Asymmetric Likert-type items in research studies can present several challenges in data analysis, particularly concerning missing data. These items are often characterized by a skewed scaling, where either there is no neutral response option or an unequal number of possible positive and negative responses. The use of conventional techniques, such…

Subjects with initially extreme observations upon remeasurement are found closer to the population mean. This tendency of observations toward the mean is called regression to the mean (RTM) and can make natural variation in repeated data look like real change. Studies, where subjects are selected on a baseline criterion, should be guarded against…

Previous research has identified an unfilled gap between, on the one hand, mathematical prerequisites needed for a formal treatment of least squares and, on the other hand, only teaching procedural aspects of curve fitting. As a response to this, the present study explores students' suggestions of how they think a computer or calculator does curve…

Background/Context: Predictive analytics has emerged as an indispensable tool in the education sector, offering insights that can improve student outcomes and inform more equitable policies (Friedler et al., 2019; Kleinberg et al., 2018). However, the widespread adoption of predictive models is hindered by several challenges, including the lack of…

Purpose and Background: Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation many researchers encounter is how to effectively estimate model parameters with typical sample sizes when there are many levels of (potentially disparate) nesting. We develop a method-of-moment corrected maximum likelihood…

Introduction: In an increasingly diverse world, there has been a call for psychology educators to make efforts to integrate diversity into the psychology curriculum. Statement of the Problem: Researchers who have surveyed psychology faculty have found the amount of time devoted to diversity content in nondiversity-focused courses is limited, with…

Cognitive diagnosis models (CDMs) are widely used to assess individuals' latent characteristics, offering detailed diagnostic insights for tailored instructional development. Maximum likelihood estimation using the expectation-maximization algorithm (MLE-EM) or its variants, such as the EM algorithm with monotonic constraints and Bayes modal…

The rapidly increasing capacity of technology to collect, organize, and manage data has spurred changes in the practice of statistics: new methods of collecting data, large data sets, new forms of data, different ways to visualize and represent data, and recognition of the importance of being able to understand and to communicate data-based…

Binary outcomes are often analyzed in cluster randomized trials (CRTs) using logistic regression and cluster robust standard errors (CRSEs) are routinely used to account for the dependent nature of nested data in such models. However, CRSEs can be problematic when the number of clusters is low (e.g., < 50) and, with CRTs, a low number of…

Microblogging sites have become important data sources for studying network dynamics and information transmission. Both areas of study, however, require accurate counts of indegree, or follower counts; unfortunately, collection of complete time series on follower counts can be limited by application programming interface constraints, system…

Statistical literacy has a large and important role in the teaching of statistics. Most mathematics and statistics courses are hierarchical, and the earlier material forms the foundation for later material. We construct a hierarchical structure for an introductory statistics course using Rasch analysis of the student scripts for the final…

Multivariate count data is ubiquitous in many areas of research including the physical, biological, and social sciences. These data are traditionally modeled with the Dirichlet Multinomial distribution (DM). A new, more flexible Dirichlet-Tree Multinomial (DTM) model is gaining in popularity. Here, we consider Bayesian DTM regression models. Our…

A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study…

Background/purpose: This study aims to examine the inferential statistical literacy of mathematics education students based on their cognitive styles--field-dependent (FD) and field-independent (FI). Inferential statistical literacy involves not only understanding statistical concepts such as hypothesis testing and parameter estimation but also…

The reproducibility crisis in the sciences is now well established. Curiously, only sporadic attention has been paid to it in kinesiology. This is a mistake. The scientific research produced in kinesiology is not exempt from the causes of the crisis. These causes include human, statistical and philosophical limitations inherent to the scientific…