Survival Analysis for Predicting Employee Turnover

Primary source: Hom, P. W., & Griffeth, R. W. (1995). Employee turnover. Cincinnati, OH: Southwestern College Publishing.

Survival Analysis and the Proportional Hazards Model for Predicting Employee Turnover

Tom [email protected]

November 2014

[ 2 ][email protected] NOVEMBER 2014

AUDIENCE SURVEY


“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)


“In this world nothing can be said to be certain, except death, taxes, and employee turnover.”

--George Mason Student (2014)


ROAD MAP

BACKGROUNDWHY

Survival Analysis

Survival AnalysisRESULTS


BACKGROUND


FIRST PIONEERS

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.

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.

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.


WHO IS THIS MAN?


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.


BY ANY OTHER NAME•  Survival analysis StaCsCcs

•  Reliability theory •  Reliability analysis Engineering

•  DuraCon analysis •  DuraCon modeling Economics

•  Event history analysis Sociology


WHY SURVIVAL ANALYSIS


WHAT SIZE IS THE HERD?



A. 39 SHEEP



B. 40 SHEEP



C. DON’T KNOW



B. 40 SHEEP

A. 39 SHEEP

C. DON’T KNOW



C. DON’T KNOW - CORRECT!


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!


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


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?


VISUAL: CENSORING

Right-censoring most common in turnover research; an employee could quit the day after the study ends!

leZ

stayed


SURVIVAL ANALYSIS RESULTS


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.


SURVIVAL RATES

0.80

0.85

0.90

0.95

1.00

1.05

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Cum

ulat

ive

Surv

ival

Rat

e

Tenure (in months)

Survival Rates for New Staff Accountants


SURVIVAL PREDICTORS

0.80

0.85

0.90

0.95

1.00

1.05

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Cum

ulat

ive

Surv

ival

Rat

e

Tenure (in months)

Survival Rates for New Staff Accountants as Functions of RJPs and Job Tenure

Traditional Job Preview Realistic Job Preview


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


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)


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)


CONCLUSIONSurvival analysis and the proportional hazard model can offer a compelling alternative to cross-sectional methodology for investigating dynamic relations between turnover and antecedents.

Contact:

Tom [email protected] @twbriggs

Data & Analytics

Survival Analysis for Predicting Employee Turnover