Psychometric Models of Individual Differences in Reading Comprehension: A Reanalysis of Freed, Hamilton, and Long (2017)

Abstract

It is well established that reading comprehension ability varies among individuals. However, there is disagreement regarding whether those differences can be attributed to differences in domain-general abilities (e.g., working memory capacity, WMC; Just & Carpenter, 1992) or domain-specific abilities (e.g., language experience; MacDonald & Christiansen, 2002). Recently, Freed, Hamilton, and Long (2017) reported that individual differences in reading comprehension can be largely attributed to language experience and concluded that WMC is not an important factor. After re-analyzing structural equation models from Freed et al. and generating a psychometric network model of their data, we find that WMC is more important to individual differences in reading comprehension than was suggested by Freed et al. Overall, it was confirmed that both domain-general and domain-specific processes are associated with variation in reading comprehension.

Teacher perceptions of working memory and executive function improvements following school-day cognitive training

“…Word decoding skills, reading comprehension, and mathematical abilities have all been linked to WM processing (Peng et al, 2016, 2018; Swanson, 1999; Zheng et al, 2011). Visuospatial and phonological processing help students to make meaning from letters and words to aid in the reading process (Goring et al, 2021; Morris & Lonigan, 2022; Swanson, 1999; Swanson & Kong, 2018), engage in mental manipulation of numbers to complete mathematical problems (Gunderson et al, 2012; Ramirez et al, 2013; Zheng et al, 2011), and process information quickly and efficiently (Goring et al, 2021; Pickering, 2001; Tourva & Spanoudis, 2020). WM skills increase over time, giving children greater attentional resources, processing speed, and memory capacity to further engage with increasingly difficult academic content as they progress through school (Alloway & Carpenter, 2020; Pickering, 2001).…”

Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

“…We agree that partial correlations can be used for this purpose; however, this was not the rationale behind the Schmank et al ( 2019 ) study. Psychometric network modeling offers an alternative approach to investigations of the underlying structure of individual differences in cognitive abilities (i.e., intelligence), without the assumptions that come with reflective latent variable models ( Conway et al 2021 ; see also Goring et al 2019 ). According to the view of intelligence endorsed by Schmank et al ( 2019 ), process overlap theory ( Kovacs and Conway 2016 ), there is no need to assume that (all or some of) the factors in higher-order factor models represent common causes of individual differences in test performance.…”