6 research outputs found

    Community singing, wellbeing and older people: implementing and evaluating an English singing tool for health intervention in Rome

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    Aim: The aim of this research was to explore the transferability and effectiveness of the English Silver Song Clubs model for older people in a different social and cultural context, i.e. in the capital city of Italy, Rome. Methods: A single condition, pre-test, post-test design was implemented. Participants completed two questionnaires: EQ-5D and York SF-12. Results: After the singing experience, participants showed a decrease in their levels of anxiety and depression. An improvement was also found from baseline to follow up in reported performance of usual activities. The English study showed a difference between the singing and non-singing groups at three and six months on mental health, and after three months on specific anxiety and depression measures. The current (Rome) study shows similar findings with an improvement on specific anxiety and depression items. Conclusions: Policy makers in different national contexts should consider social singing activities to promote the health and wellbeing of older adults as they are inexpensive to run and have been shown to be enjoyable and effective

    Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

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    Bayes factor design analysis: Planning for compelling evidence

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    A sizeable literature exists on the use of frequentist power analysis in the null-hypothesis significance testing (NHST) paradigm to facilitate the design of informative experiments. In contrast, there is almost no literature that discusses the design of experiments when Bayes factors (BFs) are used as a measure of evidence. Here we explore Bayes Factor Design Analysis (BFDA) as a useful tool to design studies for maximum efficiency and informativeness. We elaborate on three possible BF designs, (a) a fixed-n design, (b) an open-ended Sequential Bayes Factor (SBF) design, where researchers can test after each participant and can stop data collection whenever there is strong evidence for either (Formula presented.) or (Formula presented.), and (c) a modified SBF design that defines a maximal sample size where data collection is stopped regardless of the current state of evidence. We demonstrate how the properties of each design (i.e., expected strength of evidence, expected sample size, expected probability of misleading evidence, expected probability of weak evidence) can be evaluated using Monte Carlo simulations and equip researchers with the necessary information to compute their own Bayesian design analyses
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