Kim Soowon, Egerter Susan, Cubbin Catherine, Takahashi Eugene R, Braveman Paula
Center on Social Disparities in Health, Department of Family and Community Medicine, University of California, San Francisco, 500 Parnassus Ave, San Francisco, CA 94143-0900, USA.
Public Health Rep. 2007 Nov-Dec;122(6):753-63. doi: 10.1177/003335490712200607.
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.
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.
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.
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.
相当大比例的调查参与者的收入数据常常缺失,这些记录在分析时往往被剔除。为探讨排除收入缺失记录的影响,我们研究了有和没有收入信息的调查参与者的特征。
利用加利福尼亚母婴健康评估基于全州人口的产后调查数据,我们比较了有和没有报告收入数据的女性的年龄、教育程度、产次、婚姻状况、产前护理及时开始情况以及邻里贫困特征,总体上以及按种族/族裔/出生地进行比较。
总体而言,与报告了收入的受访者相比,收入信息缺失的受访者通常显得更年轻、受教育程度更低、产次更少。他们更有可能未婚、接受延迟或未接受产前护理,并且居住在贫困社区;而且他们总体上看起来与低收入女性比高收入女性更相似。然而,这些模式似乎因种族/族裔/出生地群体而异。例如,在美国出生的非裔美国女性中,收入缺失组的特征通常与低收入女性相似,而收入信息缺失的欧洲裔美国女性更类似于中等收入女性。
收入信息缺失的受访者可能不是基于人群的调查参与者的随机子集,并且在其他相关社会人口特征方面可能存在差异。在决定如何在分析中处理收入缺失信息之前,研究人员应检查相关特征,并考虑不同方法可能如何影响研究结果。特别是对于种族多样化的人群,我们建议包括一个收入缺失类别或采用多重填补技术,而不是排除那些记录。