Latent class analysis of response inconsistencies across modes of data collection
Latent class analysis of response inconsistencies across modes of data collection
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DOI：
10.1016/j.ssresearch.2012.05.006
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发表时间：
2012-09-01
影响因子：
2.5
通讯作者：
Tourangeau, Roger
中科院分区：
文献类型：
--
作者：
Yan, Ting;Kreuter, Frauke;Tourangeau, Roger
潜在类分析（LCA）被誉为研究调查测量误差的一种很有前途的技术，因为该模型产生与给定问题相关的错误率的估计。然而，问题是这些误差估计有多准确，以及在什么情况下可以依赖它们。怀疑论者认为，潜在类别模型可能低估了真实的错误率，至少有一篇论文（Kreuter et al., 2008）从经验上证明了这种低估。我们将潜在类别模型应用于来自全国家庭增长调查（NSFG）的两波数据，重点关注在不同数据收集模式下管理的一对关于堕胎的类似项目。第一项采用计算机辅助个人访谈（CAPI）；第二种是音频计算机辅助自我访谈（ACASI）。有证据表明，堕胎在NSFG中被低估了，传统观点认为，ACASI项目比CAPI项目产生的假阴性更少。为了评估这些项目，我们对人口中各个子群体的错误率进行了假设；这些假设是实现可识别的LCA模型所必需的。由于有关于堕胎实际流行率的外部数据（按子组），我们能够形成可能（大致）满足识别限制的子组和可能违反假设的其他子组。我们还运行了更复杂的模型，将亚组内潜在的异质性考虑在内。大多数模型产生了令人难以置信的低错误率，支持了在特定条件下LCA模型低估错误率的论点。(c) 2012 Elsevier Inc.版权所有。
Latent class analysis (LCA) has been hailed as a promising technique for studying measurement errors in surveys, because the models produce estimates of the error rates associated with a given question. Still, the issue arises as to how accurate these error estimates are and under what circumstances they can be relied on. Skeptics argue that latent class models can understate the true error rates and at least one paper (Kreuter et al., 2008) demonstrates such underestimation empirically. We applied latent class models to data from two waves of the National Survey of Family Growth (NSFG), focusing on a pair of similar items about abortion that are administered under different modes of data collection. The first item is administered by computer-assisted personal interviewing (CAPI); the second, by audio computer-assisted self-interviewing (ACASI). Evidence shows that abortions are underreported in the NSFG and the conventional wisdom is that ACASI item yields fewer false negatives than the CAPI item. To evaluate these items, we made assumptions about the error rates within various subgroups of the population; these assumptions were needed to achieve an identifiable LCA model. Because there are external data available on the actual prevalence of abortion (by subgroup), we were able to form subgroups for which the identifying restrictions were likely to be (approximately) met and other subgroups for which the assumptions were likely to be violated. We also ran more complex models that took potential heterogeneity within subgroups into account. Most of the models yielded implausibly low error rates, supporting the argument that, under specific conditions, LCA models underestimate the error rates. (c) 2012 Elsevier Inc. All rights reserved.