标题： 使用可解释的回归树模型分析建立非响应情况与链接行政数据的分析 显示英文标题

标题： Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data

摘要： 为了深入了解机构的特征如何与无应答相关联，应用了一个递归划分算法，对2006年5月职业就业统计数据调查数据进行分析，以构建一个回归树。 该树模型根据某些机构特征，模拟机构对调查的应答倾向。 它基于特征生成互斥的单元格，这些单元格具有同质的应答倾向。 这使得很容易识别特征变量与机构应答倾向之间的可解释关联，而使用逻辑回归倾向模型则不容易做到这一点。 我们使用5月的数据获得的模型，对其进行了2006年11月职业就业统计数据调查数据的测试。 在具有非常大的样本量$(n=179,360)$的独立机构数据集上测试该模型，提供了证据表明回归树模型准确描述了机构特征与OES调查应答倾向之间的关联。 通过模拟将这种建模方法的准确性与逻辑回归进行了比较。 然后，该表示方法结合了与样本数据链接的框架级行政工资数据，用于研究无应答偏差的可能性。 我们表明，如果没有适当的调整，无应答确实可能带来偏差风险，并且可能是不可忽略的。 显示英文摘要

摘要： To gain insight into how characteristics of an establishment are associated with nonresponse, a recursive partitioning algorithm is applied to the Occupational Employment Statistics May 2006 survey data to build a regression tree. The tree models an establishment's propensity to respond to the survey given certain establishment characteristics. It provides mutually exclusive cells based on the characteristics with homogeneous response propensities. This makes it easy to identify interpretable associations between the characteristic variables and an establishment's propensity to respond, something not easily done using a logistic regression propensity model. We test the model obtained using the May data against data from the November 2006 Occupational Employment Statistics survey. Testing the model on a disjoint set of establishment data with a very large sample size $(n=179,360)$ offers evidence that the regression tree model accurately describes the association between the establishment characteristics and the response propensity for the OES survey. The accuracy of this modeling approach is compared to that of logistic regression through simulation. This representation is then used along with frame-level administrative wage data linked to sample data to investigate the possibility of nonresponse bias. We show that without proper adjustments the nonresponse does pose a risk of bias and is possibly nonignorable.