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Creators/Authors contains: "Haaf, Julia"

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  1. ; ; ; ; ; ; ; ; ; (, Proceedings of the National Academy of Sciences)
    Measurement literacy is required for strong scientific reasoning, effective experimental design, conceptual and empirical validation of measurement quantities, and the intelligible interpretation of error in theory construction. This discourse examines how issues in measurement are posed and resolved and addresses potential misunderstandings. Examples drawn from across the sciences are used to show that measurement literacy promotes the goals of scientific discourse and provides the necessary foundation for carving out perspectives and carrying out interventions in science. 
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  2. ; ; ; ; ; ; ; ; ; ; et al (, Computational Brain & Behavior)
    Abstract van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison. 
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