In a time when the alarms of research replicability are sounding louder than ever, mapping out studies with statistical and inferential integrity is of paramount importance. Indeed, funding agencies almost always require grant applicants to present compelling a priori power analyses to justify proposed sample sizes, as a critical part of the information considered collectively to ensure a sound investment. Unfortunately, even researchers' most sincere attempts at sample size planning are fraught with the fundamental challenge of setting numerical values not just for the focal parameters for which statistical tests are planned, but for each of the model's other, more peripheral or contextual parameters as well. As we plainly demonstrate, regarding the latter parameters, even in very simple models, any slight deviation in well-intentioned numerical guesses can undermine power for the assessment of the more focal parameters that are of key theoretical interest. Toward remedying this all-too-common but seemingly underestimated problem in power analysis, we adopt a hope-for-the-best-but-plan-for-the-worst mindset and present new methods that attempt to (a) restore appropriate conservatism and robustness, and in turn credibility, to the sample size planning process, and (b) greatly simplify that process. Derivations and suggestions for practice are presented using the framework of measured variable path analysis models as they subsume many of the types of models (e.g., multiple linear regression, analysis of variance) for which sample size planning is of interest. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Continuous-time (CT) models are a flexible approach for modeling longitudinal data of psychological constructs. When using CT models, a researcher can assume one underlying continuous function for the phenomenon of interest. In principle, these models overcome some limitations of discrete-time (DT) models and allow researchers to compare findings across measures collected using different time intervals, such as daily, weekly, or monthly intervals. Theoretically, the parameters for equivalent models can be rescaled into a common time interval that allows for comparisons across individuals and studies, irrespective of the time interval used for sampling. In this study, we carry out a Monte Carlo simulation to examine the capability of CT autoregressive (CT-AR) models to recover the true dynamics of a process when the sampling interval is different from the time scale of the true generating process. We use two generating time intervals (daily or weekly) with varying strengths of the AR parameter and assess its recovery when sampled at different intervals (daily, weekly, or monthly). Our findings indicate that sampling at a faster time interval than the generating dynamics can mostly recover the generating AR effects. Sampling at a slower time interval requires stronger generating AR effects for satisfactory recovery, otherwise the estimation results show high bias and poor coverage. Based on our findings, we recommend researchers use sampling intervals guided by theory about the variable under study, and whenever possible, sample as frequently as possible. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Despite strong evidence of its logical and empirical problems, there remains a veritable eruption of mediational studies using nonexperimental cross-sectional data (NECSD). At best, such studies can say little about true mediation and at worse can create an inaccurate picture of the conclusions from the data. This article overviews the reasons to not frame NECSD studies as mediational and articulates reasons why it may continue despite solid reasons to stop. Associational variable analysis is introduced as an alternative framework to help articulate a more accurate depiction of findings. Articulation of the steps, examples of conducting the steps, and framing findings are provided from an empirical data set. Associational variable analysis is an alternative conceptual framework drawing on the analytic techniques developed for mediation but provides a more accurate framework for the articulation of findings from NECSD studies with mediational-type goals. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which is often tied conceptually to regulatory behavior in the affective process. The data are typically gathered using experience sampling methods, which are designed to pick up on fluctuations in affective variables as they evolve over time in naturalistic settings. In this article, we compare classical time-contingent sampling designs to episode-contingent sampling designs, which initiate sampling when an emotional episode has been signaled. We define emotional episodes as periods where an affective process strays relatively far away from its mean. Compared to time-contingent designs, episode-contingent designs leverage on increased affective variability, which can have beneficial implications for the precision of the ordinary least squares AR effect estimator. Using an extensive simulation study, we attempt to delineate which characteristics of an episode-contingent design are important to consider, and how these characteristics are related to estimation benefits. We then turn to an empirical illustration, showing how an episode-contingent design can be implemented in practice. We also show that various patterns we expect based on the theoretical parts of the article are recovered in the data. We conclude that episode-contingent designs can have marked benefits for the precision of the AR effect estimator, and discuss a number of challenges when it comes to implementing episode-contingent designs in practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model uncertainty and poor performance under publication bias. To overcome these limitations, we extend robust Bayesian meta-analysis (RoBMA) to meta-regression (RoBMA-regression). RoBMA-regression allows for moderator analyses while simultaneously taking into account the uncertainty about the presence and impact of other factors (i.e., the main effect, heterogeneity, publication bias, and other potential moderators). The methodology presents a coherent way of assessing the evidence for and against the presence of both continuous and categorical moderators. We further employ a Savage-Dickey density ratio test to quantify the evidence for and against the presence of the effect at different levels of categorical moderators. We illustrate RoBMA-regression in an empirical example and demonstrate its performance in a simulation study. We implemented the methodology in the RoBMA R package. Overall, RoBMA-regression presents researchers with a powerful and flexible tool for conducting robust and informative meta-regression analyses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Determining the number of factors is a decisive, yet very difficult decision a researcher faces when conducting an exploratory factor analysis (EFA). Over the last decades, numerous so-called factor retention criteria have been developed to infer the latent dimensionality from empirical data. While some tutorials and review articles on EFA exist which give recommendations on how to determine the number of latent factors, there is no comprehensive overview that categorizes the existing approaches and integrates the results of existing simulation studies evaluating the various methods in different data conditions. With this article, we want to provide such an overview enabling (applied) researchers to make an informed decision when choosing a factor retention criterion. Summarizing the most important results from recent simulation studies, we provide guidance when to rely on which method and call for a more thoughtful handling of overly simple heuristics. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This essay discusses a peculiarity in institutionalized psychological measurement practices. Namely, an inherent contradiction between guidelines for how scales/tests are developed and how those scales/tests are typically analyzed. Best practices for developing scales/tests emphasize developing individual items or subsets of items to capture unique aspects of constructs, such that the full construct is captured across the test. Analysis approaches, typically factor analysis or related reflective models, assume that no individual item (nor a subset of items) captures unique, construct-relevant variance. This contradiction has important implications for the use of factor analysis to support measurement claims. The implications and other critiques of factor analysis are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).