Double-Checking the Race Box: Examining Inconsistency between Survey Measures of Observed and Self-Reported Race

By Aliya Saperstein. 2006

139 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d62bd95fedf0668898f913ea468b9443dcfc4288

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Political Science and International Relations

1. Cynthia Feliciano
2. Kevin Lewis
3. Emma Pierson
4. Andreas Wimmer

In Sociology and Political Science

1. Cynthia Feliciano
2. Kevin Lewis
3. Emma Pierson
4. John V. Guttag
5. Andreas Wimmer
6. Carter T. Butts

Citations in the corpus

• Towards a critical race methodology in algorithmic fairness (2019, unknown): “This in turn can significantly impact health statistics [62, 101], population statistics [8, 74], and measures of social inequality [102].”
• Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook1 (2010, methodology): “Studies based on the General Social Survey (GSS) report that self-identified and surveyor-identified “race” corresponded in 99% of cases for whites and 97% for blacks, while the correspondence for “others” was much lower (Saperstein 2006, p. 61).”
• The Race of a Criminal Record: How Incarceration Colors Racial Perceptions (2010, unknown): “9 interviewer classified and self-identified measures of race (Ahmed et al. 2007; Campbell and Troyer 2007; Saperstein 2006), we argue that the changes should not be dismissed.7 The measure of incarceration that we use as our primary independent variable is whether the respondent was interviewed…”
• Shades of Race (2016, background): “Previous studies show that racial classification and racial self-identification are differentially associated with outcomes such as income (Bailey, Loveman, & Muniz, 2013; Saperstein, 2006; Telles & Lim, 1998) and health (C.”
• Coarse race data conceals disparities in clinical risk score performance (2023, background): “Given the instability of self-identified race across time, place, and context (Saperstein, 2006; Roth, 2016), how can we revise the process of granular race data collection to account for this uncertainty? From a methodological perspective, how do we design analyses that are robust to inconsistencie”