84 research outputs found

    Musicians have better memory than nonmusicians: A meta-analysis

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    Background Several studies have found that musicians perform better than nonmusicians in memory tasks, but this is not always the case, and the strength of this apparent advantage is unknown. Here, we conducted a meta-analysis with the aim of clarifying whether musicians perform better than nonmusicians in memory tasks. Methods Education Source; PEP (WEB)\u2014Psychoanalytic Electronic Publishing; Psychology and Behavioral Science (EBSCO); PsycINFO (Ovid); PubMed; ScienceDirect\u2014AllBooks Content (Elsevier API); SCOPUS (Elsevier API); SocINDEX with Full Text (EBSCO) and Google Scholar were searched for eligible studies. The selected studies involved two groups of participants: young adult musicians and nonmusicians. All the studies included memory tasks (loading long-term, short-term or working memory) that contained tonal, verbal or visuospatial stimuli. Three meta-analyses were run separately for long-term memory, short-term memory and working memory. Results We collected 29 studies, including 53 memory tasks. The results showed that musicians performed better than nonmusicians in terms of long-term memory, g = .29, 95% CI (.08\u2013.51), short-term memory, g = .57, 95% CI (.41\u2013.73), and working memory, g = .56, 95% CI (.33\u2013.80). To further explore the data, we included a moderator (the type of stimulus presented, i.e., tonal, verbal or visuospatial), which was found to influence the effect size for short-term and working memory, but not for long-term memory. In terms of short-term and working memory, the musicians\u2019 advantage was large with tonal stimuli, moderate with verbal stimuli, and small or null with visuospatial stimuli. Conclusions The three meta-analyses revealed a small effect size for long-term memory, and a medium effect size for short-term and working memory, suggesting that musicians perform better than nonmusicians in memory tasks. Moreover, the effect of the moderator suggested that, the type of stimuli influences this advantage

    Dyadic adjustment and parenting stress in internationally adoptive mothers and fathers: the mediating role of adult attachment dimensions.

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    Previous research has shown that a positive marital functioning represents a resource in adoptive families, leading to a decrease in parenting stress, but little is known about the factors mediating such a relationship. This study aimed to explore whether adult attachment avoidance and anxiety mediate the effect of dyadic functioning on parenting stress in 90 internationally adoptive couples (mothers and fathers) who had adopted a child (aged 3–10 years) in the last 36 months. Participants completed self-report measures of dyadic adjustment, adult attachment, and parenting stress. A series of path analyses supported the mediation hypothesis, but differentially for mothers and fathers. Among mothers, there was a direct and negative relationship between dyadic adjustment and parenting stress. In addition, a better dyadic adjustment was related to lower levels of attachment anxiety, which in turn were associated with less parenting stress. Among fathers, increased dyadic adjustment was related to lower levels of attachment avoidance, which in turn were associated with reduced parenting stress. These findings suggest the importance of including both mothers and fathers in adoption research. Adoptive parents could benefit from specific interventions aimed at reducing attachment avoidance and anxiety by supporting parental sense of competence and involvement for mothers and fathers, respectively

    L'effetto della numerositĂ  sul significato di un risultato statisticamente significativo.

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    Can a statistically significant test be interpreted regardless of the sample size used in a particular study? In this brief commentary on Benassi et al. (2013), we seek to answer this question using the same case study and method (i.e., False Positive Report Probability) proposed by the authors. We will demonstrate that, differently from Benassi et al., the interpretation of statistical significance is strongly related to sample size. The results are discussed with a special emphasis on their applied relevance

    A Bayesian approach to confirmatory factor analysis: Moving beyond dichotomous thinking

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    In recent years there has been a growing interest in Bayesian inference in numerous scientific disciplines. Structural equation models (SEM) are an important tool in the social and behavioural sciences to evaluate the structure of a model with latent and observed variables. However, the use of a Bayesian approach (BA) in this field is still underexplored. In this work, we illustrate the advantages of using the BA in a relevant SEM sub- model, i.e., confirmatory factor analysis (CFA). Specifically, the goals are to (1) compare the traditional maximum likelihood approach (MLA) with the BA in terms of parameter estimation and fit indices; (2) show how the BA allows to estimate further models that may result unidentified via the classical approach, but may better reflect the underlying psychological theory; and (3) present BA-based techniques for model diagnostic in terms of the distribution of estimated parameters as well as single case influence. To address the first aim, a simulation study was performed. Starting from a baseline two-correlated factor model, we manipulated sample size and effect size of a potential cross-loading for a specific item. For each condition, we estimated 2 bayesian CFAs (one including and one excluding the cross- loading) and 2 ML CFAs. Next, a bayesian CFA on a real case-study is presented. The magnitude of cross-loadings and residual correlations were simultaneously evaluated according to different theoretical models through different parameter prior specifications. The most plausible model was selected using the Deviance Information Criteria (DIC). Parameter posterior distributions and predictive posterior distribution of the observed data were used to examine model fit. All analyses were conducted using free software (i.e., R in combination with JAGS). Differences and similarities between the BA and MLA will be discussed. Overall, the formalization of model parameters in terms of prior probability distributions, instead of the less realistic parameters presence-or-absence formalization, provided a more flexible and informative evaluation of the latent structure of the observed data. To conclude, we will briefly review potential applications of the BA to the broader context of structural equation models

    Exploring the effect of statistical variability on children's performance in a quantity judgment task

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    Although variability is a core concept in statistics, little is known about young children\u2019s understanding of statistical variability. This study investigated the effect of variability on children\u2019s performance in a quantity judgment task. Participants were 110 children (49% boys) aged 4-6 years living in Northern Italy. Children were individually assessed using a computerized task in which they were asked to compare the quantity in two sets of chocolate bars. The lengths of the chocolate bars varied (which makes the task hard) and the variance of the lengths was manipulated. Children also completed a standardized test of numerical ability. Main results indicate that children\u2019s performance on the judgment task: 1) is affected by variability non-monotonically; and 2) is only moderately correlated with numerical skills

    Bayes Factor e p-value: cos\uec vicini, cos\uec lontani

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    In recent years, the inferential approach most widely used in psychological research, i.e. Null Hypothesis Significance Testing (NHST), has been subject to profound criticism accompanied by a renewed interest in alternative approaches. Among these, the Bayesian inference approach is one of the most relevant. Despite several studies have shown differences between the two approaches, little attention has been devoted to their commonalities. The current study aims to analyze the relation between the two approaches from both a mathematical and an applied perspective. As an illustrative example, we considered the independent samples t-test. In particular, the pvalue associated to NHST and the Bayes Factor (BF) derived from Bayesian approach were compared on different levels of sample size and effect size via Monte Carlo study. Results indicate that, even if the relation between p-value and BF is substantially deterministic, the interpretation of the two indices can lead to different decisions. Discrepancies occur especially in cases of small and large sample size as well as low effect siz
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