Survival analysis, or event history analysis as it is often labeled in the social sciences, includes statistical methods for analyzing the time at which “failures” or events of interest occur. Common examples of event times of interest in the survival analysis literature include light bulb or motor longevity (engineering), time of death, time to disease incidence or recovery (medicine and public health), time to unemployment, time to divorce, or time to retirement (social sciences). Survival analysis is an important statistical topic that when treated in its full depth and breadth fills entire volumes. This chapter will scratch only the surface of this topic, focusing on basic theory (Section 10.2) and application for three survival analysis techniques that are commonly used with complex sample survey data. Section 10.3 will introduce nonparametric Kaplan-Meier (K-M) analysis of the survivorship function. The Cox proportional hazards (CPH) model will be covered in Section 10.4, and Section 10.5 presents a description and application of logit and complementary log-log (CLL) models for discrete time event history data. Readers interested in the general theory and applications of survival analysis methods are referred to classic texts including Kalbfleisch and Prentice (2002), Lee (1992), and Miller (1981). Newer texts on applied methods (Hosmer, Lemeshow, and May, 2008) and several excellent user guides are also available that illustrate procedures for survival analysis in SAS (Allison, 1995) and Stata (Cleves et al., 2008).