First Advisor

Kimberly Barsamian Kahn

Term of Graduation

Winter 2024

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Applied Psychology






Discrimination, Explicit bias, Implicit bias, Islamophobia, Prejudice



Physical Description

1 online resource (viii, 313 pages)


This dissertation consists of two manuscripts addressing the multifaceted nature of Islamophobia in the United States by examining explicit and implicit anti-Muslim bias on individual and structural levels. The first manuscript (Granger et al., 2023, see chapter II) tests an ideology-threat-attitude-behavior model by estimating the simultaneous mediating effects of threat perceptions on the relationships between individual differences in ideology, Islamophobia (fear of Muslims), and support for an anti-Muslim police surveillance policy. This study (N = 603) finds that individuals who are higher in Social Dominance Orientation (SDO), Right-wing Authoritarianism (RWA), and Nationalism are more likely to perceive Muslims as realistic (power), symbolic (value), and terroristic (safety) threats. Higher perceived threats are then associated with higher levels of Islamophobia, which mediates the relationships between perceived threats and anti-Muslim policy support. This detailed framework on anti-Muslim prejudice underscores the distinct and combined influences of ideology and perceived threats on prejudice and discrimination against Muslims. The results suggest that policy decisions that emphasize perceived threats may be particularly influential among individuals with strong tendencies toward SDO, RWA, and Nationalism.

The second manuscript (Granger & Kahn, 2023, see chapter III) takes a contextual approach to implicit bias and examines the effects of a real-world United States immigration policy that targeted Muslims (the "Muslim ban") on implicit anti-Muslim bias across different political contexts and among people with different political beliefs in the United States. Using a large dataset of Implicit Association Test (IAT) scores from Project Implicit (N = 263,168), this study utilized multilevel modeling to examine how Muslim ban activity (e.g., President Trump signing the Muslim ban) influenced state-level implicit bias across political contexts (i.e., state-level margin of victory for Democrat or Republican candidates in previous presidential elections) and how this interacted with participants’ individual political beliefs. Results find that, while the Muslim ban was associated with lower levels of anti-Muslim implicit bias overall, this effect was primarily driven by those who are more liberal. Further, a cross-level interaction between state voting behaviors and individual political orientation revealed that more conservative individuals in Democrat-voting states had marginally higher levels of anti-Muslim implicit bias during Muslim ban periods. These findings point to several types of backlash effects, including a general policy backlash effect (i.e., lower levels of bias during ban periods for liberals) and a potential context backlash effect (i.e., marginally higher levels of bias for conservatives in Democrat-voting states), which hold implications for how people with different political beliefs implicitly react to the same policy across different political contexts. Together, these studies investigate the implicit and explicit components of anti-Muslim bias that occur on individual and structural levels in the United States. Results provide a foundation for considering how policies can be presented in different contexts and among people of differing political backgrounds and ideological beliefs. Collectively, these studies offer insight into how to anticipate responses to discriminatory policies, as well as how to better communicate different policies to manage reactance among different political and ideological groups in the United States.


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