Mover-Stayer Models for the Analysis of Event-History Data in Social Science Research: A Project Description

这个项目开发了三组动子-施工者模型，用于分析事件历史数据。搬家-留宿模型采用了一对回归：留在者比例的“S-回归”，其中留在者是发生事件风险为零的“人”的潜在人口；以及“M-回归”，在搬家者中发生事件的条件比率，搬迁者是发生事件风险非零的潜在人群。移动者-滞留者模型提供了从传统危险率模型的结果中无法获得的信息。本研究通过以下方式进一步发展了移动-滞留模型：(1)扩展一般移动-滞留模型以允许使用时变预测器(2)开发专门为分析扩散过程(如信息、疾病或创新的扩散)而设计的移动-滞留模型，以识别较高扩散速度的预测因子是否意味着更快的扩散，或更多的人受扩散事件的影响，或两者兼而有之；(3)为带有一些缺失信息的事件历史数据开发移动-滞留模型。将这些新的方法工具应用于分析初婚和再婚、毒品使用进展和居住流动性，将提供重要的新的实质性见解。

This project develops three groups of mover-stayer models for the analysis of event-history data. Mover-stayer models employ a pair of regressions: an `S-regression` for the proportion of stayers, where stayers are a latent population of `people` with zero risk of having the event, and an `M-regression` for the conditional rate of having the event among movers, where movers are a latent population of people with nonzero risk of having the event. Mover-stayer models provide information that cannot be obtained from the results of conventional hazard rate models. This research further develops mover-stayer models by: (1) extending general mover-stayer models to permit the use of time-varying predictors (2) developing mover-stayer models specifically designed for the analysis of diffusion processes (such as diffusion of information, diseases, or innovation) to identify whether a predictor of a higher rate of diffusion implies either more rapid diffusion, or a larger number of people who are affected by the diffusion event, or both; (3) developing mover-stayer models for event-history data with some missing information. Applications of these new methodological tools to analyses of first marriages and remarriages, drug-use progression, and residential mobility will provide important new substantive insights.