The call for greater openness in research data is quickly growing in many scientific fields. Psychology as a field, however, still falls short in this regard. Research is vulnerable to human error, inaccurate interpretation, and reporting of study results, and decisions during the research process being biased toward favorable results. Despite the obligation to share data for verification and the importance of this practice for protecting against human error, many psychologists do not fulfill their ethical responsibility of sharing their research data. This has implications for the accurate and ethical dissemination of specific research findings and the scientific development of the field more broadly. Open science practices provide promising approaches to address the ethical issues of inaccurate reporting and false-positive results in psychological research literature that hinder scientific growth and ultimately violate several relevant ethical principles and standards from the American Psychological Association's (APA's) Ethical Principles of Psychologists Code of Conduct (APA, 2017). Still, current incentive structures in the field for publishing and professional advancement appear to induce hesitancy in applying these practices. With each of these considerations in mind, recommendations on how psychologists can ethically proceed through open science practices and incentive restructuring-in particular, data management, data and code sharing, study preregistration, and registered reports-are provided.

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These supplemental materials include applied illustrations, in the first section, and recommended readings, in the second section, for each of the ten tips discussed in Where did I go wrong with my model? Ten tips for getting results in SEM. References can be found in the main paper. IllustrationsThe following analyses illustrate select points made for each of the ten tips using data from the Later Life Study of Social Exchanges (LLSSE; Newsom et al., 2005; Sorkin & Rook, 2004) described in the main manuscript. The analyses were conducted with the R lavaan package (Rosseel, 2012) with robust maximum likelihood for missing data (estimator = MLR; Yuan & Bentler, 2000). The R code used to analyze the illustrations for each tip can be found following brief descriptions of the tip and the accompanying illustration.

After the attacks on 9/11, Muslims in the United States were the targets of increased surveillance by law enforcement on the basis of their religious identity, often resulting in mistreatment and unjustified imprisonment. The current study examined ideologies that are associated with Islamophobia and support for police surveillance of Muslims, as well as specific types of intergroup threat perceptions that mediate these relationships. Participants (N = 603) completed a survey measuring Social Dominance Orientation (SDO), Right‐wing Authoritarianism (RWA), Nationalism, intergroup threat perceptions, Islamophobia, and support for an anti‐Muslim police surveillance policy. Results demonstrated that higher levels of SDO, RWA, and Nationalism were each independently associated with Islamophobia through increased realistic, symbolic, and terroristic threat perceptions. Further, higher levels of Islamophobia mediated the relationships between each type of perceived threat and support for a Muslim surveillance policy. This comprehensive model of anti‐Muslim bias highlights the relative, independent effects of ideology and threat perceptions on anti‐Muslim prejudice and discrimination. Findings hold implications for the use of threat‐based language and stereotyping in policy decisions, particularly among those high in SDO, RWA, and Nationalism.