What is the best way to elicit forecasts if the goal is to measure the skill of forecasters? For continuous outcomes, there are two common approaches: elicit probabilities at fixed quantiles of the outcome distribution (e.g., what is the probability gas will cost less than $4.00 per gallon in 1 month?), or elicit quantiles at fixed probabilities (e.g., what is the price such that there is a 25% chance a gallon of gas will cost less than it in 1 month?). We compare these methods using a simulation study and a longitudinal survey in which 1,194 participants provided five waves of forecasts in both formats on a total of 36 real-world questions. Although probability elicitation is more ubiquitous in the literature, we show in our simulation and survey data that quantile elicitation allows us to estimate forecaster skill more efficiently with less measurement error. While quantile elicitation is time-consuming and associated with comprehension difficulty indicated by incoherent forecasts (violations of monotonicity), there is a clear reduction in incoherence across survey waves, suggesting comprehension of the quantile elicitation format improves with practice and is trainable.

Youths experience mental health challenges which are associated with their biopsychosocial development. However, this has been exacerbated by technological advances and their attendant challenges. Therefore, there is a need to periodically screen youths’ mental health using psychometrically valid scales so as to offer appropriate preventive and interventional programmes for them. This study examined the preliminary psychometric properties of eight mental health scales and their relationships among youths in Hong Kong. A total of 428 youths were conveniently recruited to respond to measures on depression, anxiety, somatic symptoms, smartphone application-based addiction, obsessive–compulsive symptoms, internet addiction, insomnia, life satisfaction, stress and cognitive fatigue. Data were analysed using Confirmatory factor analysis (CFA) and Pearson’s r. Generally, CFA fits (comparative fit index >0.9, Tucker–Lewis index >0.9, root mean square error of approximation <0.08 and standardised root mean square residual <0.08), average variance extracted (ranging between 0.531 and 0.724), composite reliability (ranging between 0.875 and 0.946) and internal reliability (Cronbach’s α = 0.834–0.918 and McDonald’s ω = 0.860–0.925) for the scales were acceptable. Correlations were significant among all measures (ps < 0.001). The CFA indices and correlational analyses suggest that all eight mental health measures have acceptable reliability and construct–convergent validity. Health and research professionals can use these scales to promote mental health activities among youths in Hong Kong.

In the context of classical test theory, we study under which conditions addition of an item to a unifactorial test increases the reliability of the test score. We distinguish item weights used to construct a sum score and item factor loadings. We show that the reliability of a weighted sum score increases if the scoring weight of the new item is set sufficiently low. We conclude that any negative contribution to the reliability is not caused by inclusion of the new item per se, but by inadequate weighting of the new item. We also show that if the unweighted sum score is being used with items that have equal error variances, inclusion of a new item increases reliability if the factor loading of the new item is greater than about half of the mean loading of the other items. For standardized items, with loadings based on correlations, we derive lower bounds for the loading of a new item based on the mean and variance of the existing loadings. Instead of removing items when their factor loading is below 0.30, we recommend lowering their scoring weight if they reduce reliability.

Over 45 years ago, William Revelle proposed a reliability measure based on the worst split-half of a test or scale, commonly known as Revelle’s beta, to assess the general factor saturation. However, to this day, there is no reliable method for computing this measure, as existing approaches are either computationally infeasible or insufficiently accurate in identifying the worst split-half. This difficulty arises because the number of candidate splits increases exponentially with the number of items. In this article, we show that computing Revelle’s beta is conceptually equivalent to divisive (“top-down”) hierarchical clustering. This insight allows us to reduce the number of candidate splits to a quadratic problem, making the computation feasible. We specify theoretical conditions under which this approach is guaranteed to recover the worst split-half. To validate the efficiency of our approach, we conduct simulation studies and analyze real-world data. Code implementations accompanying this work are available online, together with Supplementary Material.

