Psychological methods最新文献

The statistical and pragmatic tension between explanation and prediction is well recognized in psychology. Yarkoni and Westfall (2017) suggested focusing more on predictions, which will ultimately produce better calibrated interpretations. Variable selection methods, such as regularization, are strongly recommended because it will help construct interpretable models while optimizing prediction accuracy. However, when the data contain a nonignorable proportion of missingness, variable selection and model building via penalized regression methods are not straightforward. What further complicates the analysis protocol is when the model performance is evaluated on both prediction accuracy and fairness, the latter is of increasing attention when the predictive outcome has societal implications. This study explored two methods for variable selection with incomplete data: the bootstrap imputation-stability selection (BI-SS) method and the stacked elastic net (SENET) method. Both methods work with multiply imputed data sets but in different ways. BI-SS implements variable selection separately on each imputed bootstrap data set and aggregates the results via stability selection, while SENET stacks all imputed data sets and fits a single pooled model. We thoroughly evaluated their performance using a suite of metrics (including area under the curve, F1 score, and fairness criteria) via three increasingly complex simulation studies. Results reveal that while BI-SS and SENET methods perform almost equally well in settings with generalized linear models, only BI-SS fares well with nested data design because of high computation demand in fitting the regularized generalized linear mixed effects models. Finally, we demonstrated both methods with an example using rich electronic health data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We introduce varying and irregular sampling time-series analysis (VISTA), a clustering approach for multivariate and irregularly sampled time series based on a parametric state-space mixture model. VISTA is specifically designed for the unsupervised identification of groups in data sets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian state-space models (LGSSMs) to provide a flexible parametric framework for fitting a wide range of time series dynamics. The clustering approach itself is based on the assumption that the population can be represented as a mixture of a fixed number of LGSSMs. VISTA's model formulation allows for an explicit derivation of the log-likelihood function, from which we develop an expectation-maximization scheme for fitting model parameters to the observed data samples. Our algorithmic implementation is designed to handle populations of multivariate time series that can exhibit large changes in sampling rate as well as irregular sampling. We evaluate the versatility and accuracy of our approach on simulated and real-world data sets, including demographic trends, wearable sensor data, epidemiological time series, and ecological momentary assessments. Our results indicate that VISTA outperforms most comparable standard times series clustering methods. We provide an open-source implementation of VISTA in Python. (PsycInfo Database Record (c) 2025 APA, all rights reserved).