Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

Over the last ten years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgments of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries, and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods, correlate and rotate the dimension reduction solution.

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

The slippery slope framework of tax compliance emphasizes the importance of trust in authorities as a substantial determinant of tax compliance alongside traditional enforcement tools like audits and fines. Using data from an experimental scenario study in 44 nations from five continents (N = 14,509), we find that trust in authorities and power of authorities, as defined in the slippery slope framework, increase tax compliance intentions and mitigate intended tax evasion across societies that differ in economic, sociodemographic, political, and cultural backgrounds. We also show that trust and power foster compliance through different channels:trusted authorities (those perceived as benevolent and enhancing the common good) register the highest voluntary compliance, while powerful authorities (those perceived as effectively controlling evasion) register the highest enforced compliance. In contrast to some previous studies, the results suggest that trust and power are not fully complementary, as indicated by a negative interaction effect. Despite some between-country variations, trust and power are identified as important determinants of tax compliance across all nations. These findings have clear implications for authorities across the globe that need to choose best practices for tax collection.

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Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses--- where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.

Mental accounting describes a series of cognitive operations that help organize financial activities and facilitate money management. Self-employed taxpayers who make use of a separate mental account for future income tax payments or collected value added tax (VAT) might find it easier to declare their taxes correctly than taxpayers who do not. This study used a questionnaire to investigate whether selfemployed taxpayers (N = 350) use mental accounting to manage their income tax and VAT obligations, whether mental accounting relates to tax knowledge, business and personality characteristics, and to what extent mental accounting is related to intended tax behavior. Our results reveal that some taxpayers mentally segregate taxes from turnover (segregators) while others do not (integrators). We found small differences in mental accounting between income taxes and VAT. Moreover, confirmatory factor analyses suggested that tax knowledge and mental accounting are distinct constructs. Segregation of taxes was related to lower impulsivity and more positive attitudes toward taxation. Individuals who stated they segregate taxes due from turnover more often claimed to run financially prosperous businesses. Mental accounting was not related to intentions of evading taxes, but individuals with higher mental accounting scores reported more pronounced levels of tax planning. While our research design does not allow drawing causal inferences, these findings could suggest that increasing self-employed taxpayers' ability to organize their financial activities might be a promising strategy to strengthen the competitiveness of their businesses.

Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses---where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.