SAY763 - APPLIED SURVIVAL and EVENT HISTORY ANALYSIS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| APPLIED SURVIVAL and EVENT HISTORY ANALYSIS | SAY763 | Any Semester/Year | 2 | 2 | 3 | 10 |
| Prequisites | None | |||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Drill and Practice | |||||
| Instructor (s) | Asst. Prof. Ayşe Abbasoğlu-Özgören | |||||
| Course objective | Learning the methods for analyzing questions related to mortality (what is associated with how long people live), entry to or exit from employment (how long they stay unemployed, or employed), marriage and/or divorce (when people start/end a family), etc., and learning how to conduct event-history analysis in Stata, and carry out an event history project with their own data. | |||||
| Learning outcomes |
| |||||
| Course Content | What is event history analysis and why is it used? Basic concepts and event-history data Stata and basic commands Survivor, hazard and cumulative hazard functions Nonparametric models: Life tables and Kaplan-Meier estimates Semi-parametric and parametric regression models Time-varying covariates and interactions Model specification and post-estimation Further issues: Non-proportionality, frailty, repeated events, etc. | |||||
| References | Blossfeld, H.-P., Golsch, K., & Rohwer, G. (2007). Event History Analysis with Stata, Lawrence Erlbaum Associates, New York. Cleves, M., Gutierrez, R.G., Gould, W., & Marchenko, Y.V. 2008. An Introduction to Survival Analysis Using Stata. Stata Press. Kleinbaum, D.G./Klein, M. (2012). Survival Analysis: A Self-Learning Text. 3rd Edition. New York: Springer Allison, P. D. (1984). Event history analysis: Regression for longitudinal event data. Beverly Hills, CA: SAGE. Allison, P. D. (1995). Survival analysis using the SAS system: A practical guide. Cary, NC: SAS Institute. Allison, P. D. (2010). Survival analysis. The Reviewer's Guide to Quantitative Methods in the Social Sciences. Eds. Hancock, G. R., Mueller, R. O. New York: Routledge. Box-Steffensmeier, J.M., & Jones, B.S. (2004). Event History Modeling: a Guide for Social Scientists, Cambridge University Press, Cambridge. Dykstra, P. A., & van Wissen, L. J. G. (1999). Introduction: The Life Course Approach as an Interdisciplinary Framework for Population Studies. In L. J. G. van Wissen & P. A. Dykstra (Eds.), Population Issues: An Interdisciplinary Focus (pp. 1-22). Dordrecht: Springer Netherlands. Steele, Fiona (2005). Event History Analysis. A National Centre for Research Methods Briefing Paper. ESRC National Centre for Research Methods. NCRM Methods Review Papers, NCRM/004. University of Bristol, Bristol. http://eprints.ncrm.ac.uk/88/ Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge MA. Yamaguchi, K. 1991. Event History Analysis. Newbury Park, CA: SAGE. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | What is event history analysis and why is it used? |
| Week 2 | Stata and Basic Commands |
| Week 3 | Survivor, hazard and cumulative hazard functions |
| Week 4 | Nonparametric models: Life tables and Kaplan-Meier estimates |
| Week 5 | Stata exercise (Life tables and Kaplan-Meier estimates) |
| Week 6 | Survival Functions by Subgroups |
| Week 7 | Semi-parametric and parametric regression models (II) |
| Week 8 | Stata exercise (exponential and piecewise exponential regression, and Cox regression) |
| Week 9 | Midterm Exam |
| Week 10 | Time-varying covariates and interactions |
| Week 11 | Stata exercise (time-varying covariates and interactions) |
| Week 12 | Model Fit |
| Week 13 | Model specification and post-estimation (with Stata exercise) |
| Week 14 | Further issues: Non-proportionality, frailty, repeated events, etc. (with Stata exercise) |
| Week 15 | Preparation for Final Exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 6 | 20 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 1 | 5 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 8 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 2 | 32 |
| Laboratory | 0 | 0 | 0 |
| Application | 5 | 2 | 10 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 8 | 40 |
| Midterms (Study duration) | 1 | 44 | 44 |
| Final Exam (Study duration) | 1 | 90 | 90 |
| Total Workload | 42 | 152 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Gains theoretical knowledge on research methodology, concepts on quantitative research methods, quantitative sampling techniques, learning, implementing, and developing models and techniques in quantitative analysis, theory, and develops skills to transform and improve knowledge into design, implementation, analysis. | X | ||||
| 2. Gains theoretical knowledge on qualitative research methods, theory, and skills to transform and improve knowledge into design, sampling, generating data and analysis, knowledge on differences between methods and their use, improvement/combined use of these methods, evaluation with experts and institutions, focus groups and in-depth interviews, skills to implement them. | X | ||||
| 3. Gains ability to evaluate quality of researches, interpret/improve results with experts and institutions, transform the results into oral/written presentations, and present them at (inter)national meetings. | X | ||||
| 4. Develops skills to conduct generalizable research, interpret results of analyses, transform them into publications abiding by ethics, gain ability and consciousness to prepare (inter)national projects, evaluate them with experts and institutions. | X | ||||
| 5. Contributes to development of methods by producing a thesis using research methods. | X | ||||
| 6. Follows international publications, communicate with colleguaes, using English at a level no lower than the Common European Framework of References for Languages B2. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest