Bias Of Crowds: Analyzing Anti-Arab Bias At The Individual And Regional Levels In Germany Using Implicit Association Tests - Info and Reading Options

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The study examines implicit and explicit prejudices against Arab and white people in Germany, applying the 'bias of crowds' approach of Payne et al. (2017). This approach argues that implicit biases are shaped by situational and social contexts rather than stable individual characteristics (Payne et al., 2017, 2022). According to this argument, measures of implicit bias at the individual level can be unreliable and weakly linked to behavior, whereas aggregating data across individuals reveals meaningful patterns, like the 'wisdom of crowds' phenomenon. Using the online platform MIND.set and the Implicit Association Test (IAT), we investigate how these prejudices against Arab people are related to regional characteristics in German regions (aggregated to NUTS-3 level). The study will utilize data from two additional surveys (“Zusatzbefragungen”) of the DeZIM Panel. The IAT is employed to measure implicit associations between individuals perceived as Arab or white and positive or negative attributes. The first survey will be conducted starting in December 2024, and the second wave starting in March 2025. For the first additional survey, n = 7000 people from the panel will be invited to take part, with an expected maximum participation of n = 3,700 based on prior response rates. In the following survey, only participants who have completed the first IAT and for whom meaningful d-scores could be calculated will be invited to participate in the additional survey with the IAT again. The study will leverage the DeZIM Online Access Panel https://www.dezim-institut.de/en/dezimfdz/online-access-panel/, which includes participants with and without migration backgrounds. The survey oversamples persons with migration background and is administered in Arab, German, English, Turkish and Russian language. This design makes it possible to investigate the bias of crowds hypothesis and to test the retest reliability at both individual and regional level. We will compare the results of analyses at the individual level to regional level models. To do so, we will use individual and regionally aggregated information (d-scores, explicit attitude measures and other potentially relevant characteristics, e.g. party preferences) as well as regional characteristics (e.g., vote shares, regional disparities, crime rates). The bias of crowds approach addresses IAT critiques regarding the weak predictive validity of IATs at the individual level and psychometric problems, such as low retest realibility. The bias of crowds' approach argues that “implicit bias reflects largely transient activation of associations cued by stereotypes and inequalities in social environments. For any individual, activated biases may be idiosyncratic and ephemeral; however, implicit bias operates like the “wisdom of crowds” phenomenon, in which independently assessed knowledge, when aggregated, tends to be more accurate than the partial knowledge of any individual” (Payne et al., 2019). While there is empirical evidence supporting this view from the U.S. with Project Implicit data (Ekstrom et al., 2022; Payne et al., 2019; Sawyer & Gampa, 2018; Stelter et al., 2022; but see also Connor & Evers, 2020) and at national level (Nosek et al., 2009), empirical evidence at regional level from the European context is still rare. Payne and colleagues replied to Connor & Evers (2020) critique of the bias of crowds approach: “Our intended claim was that the variance between situations is more systematic than the variance between individuals. By “more systematic,” we mean more reliable and valid, as evidenced by testretest reliability and correlations with criterion variables” (Payne et al., 2022, p. 608). With our unique data at hand, we will compare correlations with criterion variables and retest reliability between the individual and regional level.

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