Educational Applications of Hierarchical Linear Models: A Review
Educational Applications of Hierarchical Linear Models: A Review
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DOI：
10.3102/10769986013002085
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发表时间：
1988-06
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影响因子：
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通讯作者：
S. Raudenbush
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文献类型：
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作者：
S. Raudenbush
在过去15年中，为分层、多层次数据寻找合适的统计方法一直是教育统计的一个突出主题。作为这种研究的结果，出现了一类重要的模型，本文称之为层次线性模型。在这些模型的范例应用中，每个群体（例如，教室或学校）内的观察结果作为群体水平或“微参数”的函数而变化。然而，作为“宏观参数”的函数，这些微参数在群体总体中随机变化。研究兴趣主要集中在微观和宏观参数的估计上。本文综述了这类模型的估计理论及其应用。此外，这些方法的逻辑扩展到范例案例之外，包括研究领域，如小组研究，元分析和经典测试理论。要估计的微参数可能是多种多样的，如平均值、比例、方差、线性回归系数和logit线性回归系数。从贝叶斯和经验贝叶斯的观点回顾了估计理论，所考虑的例子涉及两个层次的数据集。
The search for appropriate statistical methods for hierarchical, multilevel data has been a prominent theme in educational statistics over the past 15 years. As a result of this search, an important class of models, termed hierarchical linear models by this review, has emerged. In the paradigmatic application of such models, observations within each group (e.g., classroom or school) vary as a function of group-level or “microparameters.” However, these microparameters vary randomly across the population of groups as a function of “macroparameters.” Research interest has focused on estimation of both micro- and macroparameters. This paper reviews estimation theory and application of such models. Also, the logic of these methods is extended beyond the paradigmatic case to include research domains as diverse as panel studies, meta-analysis, and classical test theory. Microparameters to be estimated may be as diverse as means, proportions, variances, linear regression coefficients, and logit linear regression coefficients. Estimation theory is reviewed from Bayes and empirical Bayes viewpoints and the examples considered involve data sets with two levels of hierarchy.