Threatened Restrictions: The Role of Symbolic and Realistic Threat in Anti-Transgender Legislation Support

Threatened Restrictions: The Role of Symbolic and Realistic Threat in Anti-Transgender Legislation Support | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Threatened Restrictions: The Role of Symbolic and Realistic Threat in Anti-Transgender Legislation Support Rebecca Totton, Ali Eppinga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7753662/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: There has been a recent surge of anti-transgender legislation with growing support from American voters. Research in Intergroup Threat Theory (ITT) points to symbolic threat as a better predictor of anti-LGBT attitudes than realistic threat. However, support for anti-transgender legislation may be distinct from “LGBT attitudes”. Methods: Two studies evaluated the impact of both symbolic and realistic threats as predictors of support for restrictive legislation and prejudice. Study 1 (n = 246, collected in June of 2024) used a correlational design to establish the link between realistic threat, symbolic threat, perceived deception, and perceived confusion with prejudice and support for anti-transgender legislation. Study 2 (n = 367, collected in October of 2024) used a two-condition design (“gay people”, “transgender people”) to evaluate symbolic and realistic threats as mediators of prejudice and legislation support. Results: Study 1 demonstrated that both symbolic and realistic threats were associated with anti-transgender legislation and prejudice, and were better predictors than perceived deceptiveness or confusion. Study 2 found that while symbolic threat was a better mediator of prejudice, both symbolic and realistic threats independently mediated support for anti-transgender legislation. Conclusion and Policy Implications: Past research has established the adverse impacts of anti-transgender legislation (e.g., Dhanani & Totton, 2023; Lee et al., 2024). Our studies demonstrate that support for restrictive legislation is more complex than negative attitudes and highlight the need for pro-transgender advocacy to take a multifaceted approach to bias reduction. Transgender Legislation LGBTQ+ Intergroup Threat Theory Symbolic Threat Realistic Threat Figures Figure 1 Introduction Beginning in 2019, the United States has seen a consistent and dramatic rise in anti-transgender legislation. In the 2019 legislative session, 32 pieces of anti-transgender legislation were proposed nationally. By 2024, that number had skyrocketed to 701 pieces of anti-transgender legislation, a record which was surpassed within the first 4 months of 2025 ( Translegislation.com ). On top of state-level legislation, the Trump administration has signed numerous executive orders directed at restricting the rights of transgender individuals since taking office in January of 2025 (Warbelow, 2025). The focus topics of anti-transgender legislation are varied, but most prominently tend to center on restricting discussion of transgender identities or rights of transgender students in educational settings, limiting access to gender affirming care, limiting participation in sports, and restricting access to gender aligned bathrooms or changing spaces ( Translegislation.com , 2025). The surge in anti-transgender legislation has been accompanied by changes in public attitudes about transgender individuals. Americans have become increasingly more supportive of legislation that restricts transgender rights since 2022 (Pew, 2025), a trend that more Conservative political groups have attended to. Despite transgender individuals making up approximately 1.3% of the US population (Jones, 2025), the Trump campaign and Republican interest groups spent more than 21-million dollars on anti-transgender advertisements prior to the 2024 election (Alfonseca & Kim, 2024), highlighting the current political interest and perceived political strategy associated with anti-transgender legislation and anti-transgender sentiments. Consequences of Anti-transgender Legislation The combination of increased legislation and decreased support for transgender rights is particularly dangerous for the health and safety of transgender individuals. Transgender individuals, and specifically trans youth, are at higher risk of depression and anxiety (Hajek et al., 2023 ), suicidality (Wolford-Clevenger et al., 2017 ), and a host of other mental health-related effects than their cisgender counterparts (Cotaina et al., 2022 ; Puckett et al., 2023 ; Wanta et al., 2019 ). Pervasive health disparities that exist between transgender and cisgender Americans are magnified by legislation that restricts rights for transgender individuals (Reisner et al., 2014 ; Su et al., 2016 ; Thoma et al., 2019 ). When legislation is proposed and receives media coverage, transgender individuals face increased rumination, anxiety, and mental health consequences (Dhanani & Totton, 2023 ). Internet searches for depression and suicide related topics increased when anti-transgender laws were passed, and this increase was more pronounced in states with higher LGBTQ + populations (Cunningham et al., 2022 ). Within two-years after the passage of anti-transgender legislation, suicide rates of transgender teens and young adults increase within states that enacted those laws (Lee et al., 2024 ). Collectively, previous research underscores the adverse impacts of anti-transgender legislation and highlights the importance of understanding and decreasing support for such legislation. Intergroup Threat Theory Intergroup Threat Theory (ITT) offers a potential lens to examine support for anti-transgender attitudes and support for anti-transgender legislation. ITT proposes that outgroup prejudice stems from the perception of that group as threatening (Rios et al., 2018 ). More specifically, ITT posits two primary drivers of threat, and thus prejudice and discrimination. The first are symbolic threats, which are threats to cultural values, social norms, morals, or personal beliefs. The second are realistic threats, which include threats to physical safety, health, financial security, or political power. Intergroup Threat Theory has been used to explain prejudice toward a host of different minoritized groups including immigrants (e.g., Abrams & Travaglino, 2018 ), racial minorities (e.g., Aberson et al., 2021 ; Danbold & Huo, 2022 ; Osborn et al., 2020 ), and religious minorities (e.g., Cook et al., 2015 ; Velasco et al., 2008). Symbolic threat, moreso than realistic threat, has been tied to anti-LGBT attitudes. For example, Brambilla and Butz (2013) investigated the effects of perceived threat on attitudes toward gay rights. Findings revealed that Italian college students in the symbolic threat condition expressed significantly less support for gay rights compared to two types of control conditions. Similarly, Aberson and colleagues ( 2021 ) found that although realistic and symbolic threats were both related to stereotypes, only symbolic threats were related to explicit prejudice toward gay men. These results extend beyond gay men to broader LGBT identities. Mackey and Rios ( 2025 ) primed participants with information suggesting that LGBT groups were becoming more prevalent across the United States. Across a single-paper meta-analysis, they found that while both realistic and symbolic threats were related to anti-LGBT prejudice, symbolic threat was a better predictor of anti-LGBT attitudes than realistic threat. These studies collectively point to the role of symbolic threat as a stronger predictor of anti-gay and anti-LGBT attitudes. However, research has not examined legislation support, nor attitudes specifically toward transgender individuals. Gender theorists argue against overgeneralizations of “LGBT”, suggesting that the title erases the experiences of transgender individuals, who are most likely to experience harassment and negative stereotypes based on their identity (e.g., Bey, 2021 ; Fassinger & Arseneau, 2007 ; McCarthy, 2003 ). Similarly, psychological research has shown that although anti-transgender attitudes are related to anti-LGB attitudes (Hill & Willoughby, 2005 ), anti-transgender attitudes are more extreme and more negative than anti-gay attitudes (Norton & Herek, 2012), and may stem from different underlying perceptions. For example, previous research found that transgender individuals were more likely to be viewed as both deceptive and as confused about their identity than gay men, and that perceived deceptiveness (but not perceived confusion) mediated the relationship with greater prejudice levels toward transgender targets (Totton & Rios, 2021 ). This suggests that anti-transgender attitudes may stem from different beliefs than anti-gay attitudes, and highlights the importance of examining anti-transgender prejudice as a separate category from anti-gay or anti-LGBT attitudes. Although previous research has highlighted the role of deception (but not confusion) for anti-trangender attitudes, research has not yet examined whether these effects hold for support for anti-transgender legislation. Predictors of Anti-Transgender Legislation Legislation support may stem from different threat perceptions than attitudes alone. Voting support is complex and voting behaviors may stem from realistic threats, symbolic threats, or a host of other attitudes (Friese et al., 2012 ; Rios et al., 2018 ). For instance, realistic threats related to race mediated voting decisions amongst participants with a high racial identification in the 2008 US election (Böhm, et al., 2010 ). Alternatively, Donald Trump’s “Make America Great Again” campaign slogan maps directly onto symbolic threats, and was deeply influential in the 2016 US election (Azevedo et al., 2017 ), showcasing the importance of both constructs in voting behavior. Anti-transgender legislation often targets aspects of identity that are less relevant to LGB cisgender individuals (e.g., bathrooms, legal name changes, healthcare, sports, etc.). Indeed, prior research on the drivers of support for anti-transgender legislation are extremely limited. Knutson and colleagues (2021) found that political orientation moderated a relationship between religiosity and support for restrictive bathroom legislation. While religiosity was related to greater levels of support across the political spectrum, this effect was more pronounced for liberal than conservative participants. Knowledge and accuracy of information about transgender identities also impacts support around trans legislation. Participants who scored higher on a quiz assessing their knowledgability of transgender identities also tended to be more supportive of trans-inclusive policies (Zell & Burnett, 2024 ). Alternatively, belief in disinformation, specifically disinformation related to gender affirming care, predicted support for anti-transgender legislation (Elischberger, 2025 ). While this research collectively showcases the importance of ideology and knowledge of trans folk in legislation support, it does not assess the specific threat perceptions that might be driving support for restrictive legislation. Despite previous research highlighting the role of symbolic threat in anti-LGBT attitudes (Mackey & Rios, 2025 ), the purported “threats” associated with transgender individuals may be based in fears over physical safety (e.g., bathrooms), which would point to realistic threats as an important predictor. Supporting this, previous qualitative work found that “setting concerns” such as safety in public restrooms or locker rooms were often used to frame opposition to bathroom legislation (Burke et al., 2023 ). Alternatively, “threats” associated with transgender individuals could be based in symbolic threats (e.g. gender ideology in schools) and thus might stem from symbolic threats. Similarly, previous research has identified perceived deception and confusion as predictors of anti-transgender attitudes (Totton & Rios, 2021 ). However, no research to our knowledge has evaluated these constructs as predictors of support for legislation. It is possible that perceptions of transgender individuals as confused about their identity, or as being deceptive could drive support for restrictive legislation. For example, the perception that transgender individuals are confused about their identity could lead to support for restrictive legislation with the underlying belief that doing so “allows the person time to understand their identity”. Alternatively, the perception of transgender individuals as deceptive could lead to support for restrictive legislation based on the perception of malicious intentions. As such, the current research will examine symbolic threats, realistic threats, perceived confusion, and perceived deceptiveness as potential predictors of both anti-transgender prejudice (a previously examined topic) and anti-transgender legislation. Given the previous research connecting both symbolic and realistic threats (but particularly symbolic) to anti-LGBT attitudes, and perceived confusion and deception (but particularly deception) to anti-transgender attitudes, the current research will evaluate the relationship