Predicting Nontraditional Freshman Retention Using Pre-enrollment Data
| dc.contributor.advisor | Dimitrov, Dimiter M. | |
| dc.contributor.author | Boldbataar, Bolorchimeg | |
| dc.creator | Boldbaatar, Bolorchimeg | |
| dc.date | 2014-04-24 | |
| dc.date.accessioned | 2014-07-21T19:46:36Z | |
| dc.date.available | 2014-07-21T19:46:36Z | |
| dc.date.issued | 2014-07-21 | |
| dc.description.abstract | This thesis studies the pre-enrollment variables that predict nontraditional freshman retention and includes data from the National Education Longitudinal Study (NELS: 88-2000). Pre-enrollment variables were selected based on the availability of variables in the beginning of the fall term. This thesis examines the pre-enrollment of 10 high school and 15 college variables, and levels of nontraditional students. Blockwise logistic regression was used to determine most predictive variables. The findings identified three high school variables (high school grade, attendance, and Carnegie units) and seven college variables (enrollment status, number of math and science classes, type of degree, hours of employment, campus job, grants, and marital status). After controlling these variables, it was found that the level of nontraditional students has a unique effect on student retention. The result of this finding supports previous research of nontraditional student retention: The higher the number of nontraditional characteristics that students possess, the less they are likely to be retained. These findings are discussed in terms of measurement of the variables, handling of missing data, and logistic regression analysis combined with data visualization. Implications of future research emphasize the importance of studying nontraditional students without any age restriction. | |
| dc.identifier.uri | https://hdl.handle.net/1920/8730 | |
| dc.language.iso | en | |
| dc.subject | First-year retention | |
| dc.subject | Logistic regression analysis | |
| dc.subject | Nontraditional student retention | |
| dc.subject | Pre-enrollment data | |
| dc.subject | Predicting retention | |
| dc.subject | Nontraditional characteristics | |
| dc.title | Predicting Nontraditional Freshman Retention Using Pre-enrollment Data | |
| dc.type | Thesis | |
| thesis.degree.discipline | Interdisciplinary Studies | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Arts in Interdisciplinary Studies |