Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Statistics in Psychology submitted time 2024-04-17
Abstract： Cognitive modeling has gained widespread application in psychological research. Model comparison plays a crucial role in cognitive modeling, as researchers need to select the best model for subsequent analysis or latent variable inference. Model comparison involves considering not only the fit of the models to the data (balancing overfitting and underfitting) but also the complexity of the parameter data and mathematical forms. This article categorizes and introduces three major classes of model comparison metrics commonly used in cognitive modeling, including: goodness-of-fit metrics (such as mean squared error, coefficient of determination, and ROC curves), cross-validation-based metrics (such as AIC, DIC), and marginal likelihood-based metrics. The computation methods and pros and cons of each metric are discussed, along with practical implementations in R using data from the orthogonal Go/No-Go paradigm. Based on this foundation, the article identifies the suitable contexts for each metric and discusses new approaches such as model averaging in model comparison.
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Subjects: Psychology >> Statistics in Psychology Subjects: Psychology >> Experimental Psychology submitted time 2023-08-11
Abstract： Compared to the traditional statistical methods, Bayesian linear mixed-effects modeling (BLMM) has a great number of advantages in dealing with the hierarchical structures underlying datasets and providing more intuitive statistical results. These advantages together popularize BLMM in psychological and other field research. However, there is still a lack of tutorials on the practical applications of BLMM in psychology studies in China. Therefore, we first briefly introduced the basic concepts and rationales of BLMM. Then we employed a simulated dataset to demonstrate how to understand fixed effects and random effects, and how to use the popular brms R package to specify models for BLMM based on the experimental design. We additionally covered the procedure of pre-specifying priors with prior predictive checks, and the steps of performing hypothesis testing using the Bayes Factor. BLMM, with its extensions such as Generalized BLMM, has great flexibility and capability, they can and should be applied in various psychology research.
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