Defined by DSM-5-TR as a neurodevelopmental disorder, attention-deficit/hyperactivity disorder (ADHD) has attracted ever-mounting attention from the public, coupled with a growing interest from clinicians, researchers, and patients. This is reflected in significantly higher demand for clinical assessments and frequent media reports of a surge in ADHD cases across the lifespan. These trends are puzzling as it is unknown what they truly reflect: an improvement in clinical detection or a concerning degree of overdiagnosis? A key reason for this uncertainty is our limited understanding of the disorder and imprecision of the diagnosis – a long-running subject of criticism. To better understand these issues, in this article, we deconstruct ADHD through the lens of its DSM-5-TR diagnostic criteria – the basis upon which the diagnosis is routinely made. Our in-depth analysis reveals major problems associated with the diagnostic criteria with respect to their arbitrariness, vagueness, redundancy, and context-dependent normality, which together substantially undermine the validity and reliability of the diagnosis, and the ADHD construct itself, blunting the precision of ADHD research, clinical decisions, and the effectiveness of treatment – all of which are contingent on having a robust diagnosis in the first place. Hence, our detailed deconstruction of the diagnosis of ADHD is critical as it provides the necessary groundwork for its accurate reconstruction – an essential step towards developing a valid, reliable, and clinically meaningful diagnostic foundation that will inform research and improve clinical care for patients with attentional and hyperactivity–impulsivity problems.

Pfadt et al. present accessible methods for estimating conditional standard errors of measurement (CSEMs) and implement them in the open-source software JASP. Their emphasis on individual-level precision represents an important contribution to applied measurement practice. This commentary discusses several conceptual issues that clarify and extend the authors’ treatment of dimensionality, error definition, and score interpretation. The aim is to strengthen alignment between CSEM estimation and the interpretive purposes for which test scores are used.

A discussion is provided of several issues related to behavioral measurement that arise from Pfadt et al. (2026, Psychometrika, 2026, 1–35). The note may be viewed in part as a complement to their developments regarding precision estimation for individual test scores.

Intraclass correlation coefficient (ICC) estimates are necessary for several statistical techniques. Researchers need accurate ICC estimates when conducting prospective power analyses for clustered data scenarios. In addition, meta-analysts require reasonable ICC values when adjusting effect size estimates to account for clustered primary study data or to correct for psychometric artifacts when using the ICC as a reliability measure. The validity of these analyses hinges on the accuracy of the ICC estimate. Beyond these secondary analyses, ICC estimates have been used as the focal outcome of meta-analysis itself to obtain a pooled measure of agreement, reliability, or the influence of a cluster’s effect. This study evaluates how well meta-analytically pooled ICC estimates recover the population ICC parameter value when using different ICC variance formulas as the inverse variance weights used in the pooling. We found that the variance formula that uses a normalizing transformation performs best across most conditions.

Punching shear failure in slab-column connections is a brittle collapse mode that threatens the safety of flat reinforced concrete (RC) slabs. Conventional design provisions are generally conservative but exhibit inconsistencies across geometric and material variations. This study develops an eXtreme Gradient Boosting (XGBoost) model to predict the ultimate punching shear capacity of flat RC slabs, using a database of experimental results categorized by four different geometric domains, including square slab with square column, circular slab with circular column, square slab with circular column, and circular slab with square column, covering the geometric, materials strength, and reinforcement properties of input parameters. The model achieved high predictive accuracy across the domains with coefficient of determination (R2) values > 0.930 in unseen testing datasets with minimal bias (0.994–1.006) and reduced scatter. Model interpretability, addressed through the SHapley Additive exPlanations analysis, confirmed slab thickness and average effective depth as the most critical predictors of shear capacity, followed by concrete strength and reinforcement parameters, while boundary condition parameters showed negligible influence due to the predominance of interior column cases. These findings demonstrate that XGBoost provides accurate, reliable, and interpretable predictions of punching shear capacity, offering a data-driven alternative to code-based methods and supporting safer and more consistent design of flat RC slabs.