[Article Review] Unlocking Potential: Evaluating the NIH Toolbox for Measuring Cognitive Change in Individuals with Intellectual Disabilities

Reference

Shields, R. H., Kaat, A., Sansone, S. M., Michalak, C., Coleman, J., Thompson, T., McKenzie, F. J., Dakopolos, A., Riley, K., Berry-Kravis, E., Widaman, K. F., Gershon, R. C., & Hessl, D. (2023). Sensitivity of the NIH Toolbox to detect cognitive change in individuals with intellectual and developmental disability. Neurology, 100(8), e778-e789. https://doi.org/10.1212/WNL.0000000000201528

Review

In their 2023 study, Shields et al. aimed to evaluate the sensitivity of the National Institutes of Health Toolbox Cognition Battery (NIHTB-CB) in detecting the cognitive change in individuals with intellectual disabilities (ID), specifically in those with fragile X syndrome (FXS), Down syndrome (DS), and other ID (OID). The study sought to provide further support for the use of NIHTB-CB as an outcome measure in clinical trials and other intervention studies targeting individuals with ID.

The researchers administered the NIHTB-CB and a reference standard cross-validation measure (Stanford-Binet Intelligence Scales, Fifth Edition [SB5]) to 256 participants with FXS, DS, and OID aged between 6 and 27 years. After two years, 197 individuals were retested. The study employed latent change score models to assess group developmental changes in each cognitive domain of the NIHTB-CB and SB5. Additionally, two-year growth was examined at three age points (10, 16, and 22 years).

Shields et al. (2023) found that the effect sizes of growth measured by the NIHTB-CB tests were comparable to or exceeded those of the SB5. The NIHTB-CB demonstrated significant gains in almost all domains in the OID group at younger ages (10 years), with continued gains at 16 years and stability in early adulthood (22 years). The FXS group exhibited delayed gains in attention and inhibitory control compared to the OID group. Meanwhile, the DS group showed delayed gains in receptive vocabulary compared to the OID group. Notably, the DS group experienced significant growth in early adulthood in two domains (working memory and attention/inhibitory control). Each group's pattern of NIHTB-CB growth across development corresponded to their respective pattern of SB5 growth.

The study's results support the sensitivity of the NIHTB-CB in detecting developmental changes in individuals with ID, making it a promising tool for clinical trials and intervention studies. However, the authors note that future research is needed to establish sensitivity to change within the context of treatment studies and to delineate clinically meaningful changes in NIHTB-CB scores linked to daily functioning.

[Article Review] Exploring the Performance of Coefficient Alpha and Its Alternatives in Non-Normal Data

Reference

Xiao, L., & Hau, K.-T. (2023). Performance of Coefficient Alpha and Its Alternatives: Effects of Different Types of Non-Normality. Educational and Psychological Measurement, 83(1), 5-27. https://doi.org/10.1177/00131644221088240

Review

In the article "Performance of Coefficient Alpha and Its Alternatives: Effects of Different Types of Non-Normality" by Leifeng Xiao and Kit-Tai Hau (2023), the authors evaluate the performance of coefficient alpha and several alternatives under different non-normal data conditions. They tested indices such as ordinal alpha, omega total, omega RT, omega h, GLB, and coefficient H on continuous and discrete data with varying degrees of non-normality.

The study found that the estimation bias was acceptable for continuous data with different levels of non-normality when the scales were strong. However, the bias increased with moderate strength scales and grew larger as non-normality increased. For Likert-type scales, most indices were acceptable with non-normal data with at least four points, with more points resulting in better performance. The authors discovered that omega RT and GLB were robust for different exponentially distributed data, but the bias of other indices for binomial-beta distribution was generally large.

Xiao and Hau (2023) concluded that the demand for continuous and normally distributed data for alpha might not be necessary for less severely non-normal data. For severely non-normal data, at least four scale points should be used, with more points being better. Furthermore, the authors emphasized that no single golden standard exists for all data types and that other factors such as scale loading, model structure, or scale length are also essential.