This study concludes that traditional logistic regression models, particularly those using ACT Composite scores, tend to demonstrate better fairness metrics across subgroups compared to a fairness-aware machine learning gradient-boosted machine model. The exclusion of race/ethnicity from predictive models does not introduce notable bias and may…

In today's polarizing political climate, there is a need to build citizens' statistical literacy to combat misinformation and support data-based arguments. To that aim, we investigate K-12 standards documents for their alignment with the American Statistical Association's Guidelines for the Assessment and Instruction in Statistics Education (GAISE…

Previous psychophysical studies reported a positive aftereffect in tactile temporal order judgments, which can be explained by the Bayesian estimation model ('Bayesian calibration'). We investigated the relationship between Bayesian calibration and autistic traits in participants with typical development (TD) and autism spectrum disorder (ASD).…

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test…

Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this…

Purpose: The purpose of this study was twofold: (1) to determine whether there are speech rhythm differences between preschool-age children who stutter that were eventually diagnosed as persisting (CWS-Per) or recovered (CWS-Rec) and children who do not stutter (CWNS), using empirical spectral analysis and empirical mode decomposition of the…

Students typically find research methods and statistics classes intimidating. In particular, learning different types of data measurement and operationalization can take significant time and practice to understand. Utilizing a pedagogical approach that turns a party game into a fun exercise, students collaboratively learn to operationalize…

Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were…

This study analyses academics' commitment and job satisfaction according to full-time non-tenure-track (FTNT) and full-time tenure-track (FTT) in South Korea's changing academic environment. Data were collected from the 2018 Academic Profession in the Knowledge-Based Society Survey. One-way analyses of variance show no statistically significant…

To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse…

ChatGPT is one of many generative artificial intelligence (AI) tools that has emerged recently, creating controversy in the education community with concerns about its potential to be used for plagiarism and to undermine students' ability to think independently. Recent publications have criticized the use of ChatGPT and other generative AI tools…

In recent years, big data has become ubiquitous in our day-to-day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and…

In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal…

This study builds on previous studies that have examined guided project-based learning in undergraduate statistics courses to examine students' attitudes toward statistics after participating in a graduate-level statistics course that used guided project-based learning as an andragogical technique. This phenomenological qualitative case study…

Though statistical testing is commonly practiced, the logic of statistical tests is confusing, thinking about distributions is difficult, and the way statisticians formulate expectations as probability distributions is poorly understood. To support instruction, the statistics education community has increasingly utilized simulation-based…