Comparing methods to forecast college enrollment : a statistical approach /
This article discusses how to forecast college enrollment as well as the number of credit hours demanded. The case studies a university in Central Asia that despite a strong reputation has been losing enrollment for nearly a decade. The effects of demographics on enrollment appear stronger than thos...
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| Format: | eBook |
| Language: | English |
| Published: |
London :
SAGE Publications Ltd,
2019.
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| Series: | SAGE Research Methods. Cases.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | This article discusses how to forecast college enrollment as well as the number of credit hours demanded. The case studies a university in Central Asia that despite a strong reputation has been losing enrollment for nearly a decade. The effects of demographics on enrollment appear stronger than those of such traditional factors as tuition and income; but enrollment and hours may also respond to such characteristics of the university as prerequisite courses that are difficult. The article compares three ways of forecasting enrollment and credits: a structural approach, which predicts the effects of such determinants as tuition and student traits; a univariate approach, which predicts enrollment based on past enrollment; and data mining, which discerns patterns in big datasets through such new models as artificial neural networks. Of the three approaches, the structural one may be the best at explaining enrollment changes but does not necessarily yield the most accurate forecasts, as measured by the percentage error in the modeĺs predictions of past enrollment. |
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| Physical Description: | 1 online resource. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781526473066 1526473062 |