Mixture multigroup structural equation modeling: A novel method for comparing structural relations across many groups.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Andres F Perez Alonso,Yves Rosseel,Jeroen K Vermunt,Kim De Roover
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Abstract

Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relate to life satisfaction), and structural equation modeling (SEM) is the state-of-the-art for doing so. When comparing these "structural relations" among many groups, they likely differ across the groups. However, it is equally likely that some groups share the same relations so that clusters of groups emerge. Latent variables are measured indirectly by questionnaires and, for validly comparing their relations among groups, the measurement of the latent variables should be invariant across the groups (i.e., measurement invariance). However, across many groups, often at least some measurement parameters differ. Restricting these measurement parameters to be invariant, when they are not, causes the structural relations to be estimated incorrectly and invalidates their comparison. We propose mixture multigroup SEM (MMG-SEM) to gather groups with equivalent structural relations in clusters while accounting for the reality of measurement noninvariance. Specifically, MMG-SEM obtains a clustering of groups focused on the structural relations by making them cluster-specific, while capturing measurement noninvariances with group-specific measurement parameters. In this way, MMG-SEM ensures that the clustering is valid and unaffected by differences in measurement. This article proposes an estimation procedure built around the R package "lavaan" and evaluates MMG-SEM's performance through two simulation studies. The results demonstrate that MMG-SEM successfully recovers the group-clustering as well as the cluster-specific relations and the partially group-specific measurement parameters. To illustrate its empirical value, we apply MMG-SEM to cross-cultural data on the relations between experienced emotions and life satisfaction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
混合多组结构方程模型:一种比较多组结构关系的新方法。
行为科学家通常会研究两个或多个潜在变量之间的关系(例如,情绪与生活满意度之间的关系),而结构方程建模(SEM)是最先进的研究方法。在比较多个群体之间的这些 "结构关系 "时,不同群体之间的 "结构关系 "很可能是不同的。然而,同样有可能的是,某些群体具有相同的关系,从而出现群体集群。潜变量是通过问卷间接测量的,为了有效地比较各组之间的关系,对各组潜变量的测 量应该是不变的(即测量不变性)。然而,在许多群体中,往往至少有一些测量参数是不同的。如果限制这些测量参数不变,就会导致对结构关系的估计错误,并使其比较无效。我们提出了混合多组 SEM (MMG-SEM),在考虑测量非不变性现实的同时,将具有等效结构关系的组集中在聚类中。具体来说,MMG-SEM 通过将结构关系特定于群组,获得了以结构关系为重点的群组聚类,同时用特定于群组的测量参数来捕捉测量非方差。这样,MMG-SEM 就能确保聚类的有效性,并且不受测量差异的影响。本文提出了一种基于 R 软件包 "lavaan "的估计程序,并通过两项模拟研究评估了 MMG-SEM 的性能。结果表明,MMG-SEM 成功地恢复了分组聚类以及分组特定关系和部分分组特定测量参数。为了说明 MMG-SEM 的实证价值,我们将其应用于体验情绪与生活满意度之间关系的跨文化数据。(PsycInfo Database Record (c) 2024 APA, all rights reserved)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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