| 1. |
- Ivarsson, Andreas, et al.
(författare)
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Things We Still Haven't Learned (So Far)
- 2015
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Ingår i: Journal of Sport & Exercise Psychology (JSEP). - Champaign, IL : Human Kinetics. - 0895-2779 .- 1543-2904. ; 37:4, s. 449-461
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Tidskriftsartikel (refereegranskat)abstract
- Null hypothesis significance testing (NHST) is like an immortal horse that some researchers have been trying to beat to death for over 50 years but without any success. In this article we discuss the flaws in NHST, the historical background in relation to both Fisher's and Neyman and Pearson's statistical ideas, the common misunderstandings of what p < .05 actually means, and the 2010 APA publication manual's clear, but most often ignored, instructions to report effect sizes and to interpret what they all mean in the real world. In addition, we discuss how Bayesian statistics can be used to overcome some of the problems with NHST. We then analyze quantitative articles published over the past three years (2012-2014) in two top-rated sport and exercise psychology journals to determine whether we have learned what we should have learned decades ago about our use and meaningful interpretations of statistics.
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| 2. |
- Stenling, Andreas, 1982-, et al.
(författare)
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Bayesian Structural Equation Modeling in Sport and Exercise Psychology
- 2015
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Ingår i: Journal of Sport & Exercise Psychology (JSEP). - Champaign, IL : Human Kinetics. - 0895-2779 .- 1543-2904. ; 37:4, s. 410-420
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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