The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, <i>SD</i> = 0.41) were divided into good (<i>n</i> = 154), competent (<i>n</i> = 147), and struggling (<i>n</i> = 152) readers based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.