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Cited by 878

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

It's about Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events

Singer, Judith D., Willett, John B.·1993·Journal of Educational Statistics

Abstract

Educational researchers frequently ask whether and, if so, when events occur. Until relatively recently, however, sound statistical methods for answering such questions have not been readily available. In this article, by empirical example and mathematical argument, we demonstrate how the methods of discrete-time survival analysis provide educational statisticians with an ideal framework for studying event occurrence. Using longitudinal data on the career paths of 3,941 special educators as a springboard, we derive maximum likelihood estimators for the parameters of a discrete-time hazard model, and we show how the model can befit using standard logistic regression software. We then distinguish among the several types of main effects and interactions that can be included as predictors in the model, offering data analytic advice for the practitioner. To aid educational statisticians interested in conducting discrete-time survival analysis, we provide illustrative computer code ( SAS, 1989 ) for fitting discrete-time hazard models and for recapturing fitted hazard and survival functions.

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Metadata

Authors
Singer, J. D., & Willett, J. B.
Venue
Journal of Educational Statistics
Publisher
JSTOR
Published
1993
Volume
18
Issue
2
Pages
155
ISSN
0362-9791 (ISSN-L)
OpenAlex
W2114717670
Cited by
878
References
4
FWCI
6.95
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: It's about Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events. https://app.cochat.ai/papers/10.2307/1165085
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Singer, J. D., & Willett, J. B. (1993). It's about Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events. Journal of Educational Statistics. https://doi.org/10.2307/1165085

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.