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 reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed

Aging and Models of Cognitive Ability: Exploratory and Confirmatory Analyses of Hungarian WAIS-IV Data

This poster was scheduled to be presented at the Cognitive Aging Conference (April, 2020), which was postponed due to the COVID-19 pandemic

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

Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs & Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom & Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension

A Commentary and Reanalysis of McFarland (2020)

Commentary on inaccurate statements from McFarland (2020) and reanalysis of uncorrected correlation matrices used

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