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...

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Autores principales:	Eric Leonardo Huerta-Manzanilla, Matthew W. Ohland, Manuel Toledano-Ayala, Juan Carlos Jáuregui-Correa
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Student retention in college Logistic regression in education Receiver operating characteristics roc curves Social network relations strength Sensitivity and specificity Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/fd6887e6b6a243c281cd0cbb21a3ee95
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spelling	oai:doaj.org-article:fd6887e6b6a243c281cd0cbb21a3ee952021-11-04T04:32:27ZLogit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.2352-340910.1016/j.dib.2021.107509https://doaj.org/article/fd6887e6b6a243c281cd0cbb21a3ee952021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921007812https://doaj.org/toc/2352-3409This 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.Eric Leonardo Huerta-ManzanillaMatthew W. OhlandManuel Toledano-AyalaJuan Carlos Jáuregui-CorreaElsevierarticleStudent retention in collegeLogistic regression in educationReceiver operating characteristics roc curvesSocial network relations strengthSensitivity and specificityComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107509- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Student retention in college Logistic regression in education Receiver operating characteristics roc curves Social network relations strength Sensitivity and specificity Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Student retention in college Logistic regression in education Receiver operating characteristics roc curves Social network relations strength Sensitivity and specificity Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Eric Leonardo Huerta-Manzanilla Matthew W. Ohland Manuel Toledano-Ayala Juan Carlos Jáuregui-Correa Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.
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	article
author	Eric Leonardo Huerta-Manzanilla Matthew W. Ohland Manuel Toledano-Ayala Juan Carlos Jáuregui-Correa
author_facet	Eric Leonardo Huerta-Manzanilla Matthew W. Ohland Manuel Toledano-Ayala Juan Carlos Jáuregui-Correa
author_sort	Eric Leonardo Huerta-Manzanilla
title	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_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_sort	logit models, the area under receiver characteristic curves, sensitivity, and specificity for co-enrollment density in college networks dataset.
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fd6887e6b6a243c281cd0cbb21a3ee95
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Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.

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