [ 22 ] NOVEMBER 2014
SURVIVAL ANALYSIS RESULTS 
• Generates conditional probabilities – the 
“hazard rate” – that employees will quit 
during a...
SURVIVAL RATES 
1.05 
1.00 
Survival Rates for New Staff Accountants 
Cumulative Survival Rate Tenure (in months) 
0.95 
0...
SURVIVAL PREDICTORS 
1.05 
1.00 
0.95 
0.90 
0.85 
0.80 
Survival Rates for New Staff Accountants as Functions of 
RJPs an...
PROPORTIONAL HAZARD 
• Profile comparisons “ill-suited for estimating 
the temporal effects of continuous predictors 
and ...
PROPORTIONAL HAZARD 
BENEFITS 
• Can examine multiple predictors (continuous 
or categorical) and estimate unique 
contrib...
HAZARDS OF 
PROPORTIONAL HAZARD 
• Assumes different predictors all have same 
log-hazard shape – Singer and Willett (1991...
CONCLUSION 
Survival analysis and the proportional 
hazard model can offer a compelling 
alternative to cross-sectional 
m...
Contact: 
Tom Briggs 
tbriggs@gmu.edu 
Twitter @twbriggs
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Survival Analysis for Predicting Employee Turnover

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

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Survival Analysis for Predicting Employee Turnover

  1. 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. 2. AUDIENCE SURVEY TBRIGGS@GMU.EDU [ 2 ] NOVEMBER 2014
  3. 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. 4. “In this world nothing can be said to be certain, except death, taxes, and employee turnover.” --George Mason Student (2014) TBRIGGS@GMU.EDU [ 4 ] NOVEMBER 2014
  5. 5. ROAD MAP BACKGROUND WHY Survival Analysis Survival Analysis RESULTS TBRIGGS@GMU.EDU [ 5 ] NOVEMBER 2014
  6. 6. BACKGROUND TBRIGGS@GMU.EDU [ 6 ] NOVEMBER 2014
  7. 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. TBRIGGS@GMU.EDU [ 7 ] NOVEMBER 2014
  8. 8. WHO IS THIS MAN? TBRIGGS@GMU.EDU [ 8 ] NOVEMBER 2014
  9. 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. TBRIGGS@GMU.EDU [ 9 ] NOVEMBER 2014
  10. 10. BY ANY OTHER NAME StaCsCcs • Survival analysis • Reliability theory Engineering • Reliability analysis • DuraCon analysis Economics • DuraCon modeling Sociology • Event history analysis TBRIGGS@GMU.EDU [ 10 ] NOVEMBER 2014
  11. 11. WHY SURVIVAL ANALYSIS TBRIGGS@GMU.EDU [ 11 ] NOVEMBER 2014
  12. 12. WHAT SIZE IS THE HERD? TBRIGGS@GMU.EDU [ 12 ] NOVEMBER 2014
  13. 13. WHAT SIZE IS THE HERD? A. 39 SHEEP TBRIGGS@GMU.EDU [ 13 ] NOVEMBER 2014
  14. 14. WHAT SIZE IS THE HERD? B. 40 SHEEP TBRIGGS@GMU.EDU [ 14 ] NOVEMBER 2014
  15. 15. WHAT SIZE IS THE HERD? C. DON’T KNOW TBRIGGS@GMU.EDU [ 15 ] NOVEMBER 2014
  16. 16. WHAT SIZE IS THE HERD? A. 39 SHEEP B. 40 SHEEP C. DON’T KNOW TBRIGGS@GMU.EDU [ 16 ] NOVEMBER 2014
  17. 17. WHAT SIZE IS THE HERD? C. DON’T KNOW - CORRECT! TBRIGGS@GMU.EDU [ 17 ] NOVEMBER 2014
  18. 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! TBRIGGS@GMU.EDU [ 18 ] NOVEMBER 2014
  19. 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 TBRIGGS@GMU.EDU [ 19 ] NOVEMBER 2014
  20. 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? TBRIGGS@GMU.EDU [ 20 ] NOVEMBER 2014
  21. 21. VISUAL: CENSORING leZ stayed Right-censoring most common in turnover research; an employee could quit the day after the study ends! TBRIGGS@GMU.EDU [ 21 ] NOVEMBER 2014
  22. 22. SURVIVAL ANALYSIS RESULTS TBRIGGS@GMU.EDU [ 22 ] NOVEMBER 2014
  23. 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. TBRIGGS@GMU.EDU [ 23 ] NOVEMBER 2014
  24. 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 TBRIGGS@GMU.EDU [ 24 ] NOVEMBER 2014
  25. 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 TBRIGGS@GMU.EDU [ 25 ] NOVEMBER 2014
  26. 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 TBRIGGS@GMU.EDU [ 26 ] NOVEMBER 2014
  27. 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) TBRIGGS@GMU.EDU [ 27 ] NOVEMBER 2014
  28. 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) TBRIGGS@GMU.EDU [ 28 ] NOVEMBER 2014
  29. 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. TBRIGGS@GMU.EDU [ 29 ] NOVEMBER 2014
  30. 30. Contact: Tom Briggs tbriggs@gmu.edu Twitter @twbriggs

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