Linking response quality to survey engagement: A combined random scale and latent variable approach
Linking response quality to survey engagement: A combined random scale and latent variable approach
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DOI：
10.1016/j.jocm.2013.03.005
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发表时间：
2013-06-01
影响因子：
2.4
通讯作者：
Stathopoulos, Amanda
中科院分区：
文献类型：
--
作者：
Hess, Stephane;Stathopoulos, Amanda
最近对离散选择数据中的随机尺度异质性主题的兴趣导致了 G-MNL 模型等专用工具的开发，并且不断有人声称，未能将尺度异质性与个体系数异质性分开的研究可能会产生有偏差的结果。与此相反，Hess 和 Rose（2012）表明，在使用典型线性参数规范的随机系数模型中实际上不可能单独识别这两个分量，并且性能的任何增益都可能只是更灵活的分布假设的结果。另一方面，将规模异质性与受访者的测量特征联系起来可能只能产生有限的见解，而使用受访者报告的调查理解测量或分析师捕获的测量（例如调查响应时间）会使分析师面临测量误差和内生性偏差的风险。本文的贡献是提出了一种混合模型，其中调查参与度被视为潜在变量，用于对测量模型组件中调查参与度的许多指标的值进行建模，并解释选择模型内的规模异质性。该模型克服了早期工作的一些缺点，使我们能够将受访者之间的部分异质性与规模差异联系起来，同时也使我们能够利用调查参与度指标，而不会出现内生性偏差的风险。实证应用的结果表明，两个模型组件之间存在很强的联系，并且选择模型组件的实质性输出可以说是更合理的。 (C) 2013 Elsevier B.V. 保留所有权利。
Recent interest in the topic of random scale heterogeneity in discrete choice data has led to the development of specialised tools such as the G-MNL model, as well as repeated claims that studies which fail to separate scale heterogeneity from heterogeneity in individual coefficients are likely to produce biased results. Contrary to this, Hess and Rose (2012) show that separate identification of the two components is not in fact possible in a random coefficients model using a typical linear in parameters specification, and that any gains in performance are potentially just the result of more flexible distributional assumptions. On the other hand, linking scale heterogeneity to measured characteristics of the respondents is likely to yield only limited insights, while using respondent reported measures of survey understanding or analyst captured measures such as survey response time puts an analyst at risk of measurement error and endogeneity bias. The contribution made in this paper is to put forward a hybrid model in which survey engagement is treated as a latent variable which is used to model the values of a number of indicators of survey engagement in a measurement model component, as well as explaining scale heterogeneity within the choice model. This model overcomes some of the shortcomings of earlier work, permitting us to link part of the heterogeneity across respondents to differences in scale, while also allowing us to make use of indicators of survey engagement without risk of endogeneity bias. Results from an empirical application show a strong link between the two model components as well as arguably more reasonable substantive outputs for the choice model component. (C) 2013 Elsevier B.V. All rights reserved.