Ghisletta Paolo, McArdle John J
Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland, Distance Learning University, Sierre, Switzerland, 4136 UniMail, Boulevard du Pont d'Arve 40, 1211 Geneva, Switzerland,
Department of Psychology, University of Southern California, USA 3620 South McClintock Avenue, Los Angeles, CA 90089-1061
Struct Equ Modeling. 2012;19(4):651-682. doi: 10.1080/10705511.2012.713275.
In recent years the use of the Latent Curve Model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and to the availability of specialized literature. Extensions of the LCM, like the the Latent Change Score Model (LCSM), have also increased in popularity. At the same time, the R statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, & McArdle, 2004. The data and all scripts used here are available on the first author's website.
近年来,社会科学领域的研究人员对潜在曲线模型(LCM)的使用显著增加,这可能得益于当代软件的发展以及专业文献的可得性。潜在曲线模型的扩展,如潜在变化得分模型(LCSM),也越来越受欢迎。与此同时,R统计语言和环境是开源的,可在多种操作系统上运行,正成为应用统计学的领先软件。我们展示了如何使用R软件的sem、lavaan和OpenMx包来估计LCM和LCSM。我们还说明了在分析之前如何读取、汇总和绘制数据。文中提供了费雷尔、滨上和麦卡德尔(2004年)之前所阐述数据的示例。此处使用的数据和所有脚本可在第一作者的网站上获取。