Compare methods

	Event History Turnover Analysis	Organizational Network Analysis
Field	Organizational Behavior	Organizational Behavior
Family	Survival analysis	Process / pipeline
Year of origin	1993	1984
Originator	Paul D. Allison; June G. Morita, Thomas W. Lee & Richard T. Mowday	Daniel J. Brass; David Krackhardt; Herminia Ibarra
Type	Time-to-event modeling of employee turnover	Intraorganizational social network mapping and position-to-outcome pipeline
Seminal source	Morita, J. G., Lee, T. W., & Mowday, R. T. (1993). The regression-analog to survival analysis: A selected application to turnover research. Academy of Management Journal, 36(6), 1430-1464.	Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342-369.
Aliases	Survival Analysis of Turnover, Hazard Modeling of Employee Turnover, Time-to-Turnover Analysis, Employee Tenure Survival Models	ONA, Intraorganizational Network Analysis, Workplace Social Network Analysis, Advice and Friendship Network Analysis
Related	3	3
Summary	Event history turnover analysis models not just whether employees leave but when they leave, treating tenure as a duration and the act of quitting as an event whose timing carries information. Paul Allison's 1984 monograph brought event history methods — survival and hazard models — into the social sciences with a regression-oriented treatment that handles the censoring inherent in longitudinal data. Morita, Lee, and Mowday's 1993 Academy of Management Journal paper applied these techniques to turnover research, showing organizational scholars how to model the hazard of leaving and why time-to-event methods are superior to simple stayed-versus-left comparisons. The core object is the hazard function, the instantaneous risk of quitting given that one has stayed so far, which can depend on tenure and on employee and job characteristics. Because some employees are still present when the study ends, the analysis must correctly handle censored observations rather than discarding or mis-coding them. The result is a model that explains and predicts the timing of turnover.	Organizational network analysis studies the informal web of relationships — who goes to whom for advice, who is friends with whom, who works with whom — that runs alongside the formal org chart and often determines who actually gets things done. Daniel Brass's 1984 study of a newspaper publishing company showed that an employee's position in workflow, communication, and friendship networks predicted perceived influence and promotion better than formal rank. David Krackhardt's 1990 work added a cognitive twist, demonstrating that accurately perceiving the informal network is itself a source of power. Herminia Ibarra's 1993 study related network centrality to involvement in technical and administrative innovation, distinguishing the network bases of different kinds of influence. Together these works established a pipeline: collect relational data on the organization, compute each member's structural position, and link those positions to power, influence, and innovation. The approach treats the organization as a structure of relationships rather than a hierarchy of boxes.