In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis (CNA). We further examine how such imbalances relate to fragmentation and noise in data. We show that even extreme data…

We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates C includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly…

Children rely on their Approximate Number System to intuitively perceive number. Such adaptations often exhibit sensitivity to real-world statistics. This study investigates a potential manifestation of the ANS's sensitivity to real-world statistics: a negative power-law distribution of objects in natural scenes should be reflected in children's…

Value-Added Models (VAMs) are both common and controversial in education policy and accountability research. While the sensitivity of VAMs to model specification and covariate selection is well documented, the extent to which test scoring methods (e.g., mean scores vs. IRT-based scores) may affect VA estimates is less studied. We examine the…

The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center,…

This paper introduces an approach that uses latent class analysis to identify cut scores (LCA-CS) and categorize respondents based on context scales derived from largescale assessments like PIRLS, TIMSS, and NAEP. Context scales use Likert scale items to measure latent constructs of interest and classify respondents into meaningful ordered…