Psychological methods最新文献

{"title":"A gain-probability way to interpret correlation coefficients: A tutorial.","authors":"David Trafimow","doi":"10.1037/met0000798","DOIUrl":"10.1037/met0000798","url":null,"abstract":"<p><p>The interpretation of correlation coefficients has invoked considerable discussion over many decades. One interpretive procedure is to use the coefficient of determination-the squared correlation coefficient-to index variance accounted for in one variable by variance in the other variable. A second interpretive procedure is to construct binomial effect size displays that involve dichotomizing continuous dependent variables. The present goal is to present a third interpretive procedure, with tutorial, to estimate probabilistic (dis)advantages implied by correlation coefficients and construct gain-probability diagrams. The proposed procedure does not involve dichotomizing continuous dependent variables, thereby losing information. In addition, the proposed procedure extends well to comparing correlation coefficients and facilitates subtle and nuanced implications that can enhance theoretical specificity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"271-280"},"PeriodicalIF":7.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145378569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To detrend, or not to detrend, that is the question? The effects of detrending on cross-lagged effects in panel models.","authors":"Fredrik Falkenström, Nili Solomonov, Julian Rubel","doi":"10.1037/met0000632","DOIUrl":"10.1037/met0000632","url":null,"abstract":"<p><p>Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test the arguments for and against detrending CLPMs in the presence of an intervention effect. We conducted Monte Carlo simulations to examine the impact of trends on estimates of cross-lagged effects from several longitudinal structural equation models. Our simulations suggested that ignoring time trends led to biased estimates of auto- and cross-lagged effects in some conditions, while detrending did not introduce bias in any of the models. We used real data from an intervention study to illustrate how detrending may affect results. This example showed that models that separated trends from cross-lagged effects fit better to the data and showed nonsignificant effect of the mechanism on outcome, while models that ignored trends showed significant effects. We conclude that ignoring trends increases the risk of bias in estimates of auto- and cross-lagged parameters and may lead to spurious findings. Researchers can test for the presence of trends by comparing model fit of models that take into account individual differences in trends (e.g., autoregressive latent trajectory model, the latent curve model with structured residuals, or the general cross-lagged model). (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"252-270"},"PeriodicalIF":7.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models.","authors":"Daniel McNeish","doi":"10.1037/met0000620","DOIUrl":"10.1037/met0000620","url":null,"abstract":"<p><p>Psychological data are often clustered within organizational units, which violates the independence assumption in standard regression models. Clustered errors, multilevel models, and fixed-effects models all address this issue, but in different ways. Disciplinary preferences for approaching clustered data are strong, which can restrict questions researchers ask because certain approaches are better equipped to handle particular types of questions. Resources comparing approaches to facilitate broader understanding of clustered data approaches exist for economists, political scientists, and biostatisticians. These existing resources use concepts and terminology consistent with statistical training in other disciplines, so this article provides a resource using language and principles familiar to psychologists. The article starts by walking through the origin and importance of the independence assumption to motivate the problem and emergence of different solutions in different fields. Then, information on clustered errors, multilevel models, and fixed-effect models is provided, including (a) how each approach addresses independence violations, (b) research questions ideally suited for each approach, and (c) example analyses highlighting advantages and disadvantages. The article then discusses how these approaches are not mutually exclusive but instead can be blended together to create tailor-made models that flexibly accommodate idiosyncrasies in research questions and are robust to nuances of a particular data set. The broader theme is that there is no one-size-fits-all approach to clustered data. The research question-not disciplinary preferences-should inform the statistical approach. Wider appreciation of the landscape of clustered data approaches can expand the questions researchers ask and improve the theoretical foundation of statistical models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"225-251"},"PeriodicalIF":7.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92156262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A primer on equivalence (negligible effect) testing.","authors":"Nataly Beribisky, Robert A Cribbie","doi":"10.1037/met0000800","DOIUrl":"10.1037/met0000800","url":null,"abstract":"<p><p>Equivalence testing, also called negligible effect significance testing (NEST), is appropriate when a researcher would like to find evidence of a negligible association. However, since equivalence testing/NEST procedures are newer and considerably less popular than traditional difference-based null hypothesis significance testing, it is useful to give a gentle introduction to these methods. Accordingly, this tutorial article aims to provide an overview of NEST/equivalence testing procedures by describing the nature of the procedures, explaining when they should be used, defining what considerations should go into their application (including selecting a minimally meaningful effect size), and outlining how they may be conducted and interpreted. The tutorial article also includes examples and code in open-source software to illustrate how these procedures may be applied to real data. