New Directions in Education Research: Using Data Mining Techniques to Explore Predictors of Grade Retention
| dc.contributor.advisor | Sutton, Clifton D. | |
| dc.contributor.author | Kelly-Winstead, Deanna | |
| dc.creator | Kelly-Winstead, Deanna | |
| dc.date | 2010-02-16 | |
| dc.date.accessioned | 2010-05-19T13:55:47Z | |
| dc.date.available | NO_RESTRICTION | |
| dc.date.available | 2010-05-19T13:55:47Z | |
| dc.date.issued | 2010-05-19T13:55:47Z | |
| dc.description.abstract | The purpose of this study was to use classification trees and logistic regression to identify subgroups of students more likely to be retained. The National Educational Longitudinal Study of 1988 (NELS:88) was used to identify the sociodemographic, family background and school related factors associated with grade retention. The sample size for this study consisted of 10,140 students, 1,570 of which had been held back. The NELS data were obtained from student questionnaires and surveys with the students’ parents, teachers, and school administrators. In order to identify the predictors of students more likely to be held back, models were built using classification trees and logistic regression. Overall, the current study identified the predictive factors of grade retention. Moreover, this study demonstrates the effectiveness of using classification trees in conjunction with stepwise logistic regression in educational research. | |
| dc.identifier.uri | https://hdl.handle.net/1920/5818 | |
| dc.language.iso | en | |
| dc.subject | Classification trees | |
| dc.subject | Grade retention | |
| dc.subject | Logistic regression | |
| dc.subject | Held back | |
| dc.subject | Risk factors | |
| dc.title | New Directions in Education Research: Using Data Mining Techniques to Explore Predictors of Grade Retention | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Education | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosoy in Education |