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

本计画发展三组移动者-滞留者模式，以分析事件历史资料。 移动者-滞留者模型采用一对回归："S-回归“用于滞留者的比例，其中滞留者是具有零事件风险的”人“的潜在人口，”M-回归“用于移动者中具有事件的条件率，其中移动者是具有非零事件风险的人的潜在人口。 移动者-滞留者模型提供的信息不能从传统的危险率模型的结果中获得。 这项研究通过以下方式进一步发展了搬家者-住宿者模型：（1）扩展一般的迁移-滞留模型，允许使用时变预报因子（2）开发专门用于扩散过程分析的迁移-滞留模型（如信息、疾病或创新的扩散），以确定更高扩散率的预测因素是否意味着更快的扩散，或受扩散事件影响的大量人员，或两者;（3）为具有一些缺失信息的事件历史数据开发移动者-滞留者模型。 应用这些新的方法工具来分析初婚和再婚、吸毒进展和居住流动性，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.