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.
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.
Authors: Catherine Cubbin; Paula A Braveman; Kristen S Marchi; Gilberto F Chavez; John S Santelli; Brenda J Colley Gilbert Journal: Matern Child Health J Date: 2002-12
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