Latent growth modeling allows social behavioral researchers to investigate within-person change and between-person differences in within-person change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution assumptions, have rarely been used. This article describes the latent growth curve model with categorical variables, and illustrates applications using Mplus software that are applicable to social behavioral research. The illustrations use marital instability data from the Iowa Youth and Family Project. We close with recommendations for the specification and parameterization of growth models that use both logit and probit link functions.

中文翻译：

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具有分类响应的潜在增长曲线分析在社会行为研究中的应用

潜在增长模型允许社会行为研究人员调查人内变化和人内变化中的人与人之间的差异。通常，传统的潜在增长曲线模型应用于连续变量，其中残差被假定为正态分布，而分类变量（即，二元和有序变量）不符合正态分布假设，很少使用。本文描述了具有分类变量的潜在增长曲线模型，并说明了使用 Mplus 软件的适用于社会行为研究的应用程序。这些插图使用了爱荷华州青年和家庭项目的婚姻不稳定数据。我们最后提出了对使用 logit 和 probit 链接函数的增长模型的规范和参数化的建议。