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Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time
Christopher J Cappelli1, Audrey J Leroux1, Katherine E Masyn2
1Department of Educational Policy Studies, Georgia State University.
Multivariate Behavioral Research
|September 4, 2023
Summary
Ignoring cross-classified data structures in survival analysis can lead to biased model parameters and unreliable confidence intervals. Using a cross-classified discrete-time survival model is crucial for accurate event-history data analysis when individuals move between groups.
Area of Science:
- Statistics
- Social Sciences Methodology
Background:
- Discrete-time survival models are used for event-history data.
- Hierarchical data can be analyzed with multilevel models, but mobility across clusters requires advanced approaches.
Purpose of the Study:
- To evaluate the performance of cross-classified discrete-time survival models.
- To assess the impact of ignoring cross-classification in survival analysis.
Main Methods:
- Monte Carlo simulation was employed to test three discrete-time survival models.
- Simulation factors included cluster variance, sample size, and mobility rates.
Main Results:
- Ignoring cross-classified structures caused significant parameter bias and poor confidence interval coverage.
- Standard errors were severely biased when the cross-classified nature of data was not accounted for.
Conclusions:
- Cross-classified discrete-time survival models are essential for accurate analysis of event-history data with individual mobility.
- Methodologists and practitioners in fields like education and public health should utilize appropriate models to avoid analytical errors.
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