Psychological methods最新文献

{"title":"The semipartial correlation needs no correction. Comment on Park and Yi (2025).","authors":"Peter de Heus","doi":"10.1037/met0000833","DOIUrl":"https://doi.org/10.1037/met0000833","url":null,"abstract":"<p><p>In a recent article in this journal, Park and Yi (see record 2026-37646-001) try to remedy the perceived problem that <i>R</i>²_med, a measure of effect size in mediation analysis, is usually nonzero when the second part of the indirect effect (<i>b</i>) is zero. They do this by redefining the semipartial correlation for the independent variable as equal to its zero-order correlation when <i>b</i> is zero. Against this, I argue that the perceived problem is just a fact, the supposed proof that <i>R</i>²_med must be zero is false, and the proposed solution creates many new problems, so it should be rejected. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"31 5","pages":"1280-1285"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220593","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":"How to conduct an integrative mixed methods meta-analysis: A tutorial for the systematic review of quantitative and qualitative evidence.","authors":"Heidi M Levitt","doi":"10.1037/met0000675","DOIUrl":"10.1037/met0000675","url":null,"abstract":"<p><p>This article is a guide on how to conduct mixed methods meta-analyses (sometimes called mixed methods systematic reviews, integrative meta-analyses, or integrative meta-syntheses), using an integrative approach. These aggregative methods allow researchers to synthesize qualitative and quantitative findings from a research literature in order to benefit from the strengths of both forms of analysis. The article articulates distinctions in how qualitative and quantitative methodologies work with variation to develop a coherent theoretical basis for their integration. In advancing this methodological approach to integrative mixed methods meta-analysis (IMMMA), I provide rationales for procedural decisions that support methodological integrity and address prior misconceptions that may explain why these methods have not been as commonly used as might be expected. Features of questions and subject matters that lead them to be amenable to this research approach are considered. The steps to conducting an IMMMA then are described, with illustrative examples, and in a manner open to the use of a range of qualitative and quantitative meta-analytic approaches. These steps include the development of research aims, the selection of primary research articles, the generation of units for analysis, and the development of themes and findings. The tutorial provides guidance on how to develop IMMMA findings that have methodological integrity and are based upon the appreciation of the distinctive approaches to modeling variation in quantitative and qualitative methodologies. The article concludes with guidance for report writing and developing principles for practice. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"999-1014"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366352","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":"Are factor scores measurement invariant?","authors":"Mark H C Lai, Winnie W-Y Tse","doi":"10.1037/met0000658","DOIUrl":"10.1037/met0000658","url":null,"abstract":"<p><p>There has been increased interest in practical methods for integrative analysis of data from multiple studies or samples, and using factor scores to represent constructs has become a popular and practical alternative to latent variable models with all individual items. Although researchers are aware that scores representing the same construct should be on a similar metric across samples-namely they should be measurement invariant-for integrative data analysis, the methodological literature is unclear whether factor scores would satisfy such a requirement. In this note, we show that even when researchers successfully calibrate the latent factors to the same metric across samples, factor scores-which are estimates of the latent factors but not the factors themselves-may not be measurement invariant. Specifically, we prove that factor scores computed based on the popular regression method are generally not measurement invariant. Surprisingly, such scores can be noninvariant even when the items are invariant. We also demonstrate that our conclusions generalize to similar shrinkage scores in item response models for discrete items, namely the expected a posteriori scores and the maximum a posteriori scores. Researchers should be cautious in directly using factor scores for cross-sample analyses, even when such scores are obtained from measurement models that account for noninvariance. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"990-998"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140857844","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":"Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology.","authors":"Kim-Laura Speck, Kristin Jankowsky, Florian Scharf, Ulrich Schroeders","doi":"10.1037/met0000832","DOIUrl":"10.1037/met0000832","url":null,"abstract":"<p><p>Although machine learning (ML) methods are gaining popularity in psychological research, the debate about their usefulness ranges from hype to disillusionment. The discrepancy between the hopes placed in ML methods and the empirical reality is often attributed to the quality of psychological data sets, which tend to be small and subject to imprecise measurement. In this simulation study, we examined the data requirements necessary for ML methods to perform well. We compared the performance of elastic net regressions with and without prespecified interactions, random forests, and gradient boosting machines for different data-generating processes (including interaction, stepwise, or piecewise linear effects) and under various conditions: (a) sample size, (b) number of irrelevant predictors, (c) predictor reliability, (d) effect size, and (e) nature of the data-generating process (i.e., linear vs. nonlinear