Predicting participation in higher education: a comparative evaluation of the performance of geodemographic classifications
Predicting participation in higher education: a comparative evaluation of the performance of geodemographic classifications
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DOI：
10.1111/j.1467-985x.2010.00641.x
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发表时间：
2011-01
期刊：
影响因子：
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通讯作者：
C. Brunsdon;P. Longley;A. Singleton;David I. Ashby
中科院分区：
文献类型：
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作者：
C. Brunsdon;P. Longley;A. Singleton;David I. Ashby
总结。英国高等教育的参与通过使用泊松回归技术进行建模。使用不同详细程度的社区地理人口分类的模型与使用直接来自人口普查的变量的模型进行比较，使用交叉验证方法。总的来说，增加地理人口分类器的详细程度似乎是合理的，尽管随着详细程度的增加，改进的程度变得更加微不足道。人口普查变量方法的表现比较好，尽管有人认为这在很大程度上取决于对预测因子的适当选择。论文的结论是在更广泛的实践导向和教学背景下讨论这些结果。
Summary. Participation in UK higher education is modelled by using Poisson regression techniques. Models using geodemographic classifications of neighbourhoods of varying levels of detail are compared with those using variables that are directly derived from the census, using a cross‐validation approach. Increasing the detail of geodemographic classifiers appears to be justified in general, although the degree of improvement becomes more marginal as the level of detail is increased. The census variable approach performs comparably, although it is argued that this depends heavily on an appropriate choice of predictors. The paper concludes by discussing these results in a broader practice‐oriented and pedagogic context.