Processing Speed is Related to the General Psychopathology Factor in Youth | SpringerLink

 Processing Speed is Related to the General Psychopathology Factor in Youth | SpringerLink 
https://link.springer.com/article/10.1007/s10802-023-01049-w

The relationship between the p factor and cognition in youth has largely focused on general cognition (IQ) and executive functions (EF). Another cognitive construct, processing speed (PS), is dissociable from IQ and EF, but has received less research attention despite being related to many different mental health symptoms. The present sample included 795 youth, ages 11–16 from the Colorado Learning Disabilities Research Center (CLDRC) sample. Confirmatory factor analyses tested multiple p factor models, with the primary model being a second-order, multi-reporter p factor. We then tested the correlation between the p factor and a latent PS factor. There was a significant, negative correlation between the p factor and PS (r(87) = -0.42, p < .001), indicating that slower processing speed is associated with higher general mental health symptoms. This association is stronger than previously reported associations with IQ or EF. This finding was robust across models that used different raters (youth and caregiver) and modeling approaches (second-order vs. bifactor). Our findings indicate that PS is related to general psychopathology symptoms. This research points to processing speed as an important transdiagnostic construct that warrants further exploration across development.

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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Specific reading disabilities.

https://psycnet.apa.org/record/2023-47082-005

Reading represents the most common academic domain in which children and young adults experience difficulties in school. Specific reading disabilities (SRDs) are neurodevelopmental disorders that emerge early in development and persist into adulthood. They are characterized by protracted difficulties reading and spelling words with accuracy, reading words and text with fluency, and constructing meaning from written text. This chapter reviews research on SRDs, a disorder characterized by protracted difficulties in reading. It begins with a review of research related to the interrelated issues of prevalence, risk, and identification. These factors are inseparable due to the underlying dimensionality of attributes of SRDs. Relative reading skill profiles define two primary subtypes of SRDs: dyslexia and specific reading comprehension disabilities. The chapter reviews recommendations for interventions for individuals with SRDs, highlighting the importance of systematic and explicit instruction in deficit skills. It concludes with open questions for future research investigating interventions for SRDs

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Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization - Ulrich Schroeders, Florian Scharf, Gabriel Olaru, 2023

 Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization - Ulrich Schroeders, Florian Scharf, Gabriel Olaru, 2023 
https://journals.sagepub.com/doi/10.1177/00131644231160552

Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in a confirmatory bifactor model, while simultaneously selecting items for the final scale. To achieve this, the algorithm follows the biological template of bees' foraging behavior: Scout bees explore new food sources, whereas onlooker bees search in the vicinity of previously explored, promising food sources. Analogously, scout bees in BSO introduce major changes to a model specification (e.g., adding or removing a specific factor), whereas onlooker bees only make minor changes (e.g., adding an item to a factor or swapping items between specific factors). Through this division of labor in an artificial bee colony, the algorithm aims to strike a balance between two opposing strategies diversification (or exploration) versus intensification (or exploitation). We demonstrate the usefulness of the algorithm to find the underlying structure in two empirical data sets (Holzinger–Swineford and short dark triad questionnaire, SDQ3). Furthermore, we illustrate the influence of relevant hyperparameters such as the number of bees in the hive, the percentage of scouts to onlookers, and the number of top solutions to be followed. Finally, useful applications of the new algorithm are discussed, as well as limitations and possible future research opportunities.

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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