between of all of these predictors with anti-transgender legislation support. Understanding the role of threats (symbolic and realistic) as well as beliefs (perceived deceptiveness and perceived confusion) in predicting anti-transgender legislation support will provide a preliminary understanding of potential methods to decrease support for restrictive legislation. Overview of Current Studies Our current studies examine symbolic and realistic threat perceptions as predictors of both prejudice (previously researched) and legislation support (a novel contribution), with the hypothesis that prejudice and legislation support may not be driven by the same threat perceptions. Given previous research highlighting the role of symbolic threat, realistic threat, perceived deception, and perceived confusion in anti-transgender attitudes (Mackey & Rios, 2025 ; Totton & Rios, 2021 ) all constructs were included as variables of interest in Study 1. In Study 2, participants answered questions about either “transgender people” or “gay people” to further evaluate the relationship between perceived threats and voting support. In investigating these research questions, we make two contributions to the literature and to future policy efforts. First, this work grows on the body of research examining the connection between anti-LGBT attitudes and perceived symbolic threats, realistic threats, confusion, and deception. While previous research has examined symbolic and realistic threats with anti-gay attitudes (Aberson et al., 2021 ; Brambilla & Butz, 2013) and anti-LGBT attitudes (Mackey & Rios, 2025 ), this is the first study to our knowledge to evaluate the connection between symbolic and realistic threats with anti-transgender attitudes. Second, we explore the relationship between perceived symbolic threats, realistic threats, perceived confusion, and perceived deceptiveness as predictors of support for restrictive legislation. Symbolic threats have been found to be a better predictor than realistic threats for anti-gay attitudes, but it is unclear if this same effect drives support for restrictive legislation. Similarly, while previous research has demonstrated that transgender targets are rated as both more confused and more deceptive than gay men, and that perceived deceptiveness (but not confusion) drives greater levels of prejudice (Totton & Rios, 2021 ), it is unclear if these perceptions also drive increased support for restrictive legislation. Understanding the impacts of perceived threats in support for legislation is critical for legislators, policy makers, and advocates who hope to reduce community support for anti-transgender legislation. Study 1 Hypotheses H1 We hypothesized that, in line with previous research on LGBT attitudes (Mackey & Rios, 2025 ) symbolic ( H1a ) and realistic ( H1b ) threats would each predict anti-transgender prejudice in their own independent models. H2 Similarly, in line with previous research on anti-transgender attitudes (Totton & Rios, 2021 ) deception ( H2a ) and confusion ( H2b ) would also each predict anti-transgender prejudice in their own independent models. H3 Given previous research suggesting that deception was a better predictor than confusion (Totton & Rios, 2021 ) and that symbolic threat was a better predictor than realistic threat (Mackey & Rios, 2025 ), we hypothesized that symbolic threat and deception would be stronger predictors than realistic threat and confusion in an additive model with all predictor variables. H4 Similar to H1 and H2, we hypothesized that symbolic threat ( H4a ) and realistic threat ( H4b ) would each independently predict support for anti-transgender legislation in independent models. Since no previous research has evaluated predictors of support for anti-transgender legislation, this hypothesis is based on research suggesting the role of both symbolic and realistic threats in legislation support more broadly (e.g., Azevedo et al., 2017 ; Böhm, et al., 2010 ; Rios et al., 2018 ). H5 Although previous research has not evaluated our predictor attitudes in relation to support for anti-transgender legislation, we hypothesized that both symbolic and realistic threats will remain significant predictors in an additive regression model with all predictor variables. This hypothesis stems from previous research pointing to both symbolic and realistic threats in legislation support (e.g., Azevedo et al., 2017 ; Böhm, et al., 2010 ; Rios et al., 2018 ). Given that there is no research to date examining deception and confusion as predictors of legislation support, these variables were included as exploratory variables. We did not have an a-priori hypothesis about their predictive role individually nor as a part of an additive model in predicting support for restrictive legislation. Methods Study 1 Participants This study was approved by the institutional review board at redacted for review. All participants provided informed consent prior to their participation in the study. Data were collected from participants who self-identified as cisgender, heterosexual, and politically moderate through Prolific Academic, an online survey platform that connects registered users to surveys for which they are eligible. We requested cisgender, heterosexual participants for this study given the sensitive nature of the questions. Political moderates were requested, in line with previous research (Mackey & Rios, 2025 ), and since they are likely to have the widest variety of attitudes about anti-transgender legislation (Parker et al., 2022 ). In total, 246 participants completed the survey. Seven participants (2.8%) indicated a sexual orientation other than heterosexual. Results did not change from excluding these participants, so their responses were retained. A power analysis based on an alpha value of .05 indicated that a sample of 224 was required to detect small to medium effect sizes, suggesting we had a sufficient sample. Among the participants, 96 (39.0%) identified as male and 150 (61.0%) identified as female. The average age was 40.86 ( SD = 13.00). The majority identified their race as White (70.7%), with 15.9% identifying as Black/African American, 2.0% as American Indian or Alaska Native, 11.0% as Asian or Pacific Islander, 7.7% as Hispanic, 1.2% as Middle Eastern or Arab American, and .8% as another racial/ethnic identity. Approximately half of participants held a college degree or higher (47.2%). Participants were asked about their political orientation using two questions, one about their social attitudes and one about their economic attitudes, on a 1 (very liberal) to 7 (very conservative) scale. These two questions were averaged to examine participants’ overall political attitudes. In line with participants' self-identification on Prolific, political attitudes were generally moderate ( M = 3.98, SD = .88). Measures Realistic Threat Perceived realistic threat from transgender individuals was measured using a four-item measure adapted from Mackey and Rios ( 2025 ) (e.g., “Transgender people are a safety threat when they use public restrooms or changing rooms”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together ( M = 2.78, SD = 1.39, α = .89). Symbolic Threat Perceived symbolic threat from transgender individuals was measured using a four-item scale adapted from previous research (Mackey & Rios, 2025 ) (e.g., “Transgender people are harming traditional gender roles”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together ( M = 3.51, SD = 1.72, α = .95). Confusion Perceived confusion of transgender individuals was measured using a four-item scale adapted from previous research (Totton & Rios, 2021 ) (e.g., “Transgender people are still figuring out who they are”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together ( M = 4.41, SD = 1.26, α = .83). Deception Perceived deceptiveness of transgender individuals was measured using a four-item scale adapted from previous research (Totton & Rios, 2021 ) (e.g., “I would feel deceived if I found out someone I knew was transgender”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together ( M = 3.08, SD = 1.36, α = .82). Prejudice To get a baseline understanding of participants' prejudice levels, participants were asked about their general attitude toward six groups: women, Republicans, Black/African-Americans, nonreligious individuals, transgender individuals, and those who have recently moved residences from 1 (very negative) to 9 (very positive). This item was used to compare groups to examine participants' attitudes toward transgender individuals as opposed to other groups in society. Legislation Support for anti-transgender legislation was assessed using a four-item scale (e.g., “Please indicate your support for legislation that requires people to use the bathroom of their gender assigned at birth”). Items were based on the four most common topics of anti-transgender legislation, as categorized by translegislation.com (2025). Items were measured on a 1 (strongly oppose) to 7 (strongly support) scale and were averaged together to create a composite legislation support measure ( M = 4.31, SD = 1.48, α = .83). The full list of questions can be found in the supplemental material. Study 1 Results Correlations between all variables are presented in Table 1 . A principal components analysis of all items included in the symbolic threat, realistic threat, confusion, deception, and legislation scales using varimax rotation revealed a four-factor solution, with realistic threat (eigenvalue = 1.02), symbolic threat (eigenvalue = 0.88), confusion (eigenvalue = 1.05), and deception items (eigenvalue = 1.05) each producing loadings of at least .82 onto separate factors, suggesting each construct was independent. Table 1 Study 1 Means, Standard Deviations, and Intercorrelations 1 2 3 4 5 6 7 8 9 1. Age 2. Gender − .27*** 3. Education − .001 − .03 4. Political Orientation .02 − .08 5. Realistic Threat − .07 − .01 − .17* .39*** 6. Symbolic Threat − .07 .02 − .10 .38*** .78*** 7. Confusion − .12 − .08 .03 .16* .44*** .53*** 8. Deception .04 .05 − .09 .002** .613*** .61*** .41*** 9. Prejudice .07 − .01 .01 − .23*** − .61*** − .69*** − .37*** − .51*** 10. Legislation Support − .06 .05 − .14* .43*** .73*** .38*** .44*** .53*** − .57*** Note. Gender is coded 1 for female and 2 male Paired samples t-tests were used to evaluate differences in baseline prejudice levels. We then examined the relationships between symbolic threat, realistic threat, perceived deception, prejudice toward transgender individuals, and perceived confusion with support for legislation using a series of regressions. Prejudice toward Transgender Individuals Paired sample t-tests revealed that transgender individuals ( M = 5.72, SD = 2.0) were rated more negatively than women ( M = 7.62, SD = 1.44), Black individuals ( M = 7.01, SD = 1.67), nonreligious individuals ( M = 6.43, SD = 1.72), and people who have recently moved residences ( M = 6.15, SD = 1.56) (all p’s < .001). However, transgender individuals were rated more positively than republicans ( M = 4.71, SD = 2.12) (p < .001). Predictors of anti-transgender legislation Next, we conducted a series of regression analyses to evaluate the relationships between symbolic threat, realistic threat, perceived deception, and perceived confusion with prejudice and legislation support. Political orientation was entered into Step 1 of the regression. Each of the predictors (symbolic threat, realistic threat, deception, confusion) were each entered independently as Step 2 of four separate models to understand individual influence. In the first analysis, prejudice was treated as the outcome variable. Each predictor independently significantly predicted support for anti-transgender legislation (all p’s < .001). Results for each of these models are available in the supplementary materials. These findings supported Hypotheses 1 and 2. Next, all predictors were entered into an additive model. Results indicated that, after controlling for relevant demographic variables, only symbolic ( b = − .58, 95% CI [-.765, − .392], p < .001) and realistic threat ( b = − .29, 95% CI [-.508, − .067], p = .011) remained significant. Deception ( b = − .14, 95% CI [-.321, .037], p = .119) and confusion ( b = .03, 95% CI [-.143, .201], p = .739) were no longer significant predictors. This provided partial, but not full support for Hypothesis 3. In the second set of analyses, legislation support was treated as the outcome variable. Independently, each predictor significantly predicted support for anti-transgender legislation (all p’s < .001). Results for each of these models are available in the supplementary materials. These findings supported Hypothesis 4. Next, all predictors were entered into an additive model. Results indicated that, after controlling for relevant demographic variables, only symbolic ( b = .41, 95% CI [.290, .521], p < .001) and realistic threat ( b = .29, 95% CI [.149, .423], p < .001) remained significant. Deception ( b = .01, 95% CI [-.098, .124], p = .819), and confusion ( b = .06, 95% CI [-.049, .164], p = .291) were no longer significant predictors. These findings supported Hypothesis 5. Results for both