Regional intergroup bias

Calanchini, Jimmy; Hehman, Eric; Ebert, Tobias; Esposito, Emily; Simon, Deja; Wilson, Liz

doi:10.1016/bs.aesp.2022.04.003

Cited by 12 publications

(10 citation statements)

References 145 publications

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“…Additionally, we predicted that both measures would correspond with same-gender-couple residency. Generally, we found consistency between predictors of aggregated implicit associations and explicit bias (e.g., LGB policies, Christian religiosity, education, conservatism), suggesting that measures of regional implicit and explicit bias may capture similar constructs (i.e., regional climate or culture of bias), as other researchers have argued (Calanchini et al, 2022; Hehman et al, 2019).…”

Section: Discussionsupporting

confidence: 77%

“…The aggregation of implicit measures such as the IAT substantially improves their reliability and predictive validity (Hehman et al, 2019;Payne & Hannay, 2021). Aggregated implicit associations correspond with other measures of systemic bias, most notably, measures of systemic racism (Calanchini et al, 2022). Strikingly, Payne et al (2019) demonstrated that U.S. counties with a greater proportion of enslaved population in 1860 had higher modern-day implicit pro-White bias among modern White residents, indicating that regional systemic inequalities may manifest in residents' cognitive representations.…”

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 96%

“…Both explicit and implicit measures can be aggregated regionally to reflect group-based bias (Calanchini et al, 2022; Hehman et al, 2019), but the process of aggregation does not affect explicit and implicit measures equally. Whereas explicit measures of bias have strong face validity, predictive validity, and reliability when measured at the individual level (Henry, in press) and when aggregated (Hehman et al, 2019), the reliability and validity of implicit measures at the individual level are debated.…”

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 99%

“…Recently, some researchers have claimed that measures of regional explicit and implicit bias are measuring a similar, if not the same, construct of a region’s culture of bias toward a particular group (Calanchini et al, 2022; Hehman et al, 2019). Much of this claim has been validated by research, such as that described above, showing that explicit and implicit regional measures are strongly correlated and have similar predictive power over a range of relevant outcomes.…”

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 99%

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Regional Measures of Sexual-Orientation Bias Predict Where Same-Gender Couples Live

Snyder

Henry

2023

Psychol Sci

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Regional explicit and implicit bias are associated with real-world discrimination and marginalization. We extended this research area by focusing on sexual minorities and where same-gender couples live. Using data on 2,939 U.S. counties from Project Implicit and other publicly available sources, we found that measures with known associations with systemic anti–lesbian, gay, and bisexual (anti-LGB) bias are similarly associated with regional implicit and explicit anti-LGB bias. Furthermore, we found that fewer same-gender couples reside in counties with more explicit and implicit anti-LGB bias, above and beyond other factors that likely influence same-gender-couple residency. These findings further suggest that explicit and implicit measures of regional bias are capturing similar, if not the same, construct of a region’s culture of bias toward particular groups. Couched specifically within the ongoing systemic political antagonization of the lesbian, gay, bisexual, transgender, queer, plus (LGBTQ+) community, these findings also highlight the importance of considering contextual (in addition to individual) factors that reinforce systemic inequality.

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Section: Discussionsupporting

confidence: 77%

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 96%

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 99%

Section: Regionally Aggregated Measures As a Reflection Of Systemic Biasmentioning

confidence: 99%

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Regional Measures of Sexual-Orientation Bias Predict Where Same-Gender Couples Live

Snyder

Henry

2023

Psychol Sci

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“…Distance-based weighting is a commonly used technique in geography and the wider social sciences (e.g., Buddemeier et al, 2011; Hough et al, 2004; Mann et al, 2002). Although an emerging set of recent studies highlighted the promise of distance-based weighting for mapping psychological data (Buecker et al, 2021; Calanchini et al, in press; Ebert et al, 2021, 2022), so far, this technique has been used in only a very few studies and by a narrow circle of researchers.…”

Section: Mapping Based On Distance Weightsmentioning

confidence: 99%

Effective Maps, Easily Done: Visualizing Geo-Psychological Differences Using Distance Weights

Ebert

Mewes

Götz

et al. 2022

Advances in Methods and Practices in Psychological Science

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Psychologists of many subfields are becoming increasingly interested in the geographical distribution of psychological phenomena. An integral part of this new stream of geo-psychological studies is to visualize spatial distributions of psychological phenomena in maps. However, most psychologists are not trained in visualizing spatial data. As a result, almost all existing geo-psychological studies rely on the most basic mapping technique: color-coding disaggregated data (i.e., grouping individuals into predefined spatial units and then mapping out average scores across these spatial units). Although this basic mapping technique is not wrong, it often leaves unleveraged potential to effectively visualize spatial patterns. The aim of this tutorial is to introduce psychologists to an alternative, easy-to-use mapping technique: distance-based weighting (i.e., calculating area estimates that represent distance-weighted averages of all measurement locations). We outline the basic idea of distance-based weighting and explain how to implement this technique so that it is effective for geo-psychological research. Using large-scale mental-health data from the United States ( N = 2,058,249), we empirically demonstrate how distance-based weighting may complement the commonly used basic mapping technique. We provide fully annotated R code and open access to all data used in our analyses.

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