a Department of Psychology , University of Pittsburgh.
J Pers Assess. 2014;96(3):253-5. doi: 10.1080/00223891.2013.866572. Epub 2013 Dec 30.
Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.
潜变量模型为评估心理结构（包括人格和精神病理学）的潜在结构提供了一个概念和统计框架。使用混合建模方法可以适应结合或比较分类和维度潜变量的复杂结构，该方法为测试关于心理结构的细微理论提供了强大的框架。本特刊包括关于横断和纵向混合建模的入门介绍，以及将这些技术应用于临床环境中收集的实际数据的实证示例。这组文章旨在向人格评估科学家和从业者介绍一个通用的潜变量框架，我们希望这将激发对人格及其病理学的评估中混合模型的新研究和应用。