of the additive models are available in Table 2 . Table 2 Study 1 Regression Table of Prejudice and Legislation Support with all Predictor Variables Prejudice Legislation Support Variable β SE ΔR 2 β SE ΔR 2 Step 1 Political Orientation − .23*** .14 .43*** .10 .488 .64 Step 2 Confusion .01 .09 .04 .06 Deception − .09 .09 .02 .06 Realistic Threat − .19* .11 .27*** .07 Symbolic Threat − .51*** .09 .48*** .06 Note. All VIF statistics were less than 5. Study 1 Discussion Taken together, the findings of Study 1 lend support to the idea that both symbolic and realistic threats play an important role in anti-transgender prejudice and legislation support. Moreover, Study 1 suggests that the effects of perceived deceptiveness and perceived confusion may be better explained by symbolic and realistic threat perceptions. Given the findings of Study 1, Study 2 focused on symbolic and realistic threats as potential mediators. More specifically, Study 2 aims to further illuminate differences in support for anti-transgender legislation compared to support for “anti-gay” legislation. Methods Study 2 Participants This study was approved by the institutional review board at redacted for review. All participants provided informed consent prior to their participation in the study. Data were collected from participants who self-identified as cisgender, heterosexual, and politically moderate through Prolific Academic, an online survey platform that connects registered users to surveys for which they are eligible. In total, 674 participants completed the survey. Twenty-two participants (3.7%) indicated a sexual orientation or gender identity other than cisgender and heterosexual. Results did not change from excluding these participants, so their responses were retained. A power analysis based on an alpha value of .05 indicated that a sample of 619 was required to detect small to medium effect sizes, suggesting we had a sufficient sample. Among the participants, 314 (46.4%) identified as male and 360 (53.3%) identified as female. The average age was 41.05 ( SD = 12.92). The majority identified their race as White (71.0%), with 16.0% identifying as Black/African American, 1.6% as American Indian or Alaska Native, 8.4% as Asian or Pacific Islander, 6.7% as Hispanic, 1.3% as Middle Eastern or Arab American, and .6% as another racial/ethnic identity. Similarly to Study 1, participants indicated they were politically moderate ( M = 3.97, SD = .91). Measures All scales and measures were identical to Study 1 outside of two changes. Participants in the “gay” condition read questions that applied to “gay people” rather than “transgender people”. Additionally, the legislation questions were reframed to be applicable to both gay people and transgender people (e.g., “Please indicate your support for legislation that bans conversations about transgender (gay) identities in schools and educational settings”). The full list of questions can be found in the supplemental material. Means, standard deviations, and alpha levels (where appropriate) for each scale can be found in Table 3 . Table 3 Study 2 Means, Standard Deviations, and alpha levels (where appropriate) by condition. Transgender Condition Gay Condition Mean SE Mean SE Scale α Symbolic threat 3.72 .10 2.65 .10 .95 Realistic threat 2.96 .08 1.99 .07 .92 Prejudice 5.65 .11 6.47 .11 – Legislation 3.19 .09 2.48 .09 .87 Study 2 Hypotheses H1 The transgender condition will elicit greater levels of symbolic threat ( H1a ), realistic threat ( H1b ), prejudice ( H1c ), and legislation support ( H1d ) than the gay condition. This is based on previous research demonstrating that attitudes toward transgender individuals are more negative and more extreme than those toward gay men (Norton & Herek, 2012; Totton & Rios, 2021 ). H2 In line with Mackey and Rios ( 2025 ), we expect that symbolic threats, but not realistic threats, will mediate the relationship between condition and prejudice. H3 In line with previous research suggesting the importance of both symbolic and realistic threats in legislation support (e.g., Azevedo et al., 2017 ; Böhm, et al., 2010 ; Rios et al., 2018 ), we hypothesized that both symbolic and realistic threats would mediate the relationship between condition and legislation support. Study 2 Results Similar to Study 1, a principal components analysis of all items included in the symbolic threat, realistic threat, confusion, deception, and legislation scales using varimax rotation revealed a four-factor solution, with realistic threat (eigenvalue = .82), symbolic threat (eigenvalue = 1.05), confusion (eigenvalue = 1.09), and deception items (eigenvalue = 1.04) each producing loadings of at least .76 onto separate factors, suggesting each construct was independent. We tested the effects of condition on realistic threat, symbolic threat, deception, confusion, prejudice, and voting support using one-way ANCOVAs, controlling for political orientation. Given the findings of Study 1 as well as previous research (e.g., Totton & Rios, 2021 ; Mackey & Rios, 2025 ), we hypothesized that participants in the transgender condition would express higher levels of realistic threat, symbolic threat, prejudice, and importantly, greater support for restrictive legislation. Although Study 1 found that both symbolic and realistic threats were connected to prejudice, previous research has demonstrated that symbolic threats are a better predictor of anti-LGBT attitudes in a single-paper meta-analysis (e.g., Mackey & Rios, 2025 ). As such, in line with previous research, we hypothesized that symbolic threats would be a better predictor of anti-transgender attitudes. However, previous research has not yet evaluated anti-transgender legislation support. Given the results of Study 1 as well as previous research connecting realistic threats with predictors of voting (e.g., Böhm, et al., 2010 ; Rios et al., 2018 ), we hypothesized that both symbolic and realistic threats would mediate the relationship between condition and legislation support. Given the results of Study 1, which showed that deception and confusion were not as strong of predictors of either anti-transgender attitudes or legislation support, analyses and descriptives for deception and confusion are presented in the supplementary materials as an exploratory analysis. Symbolic Threat In line with Hypothesis 1a, participants in the transgender condition ( M = 3.72, SE = .09) expressed significantly higher levels of symbolic threat than the gay condition ( M = 2.62, SE = .09), F (1, 639) = 69.47, p < .001, η2p = .098. The effect of political orientation was also significant, F (1, 639) = 97.58, p < .001, η2p = .132, with more conservative participants perceiving higher levels of symbolic threat. Realistic Threat In line with Hypothesis 1b, participants in the transgender condition ( M = 2.96, SE = .071) expressed significantly higher levels of realistic threat than the gay condition ( M = 2.00, SE = .071), F (1, 639) = 91.37, p < .001, η2p = .125. The effect of political orientation was also significant, F (1, 639) = 66.80, p < .001, η2p = .095, with more conservative participants perceiving higher levels of realistic threat. Prejudice In line with Hypothesis 1c, participants in the transgender condition ( M = 5.65, SE = .112) expressed significantly higher levels of prejudice than participants in the gay condition ( M = 6.47, SE = .111) F (1, 639) = 27.14, p < .001, η2p = .041. The effect of political orientation was also significant, F (1, 639) = 15.01, p < .001, η2p = .041, with more conservative participants scoring higher in prejudice. Since we collected attitudes toward a variety of other groups (Women, Republicans, Black/African American individuals, Nonreligious individuals, and Those who have recently moved residences), we also looked for conditional differences between those groups. No other differences were found between other target groups based on condition (all p’s > .05). A table of these comparisons are provided in the supplemental material section. Voting Behavior In line with Hypothesis 1d, participants in the transgender condition ( M = 3.19, SE = .082) expressed significantly greater support for restrictive legislation than the gay condition ( M = 2.49, SE = .081), F (1, 635) = 37.18, p < .001, η2p = .055. The effect of political orientation was also significant, F (1, 639) = 80.98, p < .001, η2p = .113, with more conservative participants being more supportive of restrictive legislation. Mediation Given the results of Study 1, We conducted a series of mediation analyses using PROCESS model 4 (Hayes, 2017 ) to examine symbolic threat and realistic threat as potential mediators of the relationships between condition (0 = gay, 1 = trans) and the relevant outcome variable (prejudice and legislation support). In both analyses, participant political orientation was added as a covariate. We then evaluated contrasts of indirect effects to determine if one mediator was stronger than the other (Coutts & Hayes, 2023 ). First, we evaluated both possible mediators of the relationship between condition (predictor) and prejudice (outcome). A test of the indirect effects revealed that symbolic threat (b = 0.679, SE = .103, 95% CI = [.485, .892]) and realistic threat (b = 0.234, SE = .083, 95% CI = [.084, .406]) significantly mediated the relationship between condition and prejudice. However, a comparison of contrasts revealed that symbolic threat was a stronger mediator than realistic threat (b = 0.445, SE = .150, 95% CI = [.163, .748]). Supporting the work of Mackey & Rios ( 2025 ) as well as Hypothesis 2, this suggests that symbolic threat is the strongest mediator of the relationship between condition and prejudice. Next, we examined both symbolic and realistic threats as mediators in a model with support for legislation as the outcome variable. A test of the indirect effects revealed that symbolic threat (b = -0.410, SE = .065, 95% CI = [-.541, − .288]) and realistic threat (b = 0.456, SE = .070, 95% CI = [-.602, − .330]) both mediated the relationship between condition and voting behavior. A comparison of contrasts of indirect effects demonstrated that the difference between indirect effects of symbolic threat and realistic threat (b = 0.051, SE = .096, 95% CI = [− .138, .238]) was not significant. Therefore, both symbolic threat and realistic threat were found to partially mediate the relation between condition and voting behavior. This supports Hypothesis 3. This relationship can be seen in Fig. 1 . Study 2 Discussion Results of Study 2 suggest that symbolic threats are a better predictor of anti-transgender prejudice than realistic threats. This supports previous work looking at anti-LGBT attitudes (e.g., Mackey & Rios, 2025 ) and suggests that, despite being more specified in their target, anti-transgender prejudice may stem from similar threats to those behind more general anti-LGBT prejudices. However, Study 2 demonstrates that support for anti-transgender legislation is mediated by both symbolic and realistic threats, suggesting a more complex picture of legislation support than prejudice. General Discussion The goal of the current studies was to evaluate predictors of support for anti-transgender legislation. Study 1 demonstrated that symbolic and realistic threats were better predictors of both anti-transgender prejudice and legislation support than perceived deceptiveness or confusion. Importantly, we found support in Study 2 for previous research suggesting that anti-transgender attitudes are better mediated by symbolic (rather than realistic) threats (e.g., Mackey & Rios, 2025 ). However, central to our research question, we also demonstrated that this effect does not extend to support for anti-transgender legislation. Across both studies, we found that symbolic and realistic threats each drove support for anti-transgender legislation. Specifically, Study 2 showed that both symbolic and realistic threats mediated the relationship between condition and support for restrictive legislation. This suggests that legislation support has more complex drivers than attitudes alone, and thus, more complex approaches may be required to reduce it. Collectively, our research adds to the body of literature in Intergroup Threat Theory, highlighting the important role of symbolic and realistic threats in both prejudice and voting behaviors. However, our work expands previous research by highlighting the complex nature of support for anti-transgender legislation and pointing to the need to interrupt both symbolic and realistic threats to decrease support for restrictive legislation. Social-Policy Implications To our knowledge, this work is the first to evaluate how threat perceptions drive support for anti-transgender legislation. Previous research has highlighted the myriad of negative impacts of anti-transgender legislation on the mental and physical health of transgender individuals (e.g., Dhanani & Totton, 2023 ; Lee et al., 2024 ). Given the proliferation of anti-transgender legislation as well as increased support for restrictive legislation (Translegislation.com, 2025; Pew, 2025), this provides a timely analysis for legislators and activists