Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…

Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs…

We explore ways in which statistics can be used to understand disease spread and support decision-making by governments. "Past performance does not guarantee future results"--we hope. We discuss and show examples from the National Science Foundation (NSF)-funded COVID-Inspired Data Science Education through Epidemiology (CIDSEE) project.…

Algorithmic bias is a pressing concern in educational data mining (EDM), as it risks amplifying inequities in learning outcomes. The Area Between ROC Curves (ABROCA) metric is frequently used to measure discrepancies in model performance across demographic groups to quantify overall model fairness. However, its skewed distribution--especially when…

This scope-review presents the milestones of how Hierarchical Rater Models (HRMs) become operable to used in automated essay scoring (AES) to improve instructional evaluation. Although essay evaluations--a useful instrument for evaluating higher-order cognitive abilities--have always depended on human raters, concerns regarding rater bias,…

The purpose of this study was to inform the dissemination of a project-based statistics curriculum by identifying institutional and instructor characteristics that predict its implementation. Data were drawn from pre- and post-workshop surveys completed by 67 instructors attending a one-and-a-half-day professional development workshop on…

Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181-214, 2000). We investigated…

Biostatisticians apply statistical methods to solve problems in the biological sciences. Successful practioners of biostatistics have advanced technical knowledge, are skilled communicators, and can seamlesslessly integrate with interdisciplinary scientific teams. Despite the breadth of skills required for success in this field, most biostatistics…

In order to detect a wide range of aberrant behaviors, it can be useful to incorporate information beyond the dichotomous item scores. In this paper, we extend the l[subscript z] and l*[subscript z] person-fit statistics so that unusual behavior in item scores and unusual behavior in item distractors can be used as indicators of aberrance. Through…

Teaching upper division statistical physics can often be clouded by the theory and complex examples used. To better help students appreciate the fundamental statistical concepts and how they are connected to thermodynamic principles this paper suggests using a simple abstract model. Using Atkins' Model students can see these ideas clearly…

A compelling demonstration of implicit learning is the human ability to unconsciously detect and internalize statistical patterns of complex environmental input. This ability, called statistical learning, has been investigated in people with dyslexia using various tasks in different orthographies. However, conclusions regarding impaired or intact…

We demonstrate how developmental theories may be instantiated as statistical models, using hierarchical continuous-time dynamic systems. This approach offers a flexible specification and an often more direct link between theory and model parameters than common modeling frameworks. We address developmental theories of the relation between the…

Learning to interpret data in context is an important educational outcome. To assess students' attainment of this outcome, it is necessary to examine the interplay between their contextual and statistical reasoning. We describe a research method designed to do so. The method draws upon Toulmin's (1958, 2003) model of argumentation for the first…

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like SRMR, RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the…

Measurement of latent constructs is one of the most challenging tasks in psychological research. Unlike physical variables, latent constructs are not directly observable but are inferred through individuals' responses to a set of items often referred to as measurement instruments, tests, surveys, or assessments. For decades, exploratory factor…