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"313-326"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nested model comparisons between common factors and composites.","authors":"Danielle Siegel, Victoria Savalei, Mijke Rhemtulla","doi":"10.1037/met0000806","DOIUrl":"10.1037/met0000806","url":null,"abstract":"<p><p>In psychological research, a common factor model is the most popular measurement model for scale items. However, there is increasing awareness that alternative measurement models, such as formative models, may make more theoretical sense for many kinds of psychological data. We demonstrate the nesting structure of three models specified in a structural equation modeling framework: a reflective confirmatory factor analysis (CFA), a formative Henseler-Ogasawara confirmatory composite analysis, and a formative pseudo-indicator model. Unlike CFA, Henseler-Ogasawara confirmatory composite analysis and pseudo-indicator model allow for the specification of composites in the structural equation modeling framework. In this article, we establish both theoretically and empirically that these three models are nested within one another, as long as the structural part of each model is saturated. As such, the three models can be compared via a chi-square difference test and other fit indices developed for nested models. We report on the results of a small simulation to evaluate whether the chi-square difference test and the root-mean-square error of approximation (RMSEA) based on it (RMSEA_D) can reliably discern whether data were sampled from a CFA or a formative measurement model, varying sample size, indicator weights, and the strength of the correlation with another concept. In two empirical examples, we illustrate how tools for nested model comparison can be used to distinguish among reflective and formative measurement models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"363-383"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One step at a time: A statistical approach for distinguishing mediators, confounders, and colliders using direction dependence analysis.","authors":"Dexin Shi, Amanda J Fairchild, Wolfgang Wiedermann","doi":"10.1037/met0000619","DOIUrl":"10.1037/met0000619","url":null,"abstract":"<p><p>In observational data, understanding the causal link when estimating the causal effect of an independent variable (<i>x</i>) on a dependent variable (<i>y</i>) often requires researchers to identify the role of a third variable in the <i>x</i> → <i>y</i> relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions. Commonly used covariance-based statistical methods, such as linear regression and structural equation modeling, cannot distinguish these effects in practice, however. In this study, we introduce a statistical approach for distinguishing mediators, confounders, colliders, and potential M-bias structures that uses higher-order moment information from the data. We propose a two-step procedure that uses the Hilbert-Schmidt independence criterion within the direction dependence analysis framework. Results from Monte Carlo simulations show that our proposed approach accurately recovers the true data-generating process of the third variable. We provide an empirical example to demonstrate the application of our proposed approach in psychological research. Finally, we discuss implications and future directions of our work. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"201-224"},"PeriodicalIF":7.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138831226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data\" by Castro-Alvarez et al. (2025).","authors":"","doi":"10.1037/met0000811","DOIUrl":"10.1037/met0000811","url":null,"abstract":"<p><p>Reports an error in \"The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data\" by Sebastian Castro-Alvarez, Laura F. Bringmann, Jason Back and Siwei Liu (<i>Psychological Methods</i>, Advanced Online Publication, Oct 20, 2025, np; see record 2026-77126-001). The authors discovered an error in the computation of the reliability coefficients according to the 2RDM. Upon correcting this, several results required minor updates. Specifically, the corrections affect a couple of sentences in the Results and Discussion sections, Figure 3, and the corresponding parts of the Supplementary Material. While the overall conclusions of the paper remain unchanged, the article has been updated to accurately reflect the corrected computations. The article was published with the impact statement from an unrelated paper. The impact statement has been replaced with correct version. All versions of this article have been corrected (The following abstract of the original article appeared in record 2026-77126-001.) Reliability is a key concept in psychology that has been broadly studied since the introduction of Cronbach's α, which is a measure of internal consistency. Despite its importance, reliability has been relatively understudied when dealing with intensive longitudinal data. Although intensive longitudinal measurements are often considered more ecologically valid and less prone to recall bias than survey data collected using traditional methods, there