alike to understand the factors that may increase voter support for anti-transgender legislation. Crucially, it highlights the distinction between anti-transgender legislation support and anti-transgender prejudice and demonstrates that support for anti-transgender legislation has more complex underpinnings than prejudice alone. Previous research examining anti-transgender legislation support has noted the impacts of both religiosity and political orientation (Knutson et al., 2021), which may be more challenging to change or address than threat perceptions. For policy makers or organizations looking to support transgender individuals by decreasing community support for restrictive legislation, this work points to the need to address both perceived symbolic threats and perceived realistic threats surrounding transgender individuals. Limitations and Future Directions There are limitations of our study that should be considered alongside its contributions. Primarily, although our samples were diverse in many ways (e.g., gender, age, education), both studies focused on cisgender, heterosexual, self-declared political “moderates.” While this group represents a critical part of the American voter pool (Brenan, 2025 ), and although political orientation was measured and included in analyses, it is possible that more firmly identified conservatives or liberals might be driven by different threats. For example, more conservative participants may be better persuaded by specific realistic threats (e.g., the belief that transgender women are a threat in bathrooms) than more liberal participants (Knutson et al., 2021). As such, future research should extend this research to a broader array of political ideologies and evaluate specific policies such as bathroom laws, education based laws, or sports related laws separately. Moreover, while the current work points to factors that drive support for anti-transgender legislation, it does not elucidate effective methods to reduce symbolic threat or realistic threat. Indeed, limited work thus far has highlighted methods of reducing perceived symbolic or realistic threats (see Rios et al., 2018 for review). Future research should evaluate effective methods of reducing threat perceptions as a means of reducing support for restrictive legislation. Conclusion Anti-transgender legislation has recently become a hot-button issue for politicians, legislators, and voters. Despite evidence that restrictive legislation has adverse effects on transgender individuals (notably trans youth and young adults), such legislation has continued to surge. This study provides insight to the threat perceptions driving voter support for anti-transgender legislation. Specifically, it underscores that, unlike negative attitudes, which seem to be driven primarily by perceived symbolic threats, legislation support has more complex underpinnings with support stemming from both symbolic threats and realistic threats. This points to the complex nature of support for anti-transgender legislation and provides a starting point for reduction efforts. We urge activists and researchers to evaluate methods to decrease perceived threats posed by transgender individuals as a means of decreasing support for restrictive policies. Moreover, we call on politicians and policymakers to reduce the spread of both symbolic and realistic threats by ending anti-transgender campaign ads and restrictive legislative policies. Declarations Funding The author declares that no funds, grants, or other support were received during the preparation of this manuscript. Conflicts of interest/Competing interests The author has no relevant financial or non-financial interests to disclose. Availability of data and material All data for this study are available at https://osf.io/8yxh4/?view_only=542e29128e9249d2ba4cfe915d0f0643. The materials included in this study are included in the supplementary materials of this paper. Ethics Approval The studies presented in this paper were approved by the Institutional Review Board at redacted for review. Consent to participate Informed consent was obtained from all individual participants included in the study. References 2025 Anti-Trans Bills: Trans legislation tracker . 2025 Anti-Trans Bills: Trans Legislation Tracker. (2025). https://translegislation.com/ Aberson, C. L., Ferguson, H., & Allen, J. (2021). Contact, threat, and prejudice: A test of intergroup threat theory across three samples and multiple measures of prejudice. 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(2025). Do Demographic Increases in LGBT and Nonreligious Americans Increase Threat? Personality and Social Psychology Bulletin , 51 (2), 253–267. https://doi.org/10.1177/01461672231188278 McCarthy, L. (2003). What About the T? Is Multicultural Education Ready to Address Transgender Issues? Multicultural Perspectives: An Official Journal of the National Association for Multicultural Education , 5 (4), 46–48. https://DOI.org/10.1207/S15327892MCP0504_11 Osborn, H., Sosa, N., & Rios, K. (2020). Perceiving demographic diversity as a threat: Divergent effects of multiculturalism and polyculturalism. Group Processes & Intergroup Relations , 23 (7), 1014–1031. https://doi.org/10.1177/1368430219880606 Parker, K., Menasce-Horowitz, J., & Brown, A. (2022). Americans’ Complex Views on Gender Identity and Transgender Issues. 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European Review of Social Psychology , 29 (1), 212–255. https://doi.org/10.1080/10463283.2018.1537049 Reisner, S. L., White, J. M., Bradford, J. B., & Mimiaga, M. J. (2014). Transgender Health Disparities: Comparing Full Cohort and Nested Matched-Pair Study Designs in a Community Health Center. LGBT health , 1 (3), 177–184. https://doi.org/10.1089/lgbt.2014.0009 Stephan, W. G., Ybarra, O., & Rios, K. (2015). Intergroup threat theory. Handbook of prejudice, stereotyping, and discrimination (pp. 255–278). Psychology. Su, D., Irwin, J. A., Fisher, C., Ramos, A., Kelley, M., Mendoza, D. A. R., & Coleman, J. D. (2016). Mental Health Disparities Within the LGBT Population: A Comparison Between Transgender and Nontransgender Individuals. Transgender health , 1 (1), 12–20. https://doi.org/10.1089/trgh.2015.0001 Thoma, B. C., Salk, R. H., Choukas-Bradley, S., Goldstein, T. R., Levine, M. D., & Marshal, M. P. (2019). Suicidality Disparities Between Transgender and Cisgender Adolescents. Pediatrics , 144 (5), e20191183. https://doi.org/10.1542/peds.2019-1183 Totton, R., & Rios, K. (2021). Predictors of anti-transgender attitudes: Identity-confusion and deception as aspects of distrust. Self and Identity , 20 (4), 496–514. https://doi.org/10.1080/15298868.2019.1621928 Velasco González, K., Verkuyten, M., Weesie, J., & Poppe, E. (2008). Prejudice towards Muslims in the Netherlands: Testing integrated threat theory. British journal of social psychology , 47 (4), 667–685. https://doi.org/10.1348/014466608X284443 Wanta, J. W., Niforatos, J. D., Durbak, E., Viguera, A., & Altinay, M. (2019). Mental Health Diagnoses Among Transgender Patients in the Clinical Setting: An All-Payer Electronic Health Record Study. Transgender health , 4 (1), 313–315. https://doi.org/10.1089/trgh.2019.0029 Warbelow, S. (2024, February 6). Understanding Executive Orders and What They Mean for the LGBTQ + Community . Human Rights Campaign. https://www.hrc.org/news/understanding-executive-orders-and-what-they-mean-for-the-lgbtq-community Wolford-Clevenger, C., Cannon, C. J., Flores, L. Y., Smith, P. N., & Stuart, G. L. (2017). Suicide Risk Among Transgender People: A Prevalent Problem in Critical Need of Empirical and Theoretical Research. Violence and gender , 4 (3), 69–72. https://doi.org/10.1089/vio.2017.0006 Zell, E., & Burnett, M. I. (2024). Transgender Knowledge Mediates US Political Differences in Prejudice and Support for Trans-inclusive Policies. Sex Roles , 90 (12), 1879–1890. https://doi.org/10.1007/s11199-024-01539-1 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":183996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy 2 Mediation of Legislation Support (Standardized Betas).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7753662/v1/60683a58cff00e77c2e8b0e6.png"},{"id":94597362,"identity":"74815dfa-63f1-4a8d-9b96-b0a423443bc1","added_by":"auto","created_at":"2025-10-28 18:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1196265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7753662/v1/0c8d1212-3889-46f4-8a59-06f203dd20f4.pdf"},{"id":94592684,"identity":"91e04842-77d5-4577-8e57-d3df4cf8acce","added_by":"auto","created_at":"2025-10-28 18:23:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17613,"visible":true,"origin":"","legend":"","description":"","filename":"ITTlegislationpaperSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7753662/v1/9a729d3bcd720d4a9840ce3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Threatened Restrictions: The Role of Symbolic and Realistic Threat in Anti-Transgender Legislation Support","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBeginning in 2019, the United States has seen a consistent and dramatic rise in anti-transgender legislation. In the 2019 legislative session, 32 pieces of anti-transgender legislation were proposed nationally. By 2024, that number had skyrocketed to 701 pieces of anti-transgender legislation, a record which was surpassed within the first 4 months of 2025 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTranslegislation.com\u003c/span\u003e). On top of state-level legislation, the Trump administration has signed numerous executive orders directed at restricting the rights of transgender individuals since taking office in January of 2025 (Warbelow, 2025). The focus topics of anti-transgender legislation are varied, but most prominently tend to center on restricting discussion of transgender identities or rights of transgender students in educational settings, limiting access to gender affirming care, limiting participation in sports, and restricting access to gender aligned bathrooms or changing spaces (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTranslegislation.com\u003c/span\u003e, 2025).\u003c/p\u003e\u003cp\u003eThe surge in anti-transgender legislation has been accompanied by changes in public attitudes about transgender individuals. Americans have become increasingly more supportive of legislation that restricts transgender rights since 2022 (Pew, 2025), a trend that more Conservative political groups have attended to. Despite transgender individuals making up approximately 1.3% of the US population (Jones, 2025), the Trump campaign and Republican interest groups spent more than 21-million dollars on anti-transgender advertisements prior to the 2024 election (Alfonseca \u0026amp; Kim, 2024), highlighting the current political interest and perceived political strategy associated with anti-transgender legislation and anti-transgender sentiments.\u003c/p\u003e\n\u003ch3\u003eConsequences of Anti-transgender Legislation\u003c/h3\u003e\n\u003cp\u003eThe combination of increased legislation and decreased support for transgender rights is particularly dangerous for the health and safety of transgender individuals. Transgender individuals, and specifically trans youth, are at higher risk of depression and anxiety (Hajek et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suicidality (Wolford-Clevenger et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and a host of other mental health-related effects than their cisgender counterparts (Cotaina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Puckett et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wanta et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pervasive health disparities that exist between transgender and cisgender Americans are magnified by legislation that restricts rights for transgender individuals (Reisner et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Thoma et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When legislation is proposed and receives media coverage, transgender individuals face increased rumination, anxiety, and mental health consequences (Dhanani \u0026amp; Totton, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Internet searches for depression and suicide related topics increased when anti-transgender laws were passed, and this increase was more pronounced in states with higher LGBTQ\u0026thinsp;+\u0026thinsp;populations (Cunningham et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within two-years after the passage of anti-transgender legislation, suicide rates of transgender teens and young adults increase within states that enacted those laws (Lee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, previous research underscores the adverse impacts of anti-transgender legislation and highlights the importance of understanding and decreasing support for such legislation.