Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 0 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Databases | 2 |
| Higher Education | 2 |
| Models | 2 |
| Academic Persistence | 1 |
| Colleges | 1 |
| Data | 1 |
| Data Analysis | 1 |
| Degrees (Academic) | 1 |
| Federal Programs | 1 |
| Graduate Students | 1 |
| Graduate Study | 1 |
| More ▼ | |
Source
| New Directions for… | 2 |
Author
| Bailey, Brenda L. | 1 |
| Isaac, Paul D. | 1 |
Publication Type
| Journal Articles | 2 |
| Guides - Non-Classroom | 1 |
| Reports - Descriptive | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
| Two Year Colleges | 1 |
Audience
| Researchers | 1 |
Location
| Minnesota | 1 |
Laws, Policies, & Programs
| Higher Education Act Title IV | 1 |
Assessments and Surveys
What Works Clearinghouse Rating
Direct linkBailey, Brenda L. – New Directions for Institutional Research, 2006
Data mining of IPEDS data is used to develop models that calculate predicted graduation rates for two- and four-year institutions. (Contains 7 tables and 5 figures.)
Descriptors: Graduation Rate, Models, Data, Prediction
Peer reviewedIsaac, Paul D. – New Directions for Institutional Research, 1993
The conceptual and technical problems that need to be considered when studying graduate student retention and degree progress are examined, and practical suggestions for the institutional researcher are offered. Terms are defined, retention measures are explained, and different types of analysis are outlined. Ideas are given for database…
Descriptors: Academic Persistence, Databases, Degrees (Academic), Graduate Students
