McGrew Kevin S, Schneider W Joel, Decker Scott L, Bulut Okan
Institute for Applied Psychometrics, 1313 Pondview Lane E, St. Joseph, MN 56374, USA.
College of Education and Human Development, Temple University, Ritter Hall 358, Philadelphia, PA 19122, USA.
J Intell. 2023 Jan 16;11(1):19. doi: 10.3390/jintelligence11010019.
For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or ). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to -centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9-19 standardization sample of the Woodcock-Johnson Tests of Cognitive Ability-Fourth Edition. Results support the primary broad abilities from the Cattell-Horn-Carroll (CHC) theory and suggest that the working memory-attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a -centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, .
一个多世纪以来，智力结构一直由因素分析方法主导，这些方法假定测试是潜在实体（例如，一般智力等）的指标。最近，心理测量网络方法和理论（例如，过程重叠理论；动态共生理论）为以因素为中心的模型提供了替代方案。然而，很少有研究使用网络方法来研究当代认知测量。我们将高斯图形网络模型应用于伍德科克-约翰逊认知能力测验第四版9至19岁的标准化样本。结果支持了卡特尔-霍恩-卡罗尔（CHC）理论中的主要宽泛能力，并表明工作记忆-注意力控制复合体可能是理解CHC智力网络模型的核心。补充性多维尺度分析表明可能存在高阶维度（PPIK；三元理论；系统I-II认知加工）以及长期记忆中单独的学习和检索方面。总体而言，网络方法为具有以因素为中心偏差的因素模型（即双因素模型）提供了一种可行的替代方案，这些因素模型在超出全量表智商的测试解释中，就宽泛的CHC分数的效用得出了错误结论。
