Psychological methods最新文献

The development and evaluation of text classifiers in psychology depends on rigorous manual coding. Yet, the evaluation of manual coding and computational algorithms is usually considered separately. This is problematic because developing high-validity classifiers is a repeated process of identifying, explaining, and addressing conceptual and measurement issues during both the manual coding and classifier development stages. To address this problem, we introduce the Repeated Adjustment of Measurement Protocols (RAMP) method for developing high-validity text classifiers in psychology. The RAMP method has three stages: manual coding, classifier development, and integrative evaluation. These stages integrate the best practices of content analysis (manual coding), data science (classifier development), and psychology (integrative evaluation). Central to this integration is the concept of an inference loop, defined as the process of maximizing validity through repeated adjustments to concepts and constructs, guided by push-back from the empirical data. Inference loops operate both within each stage of the method and across related studies. We illustrate RAMP through a case study, where we manually coded 21,815 sentences for misunderstanding (Krippendorff's α = .79), and developed a rule-based classifier (Matthews correlation coefficient [MCC] = 0.22), a supervised machine learning classifier (Bidirectional Encoder Representations From Transformers; MCC = 0.69) and a large language model classifier (GPT-4o; MCC = 0.47). By integrating manual coding and classifier development stages, we were able to identify and address a concept validity problem with misunderstandings. RAMP advances existing methods by operationalizing validity as an ongoing dynamic process, where concepts and constructs are repeatedly adjusted toward increasingly widespread intersubjective agreement on their utility. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Mediation modeling using longitudinal data is an exciting field that captures the interrelations in dynamic changes, such as mediated changes, over time. Even though discrete-time vector autoregressive approaches are commonly used to estimate indirect effects in longitudinal data, they have known limitations due to the dependency of inferential results on the time intervals between successive occasions and the assumption of regular spacing between measurements. Continuous-time vector autoregressive models have been proposed as an alternative to address these issues. Previous work in the area (e.g., Deboeck & Preacher, 2015; Ryan & Hamaker, 2021) has shown how the direct, indirect, and total effects, for a range of time-interval values, can be calculated using parameters estimated from continuous-time vector autoregressive models for causal inferential purposes. However, both standardized effects size measures and methods for calculating the uncertainty around the direct, indirect, and total effects in continuous-time mediation have yet to be explored. Drawing from the mediation model literature, we present and compare results using the delta, Monte Carlo, and parametric bootstrap methods to calculate SEs and confidence intervals for the direct, indirect, and total effects in continuous-time mediation for inferential purposes. Options to automate these inferential procedures and facilitate interpretations are available in the cTMed R package. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The generally small but touted as "statistically significant" correlation coefficients in the social sciences jeopardize theory testing and prediction. To investigate these small coefficients' underlying causes, traditional equations such as Spearman's (1904) classic attenuation formula, Cronbach's (1951) alpha, and Guilford and Fruchter's (1973) equation for the effect of additional items on a scale's predictive power are considered. These equations' implications differ regarding large interitem correlations enhancing or diminishing predictive power. Contrary to conventional practice, such correlations decrease predictive power when treating items as multi-item scale components but can increase predictive power when treating items separately. The implications are wide-ranging. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories is the use of Bayesian informative hypothesis testing. An additional advantage of the use of this Bayesian approach is that combining the results from multiple studies is straightforward. In this article, we discuss the behavior of Bayes factors in the context of evaluating informative hypotheses with multiple studies. By using simple models and (partly) analytical solutions, we introduce and evaluate Bayesian evidence synthesis (BES) and compare its results to Bayesian sequential updating. By doing so, we clarify how different replications or updating questions can be evaluated. In addition, we illustrate BES with two simulations, in which multiple studies are generated to resemble conceptual replications. The studies in these simulations are too heterogeneous to be aggregated with conventional research synthesis methods. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Self-report scales are widely used in psychology to compare means in latent constructs across groups, experimental conditions, or time points. However, for these comparisons to be meaningful and unbiased, the scales must demonstrate measurement invariance (MI) across compared time points or (experimental) groups. MI testing determines whether the latent constructs are measured equivalently across groups or time, which is essential for meaningful comparisons. We conducted a systematic review of 426 psychology articles with openly available data, to (a) examine common practices in conducting and reporting of MI testing, (b) assess whether we could reproduce the reported MI results, and (c) conduct MI tests for the comparisons that enabled sufficiently powerful MI testing. We identified 96 articles that contained a total of 929 comparisons. Results showed that only 4% of the 929 comparisons underwent MI testing, and the tests were generally poorly reported. None of the reported MI tests were reproducible, and only 26% of the 174 newly performed MI tests reached sufficient (scalar) invariance, with MI failing completely in 58% of tests. Exploratory analyses suggested that in nearly half of the comparisons where configural invariance was rejected, the number of factors differed between groups. These results indicate that MI tests are rarely conducted and poorly reported in psychological studies. We observed frequent violations of MI, suggesting that reported differences between (experimental) groups may not be solely attributed to group differences in the latent constructs. We offer recommendations aimed at improving reporting and computational reproducibility practices in psychology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics. Focusing on negative affect, we provide an accessible interpretation of the DLO model parameters in terms of emotional lability, resilience, and vulnerability. We conducted a Monte Carlo study to test the DLO model performance under different empirically relevant conditions in terms of individual characteristics and sampling scheme. We used state-space models in continuous time. The results show that, under certain conditions, the DLO model is able to accurately and efficiently recover the parameters underlying the affective dynamics of a single individual. We discuss the results and the theoretical and practical implications of using this model, illustrate how to use it for studying psychological phenomena at the individual level, and provide specific recommendations on how to collect data for this purpose. We also provide a tutorial website and computer code in R to implement this approach. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Several theoretical perspectives suggest that dyadic experiences are distinguished by patterns of behavioral change that emerge during interactions. Methods for examining change in behavior over time are well elaborated for the study of change along continuous dimensions. Extensions for charting increases and decreases in individuals' use of specific, categorically defined behaviors, however, are rarely invoked. Greater accessibility of Bayesian frameworks that facilitate formulation and estimation of the requisite models is opening new possibilities. This article provides a primer on how multinomial logistic growth models can be used to examine between-dyad differences in within-dyad behavioral change over the course of an interaction. We describe and illustrate how these models are implemented in the Bayesian framework using data from support conversations between strangers (N = 118 dyads) to examine (RQ1) how six types of listeners' and disclosers' behaviors change as support conversations unfold and (RQ2) how the disclosers' preconversation distress moderates the change in conversation behaviors. The primer concludes with a series of notes on (a) implications of modeling choices, (b) flexibility in modeling nonlinear change, (c) necessity for theory that specifies how and why change trajectories differ, and (d) how multinomial logistic growth models can help refine current theory about dyadic interaction. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Factor mixture modeling (FMM) incorporates both continuous latent variables and categorical latent variables in a single analytic model clustering items and observations simultaneously. After two decades since the introduction of FMM to psychological and behavioral science research, it is an opportune time to review FMM applications to understand how these applications are utilized in real-world research. We conducted a systematic review of 76 FMM applications. We developed a comprehensive coding scheme based on the current methodological literature of FMM and evaluated common usages and practices of FMM. Based on the review, we identify challenges and issues that applied researchers encounter in the practice of FMM and provide practical suggestions to promote well-informed decision making. Lastly, we discuss future methodological directions and suggest how FMM can be expanded beyond its typical use in applied studies. (PsycInfo Database Record (c) 2025 APA, all rights reserved).