Potential implications of missing income data in population-based surveys: An example from a postpartum survey in California
Potential implications of missing income data in population-based surveys: An example from a postpartum survey in California
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DOI：
10.1177/003335490712200607
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发表时间：
2007-11-01
影响因子：
3.3
通讯作者：
Braveman, Paula
中科院分区：
文献类型：
--
作者：
Kim, Soowon;Egerter, Susan;Braveman, Paula
目标。对于相当大比例的调查参与者来说，收入数据往往是缺失的，这些记录往往从分析中被删除。为了探讨排除收入缺失记录的影响，我们检查了有和没有收入信息的调查参与者的特征。使用加州母婴健康评估的全州人口产后调查数据，我们比较了有和没有报告收入数据的妇女的年龄、受教育程度、胎次、婚姻状况、及时的产前护理开始和社区贫困特征，并按种族/民族/出生进行了总体比较。总体而言，与报告收入的受访者相比，缺乏收入信息的受访者通常显得更年轻、受教育程度更低、平等程度更低。他们更有可能未婚，接受延迟或没有产前护理，居住在贫困社区；与收入较高的女性相比，收入较低的女性通常表现得更相似。然而，这种模式似乎因种族/民族/出生群体而异。例如，在美国出生的非裔美国女性中，收入缺失群体的特征与低收入女性的特征大致相似，而收入缺失群体的欧美女性则与中等收入女性的特征更为相似。缺少收入信息的受访者可能不是基于人口的调查参与者的随机子集，并且可能在其他相关的社会人口学特征上有所不同。在决定如何分析处理缺失的收入信息之前，研究人员应该检查相关特征，并考虑不同的方法如何影响研究结果。特别是对于种族多样化的人群，我们建议包括缺失的收入类别或采用多重归算技术，而不是排除这些记录。
Objectives. Income data are often missing for substantial proportions of survey participants and these records are often dropped from analyses. To explore the implications of excluding records with missing income, we examined characteristics of survey participants with and without income information.Methods. Using statewide population-based postpartum survey data from the California Maternal and Infant Health Assessment, we compared the age, education, parity, marital status, timely prenatal care initiation, and neighborhood poverty characteristics of women with and without reported income data, overall, and by race/ethnicity/nativity.Results. Overall, compared with respondents who reported income, respondents with missing income information generally appeared younger, less educated, and of lower parity. They were more likely to be unmarried, to have received delayed or no prenatal care, and to reside in poor neighborhoods; and they generally appeared more similar to lower- than higher-income women. However, the patterns appeared to vary by racial/ethnic/nativity group. For example, among U.S.-born African American women, the characteristics of the missing-income group were generally similar to those of low-income women, while European American women with missing income information more closely resembled their moderate-income counterparts.Conclusions. Respondents with missing income information may not be a random subset of population-based survey participants and may differ on other relevant sociodemographic characteristics. Before deciding how to deal analytically with missing income information, researchers should examine relevant characteristics and consider how different approaches could affect study findings. Particularly for ethnically diverse populations, we recommend including a missing income category or employing multiple-imputation techniques rather than excluding those records.