题目：长时数据分析的增长混合模型

主讲：陈琦（美国北德州大学教育心理学副教授）

时间：1月5日上午10：00-12:00

地点：哲学学院南楼大报告厅

附一：报告英文摘要

Title：Growth Mixture Models (GMM) for Longitudinal Data Analysis（长时数据分析的增长混合模型）

Abstract: Growth Mixture Modeling (GMM) is a person-centered approach for analyzing longitudinal data. Using GMM, we can group individuals who are more similar to each other into categories. In this presentation, I will first introduce some concepts related to GMM, then present a simulation study examining the impact of ignoring a level of nesting structure in multilevel growth mixture models, and an empirical study applying GMM to investigate the differential effect of grade retention on the development of academic achievement from grade one to five on children retained in first grade over six years. Finally, I will briefly talk about the future directions of my research.