Chen Siyuan Marco, Bauer Daniel J
Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.
Psychol Methods. 2024 Aug 29. doi: 10.1037/met0000685.
In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding actual construct change with changes in measurement (i.e., differential item functioning [DIF]), threatening the validity of conclusions. An improved method that avoids such confounding is the second-order growth curve (SGC) model. It specifies a measurement model at each occasion of measurement that can be evaluated for invariance over time. The applicability of the SGC model is hindered by key limitations: (a) the SGC model treats time as continuous when modeling construct growth but as discrete when modeling measurement, reducing interpretability and parsimony; (b) the evaluation of DIF becomes increasingly error-prone given multiple timepoints and groups; (c) DIF associated with continuous covariates is difficult to incorporate. Drawing on moderated nonlinear factor analysis, we propose an alternative approach that provides a parsimonious framework for including many time points and DIF from different types of covariates. We implement this model through Bayesian estimation, allowing for incorporation of regularizing priors to facilitate efficient evaluation of DIF. We demonstrate a two-step workflow of measurement evaluation and growth modeling, with an empirical example examining changes in adolescent delinquency over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
在使用增长曲线模型分析纵向数据时，一个关键假设是观察到的测量值的变化反映的是构念的变化，而不是构念随时间表现形式的变化。然而，增长曲线模型通常适用于作为量表项目总和或平均值构建的重复测量，这隐含了测量恒常性的假设。这种做法有可能将实际的构念变化与测量变化（即项目功能差异[DIF]）混淆，从而威胁到结论的有效性。一种避免此类混淆的改进方法是二阶增长曲线（SGC）模型。它在每次测量时指定一个测量模型，该模型可针对时间不变性进行评估。SGC模型的适用性受到关键限制的阻碍：（a）SGC模型在对构念增长进行建模时将时间视为连续的，但在对测量进行建模时将时间视为离散的，这降低了可解释性和简约性；（b）鉴于多个时间点和组，DIF的评估变得越来越容易出错；（c）与连续协变量相关的DIF难以纳入。借鉴调节非线性因子分析，我们提出了一种替代方法，该方法提供了一个简约的框架，用于纳入来自不同类型协变量的多个时间点和DIF。我们通过贝叶斯估计来实现这个模型，允许纳入正则化先验以促进对DIF的有效评估。我们展示了一个测量评估和增长建模的两步工作流程，并通过一个实证例子考察了青少年犯罪随时间的变化。（《心理学文摘数据库记录》（c）2024美国心理学会，保留所有权利）
