Association of self-efficacy and mental toughness with sport performance in Brazilian futsal athletes

Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.

Mental toughness (MT) has become a popular area of investigation and practice within sport and exercise psychology over the past two decades. Since the turn of the twenty first century, there have been hundreds of studies published on mental toughness, yet concerns remain about the conceptualisation and measurement of mental toughness. In this paper, I take stock of past work with the goal of clarifying and elaborating the most fundamental and common aspects of MT. I also look to the future and outline key substantive and methodological issues that may offer the greatest potential for refining the conceptualisation of MT and contributing to theory building on this concept. My hope is that this information will provide a platform from which to foster coherent and systematic scholarly work on MT.