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…

This meta-analysis examines the impact of technology in statistics learning, comparing experimental and control groups across 34 studies, resulting in 55 effect sizes. The random effects model revealed a significant standardized mean difference (gRE = 0.50, 95% CI [0.35, 0.64], p < 0.01), indicating a positive effect of using technology in…

This study compared Stepwise Logistic Regression (Stepwise-LR) and three machine learning (ML) methods--Classification and Regression Trees (CART), Random Forest (RF), and Generalized Boosted Modeling (GBM) for estimating propensity scores (PS) applied in causal inference. A simulation study was conducted considering factors of the sample size,…

In October 2025, AACRAO created and distributed a 60-second survey to admissions professionals at higher education institutions in the United States. The survey asked these professionals to share information about their admissions staffing structures, responsibilities, current challenges, and future concerns. It received 270 institutional…

Exploratory learning before instruction typically benefits conceptual understanding compared to traditional instruction-first methods. The current study examined whether different exploration prompts impact students' exploration approaches and learning outcomes, using a quasi-experimental design. Undergraduate students (N = 164) in psychological…

Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for…

Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A…

Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6-12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL…

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low…

The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers…

Background/Context: In meta-analysis examining educational intervention, characterizing heterogeneity and exploring the sources of variation in synthesized effects have become increasingly prominent areas of interest. When combining results from a collection of studies, statistical dependency among their effects size estimates will arise when a…

We discuss an application of Bayesian factor analysis for estimation of the optimal linear combination and associated maximal reliability of a multi-component measuring instrument. The described procedure yields point and credibility interval estimates of this reliability coefficient, which are readily obtained in educational and behavioral…

Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model…

In July 2022 the Office for National Statistics (ONS) announced that they had made errors coding occupational data in the Labour Force Survey (LFS) for 2021. This error was subsequently corrected and the ONS published revised LFS data for 2021 in summer 2023. LFS data up to 2021 played a central role in the production of the Skills Imperative 2035…

This report provides projections for the UK labor market, through revised tables and figures for the occupational outlook on long-run employment prospects for the UK. The report is organized into the following sections: (1) Revised versions of the tables and figures from "Working Paper 2 -- Headline report" that were affected by ONS…