Search

Identifying significant predictors of behavioral outcomes is of great interest in many psychological studies. Lasso regression, as an alternative to stepwise regression for variable selection, has started gaining traction among psychologists. Yet, further investigation is valuable to fully understand its performance across various psychological data conditions. Using a Monte Carlo simulation and an empirical demonstration, we compared Lasso regression to stepwise regression in typical psychological datasets varying in sample size, predictor size, sparsity, and signal-to-noise ratio. We found that: (1) Lasso regression was more accurate in within-sample selection and yielded more consistent out-of-sample prediction accuracy than stepwise regression; (2) Lasso with a harsher shrinkage parameter was more accurate, parsimonious, and robust to sampling variability than the prediction-optimizing Lasso. Finally, we concluded with cautious notes and recommendations in practice on the application of Lasso regression. [ABSTRACT FROM AUTHOR]

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross‐validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10‐fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person‐mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice. [ABSTRACT FROM AUTHOR]