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Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.
This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Pe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573127/ https://www.ncbi.nlm.nih.gov/pubmed/34765702 http://dx.doi.org/10.1016/j.dib.2021.107509 |
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author | Huerta-Manzanilla, Eric Leonardo Ohland, Matthew W. Toledano-Ayala, Manuel Jáuregui-Correa, Juan Carlos |
author_facet | Huerta-Manzanilla, Eric Leonardo Ohland, Matthew W. Toledano-Ayala, Manuel Jáuregui-Correa, Juan Carlos |
author_sort | Huerta-Manzanilla, Eric Leonardo |
collection | PubMed |
description | This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed using R to estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. The R code to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included. |
format | Online Article Text |
id | pubmed-8573127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-85731272021-11-10 Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. Huerta-Manzanilla, Eric Leonardo Ohland, Matthew W. Toledano-Ayala, Manuel Jáuregui-Correa, Juan Carlos Data Brief Data Article This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed using R to estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. The R code to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included. Elsevier 2021-10-26 /pmc/articles/PMC8573127/ /pubmed/34765702 http://dx.doi.org/10.1016/j.dib.2021.107509 Text en © 2021 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Huerta-Manzanilla, Eric Leonardo Ohland, Matthew W. Toledano-Ayala, Manuel Jáuregui-Correa, Juan Carlos Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title | Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title_full | Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title_fullStr | Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title_full_unstemmed | Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title_short | Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset. |
title_sort | logit models, the area under receiver characteristic curves, sensitivity, and specificity for co-enrollment density in college networks dataset. |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573127/ https://www.ncbi.nlm.nih.gov/pubmed/34765702 http://dx.doi.org/10.1016/j.dib.2021.107509 |
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