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIntergroup Threat Theory\u003c/h2\u003e\u003cp\u003eIntergroup Threat Theory (ITT) offers a potential lens to examine support for anti-transgender attitudes and support for anti-transgender legislation. ITT proposes that outgroup prejudice stems from the perception of that group as threatening (Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). More specifically, ITT posits two primary drivers of threat, and thus prejudice and discrimination. The first are symbolic threats, which are threats to cultural values, social norms, morals, or personal beliefs. The second are realistic threats, which include threats to physical safety, health, financial security, or political power. Intergroup Threat Theory has been used to explain prejudice toward a host of different minoritized groups including immigrants (e.g., Abrams \u0026amp; Travaglino, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), racial minorities (e.g., Aberson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Danbold \u0026amp; Huo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Osborn et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and religious minorities (e.g., Cook et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Velasco et al., 2008). Symbolic threat, moreso than realistic threat, has been tied to anti-LGBT attitudes. For example, Brambilla and Butz (2013) investigated the effects of perceived threat on attitudes toward gay rights. Findings revealed that Italian college students in the symbolic threat condition expressed significantly less support for gay rights compared to two types of control conditions. Similarly, Aberson and colleagues (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that although realistic and symbolic threats were both related to stereotypes, only symbolic threats were related to explicit prejudice toward gay men. These results extend beyond gay men to broader LGBT identities. Mackey and Rios (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) primed participants with information suggesting that LGBT groups were becoming more prevalent across the United States. Across a single-paper meta-analysis, they found that while both realistic and symbolic threats were related to anti-LGBT prejudice, symbolic threat was a better predictor of anti-LGBT attitudes than realistic threat. These studies collectively point to the role of symbolic threat as a stronger predictor of anti-gay and anti-LGBT attitudes.\u003c/p\u003e\u003cp\u003eHowever, research has not examined legislation support, nor attitudes specifically toward transgender individuals. Gender theorists argue against overgeneralizations of \u0026ldquo;LGBT\u0026rdquo;, suggesting that the title erases the experiences of transgender individuals, who are most likely to experience harassment and negative stereotypes based on their identity (e.g., Bey, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fassinger \u0026amp; Arseneau, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McCarthy, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Similarly, psychological research has shown that although anti-transgender attitudes are related to anti-LGB attitudes (Hill \u0026amp; Willoughby, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), anti-transgender attitudes are more extreme and more negative than anti-gay attitudes (Norton \u0026amp; Herek, 2012), and may stem from different underlying perceptions. For example, previous research found that transgender individuals were more likely to be viewed as both deceptive and as confused about their identity than gay men, and that perceived deceptiveness (but not perceived confusion) mediated the relationship with greater prejudice levels toward transgender targets (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that anti-transgender attitudes may stem from different beliefs than anti-gay attitudes, and highlights the importance of examining anti-transgender prejudice as a separate category from anti-gay or anti-LGBT attitudes. Although previous research has highlighted the role of deception (but not confusion) for anti-trangender attitudes, research has not yet examined whether these effects hold for support for anti-transgender legislation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePredictors of Anti-Transgender Legislation\u003c/h3\u003e\n\u003cp\u003eLegislation support may stem from different threat perceptions than attitudes alone. Voting support is complex and voting behaviors may stem from realistic threats, symbolic threats, or a host of other attitudes (Friese et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, realistic threats related to race mediated voting decisions amongst participants with a high racial identification in the 2008 US election (B\u0026ouml;hm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Alternatively, Donald Trump\u0026rsquo;s \u0026ldquo;Make America Great Again\u0026rdquo; campaign slogan maps directly onto symbolic threats, and was deeply influential in the 2016 US election (Azevedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), showcasing the importance of both constructs in voting behavior.\u003c/p\u003e\u003cp\u003eAnti-transgender legislation often targets aspects of identity that are less relevant to LGB cisgender individuals (e.g., bathrooms, legal name changes, healthcare, sports, etc.). Indeed, prior research on the drivers of support for anti-transgender legislation are extremely limited. Knutson and colleagues (2021) found that political orientation moderated a relationship between religiosity and support for restrictive bathroom legislation. While religiosity was related to greater levels of support across the political spectrum, this effect was more pronounced for liberal than conservative participants. Knowledge and accuracy of information about transgender identities also impacts support around trans legislation. Participants who scored higher on a quiz assessing their knowledgability of transgender identities also tended to be more supportive of trans-inclusive policies (Zell \u0026amp; Burnett, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Alternatively, belief in disinformation, specifically disinformation related to gender affirming care, predicted support for anti-transgender legislation (Elischberger, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While this research collectively showcases the importance of ideology and knowledge of trans folk in legislation support, it does not assess the specific threat perceptions that might be driving support for restrictive legislation.\u003c/p\u003e\u003cp\u003eDespite previous research highlighting the role of symbolic threat in anti-LGBT attitudes (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the purported \u0026ldquo;threats\u0026rdquo; associated with transgender individuals may be based in fears over physical safety (e.g., bathrooms), which would point to realistic threats as an important predictor. Supporting this, previous qualitative work found that \u0026ldquo;setting concerns\u0026rdquo; such as safety in public restrooms or locker rooms were often used to frame opposition to bathroom legislation (Burke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Alternatively, \u0026ldquo;threats\u0026rdquo; associated with transgender individuals could be based in symbolic threats (e.g. gender ideology in schools) and thus might stem from symbolic threats. Similarly, previous research has identified perceived deception and confusion as predictors of anti-transgender attitudes (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, no research to our knowledge has evaluated these constructs as predictors of support for legislation. It is possible that perceptions of transgender individuals as confused about their identity, or as being deceptive could drive support for restrictive legislation. For example, the perception that transgender individuals are confused about their identity could lead to support for restrictive legislation with the underlying belief that doing so \u0026ldquo;allows the person time to understand their identity\u0026rdquo;. Alternatively, the perception of transgender individuals as deceptive could lead to support for restrictive legislation based on the perception of malicious intentions. As such, the current research will examine symbolic threats, realistic threats, perceived confusion, and perceived deceptiveness as potential predictors of both anti-transgender prejudice (a previously examined topic) and anti-transgender legislation.\u003c/p\u003e\u003cp\u003eGiven the previous research connecting both symbolic and realistic threats (but particularly symbolic) to anti-LGBT attitudes, and perceived confusion and deception (but particularly deception) to anti-transgender attitudes, the current research will evaluate the relationship between of all of these predictors with anti-transgender legislation support. Understanding the role of threats (symbolic and realistic) as well as beliefs (perceived deceptiveness and perceived confusion) in predicting anti-transgender legislation support will provide a preliminary understanding of potential methods to decrease support for restrictive legislation.\u003c/p\u003e\n\u003ch3\u003eOverview of Current Studies\u003c/h3\u003e\n\u003cp\u003eOur current studies examine symbolic and realistic threat perceptions as predictors of both prejudice (previously researched) and legislation support (a novel contribution), with the hypothesis that prejudice and legislation support may not be driven by the same threat perceptions. Given previous research highlighting the role of symbolic threat, realistic threat, perceived deception, and perceived confusion in anti-transgender attitudes (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) all constructs were included as variables of interest in Study 1. In Study 2, participants answered questions about either \u0026ldquo;transgender people\u0026rdquo; or \u0026ldquo;gay people\u0026rdquo; to further evaluate the relationship between perceived threats and voting support.\u003c/p\u003e\u003cp\u003eIn investigating these research questions, we make two contributions to the literature and to future policy efforts. First, this work grows on the body of research examining the connection between anti-LGBT attitudes and perceived symbolic threats, realistic threats, confusion, and deception. While previous research has examined symbolic and realistic threats with anti-gay attitudes (Aberson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brambilla \u0026amp; Butz, 2013) and anti-LGBT attitudes (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this is the first study to our knowledge to evaluate the connection between symbolic and realistic threats with anti-transgender attitudes. Second, we explore the relationship between perceived symbolic threats, realistic threats, perceived confusion, and perceived deceptiveness as predictors of support for restrictive legislation. Symbolic threats have been found to be a better predictor than realistic threats for anti-gay attitudes, but it is unclear if this same effect drives support for restrictive legislation. Similarly, while previous research has demonstrated that transgender targets are rated as both more confused and more deceptive than gay men, and that perceived deceptiveness (but not confusion) drives greater levels of prejudice (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it is unclear if these perceptions also drive increased support for restrictive legislation. Understanding the impacts of perceived threats in support for legislation is critical for legislators, policy makers, and advocates who hope to reduce community support for anti-transgender legislation.\u003c/p\u003e"},{"header":"Study 1 Hypotheses","content":"\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe hypothesized that, in line with previous research on LGBT attitudes (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) symbolic (\u003cem\u003eH1a\u003c/em\u003e) and realistic (\u003cem\u003eH1b\u003c/em\u003e) threats would each predict anti-transgender prejudice in their own independent models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSimilarly, in line with previous research on anti-transgender attitudes (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) deception (\u003cem\u003eH2a\u003c/em\u003e) and confusion (\u003cem\u003eH2b\u003c/em\u003e) would also each predict anti-transgender prejudice in their own independent models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eGiven previous research suggesting that deception was a better predictor than confusion (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and that symbolic threat was a better predictor than realistic threat (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we hypothesized that symbolic threat and deception would be stronger predictors than realistic threat and confusion in an additive model with all predictor variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSimilar to H1 and H2, we hypothesized that symbolic threat (\u003cem\u003eH4a\u003c/em\u003e) and realistic threat (\u003cem\u003eH4b\u003c/em\u003e) would each independently predict support for anti-transgender legislation in independent models. Since no previous research has evaluated predictors of support for anti-transgender legislation, this hypothesis is based on research suggesting the role of both symbolic and realistic threats in legislation support more broadly (e.g., Azevedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Böhm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAlthough previous research has not evaluated our predictor attitudes in relation to support for anti-transgender legislation, we hypothesized that both symbolic and realistic threats will remain significant predictors in an additive regression model with all predictor variables. This hypothesis stems from previous research pointing to both symbolic and realistic threats in legislation support (e.g., Azevedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Böhm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven that there is no research to date examining deception and confusion as predictors of legislation support, these variables were included as exploratory variables. We did not have an a-priori hypothesis about their predictive role individually nor as a part of an additive model in predicting support for restrictive legislation.