Klopack Eric T, Wickrama Kandauda K A S
Department of Sociology, University of Georgia, Athens, GA.
Department of Human Development and Family Science, University of Georgia, Athens, GA.
Struct Equ Modeling. 2020;27(1):97-110. doi: 10.1080/10705511.2018.1562929. Epub 2019 Apr 25.
Many developmental and life course researchers are interested in modeling dynamic developmental processes. Latent change score (LCS) modeling is a potentially powerful modeling technique that can be used to assess complex life course processes, as well as the direction of longitudinal bivariate associations. Advances in modeling software, like Mplus, as well as widening adoption of software by researchers has made LCS modeling simpler. Thus, in the present paper, we provide 1) a theoretical overview of LCS analysis, 2) information on the interpretation of these models, 3) a practical guid7e for estimating these models in Mplus (including example syntax), 4) illustrative examples of LCS analysis, and 5) potential caveats for researchers.
许多发展与生命历程研究人员都对动态发展过程建模感兴趣。潜变化得分（LCS）建模是一种潜在的强大建模技术，可用于评估复杂的生命历程过程以及纵向双变量关联的方向。诸如Mplus等建模软件的进步，以及研究人员对该软件越来越广泛的采用，使得LCS建模变得更加简单。因此，在本文中，我们提供了：1）LCS分析的理论概述；2）关于这些模型解释的信息；3）在Mplus中估计这些模型的实用指南（包括示例语法）；4）LCS分析的说明性示例；5）给研究人员的潜在注意事项。
