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.

Following a trend across the sciences, recent studies in lithic analysis have embraced the ideal of replicability. Recent large-scale studies have demonstrated that high replicability is achievable under controlled conditions and have proposed strategies to improve it in lithic data recording. Although this focus has yielded important methodological advances, we argue that an overemphasis on replicability risks narrowing the scope of archaeological inquiry. More specifically, we show (1) that replicability alone does not guarantee reliability, interpretive value, or cost effectiveness, and (2) that archaeological data often involve unavoidable ambiguity due to preservation, analyst background, and the nature of lithic variability itself. Instead of allowing replicability to dictate research priorities, we advocate for a problem-driven, pluralistic approach that tailors methods to research questions and balances replicable measures with interpretive depth. This has practical implications for training, publishing, and funding policy. We conclude that Paleolithic archaeology must engage with the replicability movement on its own terms—preserving methodological diversity while maintaining scientific credibility.

Bilinguals vary in their daily-life language use and switching behaviours, which are also frequently studied in relation to other processes (e.g., executive control). Measuring daily-life language use and switching often relies on self-reported questionnaires, but little is known about the validity of these questionnaires. Here, we present two studies examining test–retest reliability and validity of language-use questionnaires (relative to Ecological Momentary Assessment, Study 1) and language-switching questionnaires and tasks (relative to recorded daily-life conversations, small-scale Study 2). Test–retest reliability and validity of the LSBQ (Anderson et al., 2018) were high and moderate, respectively, suggesting this questionnaire can capture daily-life language use well. Although only examined with a small sample size, Study 2 suggested relatively low validity of most language-switching questionnaires, with short language-production tasks potentially offering a more valid assessment. Together, these studies suggest that tools are available to reliably capture language use and switching with (a certain degree of) validity.

Mental health conditions among youths are increasing rapidly, taking into consideration their biological, psychological and social development in the time of technological advancement with its associated challenges. Therefore, this study examined the psychometric properties of eight mental health scales among Ghanaian youth. A total of 708 youths (62.1% females; 10–29 years) from junior high schools, senior high schools and a university were recruited to respond to measures on depression, anxiety, somatic symptoms, obsessive–compulsive symptoms, insomnia, smartphone application-based addiction, internet addiction, life satisfaction, stress and cognitive fatigue. Confirmatory factor analysis (CFA) and Pearson’s r were used to analyse the data. The findings indicated acceptable CFA fit for all scales (comparative fit index [CFI] >0.9, Tucker–Lewis index [TLI] >0.9, root mean square error of approximation [RMSEA] <0.08 and standardized root mean square residual [SRMR] <0.08), and internal reliability was satisfactory (Cronbach’s α = 0.774–0.868 and McDonald’s ω = 0.775–0.870). Correlation analyses showed significant relationships between all the measures except for life satisfaction and internet addiction, and stress and life satisfaction. Both the CFA indices and correlation analyses indicate that all the mental health measures demonstrate acceptable initial evidence of reliability and construct validity.

When using tests to assess individuals, precision of individual test scores is of great importance. Although it is generally known that different test scores are measured with varying precision, traditionally, measurement precision is quantified using a single value known as the standard error of measurement. In the practice of testing, the standard error of measurement is used as a one-size-fits-all measure for each test score. This practice emphasizes the need for a conditional precision estimate that shows which scores are precise and which scores lack precision. We discuss several conditional precision estimates based on classical test theory and item response theory, and provide open-source statistical software included in the software package JASP that enables computation of these estimates. Using conditional precision estimates, decisions based on test scores are expected to show less bias than the common unconditional standard error of measurement.

We revisit the question of how to include parameter uncertainty in univariate parametric models of losses and loss ratios. We first review the statistical theory for including parameter uncertainty based on right Haar priors (RHPs), which applies to many commonly used models. In this theory, the prior is chosen in such a way as to ensure matching between predicted probabilities and the relative frequencies of future outcomes in repeated tests. This property is known as reliability, or calibration. We then test priors for including parameter uncertainty in a number of models not covered by RHP theory. For these models, we find priors that generate predictions that are more reliable than predictions based on maximum likelihood, although they are not perfectly reliable. We discuss numerical schemes that can be used to generate Bayesian predictions, including a novel use of asymptotic expansions, and we include an example in which we show the impact of including parameter uncertainty in the modeling of extreme hurricane losses. The tail loss estimates show material increases due to the inclusion of parameter uncertainty. Finally, we describe a new software library that makes it straightforward to apply the methods we describe.