Psychometric Network Analysis of the Hungarian WAIS

The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT; Kovacs & Conway, 2016), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. From this perspective, psychometric network analysis is an attractive alternative to latent variable modeling. Network analyses display partial correlations among observed variables that demonstrate direct relationships among observed variables. To demonstrate the benefits of this approach, the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV; Wechsler, 2008) was analyzed using both psychometric network analysis and latent variable modeling. Network models were directly compared to latent variable models. Results indicate that the H-WAIS-IV data was better fit by network models than by latent variable models. We argue that POT, and network models, provide a more accurate view of the structure of intelligence than traditional approaches.

认知能力度量之间的正相关性发现——即所谓的积极流形——催生了包含一般认知能力或一般智力（g）的智力模型。此观点得到了因子分析和潜在变量模型的支持。然而，一种新的智力理论——过程重叠理论（POT；Kovacs & Conway，2016年）——提出，g并非一种心理属性，而是一个指数，代表了由相互交织的认知过程网络所产生的认知能力。从这一视角出发，心理测量网络分析成为潜在变量模型的有力替代方案。网络分析能够展示观测变量之间的部分相关性，并揭示观测变量之间的直接关系。为了证明这一方法的益处，匈牙利版韦氏成人智力测验第四版（H-WAIS-IV；Wechsler，2008年）同时采用了心理测量网络分析和潜在变量模型进行分析。网络模型与潜在变量模型进行了直接比较。结果显示，H-WAIS-IV数据更适合网络模型而非潜在变量模型。我们认为，POT以及网络模型，相较于传统方法，提供了对智力结构更为精确的视角。