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Yongtian Cheng; K. V. Petrides – Educational and Psychological Measurement, 2025
Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…
Descriptors: Psychological Studies, Artificial Intelligence, Cognitive Processes, Predictive Validity
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André Beauducel; Norbert Hilger; Tobias Kuhl – Educational and Psychological Measurement, 2024
Regression factor score predictors have the maximum factor score determinacy, that is, the maximum correlation with the corresponding factor, but they do not have the same inter-correlations as the factors. As it might be useful to compute factor score predictors that have the same inter-correlations as the factors, correlation-preserving factor…
Descriptors: Scores, Factor Analysis, Correlation, Predictor Variables
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Lisa J. Elliott; Joan Middendorf – International Journal for the Scholarship of Teaching and Learning, 2024
Teaching and learning undergraduate statistics has been a most challenging task for undergraduate psychology majors (Salkind, 2017). A seasoned statistics instructor consulted with a seasoned instructional designer on a method to improve a particularly demanding course using a performance improvement approach to address learning difficulties she…
Descriptors: Undergraduate Students, Psychological Studies, Statistics Education, Teaching Methods
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Kratochwill, Thomas R.; Levin, Joel R.; Horner, Robert H. – Remedial and Special Education, 2018
The central roles of science in the field of remedial and special education are to (a) identify basic laws of nature and (b) apply those laws in the design of practices that achieve socially valued outcomes. The scientific process is designed to allow demonstration of specific (typically positive) outcomes, and to assist in the attribution of…
Descriptors: Intervention, Educational Research, Research Methodology, Case Studies
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Golino, Hudson F.; Gomes, Cristiano M. A. – International Journal of Research & Method in Education, 2016
This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…
Descriptors: Item Response Theory, Regression (Statistics), Difficulty Level, Goodness of Fit