This paper carries out a full Bayesian analysis for a data set examined in Chen & Cesari (2015). These data were collected for assessing people’s ability in evaluating short intervals of time. Chen & Cesari (2015) showed evidence of the existence of two independent internal clocks for evaluating time intervals below and above the second. We reexamine here, the same question by performing a complete statistical Bayesian analysis of the data. The Bayesian approach can be used to analyze these data thanks to the specific trial design. Data were obtained from evaluation of time ranges from two groups of individuals. More specifically, information gathered from a nontrained group (considered as baseline) allowed us to build a prior distribution for the parameter(s) of interest, and data from the trained group determined the likelihood function. This paper’s main goals are (i) showing how the Bayesian inferential method can be used in statistical analyses and (ii) showing that the Bayesian methodology gives additional support to the findings presented in Chen & Cesari (2015) regarding the existence of two internal clocks in assessing duration of time intervals.
Yin-Hua Chen, Isabella Verdinelli and Paola Cesari
Andreas Stenling, Andreas Ivarsson, Urban Johnson and Magnus Lindwall
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.
Ken Hodge and Daniel F. Gucciardi
The purpose of this investigation was to examine whether the relationships between contextual factors and basic psychological needs were related to antisocial and prosocial behavior in sport. A two-study project employing Bayesian path analysis was conducted with competitive athletes (Study 1, n = 291; Study 2, n = 272). Coach and teammate autonomy-supportive climates had meaningful direct relations with need satisfaction and prosocial behavior. Coach and teammate controlling climates had meaningful direct relations with antisocial behavior. Need satisfaction was both directly and indirectly related with both prosocial and antisocial behavior, whereas moral disengagement was directly and indirectly related with antisocial behavior. Overall, these findings reflected substantial evidence from the literature on self-determination theory that autonomy-supportive motivational climates are important environmental influences for need satisfaction, and are important correlates of prosocial behavior in sport, whereas controlling coach and teammate climates, along with moral disengagement, were important correlates of antisocial behavior in sport.
Fabio R. Serpiello and Will G. Hopkins
was interpreted as evidence for that hypothesis, as the P value corresponds to the posterior probability of the magnitude of the true effect in a reference Bayesian analysis with a minimally informative prior. 16 , 17 The P value is reported qualitatively using the following scale: .25 to .75
Lindh * Katja Laakso * Lena Hartelius * 7 2016 20 3 233 254 10.1123/mc.2014-0068 Elite Athletes Refine Their Internal Clocks: A Bayesian Analysis Yin-Hua Chen * Isabella Verdinelli * Paola Cesari * 7 2016 20 3 255 265 10.1123/mc.2014-0070 Postural Steadiness and Ankle Force Variability in
Matthew S. Tenan, Andrew J. Tweedell and Courtney A. Haynes
.18637/jss.v066.i03 17. Erdman C , Emerson JW . BCP: an R package for performing a Bayesian analysis of change point problems . J Stat Softw . 2007 ; 23 ( 3 ): 1 – 13 . doi:10.18637/jss.v023.i03 10.18637/jss.v023.i03 18. Barry D , Hartigan JA
Matthew Ellis, Mark Noon, Tony Myers and Neil Clarke
about between-individual differences, and allow observations for individuals to be collected across different time points. 23 Bayesian analysis is more suitable for small-scale athlete studies than traditional statistics, allows direct probability statements to be made about the parameters (population
David N. Borg, Ian B. Stewart, John O. Osborne, Christopher Drovandi, Joseph T. Costello, Jamie Stanley and Geoffrey M. Minett
between conditions (Table 1 ). Perceived TL Bayesian analysis showed evidence of a condition effect for session-RPE TL ( β HT CWI : 0.6 [0.1–1.1]; β HT HWI : 0.6 [0.1–1.1]). Session-RPE TL (Figure 1 ) was statistically higher in the heat versus CON ( d = 5.95–7.29). There was also evidence that
Collin A. Webster, Diana Mindrila, Chanta Moore, Gregory Stewart, Karie Orendorff and Sally Taunton
). Comprehensive School Physical Activity Program (CSPAP) survey report . Reston, VA : Author . Asparouhov , T. , & Muthén , B. ( 2010a ). Bayesian analysis of latent variable models using Mplus. Technical Report . Version 4. Retrieved from http://www.statmodel.com/download/BayesAdvantages18.pdf Asparouhov
Katrina M. Moss, Annette J. Dobson, Kimberley L. Edwards, Kylie D. Hesketh, Yung-Ting Chang and Gita D. Mishra
, Rhodes RE , Rinaldi CM , Spence JC , Carson V . Role of parental and environmental characteristics in toddlers’ physical activity and screen time: Bayesian analysis of structural equation models . Int J Behav Nutr Phys Act . 2018 ; 15 ( 1 ): 17 . 29426324 10.1186/s12966-018-0649-5 23. Kunin