The document presents a hierarchical survival analysis using Cox models to explore predictors affecting donor behavior over time. Key variables analyzed include economic lifetime giving, inflation, economic growth, and demographic factors such as alumni status and employment. Statistical results highlight significant differentials in giving based on factors like location and employment status, with secured confidence intervals and standardized coefficients.

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

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.

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