Unraveling the Multifaceted Nature of Intelligence: A Correlated Factor Model Approach Grounded in PASS Theory.

Abstract

Intelligence, a subject of profound interest within psychology, has seen extensive exploration of its psychological and psychometric foundations. This study delves into the multifaceted nature of intelligence, using structural equation modeling techniques to examine theory-driven conceptualizations of the construct. We tested several models, including unidimensional, correlated, higher-order, and bifactor symmetrical and asymmetrical models. To enhance the reliability and generalizability of the findings, we used a large and diverse cohort based on the Planning, Attention, Simultaneous, Successive (PASS) theory and the Cognitive Assessment System 2 (CAS2), which was standardized in the United States. Results showed that the correlated factor model, which reflects relationships among cognitive domains, offers the most fitting representation of intelligence. This outcome aligns with the PASS theory's theoretical foundations, emphasizing intelligence's multifaceted nature. Also, our exploration of cultural relevance invariance underscores the importance of considering demographic-related differences in cognitive processes. By endorsing a correlated factor model, our study encourages a subtle understanding of intelligence that acknowledges the diversity and interconnectedness of cognitive processes, with potential implications for education and clinical assessment practices.