Predicting implementation of response to intervention in math using elastic net logistic regression

Method<sub>50</sub> and Method<sub>coef</sub> emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

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Publication Details

Journal
Frontiers in Psychology
Published
2024-10-02
DOI
https://doi.org/10.3389/fpsyg.2024.1410396
Citations
3
Primary Topic
Statistical Methods and Applications
Type
article
Field-Weighted Citation Impact
2.13

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article

Predicting implementation of response to intervention in math using elastic net logistic regression

Sara Comella, Garret J. Hall, Qian Zhang, Qi Wang
3 citations
Frontiers in Psychology
Statistical Methods and Applications
2.13
article

Predicting implementation of response to intervention in math using elastic net logistic regression

Sara Comella, Garret J. Hall, Qian Zhang, Qi Wang
article en
3 citations

Abstract

Method<sub>50</sub> and Method<sub>coef</sub> emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

Frontiers in PsychologyVol. 15
Florida State University (US)
National Science Foundation, American Educational Research Association
Openalex Percentile: Top 12%
Statistical Methods and Applications
2.13
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