We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the…

Perception and perceptual memory play crucial roles in fear generalization, yet their dynamic interaction remains understudied. This research (N = 80) explored their relationship through a classical differential conditioning experiment. Results revealed that while fear context perception fluctuates over time with a drift effect, perceptual memory…

In this article, we pursue two points of discussion. First, a new illustration is presented of the person-oriented tenet according to which it can be hazardous to generalize to the individual results that are based on the analysis of aggregated data. Second, it is illustrated that taking into account serial dependence information can result in not…

Learning analytics is a fast-growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of…

Purpose: A considerable number of screening and diagnostic tools for autism exist, but variability in these measures presents challenges to data harmonization and the comparability and generalizability of findings. At the same time, there is a movement away from autism symptomatology to stances that capture heterogeneity and appreciate diversity.…

This study addresses the role of awareness in learning and the variables that may facilitate adult second language (L2) implicit learning. We replicated Williams's (2005) study with a similar group of academic learners enrolled at university as well as a group of non-college-educated adults in order to explore the generalizability of the findings…

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the…

Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM…

In this article, we develop and evaluate a novel system and computing platform to structure, measure, and improve student development using points. We define student development broadly as the achievement of learning to do, know, live together, and be. The system leverages individual agency, social influences, content generation and sharing,…

Over the past ten years, propensity score methods have made an important contribution to improving generalizations from studies that do not select samples randomly from a population of inference. However, these methods require assumptions and recent work has considered the role of bounding approaches that provide a range of treatment impact…

Profile analysis is one of the main tools for studying whether differential item functioning can be related to specific features of test items. While relevant, profile analysis in its current form has two restrictions that limit its usefulness in practice: It assumes that all test items have equal discrimination parameters, and it does not test…

Despite the popularity of traditional fit index cutoffs like RMSEA [less than or equal to] 0.06 and CFI [greater than or equal to] 0.95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been proposed to avoid overgeneralization by deriving cutoffs specifically tailored to the characteristics…

Although generalizability theory (GT) designs have traditionally been analyzed within an ANOVA framework, identical results can be obtained with structural equation models (SEMs) but extended to represent multiple sources of both systematic and measurement error variance, include estimation methods less likely to produce negative variance…

Conventional methods for evaluating the utility of subscores rely on traditional indices of reliability and on correlations among subscores. One limitation of correlational methods is that they do not explicitly consider variation in subtest means. An exception is an index of score profile reliability designated as [G], which quantifies the ratio…

This article explores the potential of artificial intelligence (AI) for academic advising. Specifically, it examines how AI-powered machine interpretation and data analysis can be used to deliver advising services anytime, anywhere. This system would eliminate the need for students to physically meet with advisors and could answer their…