is no warranty that they are more reliable. Hence, empirical researchers need tools to study and report the reliability of the scales used in intensive longitudinal research. In recent years, psychologists have proposed different approaches to estimate the reliability of scales and items used when studying psychological dynamics. However, it is unclear how these approaches compare to one another, making it difficult to determine what options researchers have given a particular data set and specific research questions. Specifically, these approaches estimate reliability indices based on different statistical models, such as linear multilevel analysis, vector autoregressive models, and dynamic factor models. Furthermore, while some methods involve estimating one reliability index for the scores that applies to the whole sample, others estimate person-specific reliability indices. This wide variety of approaches can provoke some confusion. In this article, we aim to bridge this gap by reviewing and highlighting the similarities and differences of different methods used to estimate the reliability of intensive longitudinal data. We also showcase their application with empirical data. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"31 2","pages":"296"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"You must parcel carefully if you have to! Comparing eight item parceling strategies with the item-level model for bifactor predictive models.","authors":"Jinsoo Choi,Bo Zhang,Dexin Shi,Sunbeom Kwon,Leo Alexander","doi":"10.1037/met0000827","DOIUrl":"https://doi.org/10.1037/met0000827","url":null,"abstract":"The bifactor predictive model has been an appealing method for simultaneously examining the validity of the general factor and group factors of hierarchical constructs. Item parceling has been frequently applied in the bifactor context, but all of these applications in bifactor models were based on parceling strategies developed for simple factor structures where one item only loads on one factor. It remains unexplored regarding how to create parcels that can consider the double-loading structure of bifactor models and still allow users to maintain the bifactor structure after item parceling. Moreover, it is unclear whether item parceling itself is beneficial or detrimental in the context of bifactor predictive models. In the present study, we propose eight parceling strategies for bifactor models and investigate (a) whether parceling performs better than item-level modeling and (b) the impact of the eight item parceling strategies on the statistical performance of bifactor predictive models. Contrary to prior studies showing that parceling generally enhances statistical performance for nonbifactor models, the current simulations and the empirical example clearly demonstrate (a) that the item-level model tends to outperform parceling strategies overall and (b) that different parceling strategies demonstrate substantially different statistical performance. Among the eight parceling strategies, the three parceling strategies that maximize factor loading differences within each group factor performed relatively the best, often close to the item-level model. In contrast, strategies that minimize factor loading differences within each group factor performed substantially worse. This study also provides an in-depth discussion and practical guidance for applied researchers. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"4 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factored structural equation modeling in blimp.","authors":"Craig K Enders,Brian T Keller","doi":"10.1037/met0000826","DOIUrl":"https://doi.org/10.1037/met0000826","url":null,"abstract":"This tutorial introduces factored structural equation modeling (FSEM), an alternative to multivariate structural equation modeling, and demonstrates its implementation in the Blimp software and the rblimp R package. FSEM reconceptualizes the joint distribution of observed and latent variables as a set of univariate and multivariate submodels-each specified via simple regression equations-and treats latent variables as missing data to be imputed via Bayesian data augmentation. This approach seamlessly accommodates combinations of continuous (normal and nonnormal), binary, ordinal, nominal, count, and two-part variables; interactions; nonlinear effects; heteroscedasticity; and multilevel data structures without violating distributional assumptions. We first outline the theoretical foundations of factored regression specification and its connection to the probability chain rule. We then describe the Markov chain Monte Carlo estimation and missing-data imputation algorithms that underlie FSEM. Next, we present a series of illustrative models-ranging from basic confirmatory factor analysis to complex dynamic, multilevel, and hybrid generalized-linear-structural equation modeling applications-providing Blimp syntax excerpts and a real-data example. We conclude by discussing practical considerations, limitations, and directions for future methodological research. This tutorial aims to equip researchers with a flexible, user-friendly framework for modeling complex data in behavioral and social sciences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"192 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supplemental Material for Modeling Intraindividual Variability as a Predictor With Intensive Longitudinal Data","authors":"","doi":"10.1037/met0000818.supp","DOIUrl":"https://doi.org/10.1037/met0000818.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"7 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147524010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}