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S5E22 Survival Analysis in the Social Sciences: It's About Time
Apr 23, 2024
Exploring survival analysis in social sciences, the podcast discusses predicting death dates, running with scissors, logistic regression on steroids, and navigating complexities of care. They touch on the importance of these models in psychology and education, challenges in defining time windows, and analyzing behavior patterns using Kaplan Meyer curve. Cox proportional hazard regression is highlighted along with adapting research questions and promoting the podcast.
47:21
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Quick takeaways
- Survival analysis can predict time to events like alcohol use onset, useful for social sciences.
- Cox regression in survival analysis reveals how individual characteristics influence event occurrences over time.
Deep dives
Exploring Survival Analysis and Its Underutilization in Social Sciences
Survival analysis is a powerful technique allowing for predictive testing of time to events, such as predicting the age of onset of alcohol use or recidivism. Although common in health sciences, its application in social sciences remains limited. The Kaplan-Meier curve, a fundamental tool in survival analysis, reveals stepwise probabilities of events over time. Understanding peak risks and factors impacting them can provide valuable insights. Cox regression extends this analysis to explore how characteristics of individuals or environmental factors influence event occurrences over time.
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