\u003c/p\u003e\n\u003ch3\u003eMethods Study 1\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e This study was approved by the institutional review board at redacted for review. All participants provided informed consent prior to their participation in the study. Data were collected from participants who self-identified as cisgender, heterosexual, and politically moderate through Prolific Academic, an online survey platform that connects registered users to surveys for which they are eligible. We requested cisgender, heterosexual participants for this study given the sensitive nature of the questions. Political moderates were requested, in line with previous research (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and since they are likely to have the widest variety of attitudes about anti-transgender legislation (Parker et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In total, 246 participants completed the survey. Seven participants (2.8%) indicated a sexual orientation other than heterosexual. Results did not change from excluding these participants, so their responses were retained. A power analysis based on an alpha value of .05 indicated that a sample of 224 was required to detect small to medium effect sizes, suggesting we had a sufficient sample. Among the participants, 96 (39.0%) identified as male and 150 (61.0%) identified as female. The average age was 40.86 (\u003cem\u003eSD\u003c/em\u003e = 13.00). The majority identified their race as White (70.7%), with 15.9% identifying as Black/African American, 2.0% as American Indian or Alaska Native, 11.0% as Asian or Pacific Islander, 7.7% as Hispanic, 1.2% as Middle Eastern or Arab American, and .8% as another racial/ethnic identity. Approximately half of participants held a college degree or higher (47.2%). Participants were asked about their political orientation using two questions, one about their social attitudes and one about their economic attitudes, on a 1 (very liberal) to 7 (very conservative) scale. These two questions were averaged to examine participants’ overall political attitudes. In line with participants' self-identification on Prolific, political attitudes were generally moderate (\u003cem\u003eM\u003c/em\u003e = 3.98, \u003cem\u003eSD\u003c/em\u003e = .88).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eRealistic Threat\u003c/h2\u003e\u003cp\u003ePerceived realistic threat from transgender individuals was measured using a four-item measure adapted from Mackey and Rios (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (e.g., “Transgender people are a safety threat when they use public restrooms or changing rooms”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together (\u003cem\u003eM\u003c/em\u003e = 2.78, \u003cem\u003eSD\u003c/em\u003e = 1.39, α = .89).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSymbolic Threat\u003c/h2\u003e\u003cp\u003ePerceived symbolic threat from transgender individuals was measured using a four-item scale adapted from previous research (Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (e.g., “Transgender people are harming traditional gender roles”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together (\u003cem\u003eM\u003c/em\u003e = 3.51, \u003cem\u003eSD\u003c/em\u003e = 1.72, α = .95).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eConfusion\u003c/h2\u003e\u003cp\u003ePerceived confusion of transgender individuals was measured using a four-item scale adapted from previous research (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (e.g., “Transgender people are still figuring out who they are”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together (\u003cem\u003eM\u003c/em\u003e = 4.41, \u003cem\u003eSD\u003c/em\u003e = 1.26, α = .83).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDeception\u003c/h2\u003e\u003cp\u003ePerceived deceptiveness of transgender individuals was measured using a four-item scale adapted from previous research (Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (e.g., “I would feel deceived if I found out someone I knew was transgender”). Items were measured on a 1 (strongly disagree) to 7 (strongly agree) scale and were averaged together (\u003cem\u003eM\u003c/em\u003e = 3.08, \u003cem\u003eSD\u003c/em\u003e = 1.36, α = .82).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePrejudice\u003c/h2\u003e\u003cp\u003eTo get a baseline understanding of participants' prejudice levels, participants were asked about their general attitude toward six groups: women, Republicans, Black/African-Americans, nonreligious individuals, transgender individuals, and those who have recently moved residences from 1 (very negative) to 9 (very positive). This item was used to compare groups to examine participants' attitudes toward transgender individuals as opposed to other groups in society.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLegislation\u003c/h2\u003e\u003cp\u003eSupport for anti-transgender legislation was assessed using a four-item scale (e.g., “Please indicate your support for legislation that requires people to use the bathroom of their gender assigned at birth”). Items were based on the four most common topics of anti-transgender legislation, as categorized by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003etranslegislation.com\u003c/span\u003e (2025). Items were measured on a 1 (strongly oppose) to 7 (strongly support) scale and were averaged together to create a composite legislation support measure (\u003cem\u003eM\u003c/em\u003e = 4.31, \u003cem\u003eSD\u003c/em\u003e = 1.48, α = .83).\u003c/p\u003e\u003cp\u003eThe full list of questions can be found in the supplemental material.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eStudy 1 Results\u003c/h2\u003e\u003cp\u003eCorrelations between all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A principal components analysis of all items included in the symbolic threat, realistic threat, confusion, deception, and legislation scales using varimax rotation revealed a four-factor solution, with realistic threat (eigenvalue = 1.02), symbolic threat (eigenvalue = 0.88), confusion (eigenvalue = 1.05), and deception items (eigenvalue = 1.05) each producing loadings of at least .82 onto separate factors, suggesting each construct was independent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy 1 Means, Standard Deviations, and Intercorrelations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e1. Age\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2. Gender\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .27***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e3. Education\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e− .03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4. Political Orientation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e− .08\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e5. Realistic Threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .07\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e− .01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e− .17*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e.39***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e6. Symbolic Threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .07\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e− .10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e.38***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e.78***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e7. Confusion\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .12\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e− .08\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e.16*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e.44***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e.53***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e8. Deception\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e.04\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e.05\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e− .09\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e.002**\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e.613***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e.61***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e.41***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e9. Prejudice\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e.07\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e− .01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e.01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e− .23***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e− .61***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e− .69***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e− .37***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e− .51***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e10. Legislation Support\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e− .06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e.05\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e− .14*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e.43***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e.73***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e.38***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e.44***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e.53***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e− .57***\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote. Gender is coded 1 for female and 2 male\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePaired samples t-tests were used to evaluate differences in baseline prejudice levels. We then examined the relationships between symbolic threat, realistic threat, perceived deception, prejudice toward transgender individuals, and perceived confusion with support for legislation using a series of regressions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePrejudice toward Transgender Individuals\u003c/h2\u003e\u003cp\u003ePaired sample t-tests revealed that transgender individuals (\u003cem\u003eM\u003c/em\u003e = 5.72, \u003cem\u003eSD\u003c/em\u003e = 2.0) were rated more negatively than women (\u003cem\u003eM\u003c/em\u003e = 7.62, \u003cem\u003eSD\u003c/em\u003e = 1.44), Black individuals (\u003cem\u003eM\u003c/em\u003e = 7.01, \u003cem\u003eSD\u003c/em\u003e = 1.67), nonreligious individuals (\u003cem\u003eM\u003c/em\u003e = 6.43, \u003cem\u003eSD\u003c/em\u003e = 1.72), and people who have recently moved residences (\u003cem\u003eM\u003c/em\u003e = 6.15, \u003cem\u003eSD\u003c/em\u003e = 1.56) (all p’s \u0026lt; .001). However, transgender individuals were rated more positively than republicans (\u003cem\u003eM\u003c/em\u003e = 4.71, \u003cem\u003eSD\u003c/em\u003e = 2.12) (p \u0026lt; .001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of anti-transgender legislation\u003c/h2\u003e\u003cp\u003eNext, we conducted a series of regression analyses to evaluate the relationships between symbolic threat, realistic threat, perceived deception, and perceived confusion with prejudice and legislation support. Political orientation was entered into Step 1 of the regression. Each of the predictors (symbolic threat, realistic threat, deception, confusion) were each entered independently as Step 2 of four separate models to understand individual influence. In the first analysis, prejudice was treated as the outcome variable. Each predictor independently significantly predicted support for anti-transgender legislation (all \u003cem\u003ep’s\u003c/em\u003e \u0026lt; .001). Results for each of these models are available in the supplementary materials. These findings supported Hypotheses 1 and 2.\u003c/p\u003e\u003cp\u003eNext, all predictors were entered into an additive model. Results indicated that, after controlling for relevant demographic variables, only symbolic (\u003cem\u003eb\u003c/em\u003e = − .58, 95% CI [-.765, − .392], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and realistic threat (\u003cem\u003eb\u003c/em\u003e = − .29, 95% CI [-.508, − .067], \u003cem\u003ep\u003c/em\u003e = .011) remained significant. Deception (\u003cem\u003eb\u003c/em\u003e = − .14, 95% CI [-.321, .037], \u003cem\u003ep\u003c/em\u003e = .119) and confusion (\u003cem\u003eb\u003c/em\u003e = .03, 95% CI [-.143, .201], \u003cem\u003ep\u003c/em\u003e = .739) were no longer significant predictors. This provided partial, but not full support for Hypothesis 3.