Ramírez Eduar S, Jiménez Marcos, Franco Víthor Rosa, Alvarado Jesús M
Department of Psychobiology and Behavioral Sciences Methods, Faculty of Psychology, Complutense University of Madrid, Campus Somosaguas, Carretera De Húmera, s/n, 28006 Madrid, Spain.
Faculty of Health Sciences, Universidad Villanueva, Calle Costa Brava 2, 28034 Madrid, Spain.
J Intell. 2024 Jul 18;12(7):67. doi: 10.3390/jintelligence12070067.
Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.
对类比推理的研究有助于理解诸如模式识别和创造性问题解决等反应过程，已成为一种智力预测指标。虽然传统的类比测试结合了各种用于生成题目的组合规则，但当前的统计模型，如逻辑潜在特质模型（LLTM）和恩布雷森的诊断多成分潜在特质模型（MLTM-D），在处理这些过程的内在复杂性方面存在局限性，导致模型拟合和解释效果欠佳。本研究的主要目的是扩展恩布雷森的MLTM-D，以涵盖允许估计题目参数 的复杂多维模型。具体而言，我们开发了MLTM-D的三参数（3PL）版本，它能对参与者的反应过程提供更丰富的解释。我们开发了广义诊断多成分潜在特质模型（GMLTM-D），这是一种统计模型，扩展了恩布雷森的多成分模型以探索复杂的类比理论。使用先前一项关于图形类比推理测试的数据，将GMLTM-D与LLTM和MLTM-D进行比较，该测试由基于图形旋转、梯形旋转, 反射、线段减法和点移动这五条组合规则的27个题目组成。此外，我们提供了一个R包（GMLTM）用于对上述模型进行贝叶斯估计。与LLTM和MLTM-D的贝叶斯版本相比，GMLTM-D能更准确地复制观测数据，显示出更好的模型拟合和更高的预测准确性。因此，GMLTM-D是用于分析类比推理数据和校准智力测试的可靠模型。GMLTM-D包含了真实数据的复杂性，增强了对考生反应过程的理解。
