Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of group iterative multiple model estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called latent variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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使用模型隐含工具变量 (MIIV) 的潜在变量 GIMME。

心理学许多领域的研究人员越来越希望获得个人过程的个性化和可概括的动态模型。这在一系列数据类型的心理生理学、行为学和情感研究范式中都能看到。大多数数据都存在测量误差。出于这个原因，研究人员通常会收集相同潜在结构的多个指标，并使用因子分析等方法从这些指标中得出分数。除了准确测量个体之外，研究人员还需要找到最能描述潜在结构之间关系的模型。大多数当前可用的数据驱动搜索不包括潜在变量。我们提出了一种建立在组迭代多模型估计（GIMME）的强大基础之上的方法，特定过滤器和模型隐含工具变量与两阶段最小二乘估计 (MIIV-2SLS) 为研究人员提供了在其数据驱动的模型搜索中包含潜在变量的选项。由此产生的方法称为潜在变量 GIMME (LV-GIMME)。GIMME 用于数据驱动搜索潜在变量之间存在的关系。与 idiographic 过滤器等其他方法不同，LV-GIMME 不要求潜在变量模型在个体之间保持不变。通过使用 MIIV-2SLS 进行估计，此要求有所放宽。模拟数据研究表明，该方法可以可靠地检测潜在构造之间的关系，并且与直接使用观察变量相比，潜在构造提供了更多的检测效果的能力。我们使用从功能性 MRI 和每日自我报告数据中提取的经验数据示例。（PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。