\u003c/p\u003e\u003cp\u003eIn the second set of analyses, legislation support was treated as the outcome variable. Independently, each predictor significantly predicted support for anti-transgender legislation (all \u003cem\u003ep’s\u003c/em\u003e \u0026lt; .001). Results for each of these models are available in the supplementary materials. These findings supported Hypothesis 4.\u003c/p\u003e\u003cp\u003eNext, all predictors were entered into an additive model. Results indicated that, after controlling for relevant demographic variables, only symbolic (\u003cem\u003eb\u003c/em\u003e = .41, 95% CI [.290, .521], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and realistic threat (\u003cem\u003eb\u003c/em\u003e = .29, 95% CI [.149, .423], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) remained significant. Deception (\u003cem\u003eb\u003c/em\u003e = .01, 95% CI [-.098, .124], \u003cem\u003ep\u003c/em\u003e = .819), and confusion (\u003cem\u003eb\u003c/em\u003e = .06, 95% CI [-.049, .164], \u003cem\u003ep\u003c/em\u003e = .291) were no longer significant predictors. These findings supported Hypothesis 5.\u003c/p\u003e\u003cp\u003eResults for both of the additive models are available in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy 1 Regression Table of Prejudice and Legislation Support with all Predictor Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePrejudice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLegislation Support\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStep 1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePolitical Orientation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.43***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStep 2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConfusion\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDeception\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRealistic Threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .19*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.27***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSymbolic Threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.48***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cem\u003eNote. All VIF statistics were less than 5.\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStudy 1 Discussion\u003c/h2\u003e\u003cp\u003eTaken together, the findings of Study 1 lend support to the idea that both symbolic and realistic threats play an important role in anti-transgender prejudice and legislation support. Moreover, Study 1 suggests that the effects of perceived deceptiveness and perceived confusion may be better explained by symbolic and realistic threat perceptions. Given the findings of Study 1, Study 2 focused on symbolic and realistic threats as potential mediators. More specifically, Study 2 aims to further illuminate differences in support for anti-transgender legislation compared to support for “anti-gay” legislation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eMethods Study 2\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThis study was approved by the institutional review board at redacted for review. All participants provided informed consent prior to their participation in the study. Data were collected from participants who self-identified as cisgender, heterosexual, and politically moderate through Prolific Academic, an online survey platform that connects registered users to surveys for which they are eligible. In total, 674 participants completed the survey. Twenty-two participants (3.7%) indicated a sexual orientation or gender identity other than cisgender and heterosexual. Results did not change from excluding these participants, so their responses were retained. A power analysis based on an alpha value of .05 indicated that a sample of 619 was required to detect small to medium effect sizes, suggesting we had a sufficient sample. Among the participants, 314 (46.4%) identified as male and 360 (53.3%) identified as female. The average age was 41.05 (\u003cem\u003eSD\u003c/em\u003e = 12.92). The majority identified their race as White (71.0%), with 16.0% identifying as Black/African American, 1.6% as American Indian or Alaska Native, 8.4% as Asian or Pacific Islander, 6.7% as Hispanic, 1.3% as Middle Eastern or Arab American, and .6% as another racial/ethnic identity. Similarly to Study 1, participants indicated they were politically moderate (\u003cem\u003eM\u003c/em\u003e = 3.97, \u003cem\u003eSD\u003c/em\u003e = .91).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003eAll scales and measures were identical to Study 1 outside of two changes. Participants in the “gay” condition read questions that applied to “gay people” rather than “transgender people”. Additionally, the legislation questions were reframed to be applicable to both gay people and transgender people (e.g., “Please indicate your support for legislation that bans conversations about transgender (gay) identities in schools and educational settings”). The full list of questions can be found in the supplemental material. Means, standard deviations, and alpha levels (where appropriate) for each scale can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy 2 Means, Standard Deviations, and alpha levels (where appropriate) by condition.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTransgender Condition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eGay Condition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eScale α\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSymbolic threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRealistic threat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePrejudice\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e–\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLegislation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Study 2 Hypotheses","content":"\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe transgender condition will elicit greater levels of symbolic threat (\u003cem\u003eH1a\u003c/em\u003e), realistic threat (\u003cem\u003eH1b\u003c/em\u003e), prejudice (\u003cem\u003eH1c\u003c/em\u003e), and legislation support (\u003cem\u003eH1d\u003c/em\u003e) than the gay condition. This is based on previous research demonstrating that attitudes toward transgender individuals are more negative and more extreme than those toward gay men (Norton \u0026amp; Herek, 2012; Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn line with Mackey and Rios (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we expect that symbolic threats, but not realistic threats, will mediate the relationship between condition and prejudice.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn line with previous research suggesting the importance of both symbolic and realistic threats in legislation support (e.g., Azevedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Böhm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we hypothesized that both symbolic and realistic threats would mediate the relationship between condition and legislation support.\u003c/p\u003e\u003ch2\u003eStudy 2 Results\u003c/h2\u003e\u003cp\u003eSimilar to Study 1, a principal components analysis of all items included in the symbolic threat, realistic threat, confusion, deception, and legislation scales using varimax rotation revealed a four-factor solution, with realistic threat (eigenvalue = .82), symbolic threat (eigenvalue = 1.05), confusion (eigenvalue = 1.09), and deception items (eigenvalue = 1.04) each producing loadings of at least .76 onto separate factors, suggesting each construct was independent.\u003c/p\u003e\u003cp\u003eWe tested the effects of condition on realistic threat, symbolic threat, deception, confusion, prejudice, and voting support using one-way ANCOVAs, controlling for political orientation. Given the findings of Study 1 as well as previous research (e.g., Totton \u0026amp; Rios, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we hypothesized that participants in the transgender condition would express higher levels of realistic threat, symbolic threat, prejudice, and importantly, greater support for restrictive legislation. Although Study 1 found that both symbolic and realistic threats were connected to prejudice, previous research has demonstrated that symbolic threats are a \u003cem\u003ebetter\u003c/em\u003e predictor of anti-LGBT attitudes in a single-paper meta-analysis (e.g., Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As such, in line with previous research, we hypothesized that symbolic threats would be a better predictor of anti-transgender attitudes. However, previous research has not yet evaluated anti-transgender legislation support. Given the results of Study 1 as well as previous research connecting realistic threats with predictors of voting (e.g., Böhm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we hypothesized that both symbolic and realistic threats would mediate the relationship between condition and legislation support. Given the results of Study 1, which showed that deception and confusion were not as strong of predictors of either anti-transgender attitudes or legislation support, analyses and descriptives for deception and confusion are presented in the supplementary materials as an exploratory analysis.\u003c/p\u003e\u003ch2\u003eSymbolic Threat\u003c/h2\u003e\u003cp\u003eIn line with Hypothesis 1a, participants in the transgender condition (\u003cem\u003eM\u003c/em\u003e = 3.72, \u003cem\u003eSE\u003c/em\u003e = .09) expressed significantly higher levels of symbolic threat than the gay condition (\u003cem\u003eM\u003c/em\u003e = 2.62, \u003cem\u003eSE\u003c/em\u003e = .09), \u003cem\u003eF\u003c/em\u003e(1, 639) = 69.47, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .098. The effect of political orientation was also significant, \u003cem\u003eF\u003c/em\u003e(1, 639) = 97.58, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .132, with more conservative participants perceiving higher levels of symbolic threat.\u003c/p\u003e\u003ch2\u003eRealistic Threat\u003c/h2\u003e\u003cp\u003eIn line with Hypothesis 1b, participants in the transgender condition (\u003cem\u003eM\u003c/em\u003e = 2.96, \u003cem\u003eSE\u003c/em\u003e = .071) expressed significantly higher levels of realistic threat than the gay condition (\u003cem\u003eM\u003c/em\u003e = 2.00, \u003cem\u003eSE\u003c/em\u003e = .071), \u003cem\u003eF\u003c/em\u003e(1, 639) = 91.37, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .125. The effect of political orientation was also significant, \u003cem\u003eF\u003c/em\u003e(1, 639) = 66.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .095, with more conservative participants perceiving higher levels of realistic threat.\u003c/p\u003e\u003ch2\u003ePrejudice\u003c/h2\u003e\u003cp\u003eIn line with Hypothesis 1c, participants in the transgender condition (\u003cem\u003eM\u003c/em\u003e = 5.65, \u003cem\u003eSE\u003c/em\u003e = .112) expressed significantly higher levels of prejudice than participants in the gay condition (\u003cem\u003eM\u003c/em\u003e = 6.47, \u003cem\u003eSE\u003c/em\u003e = .111) \u003cem\u003eF\u003c/em\u003e(1, 639) = 27.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .041. The effect of political orientation was also significant, \u003cem\u003eF\u003c/em\u003e(1, 639) = 15.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .041, with more conservative participants scoring higher in prejudice.\u003c/p\u003e\u003cp\u003eSince we collected attitudes toward a variety of other groups (Women, Republicans, Black/African American individuals, Nonreligious individuals, and Those who have recently moved residences), we also looked for conditional differences between those groups. No other differences were found between other target groups based on condition (all p’s \u0026gt; .05). A table of these comparisons are provided in the supplemental material section.\u003c/p\u003e\u003ch2\u003eVoting Behavior\u003c/h2\u003e\u003cp\u003eIn line with Hypothesis 1d, participants in the transgender condition (\u003cem\u003eM\u003c/em\u003e = 3.19, \u003cem\u003eSE\u003c/em\u003e = .082) expressed significantly greater support for restrictive legislation than the gay condition (\u003cem\u003eM\u003c/em\u003e = 2.49, \u003cem\u003eSE\u003c/em\u003e = .081), \u003cem\u003eF\u003c/em\u003e(1, 635) = 37.18, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .055. The effect of political orientation was also significant, \u003cem\u003eF\u003c/em\u003e(1, 639) = 80.98, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003eη2p\u003c/em\u003e = .113, with more conservative participants being more supportive of restrictive legislation.