The COVID-19 pandemic altered course delivery in higher education at many universities. This article evaluates the differences between student experiences in the fall 2019 semester (pre-pandemic) and those during the fall 2020 semester (pandemic) within a multidisciplinary, project-based introductory statistics course. Results indicated that there…

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics…

Variability is underemphasized in domains such as engineering. Statistics and data science education research offers a variety of frameworks for understanding variability, but new frameworks for domain applications are necessary. This study investigated the professional practices of working engineers to develop such a framework. The Neglected,…

While many instructors are aware of the "Literary Digest" 1936 poll as an example of biased sampling methods, this article details potential further explorations for the "Digest's" 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to…

Quantitative education research is often perceived to be "objective" or "neutral." However, quantitative research has been and continues to be used to perpetuate inequities; these inequities arise as both intended effects and unintended side effects of traditional quantitative research. In this review of the literature, we…

This study examined Physical Education Pre-Service Teachers' (PSTs) perceptions of their teaching from constructivist teaching practices and their variation among PSTs' responses when aggregated by sex, teaching practices curriculum, extension project, and university type. The sample consisted of 869 Physical Education PSTs from Brazil (female =…

The following study shows how reparameterizations and constraints of the general linear model can serve to parse quantitative and qualitative aspects of predictors. We demonstrate three different approaches. The study uses data from the High School Longitudinal Study of 2009 on mathematics course-taking and achievement as an example. Results show…

Purpose: The purpose of the article is to gain more insight into factors that can explain students' success in business subjects. The focus is on the connection between performance in introductory courses in business mathematics (BM) and business statistics (BS) and success in various business subjects. Design/methodology/approach: Use of a…

Data Analytics has emerged as an essential skill for business students, and several tools are available to support their learning in this area. Due to the students' lack of programming skills and the perceived complexity of R, many business analytics courses employ no-code analytical software like IBM SPSS Modeler. Nonetheless, generative…

A modified and improved inductive inferential approach to evaluate item discriminations in a conditional maximum likelihood and Rasch modeling framework is suggested. The new approach involves the derivation of four hypothesis tests. It implies a linear restriction of the assumed set of probability distributions in the classical approach that…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

Implementation planning is a consultative educator support strategy with preliminary evidence of effectiveness from single-case design research. This meta-analysis investigates the impacts of implementation planning on educator implementation and student outcomes and explores moderators of these relationships. Principles of open science and…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

To evaluate the evolution of statistical education research aimed at the inclusion of technological resources in the initial and continuing education of teachers in Brazil, a systematic literature review (SLR) was conducted on 25 works published in the Scientific Proceedings of the National Meeting of Mathematics Education and on 7 papers from the…

Language, culture, and conceptions of uncertainty can impact the way that students respond to a statistical investigation. The aim of this small exploratory study was to gain insight into how two groups of children (aged 9-12) speaking different home dialects adopted expressions of uncertainty in a standard dialect used in school, specifically the…