This study critically examines the biases and methodological shortcomings in studies comparing deaf and hearing populations, demonstrating their implications for both the reliability and ethics of research in deaf education. Upon reviewing the 20 most-cited deaf-hearing comparison studies, we identified recurring fallacies such as the presumption…

Cross-national comparisons of educational achievement rely upon each participating country collecting nationally representative data. While obtaining high response rates is a key part of reaching this goal, other potentially important factors may also be at play. This paper focuses on one such issue--exclusion rates--which has received relatively…

RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two…

This colloquium brings information about a second cycle of OECD's International Early Learning and Well-being Study (IELS) to the early childhood community, and offers a further critique of the approach to comparative research that the IELS embodies.

In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). A multilevel mediation model is used as an illustrative example to compare different types of DIC and WAIC. More specifically, the study compares the…

To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations…

Does "X" affect "Y"? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the…

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the…

Advances in neuro-technology provide new insights into how individual students learn in educational contexts. However, applying it poses challenges for teachers in natural settings. This paper presents an example of the use and applicability of eye-tracking technology in Higher Education. We worked with a sample of 20 students from three…

In this article, we examine whether cross-national studies disclose enough information for independent researchers to evaluate the validity and reliability of the findings (evaluation transparency) or to perform a direct replication (replicability transparency). The first contribution is theoretical. We develop a heuristic theoretical model…

As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…

The purpose of this study was to compare and contrast frameworks for evaluating methodological rigor in single case research. Specifically, research on high-probability requests to increase compliance in young children was evaluated. Ten studies were identified and were coded using 4 frameworks. These frameworks were the Council for Exceptional…

Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we…

Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer…

We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…