\u003c/p\u003e\u003ch2\u003eMediation\u003c/h2\u003e\u003cp\u003eGiven the results of Study 1, We conducted a series of mediation analyses using PROCESS model 4 (Hayes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to examine symbolic threat and realistic threat as potential mediators of the relationships between condition (0 = gay, 1 = trans) and the relevant outcome variable (prejudice and legislation support). In both analyses, participant political orientation was added as a covariate. We then evaluated contrasts of indirect effects to determine if one mediator was stronger than the other (Coutts \u0026amp; Hayes, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirst, we evaluated both possible mediators of the relationship between condition (predictor) and prejudice (outcome). A test of the indirect effects revealed that symbolic threat (b = 0.679, SE = .103, 95% CI = [.485, .892]) and realistic threat (b = 0.234, SE = .083, 95% CI = [.084, .406]) significantly mediated the relationship between condition and prejudice. However, a comparison of contrasts revealed that symbolic threat was a stronger mediator than realistic threat (b = 0.445, SE = .150, 95% CI = [.163, .748]). Supporting the work of Mackey \u0026amp; Rios (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) as well as Hypothesis 2, this suggests that symbolic threat is the strongest mediator of the relationship between condition and prejudice.\u003c/p\u003e\u003cp\u003eNext, we examined both symbolic and realistic threats as mediators in a model with support for legislation as the outcome variable. A test of the indirect effects revealed that symbolic threat (b = -0.410, SE = .065, 95% CI = [-.541, − .288]) and realistic threat (b = 0.456, SE = .070, 95% CI = [-.602, − .330]) both mediated the relationship between condition and voting behavior. A comparison of contrasts of indirect effects demonstrated that the difference between indirect effects of symbolic threat and realistic threat (b = 0.051, SE = .096, 95% CI = [− .138, .238]) was not significant. Therefore, both symbolic threat and realistic threat were found to partially mediate the relation between condition and voting behavior. This supports Hypothesis 3. This relationship can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch3\u003eStudy 2 Discussion\u003c/h3\u003e\u003cp\u003eResults of Study 2 suggest that symbolic threats are a better predictor of anti-transgender prejudice than realistic threats. This supports previous work looking at anti-LGBT attitudes (e.g., Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and suggests that, despite being more specified in their target, anti-transgender prejudice may stem from similar threats to those behind more general anti-LGBT prejudices. However, Study 2 demonstrates that support for anti-transgender legislation is mediated by \u003cem\u003eboth\u003c/em\u003e symbolic and realistic threats, suggesting a more complex picture of legislation support than prejudice.\u003c/p\u003e"},{"header":"General Discussion","content":"\u003cp\u003eThe goal of the current studies was to evaluate predictors of support for anti-transgender legislation. Study 1 demonstrated that symbolic and realistic threats were better predictors of both anti-transgender prejudice and legislation support than perceived deceptiveness or confusion. Importantly, we found support in Study 2 for previous research suggesting that anti-transgender attitudes are better mediated by symbolic (rather than realistic) threats (e.g., Mackey \u0026amp; Rios, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, central to our research question, we also demonstrated that this effect does not extend to support for anti-transgender legislation. Across both studies, we found that symbolic and realistic threats each drove support for anti-transgender legislation. Specifically, Study 2 showed that both symbolic and realistic threats mediated the relationship between condition and support for restrictive legislation. This suggests that legislation support has more complex drivers than attitudes alone, and thus, more complex approaches may be required to reduce it. Collectively, our research adds to the body of literature in Intergroup Threat Theory, highlighting the important role of symbolic and realistic threats in both prejudice and voting behaviors. However, our work expands previous research by highlighting the complex nature of support for anti-transgender legislation and pointing to the need to interrupt both symbolic and realistic threats to decrease support for restrictive legislation.\u003c/p\u003e\u003ch2\u003eSocial-Policy Implications\u003c/h2\u003e\u003cp\u003eTo our knowledge, this work is the first to evaluate how threat perceptions drive support for anti-transgender legislation. Previous research has highlighted the myriad of negative impacts of anti-transgender legislation on the mental and physical health of transgender individuals (e.g., Dhanani \u0026amp; Totton, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the proliferation of anti-transgender legislation as well as increased support for restrictive legislation (Translegislation.com, 2025; Pew, 2025), this provides a timely analysis for legislators and activists alike to understand the factors that may increase voter support for anti-transgender legislation. Crucially, it highlights the distinction between anti-transgender legislation support and anti-transgender prejudice and demonstrates that support for anti-transgender legislation has more complex underpinnings than prejudice alone. Previous research examining anti-transgender legislation support has noted the impacts of both religiosity and political orientation (Knutson et al., 2021), which may be more challenging to change or address than threat perceptions. For policy makers or organizations looking to support transgender individuals by decreasing community support for restrictive legislation, this work points to the need to address both perceived symbolic threats \u003cem\u003eand\u003c/em\u003e perceived realistic threats surrounding transgender individuals.\u003c/p\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eThere are limitations of our study that should be considered alongside its contributions. Primarily, although our samples were diverse in many ways (e.g., gender, age, education), both studies focused on cisgender, heterosexual, self-declared political “moderates.” While this group represents a critical part of the American voter pool (Brenan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and although political orientation was measured and included in analyses, it is possible that more firmly identified conservatives or liberals might be driven by different threats. For example, more conservative participants may be better persuaded by specific realistic threats (e.g., the belief that transgender women are a threat in bathrooms) than more liberal participants (Knutson et al., 2021). As such, future research should extend this research to a broader array of political ideologies and evaluate specific policies such as bathroom laws, education based laws, or sports related laws separately.\u003c/p\u003e\u003cp\u003eMoreover, while the current work points to factors that drive support for anti-transgender legislation, it does not elucidate effective methods to reduce symbolic threat or realistic threat. Indeed, limited work thus far has highlighted methods of reducing perceived symbolic or realistic threats (see Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e for review). Future research should evaluate effective methods of reducing threat perceptions as a means of reducing support for restrictive legislation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAnti-transgender legislation has recently become a hot-button issue for politicians, legislators, and voters. Despite evidence that restrictive legislation has adverse effects on transgender individuals (notably trans youth and young adults), such legislation has continued to surge. This study provides insight to the threat perceptions driving voter support for anti-transgender legislation. Specifically, it underscores that, unlike negative attitudes, which seem to be driven primarily by perceived symbolic threats, legislation support has more complex underpinnings with support stemming from both symbolic threats and realistic threats. This points to the complex nature of support for anti-transgender legislation and provides a starting point for reduction efforts. We urge activists and researchers to evaluate methods to decrease perceived threats posed by transgender individuals as a means of decreasing support for restrictive policies. Moreover, we call on politicians and policymakers to reduce the spread of both symbolic and realistic threats by ending anti-transgender campaign ads and restrictive legislative policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data for this study are available at https://osf.io/8yxh4/?view_only=542e29128e9249d2ba4cfe915d0f0643. The materials included in this study are included in the supplementary materials of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies presented in this paper were approved by the Institutional Review Board at redacted for review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003e2025 Anti-Trans Bills: Trans legislation tracker\u003c/em\u003e. 2025 Anti-Trans Bills: Trans Legislation Tracker. (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://translegislation.com/\u003c/span\u003e\u003cspan address=\"https://translegislation.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAberson, C. L., Ferguson, H., \u0026amp; Allen, J. (2021). Contact, threat, and prejudice: A test of intergroup threat theory across three samples and multiple measures of prejudice. \u003cem\u003eJournal of Theoretical Social Psychology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), 404\u0026ndash;422. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jts5.107\u003c/span\u003e\u003cspan address=\"10.1002/jts5.107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbrams, D., \u0026amp; Travaglino, G. A. (2018). 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Transgender Knowledge Mediates US Political Differences in Prejudice and Support for Trans-inclusive Policies. \u003cem\u003eSex Roles\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(12), 1879\u0026ndash;1890. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11199-024-01539-1\u003c/span\u003e\u003cspan address=\"10.1007/s11199-024-01539-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transgender, Legislation, LGBTQ+, Intergroup Threat Theory, Symbolic Threat, Realistic Threat","lastPublishedDoi":"10.21203/rs.3.rs-7753662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7753662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction: There has been a recent surge of anti-transgender legislation with growing support from American voters. Research in Intergroup Threat Theory (ITT) points to symbolic threat as a better predictor of anti-LGBT attitudes than realistic threat. However, support for anti-transgender legislation may be distinct from “LGBT attitudes”.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Two studies evaluated the impact of both symbolic and realistic threats as predictors of support for restrictive legislation and prejudice. Study 1 (n = 246, collected in June of 2024) used a correlational design to establish the link between realistic threat, symbolic threat, perceived deception, and perceived confusion with prejudice and support for anti-transgender legislation. Study 2 (n = 367, collected in October of 2024) used a two-condition design (“gay people”, “transgender people”) to evaluate symbolic and realistic threats as mediators of prejudice and legislation support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Study 1 demonstrated that both symbolic and realistic threats were associated with anti-transgender legislation and prejudice, and were better predictors than perceived deceptiveness or confusion. Study 2 found that while symbolic threat was a better mediator of prejudice, both symbolic and realistic threats independently mediated support for anti-transgender legislation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion and Policy Implications: Past research has established the adverse impacts of anti-transgender legislation (e.g., Dhanani \u0026amp; Totton, 2023; Lee et al., 2024). Our studies demonstrate that support for restrictive legislation is more complex than negative attitudes and highlight the need for pro-transgender advocacy to take a multifaceted approach to bias reduction.\u003c/p\u003e","manuscriptTitle":"Threatened Restrictions: The Role of Symbolic and Realistic Threat in Anti-Transgender Legislation Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:49:06","doi":"10.21203/rs.3.rs-7753662/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18263a6f-d4b9-4cbc-bdc3-aaf2098bd785","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T14:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:49:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7753662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7753662","identity":"rs-7753662","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}