The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as "measurement"). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically…

Classical item analysis (CIA) entails summarizing items based on two key attributes: item difficulty and item discrimination, defined as the proportion of examinees answering correctly and the difference in correctness between high and low scorers. Recent insights reveal a direct link between these measures and aspects of signal detection theory…

This article outlines a readily applicable procedure for point and interval estimation of the population discrepancy between reliability and the popular Cronbach's coefficient alpha for unidimensional multi-component measuring instruments with uncorrelated errors, which are widely used in behavioral and social research. The method is developed…

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…

This article develops an index to measure the level of susceptibility of courses to cheating using ChatGPT (Chat Generative Pre-trained Transformer), an advanced text-based artificial intelligence (AI) language model. It demonstrates the application of the index to a sample of business courses in a mid-sized university. The study finds that the…

There is a need for valid and reliable instruments to assess learning outcomes in education for sustainable development (ESD). Measurement invariance (MI) needs to be established before results of these instruments can be validly compared between groups. Despite its importance, establishing MI is an often overlooked validation step. To provide an…

Previous psycholinguistic research has demonstrated that sentence processing varies according to both syntactic and discourse context. However, a systematic investigation of how such contexts influence how the processor manages low-level representations of linguistic structure has yet to be carried out. In this paper, we conduct a series of…

This study provides a comprehensive overview of methodological aspects when using heart rate variability (HRV) measures in educational research. Following PRISMA 2020 guidelines, we searched four databases for relevant studies published until March 5, 2024. From the 48 studies reviewed, we extracted data across three analytical categories: (1)…

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs.…

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the…

Educational large-scale assessment (LSA) studies like the program for international student assessment (PISA) provide important information about trends in the performance of educational indicators in cognitive domains. The change in the country means in a cognitive domain like reading between two successive assessments is an example of a trend…

Diagnostic classification models (DCMs) have grown in popularity as stakeholders increasingly desire actionable information related to students' skill competencies. Longitudinal DCMs offer a psychometric framework for providing estimates of students' proficiency status transitions over time. For both cross-sectional and longitudinal DCMs, it is…

The acquisition of quantitative measurement data has been a challenge in chemistry education for a long time, as the required measurement devices are often too old, error-prone, complicated to operate, or simply too expensive. This paper presents the LabPi digital measurement station, which addresses this problem and provides a solution for…

This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also…

Cognitive load measurement is a methodological issue of high importance in all learning settings involving a high perceptual richness, such as virtual and augmented reality. As a result of the growing number of cognitive load measurement methods and surveys, it can be difficult to choose the optimal measurement instrument for learning tasks in…