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Bob Sheldon. IN WHICH BOX? In Which Box? Bob Sheldon. Front Cover.
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Results also indicate that rates of missingness and misclassification on the NIH measure vary substantially between groups: While missingness could reflect an order effect—in which respondents skipped the seemingly repetitive, closed-ended measure because they had already completed the open-ended measure—such an effect would have produced similar rates of missingness across groups. The fact that missingness was disproportionately present in specific groups suggests that it is not due to a mere order effect.

Immigrant respondents were disproportionately likely to have missing race data on the NIH measure relative to mainland U. This finding further indicates that national, closed-ended measures may fail to capture the diversity of lived experiences of racism within specific community subgroups—in this case, immigrant groups—and the potential implications of these experiences. Given the profound, multi-generational social adaptation process that accompanies immigration, we would expect differences in concepts of race and ethnicity between immigrants and mainland U.

For these individuals, whose native languages were Cape Verdean Creole 6 and Haitian Creole 1 , the validity of such survey data is limited; this limitation, unfortunately, represents a pattern that occurs frequently in research with linguistically diverse communities. The lack of a Latina o race option in the NIH measure may undermine the measure's validity by increasing both missingness and misclassification.

Missing responses on the NIH race item, and respondents' decision to instead represent their identity through their responses to the NIH ethnicity item, may reflect these individuals' identities as ethnically Latina o s, but it does not capture experiences of racialization.

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This is particularly problematic in samples such as ours, where the majority of those endorsing White are actually racially self-identified Latina o s. From a statistical perspective, this pattern demonstrates how such misclassification of race and ethnicity creates measurement error that may bias findings regarding health disparities toward the null.

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The fact that nearly half of self-identified ethnic African-Americans skipped the NIH ethnicity question signifies that the measure is not capturing their ethnicity-related experiences. Operationalizing ethnicity in this dichotomous way also prevents researchers from understanding ethnic differences within racialized groups; for example, our findings suggest that the NIH ethnicity item misses more specific or nuanced ethnic identifications provided by many Latina o s, including self-identified Puerto Ricans and Dominicans.

These respondents would be misrepresented by the current measure; they may skip the NIH race item because there is no appropriate response but also skip the ethnicity item because their ethnicity as Brazilian cannot be captured. Their completion might be higher if African-American ethnicity was not confounded with Black race in the response options, and if there were an ethnicity option that enabled Cape Verdeans to differentiate themselves from African-Americans.

While Cape Verdeans represent a small proportion of the population nationwide, there are over 37, Cape Verdeans in the community we sampled US Census Bureau ; the measure's poor performance with this group indicates an inability to capture experiences of regionally specific groups of interest to public health researchers. The ability to capture the identities of such ethnic groups is essential given the established differences in diet, health behaviors, and health beliefs among ethnic groups and, in turn, disparities in health that are attributable to ethnic, not racial, differences Castro, Shaibi, and Boehm-Smith Our results inform current policies and practices regarding the collection of race and ethnicity data by health researchers, health plans, and health care providers.

These findings indicate that utilizing a measure that conceptually separates race and ethnicity, and that provides respondents with adequate flexibility to identify themselves both racially and ethnically, decreases missing data and misclassification and, as such, may increase validity.

A more detailed, granular measure of race and ethnicity would enhance validity. Employing an open-ended response approach requires time-consuming coding and is impractical for large-scale public health research; nevertheless, operationalization of a closed-ended, multiple-choice measure can be improved.

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Feasible changes might include the following: Broadly, future research using improved measurement tools may better elucidate race- and ethnicity-related patterns in health and may inform the development and evaluation of health prevention and intervention efforts. We wish to thank the research assistants who worked on data collection and processing, including Diana Cortes, Stephanie Moronta, and Gavin O'Brien, graduate student Nicholas Mian and Professor Alice Carter for their oversight of data collection and Professor Carter's initial contribution to conceptualization, and graduate student Hillary Hurst Bush for her initial work in data aggregation.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. An early version of these findings was presented at the Annual Convention of the American Psychological Association. The authors do not declare any potential personal conflicts of interest. Additional supporting information may be found in the online version of this article:.

National Center for Biotechnology Information , U. Journal List Health Serv Res v. Published online Dec 3. Abbey Eisenhower , Ph. Author information Article notes Copyright and License information Disclaimer. Address correspondence to Abbey Eisenhower, Ph.

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Accepted Sep This article has been cited by other articles in PMC. Abstract Objective This study examined methodological concerns with standard approaches to measuring race and ethnicity using the federally defined race and ethnicity categories, as utilized in National Institutes of Health NIH funded research.

Study Design We examined missingness and misclassification in responses to the closed-ended NIH measure of race and ethnicity compared with open-ended measures of self-identified race and ethnicity. Principal Findings Rates of missingness were 26 and 43 percent for NIH race and ethnicity items, respectively, compared with 11 and 18 percent for open-ended responses. Conclusions The NIH's current race and ethnicity measure demonstrated poor differentiation of race and ethnicity, restricted response options, and lack of an inclusive ethnicity question. Measurement of race and ethnicity, health disparities research, National Institutes of Health NIH race and ethnicity reporting, racial and ethnic self-identification.

Defining Race and Ethnicity: The Incongruence of Conceptual Understandings and Standard Measures Race is a social categorization imposed on people related to physical appearance for the purpose of making hierarchical power-based distinctions in social relations Smedley and Smedley ; Markus Don't We All Have Both? Incongruence between Participants' Understandings and Standard Measures of Race and Ethnicity The brief review above illustrates that race and ethnicity are conceptually distinct. Methods Participants Data were gathered through a larger study assessing child functioning, needs, and service preferences among economically disadvantaged caregivers of young children.

Procedures Data collection occurred during May—October of Measures This study focused on the items measuring race and ethnicity, which were preceded by questions about child functioning and family demographics. Race and ethnicity items were administered in the following order: Open-ended measures of self-identified race and ethnicity, with the below prompts followed by a blank line for participants to write responses: In your own words, what race s or racial group s do you belong to?

In your own words, to which ethnic group s do you belong?

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The standard NIH measure as follows: Which group below most accurately describes your race? Which group below most accurately describes your ethnicity? Hispanic or Latina o. Open in a separate window. Results We evaluated the NIH race and ethnicity items by examining: Rates and patterns of completion versus missingness of the NIH items relative to self-identification items. High rates of missingness on the NIH measure raise concern about whether participant characteristics used in comparisons are being fully captured.

Finally, we compared rates of completion in immigrant versus nonimmigrant participants with chi-square analyses. Rates and patterns of agreement between NIH items and self-identified race and ethnicity responses. Low match rates raise questions about what the NIH items are measuring if they are not measuring self-identified race and ethnicity.

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