Intensive Longitudinal Data Analyses With Dynamic Structural Equation Modeling
介绍了密集纵向数据的特点和分析挑战，并引入动态结构方程模型（DSEM）这一统计方法，通过三个模拟数据示例展示如何使用Mplus软件进行分析，适合组织研究者判断是否采用该方法。
Recent developments in theories and data collection methods have made intensive longitudinal data (ILD) increasingly relevant and available for organizational research. New methods for analyzing ILD have emerged under the multilevel modeling framework. In this article, we first delineate features of ILD (including autoregressive relationships, trends, cycles/seasons, and between-subject variability in temporal trends). We discuss the analytic challenges for handling ILD using traditional analytic tools familiar to organizational researchers (e.g., growth models, single-subject time series analyses). We then introduce a statistical approach for handling ILD from the multilevel modeling framework: dynamic structural equation modeling (DSEM). We provide three examples using simulated data sets to demonstrate how to apply DSEM to examine ILD with a software program familiar to organizational researchers (i.e., M plus). Finally, we discuss issues related to applying DSEM, including centering, missing data, and sample size.