[Article Review] Unraveling Brain and Cognitive Changes: A Deep Dive into GALAMMs

Reference

Sørensen, Ø., Fjell, A. M., & Walhovd, K. B. (2023). Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models. Psychometrika, 88(2), 456-486. https://doi.org/10.1007/s11336-023-09910-z

Review

In their 2023 study, Sørensen, Fjell, and Walhovd introduced generalized additive latent and mixed models (GALAMMs) to analyze clustered data. They developed these models primarily to address applications in cognitive neuroscience. Their method leverages a scalable maximum likelihood estimation algorithm, utilizing advanced computational techniques like the Laplace approximation, sparse matrix computation, and automatic differentiation. Crucially, this approach allows for a variety of mixed response types, heteroscedasticity, and crossed random effects.

The authors further illustrated the applicability of GALAMMs by presenting two case studies. The first highlighted how these models could comprehensively capture lifespan trajectories of various cognitive abilities, including episodic memory, working memory, and executive function. Such findings were drawn from widely used cognitive tests like the California Verbal Learning Test, digit span tests, and Stroop tests. In their second case, the researchers explored the impact of socioeconomic status on brain structure, specifically delving into the relationship between educational and income levels with hippocampal volumes, gauged via magnetic resonance imaging (MRI). Their results posited that by integrating semiparametric estimation with latent variable modeling, GALAMMs can offer a more nuanced depiction of how both the brain and cognition evolve throughout an individual's life.

Overall, this study presents a promising tool for the analysis of complex data structures, especially in the realm of cognitive neuroscience. While the authors provided solid evidence from their case studies, it would be beneficial to see how GALAMMs fare in a broader range of applications. Moreover, the efficacy of these models in different sample sizes, beyond moderate ones, remains a question worth exploring in future research.

[Article Review] Peering into Decision Making: A Dive into Modeling Eye Movements

Reference

Wedel, M., Pieters, R., & van der Lans, R. (2023). Modeling Eye Movements During Decision Making: A Review. Psychometrika, 88(2), 697-729. https://doi.org/10.1007/s11336-022-09876-4

Review

In the article "Modeling Eye Movements During Decision Making: A Review", authors Wedel, Pieters, and van der Lans (2023) undertake a comprehensive exploration of recent advancements in psychometric and econometric modeling of eye movements during decision-making tasks. The authors rightly identify eye movements as an instrumental method to gain insights into the otherwise elusive perceptual, cognitive, and evaluative processes that people undergo during decision-making. Their proposed theoretical framework emphasizes the intricate nature of task and strategy switching in relation to complex goals.

Building on this foundational framework, the trio proceeds to map out the existing literature, emphasizing how cognitive processes steer distinct eye-movement patterns. Their endeavor to categorize and contextualize prior works lends clarity to the field. However, a potential pitfall of the article lies in its optimistic depiction of these models. While they note the advances, more critical discussion around the challenges and limitations faced would have further enriched the narrative.

The authors call for further research and a more detailed psychometric modeling approach to understand eye movements during decision-making. This article, while shedding light on key areas of development, would benefit from a balanced perspective, accentuating not just the possibilities but also the boundaries of the domain.

[Article Review] Revolutionizing Online Test Monitoring: A Dive into Kang's Latest Research

Reference

Kang, Hyeon-Ah. (2023). Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring. Psychometrika, 88(2), 672-696. https://doi.org/10.1007/s11336-022-09871-9

Review

In her 2023 article published in Psychometrika, Kang delves into a critical dimension of psychometric testing: the continuous and intermittent monitoring of item functioning. At the heart of the article lies the introduction of sequential generalized likelihood ratio tests designed to surveil multiple item parameters across various sampling techniques. Kang's focus on the stability of item parameters across time sets significant precedence in an age where psychometric tests, especially online ones, see broad usage and necessitate consistent quality checks.

Through a combination of simulated and real assessment data, Kang validates the efficacy of the proposed monitoring procedures. One of the standout features of these methods, as highlighted in the study, is their ability to identify significant parameter shifts in a timely fashion while keeping error rates within acceptable margins. The research commendably compares these newly introduced methods against existing ones, showcasing their superior performance. Such empirical results strengthen the credibility of these procedures and their potential applicability in real-world settings.

The article suggests that multivariate parametric monitoring, anchored on robust likelihood-ratio tests, holds the promise of being a formidable tool in upholding the quality of psychometric items. Kang's emphasis on joint monitoring of multiple-item parameters provides a holistic approach to maintaining consistency and reliability. Grounded on the empirical findings, the study also offers tangible strategies for online item monitoring, an invaluable asset for practitioners in the field.