Being able to understand, interpret, and critically evaluate data is necessary for all individuals in our society. Using the PreK-12 Guidelines for Assessment and Instruction in Statistics Education-II (GAISE-II; Bargagliotti et al., 2020) curriculum framework, the current paper outlines five evidence-based recommendations that teachers can use to…

Parent rating scales are used at increasing rates across disciplines to track child development and determine diagnoses/needs. This study explored relationships among parental stress and the validity of parents' ratings of their child's behaviors. Participants include children who were referred for assessment of behavioral, social-emotional, and…

The issue of missing data raises concerns in all statistical and educational research. In this study, we focus on missing data in school-based assessment data generated by progressed high school learners (those who did not meet the promotional requirements for their current grades but were allowed to move to the next grade because of policy…

Although culturally responsive mathematics teaching is important, post-secondary education for preservice teachers (PTs) does not typically lead to learning opportunities for them to use mathematics to recognize the roles of social agents. To address this issue, we created a culturally responsive mathematical modeling task in which we invited PTs…

Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…

Missing data presents a significant challenge in Educational Data Mining (EDM). Imputation techniques aim to reconstruct missing data while preserving critical information in datasets for more accurate analysis. Although imputation techniques have gained attention in various fields in recent years, their use for addressing missing data in…

A rising number of children are permanently excluded from school each year in England. Children's experiences of exclusion are underrepresented in the literature, effectively giving prominence to the views and interpretations of researchers. This qualitative study uses semi-structured interviews to explore the ways in which excluded children story…

Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11…

Purpose: This paper aims to describe the statistical arguments produced by third-grade students (8-9 years old) and to identify the teaching support for collective argumentation in a lesson based on data comparison. A Lesson Study Group researched and planned the lesson around a problem from the official mathematics textbook.…

As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…

Data-driven marketing analytics courses are integral to modern business management degrees in universities, yet many graduates focus solely on single, separated data analysis techniques during their learning process, hindering effective integration and practical performance. This study proposes that employing the Fishbowl method, which divides…

Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of…

The methodological controversy surrounding ordinal outcome data has posed a distinct challenge to the conceptualization, design, and conduct of research in the social and behavioral sciences for more than 75 years. Accordingly, this study sought to supply a comprehensive and multidisciplinary perspective of the debate and in so doing lay the…

The practice of statistics has the power to motivate and support learning across the STEM (Science, Technology, Engineering, and Mathematics) disciplines. When connected with meaningful science contexts, statistical problem solving through the collection of data and subsequent data analysis, supported by contemporary graphing technology, presents…

In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…