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Myoung-jae Lee; Goeun Lee; Jin-young Choi – Sociological Methods & Research, 2025
A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…
Descriptors: Probability, Least Squares Statistics, Regression (Statistics), Causal Models
Jason A. Schoeneberger; Christopher Rhoads – American Journal of Evaluation, 2025
Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Multivariate Analysis
Kajal Mahawar; Punam Rattan – Education and Information Technologies, 2025
Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…
Descriptors: Electronic Learning, Artificial Intelligence, Academic Achievement, Prediction
Aarnes Gudmestad; Thomas A. Metzger – Language Learning, 2025
In this Methods Showcase Article, we illustrate mixed-effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future-time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step-by-step…
Descriptors: Second Language Learning, Spanish, Applied Linguistics, Predictor Variables
William R. Dardick; Jeffrey R. Harring – Journal of Educational and Behavioral Statistics, 2025
Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…
Descriptors: Statistics, Statistics Education, Problem Solving, Multivariate Analysis
Shabnam Ara S. J.; Tanuja Ramachandriah; Manjula S. Haladappa – Online Learning, 2025
Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…
Descriptors: Learning Analytics, Privacy, Artificial Intelligence, Regression (Statistics)