Spelling is a measure of the precision of lexical representations, which is important for word reading, comprehension, and writing. Performance on spelling tests is typically assessed in terms of correctness. We asked whether nonbinary measures of spelling errors provide additional information about lexical quality. Letter distance was the number…

Background/Context: Single-case experiment designs (SCEDs) are experimental designs in which a small number of cases are repeatedly measured over time, with manipulation of baseline and intervention phases. Because SCEDs often rely on direct behavioral observations, count data are common. To account for both the clustering and the non-normal…

The Beuk standard setting method derives cut scores through expert judgment that balances content and normative perspectives. This study developed a method to estimate confidence intervals for Beuk settings and assessed their accuracy via simulations. Simulations varied SME panel size, expert agreement, cut score locations, score distributions,…

Children are interviewed to provide information about past events in various contexts (e.g., police interviews, court proceedings, therapeutic interviews). During an interview, various factors may influence the accuracy of children's responses to questions about recent events. However, behavioral research in this area is limited. Sparling et al.…

The relationship between confidence and accuracy has long been an important and controversial topic within the field of human memory. In a recent review article, Schwartz (2024). "Inferential theories of retrospective confidence." Metacognition & Learning.) competently summarized some of the key empirical findings on this issue and…

Purpose: Although social media platforms have made information about autism more accessible to the general public, concerns have been raised about the unfiltered nature of the content they host. In the current study, we examined the reach and accuracy of videos providing informational content about autism on TikTok, a popular social media…

Children are vulnerable to disease, yet are poor at recognising and avoiding sickness. Thus, the current study aims to recruit 5- to 9-year-olds (anticipated 50% female, 60% White, 60% Hispanic/Latine) to test whether children's sickness perception is malleable and can be improved through training. We created developmentally appropriate stories…

We explored the practicality of relatively small item pools in the context of low-stakes Computer-Adaptive Testing (CAT), such as CAT procedures that might be used for quick diagnostic or screening exams. We used a basic CAT algorithm without content balancing and exposure control restrictions to reflect low stakes testing scenarios. We examined…

Theories of self-regulated learning suggest a positive link between knowledge monitoring accuracy (the ability to predict test performance) and performance on tests. Put differently, students who accurately monitor their knowledge of course content more efficiently regulate study of course materials. However, a plethora of literature indicates…

Distances derived from word embeddings can measure a range of gradational relations--similarity, hierarchy, entailment, and stereotype--and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation…

Purpose: Phonetic forced alignment has a multitude of applications in automated analysis of speech, particularly in studying nonstandard speech such as children's speech. Manual alignment is tedious but serves as the gold standard for clinical-grade alignment. Current tools do not support direct training on manual alignments. Thus, a trainable…

Although AI transformer models have demonstrated notable capability in automated scoring, it is difficult to examine how and why these models fall short in scoring some responses. This study investigated how transformer models' language processing and quantification processes can be leveraged to enhance the accuracy of automated scoring. Automated…

This study investigates the estimation of classification consistency and accuracy indices for composite summed and theta scores within the SS-MIRT framework, using five popular approaches, including the Lee, Rudner, Guo, Bayesian EAP, and Bayesian MCMC approaches. The procedures are illustrated through analysis of two real datasets and further…

This study considers the estimation of marginal reliability and conditional accuracy measures using a generalized recursion procedure with several IRT-based ability and score estimators. The estimators include MLE, TCC, and EAP abilities, and corresponding test scores obtained with different weightings of the item scores. We consider reliability…

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an…