7 research outputs found

    Embedding data skills in research methods education: preparing students for reproducible research

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    Many initiatives to improve reproducibility incentivise replication and encourage greater transparency without directly addressing the underlying skills needed for transparent and reproducible data preparation and analysis. In this paper, we argue that training in data processing and transformation should be embedded in field-specific research methods curricula. Promoting reproducibility and open science requires not only teaching relevant values and practices, but also providing the skills needed for reproducible data analysis. Improving students’ data skills will also enhance their employability within and beyond the academic context. To demonstrate the necessity of these skills, we walk through the analysis of realistic data from a classic paradigm in experimental psychology that is often used in teaching: the Stroop Interference Task. When starting from realistic raw data, nearly 80% of the data analytic effort for this task involves skills not commonly taught— namely, importing, manipulating, and transforming tabular data. Data processing and transformation is a large and inescapable part of data analysis, and so education should strive to make the work associated with it as efficient, transparent, and reproducible as possible. We conclude by considering the challenges of embedding computational data skills training in undergraduate programmes and offer some solutions

    To which world regions does the valence–dominance model of social perception apply?

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    Over the past 10 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 judgements 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 and correlate and rotate the dimension reduction solution

    A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

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    The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

    Assessing the evidentiary value of secondary data analyses : a commentary on Gangestad, Dinh, Grebe, Del Giudice, and Thompson (2019)

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    Secondary data analyses (analyses of open data from published studies) can play a critical role in hypothesis generation and in maximizing the contribution of collected data to the accumulation of scientific knowledge. However, assessing the evidentiary value of results from secondary data analyses is often challenging because analytical decisions can be biased by knowledge of the results of (and analytical choices made in) the original study and by unacknowledged exploratory analyses of open data sets (Scott & Kline, 2019; Weston, Ritchie, Rohrer, & Przybylski, 2018). Using the secondary data analyses reported by Gangestad et al. (2019) as a case study, we outline several approaches that, if implemented, would allow readers to assess the evidentiary value of results from secondary data analyses with greater confidence

    To which world regions does the valence-dominance model of social perception apply?

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    Over the past 10 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 judgements 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 and correlate and rotate the dimension reduction solution

    To Which World Regions Does the Valence-Dominance Model of Social Perception Apply?

    Get PDF
    Over the past 10 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 judgements 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 and correlate and rotate the dimension reduction solution
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