New Applications of Survival Analysis Modeling: Examining Intercollegiate Athletic Relationship Dissolution

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Co-authored by Elizabeth Wanless, Jonathan A. Jensen, and Parker Poliakoff, this talk is scheduled to be presented at the 2017 Great Lakes Analytics in Sports Conference (GLASC) on July 13, 2017

Data & Analytics

0
.25
.5
.75
1
0 2 4 6 8 10
Analysis Time (Years)
95% CI Survivor function
Kaplan-Meier Survival Estimate
.05
.1
.15
.2
0 2 4 6 8 10
Analysis Time (Years)
95% CI Smoothed hazard function
Smoothed Hazard Estimate

Table 2.
Hierarchical survival analysis modeling results
Predictor Variables Model 1 Model 2 Model 3 Model 4
Economic
Lifetime Giving -0.92 (.00) -1.01 (.00) 6.23 (.00) 6.36 (.00)
Inflation 0.43 (.02) 0.43 (.02) 1.28 (.02) 1.00 (.02)
Economic Growth 0.54 (.02) 0.63 (.02) -0.06 (.02) 0.82 (.02)
Demographic
Alumni 1.53 (.72) 1.29 (.57) 1.23 (.57)
Live in State -7.24** (.04) -7.23** (.04) -7.30** (.04)
Years Since Graduation -0.62 (.00) 0.99 (.00) 1.02 (.00)
Retired -1.28 (.07) -2.77* (.06) -2.74* (.06)
Marketing
Times Contacted -5.78** (.00) -5.79** (.04)
Athletic
NCAA Tourney Wins -2.82* (.04)
Bowl Wins 0.21 (.07)
Log-likelihood -6551.93 -6633.79 -6604.31 -6600.83
Wald χ² 1.57 57.40** 33.43** 9.56*
Results from Cox model, with the Breslow method for handling ties; Standard errors clustered
by person. Standardized coefficients are listed, with standard errors in parentheses.
* p < .01; ** p < .001.

.05
.1
.15
.2
2 4 6 8
Analysis Time (Years)
Live in State = 1 Live in State = 0
Cox Model: Donor Location Differentials
.06.08
.1
.12.14.16
Smoothedhazardfunction
2 4 6 8
Analysis Time (Years)
Retired Currently Employed
Cox Model: Donor Employment Differentials

New Applications of Survival Analysis Modeling: Examining Intercollegiate Athletic Relationship Dissolution

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4. • • • •

9. 0 .25 .5 .75 1 0 2 4 6 8 10 Analysis Time (Years) 95% CI Survivor function Kaplan-Meier Survival Estimate .05 .1 .15 .2 0 2 4 6 8 10 Analysis Time (Years) 95% CI Smoothed hazard function Smoothed Hazard Estimate

10.

11. Table 2. Hierarchical survival analysis modeling results Predictor Variables Model 1 Model 2 Model 3 Model 4 Economic Lifetime Giving -0.92 (.00) -1.01 (.00) 6.23 (.00) 6.36 (.00) Inflation 0.43 (.02) 0.43 (.02) 1.28 (.02) 1.00 (.02) Economic Growth 0.54 (.02) 0.63 (.02) -0.06 (.02) 0.82 (.02) Demographic Alumni 1.53 (.72) 1.29 (.57) 1.23 (.57) Live in State -7.24** (.04) -7.23** (.04) -7.30** (.04) Years Since Graduation -0.62 (.00) 0.99 (.00) 1.02 (.00) Retired -1.28 (.07) -2.77* (.06) -2.74* (.06) Marketing Times Contacted -5.78** (.00) -5.79** (.04) Athletic NCAA Tourney Wins -2.82* (.04) Bowl Wins 0.21 (.07) Log-likelihood -6551.93 -6633.79 -6604.31 -6600.83 Wald χ² 1.57 57.40** 33.43** 9.56* Results from Cox model, with the Breslow method for handling ties; Standard errors clustered by person. Standardized coefficients are listed, with standard errors in parentheses. * p < .01; ** p < .001.

12.

13.

14.

15.

16. .05 .1 .15 .2 2 4 6 8 Analysis Time (Years) Live in State = 1 Live in State = 0 Cox Model: Donor Location Differentials .06.08 .1 .12.14.16 Smoothedhazardfunction 2 4 6 8 Analysis Time (Years) Retired Currently Employed Cox Model: Donor Employment Differentials

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