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Youmi Suk – Journal of Educational and Behavioral Statistics, 2024
Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…
Descriptors: Artificial Intelligence, Causal Models, Statistical Inference, Maximum Likelihood Statistics
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Leslie Rutkowski; David Rutkowski – Journal of Creative Behavior, 2025
The Programme for International Student Assessment (PISA) introduced creative thinking as an innovative domain in 2022. This paper examines the unique methodological issues in international assessments and the implications of measuring creative thinking within PISA's framework, including stratified sampling, rotated form designs, and a distinct…
Descriptors: Creativity, Creative Thinking, Measurement, Sampling
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2023
In order to evaluate the effect of a policy or treatment with pre- and post-treatment outcomes, we propose an approach based on a transition model, which may be applied with multivariate outcomes and accounts for unobserved heterogeneity. This model is based on potential versions of discrete latent variables representing the individual…
Descriptors: Causal Models, Multivariate Analysis, Markov Processes, Human Capital
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Jaime León; Fernando Martínez-Abad – Large-scale Assessments in Education, 2025
Background: Grade retention is an educational aspect that concerns teachers, families, and experts. It implies an economic cost for families, as well as a personal cost for the student, who is forced to study one more year. The objective of the study was to evaluate the effect of course repetition on math, science and reading competencies, and…
Descriptors: Grade Repetition, Academic Achievement, Scores, Foreign Countries
Karabatsos, George; Walker, Stephen G. – Society for Research on Educational Effectiveness, 2011
Karabatsos and Walker (2011) introduced a new Bayesian nonparametric (BNP) regression model. Through analyses of real and simulated data, they showed that the BNP regression model outperforms other parametric and nonparametric regression models of common use, in terms of predictive accuracy of the outcome (dependent) variable. The other,…
Descriptors: Bayesian Statistics, Regression (Statistics), Nonparametric Statistics, Statistical Inference