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There is growing interest in integrating explanatory and predictive research practices in psychological research. For this integration to be successful, the psychologist's toolkit must incorporate standard procedures that enable a direct estimation of the prediction error, such as cross-validation (CV). Despite their apparent simplicity, CV methods are intricate, and thus it is crucial to adapt them to specific contexts and predictive metrics. This study delves into the performance of different CV methods in estimating the prediction error in the R 2 and MSE metrics in regression analysis, ubiquitous in psychological research. Current approaches, which rely on the 5- or 10-fold rule of thumb or on the squared correlation between predicted and observed values, present limitations when computing the prediction error in the R 2 metric, a widely used statistic in the behavioral sciences. We propose the use of an alternative method that overcomes these limitations and enables the computation of the leave-one-out (LOO) in the R 2 metric. Through two Monte Carlo simulation studies and the application of CV to the data from the Many Labs Replication Project, we show that the LOO consistently has the best performance. The CV methods discussed in the present study have been implemented in the R package OutR2., Competing Interests: Declarations. Conflicts of interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable., (© 2025. The Psychonomic Society, Inc.)

The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification using modification indices and the Saris procedure, exploratory factor analysis (EFA) with different rotation procedures (Geomin, target, and objectively refined target matrix), Bayesian structural equation modeling (BSEM), and a new set of procedures that, after fitting an unrestrictive model (i.e., EFA, BSEM), identify and retain only the relevant loadings to provide a parsimonious CFA solution (ECFA, BCFA). By means of an exhaustive Monte Carlo simulation study and a real data illustration, it is shown that CFA and BSEM are overly stiff and, consequently, do not appropriately recover the structure of slightly misspecified models. EFA usually provides the most accurate parameter estimates, although the rotation procedure choice is of major importance, especially depending on whether the latent factors are correlated or not. Finally, ECFA might be a sound option whenever an a priori structure cannot be hypothesized and the latent factors are correlated. Moreover, it is shown that the pattern of the results of a factor analytic technique can be somehow predicted based on its positioning in the confirmatory-exploratory continuum. Applied recommendations are given for the selection of the most appropriate technique under different representative scenarios by means of a detailed flowchart. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research on unconscious processing has been a valuable source of evidence in psycholinguistics for shedding light on the cognitive architecture of language. The automaticity of syntactic processing, in particular, has long been debated. One strategy to establish this automaticity involves detecting significant syntactic priming effects in tasks that limit conscious awareness of the stimuli. Criteria for assessing unconscious priming include the visibility ( d ') of masked words not differing significantly from zero and no positive correlation between visibility and priming. However, such outcomes could also arise for strictly methodological reasons, such as low statistical power in visibility tests or low reliability of dependent measures. In this study, we aimed to address these potential limitations. Through meta-analysis and Bayesian re-analysis, we find evidence of low statistical power and of participants having above-chance awareness of 'subliminal' words. Moreover, we conducted reliability analyses on a dataset from Berkovitch and Dehaene (2019), finding that low reliability in both syntactic priming and visibility tasks may better explain the absence of a significant correlation. Overall, these findings cast doubt on the validity of previous conclusions regarding the automaticity of syntactic processing based on masked priming effects. The results underscore the importance of revisiting the methods employed when exploring unconscious processing in future psycholinguistic research., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2025 The Author(s).)