In our data-driven society, it is essential for students to become statistically literate. A core domain within Statistical Literacy is Statistical Inference, the ability to draw inferences from sample data. Acquiring and applying inferences is difficult for students and, therefore, usually not included in the pre-10th-grade curriculum. However,…

The 2023 Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), provides a comprehensive overview of adults' literacy, numeracy and adaptive problem-solving skills -- skills that are fundamental for personal, economic and societal development. These key information processing skills…

Network meta-analysis has been an essential methodology of systematic reviews for comparative effectiveness research. The restricted maximum likelihood (REML) method is one of the current standard inference methods for multivariate, contrast-based meta-analysis models, but recent studies have revealed the resultant confidence intervals of average…

Increasingly, the broader public, media and policymakers are looking to qualitative research to provide answers to our most pressing social questions. While an exciting and perhaps overdue moment for qualitative researchers, it is also a time when the method is coming under increasing scrutiny for a lack of reliability and transparency. The…

Educational robots have unrivaled advantages and value in developing students' computational thinking. Currently, there are fewer studies on the overall effects of educational robots on K12 students' computational thinking, especially at the instructional style and cross-grade level. In order to investigate the overall effect of educational robots…

This is the first review that takes stock of studies that have applied an experience sampling methodology (ESM) to examine the consequences of daily ethnic-racial discrimination (ERD) on youth' psychological adjustment and academic outcomes. We summarize moderators and mediators of these associations and highlight the benefits and limitations of…

Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across…

Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about…

Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical to the judicious and wise use of funding because they serve as important starting points that guide practical discussions around sampling tradeoffs and…

This paper uses Individual Deprivation Measure data from Indonesia and South Africa to demonstrate the effects of coverage bias associated with mobile phone-based sampling and data collection approaches that restrict sampling frames to those who own or have access to a mobile phone -- a increasingly common method. Analysis of this data…

In today's data-driven and data-informed educational landscape, leaders face increasing pressure to make decisions and present results based on what appear to be comprehensive statistical analyses. However, the ethical implications of these responsibilities can be complex, particularly when statistical results carry the potential to be…

Internationally, standard observational measures of Early Childhood Education and Care (ECEC) are used to assess the quality of provision. They are applied as research tools but, significantly, also guide policy decisions, distribution of resources and public opinion. Considerable faith is placed in such measures, yet their validity, reliability…

The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both…

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…

The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a small/moderate number of students, it can be sensitive to the sample, resulting in inaccurate representations of growth of the students, low year-to-year stability, and inequities for…