Subjects: Psychology >> Statistics in Psychology submitted time 2020-05-14
Abstract： Psychological researches focus on describing, explaining and predicting behavior, and having a good understanding of the association between variables is an essential part of this process. Regression analysis, a method to evaluate the relationship between variables, is widely used in psychological studies. However, due to its highly focus on the interpretation of sample data, the traditional ordinary least squares regression has several drawbacks, such as over-fitting problem and limitation on dealing with multicollinearity, which may undermine the generalizability of the model. These drawbacks have an inevitable influence on the promotion and prediction of the model conclusion. With the rapid development of methodology, Least absolute shrinkage and selection operator (Lasso) regression has been emerged to better compensate for the limitations of traditional methods. By introducing a penalty term in the model and shrinking the regression coefficients to zero, Lasso regression can achieve a higher accuracy of model prediction and model generalizability with the cost of a certain estimation bias. Besides, Lasso regression can also effectively deal with the multicollinearity problem. Therefore, it has been widely used in medicine, economics, neuroscience and other fields. In psychology, due to the limitations of computer computing power, researchers used to mainly rely on hypothesis testing to understand the association among variables to verify theories. Now, with the rapid development of machine learning, a shift from focusing on interpretation of the regression coefficients to improving the prediction of the model has emerged and become more and more important. Therefore, based on fundamental theories and real data analysis, the aim of this paper is to introduce the principles, implementation steps and advantages of the Lasso regression. With the help of statistic science, it is promising that more and more applied researchers will be called upon to focus on the emerging statistical tools to promote the development of psychology.
Subjects: Psychology >> Statistics in Psychology submitted time 2018-12-27
Abstract： Structural equation modeling (SEM) has been widely used in psychological researches to investigate the casual relationship among latent variables. Model estimation can be conducted under both the frequentist framework (e.g., maximum-likelihood approach) and the Bayesian framework. In recent years, with the prevalence of Bayesian statistics and its advantages in dealing with small samples, missing data and complex models in SEM, Bayesian structural equation modeling (BSEM) has developed rapidly. However, in China its application in the field of psychology is still insufficient. Therefore, this paper mainly focuses on presenting this new research method to applied researchers. We explain the theoretical and methodological basis of BSEM, as well as its advantages and disadvantages compared with the traditional frequentist approach. We also introduce several commonly used BSEM models and their applications. "
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