effects). We investigated whether the models achieved the highest level of predictive performance attainable under the given simulated conditions. There were two main takeaways from our results: First, the maximum possible predictive performance was only achieved under optimal simulation conditions (<i>N</i> = 1,000, perfectly reliable predictors, predominantly linear effects, and an exceptionally large effect size of <i>R</i>² = .80), which are arguably rarely met in psychological research. Second, each ML model outperformed the others under certain conditions, but none was consistently superior or entirely robust to suboptimal data characteristics. We stress that data quality fundamentally limits predictive performance and discuss the interpretation of comparisons between flexible ML models and simpler (regularized linear) baselines in psychological research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1196-1217"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147819986","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":"Using features of dynamic networks to guide treatment selection and outcome prediction: The central role of uncertainty.","authors":"Björn S. Siepe,Matthias Kloft,Yong Zhang,Fridtjof Petersen,Laura F. Bringmann,Daniel W. Heck","doi":"10.1037/met0000872","DOIUrl":"https://doi.org/10.1037/met0000872","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"32 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149093303","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":"Applying multivariate generalizability theory to psychological assessments.","authors":"Walter P Vispoel, Hyeryung Lee, Hyeri Hong, Tingting Chen","doi":"10.1037/met0000606","DOIUrl":"10.1037/met0000606","url":null,"abstract":"<p><p>[Correction Notice: An Erratum for this article was reported in Vol 31(5) of <i>Psychological Methods</i> (see record 2028-39170-001). In the article \"Applying Multivariate Generalizability Theory to Psychological Assessments,\" by Walter P. Vispoel, Hyeryung Lee, Hyeri Hong, and Tingting Chen (Psychological Methods, advance online publication, September 7, 2023, https://doi.org/10.1037/met0000606), the author discovered an error in Equation 38. \"Estimated Reliability (Composite)\" should have been \"Estimated Reliability (Subscale)\" in the denominator of the original equation. This change was incorporated into the corrected version, and the equation was further simplified by replacing \"^r23 Estimated Reliability\" terms with equivalent terms involving true score variances and covariances. The corrected equation is noted in this Erratum.] Multivariate generalizability theory (GT) represents a comprehensive framework for quantifying score consistency, separating multiple sources contributing to measurement error, correcting correlation coefficients for such error, assessing subscale viability, and determining the best ways to change measurement procedures at different levels of score aggregation. Despite such desirable attributes, multivariate GT has rarely been applied when measuring psychological constructs and far less often than univariate techniques that are subsumed within that framework. Our purpose in this tutorial is to describe multivariate GT in a simple way and illustrate how it expands and complements univariate procedures. We begin with a review of univariate GT designs and illustrate how such designs serve as subcomponents of corresponding multivariate designs. Our empirical examples focus primarily on subscale and composite scores for objectively scored measures, but guidelines are provided for applying the same techniques to subjectively scored performance and clinical assessments. We also compare multivariate GT indices of score consistency and measurement error to those obtained using alternative GT-based procedures and across different software packages for analyzing multivariate GT designs. Our online supplemental materials include instruction, code, and output for common multivariate GT designs analyzed using <i>mGENOVA</i> and the <i>gtheory</i>, <i>glmmTMB</i>, lavaan, and related packages in R. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"785-807"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10173543","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":"Summed versus estimated factor scores: Considering uncertainties when using observed scores.","authors":"Yang Liu, Jolynn Pek","doi":"10.1037/met0000644","DOIUrl":"10.1037/met0000644","url":null,"abstract":"<p><p>Observed scores (e.g., summed scores and estimated factor scores) are assumed to reflect underlying constructs and have many uses in psychological science. Constructs are often operationalized as latent variables (LVs), which are mathematically defined by their relations with manifest variables in an LV measurement model (e.g., common factor model). We examine the performance of several types of observed scores for the purposes of (a) estimating latent scores and classifying people and (b) recovering structural relations among LVs. To better reflect practice, our evaluation takes into account different sources of uncertainty (i.e., sampling error and model error). We review psychometric properties of observed scores based on the classical test theory applied to common factor models, report on a simulation study examining their performance, and provide two empirical examples to illustrate how different scores perform under different conditions of reliability, sample size, and model error. We conclude with general recommendations for using observed scores and discuss future research directions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"825-843"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139707635","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 causal framework for explaining effect heterogeneity in conceptual replications.","authors":"Steffi Pohl, Marie-Ann Sengewald, Dennis Kondzic, Jerome Hoffmann, Mathias Twardawski, Peter M Steiner","doi":"10.1037/met0000834","DOIUrl":"10.1037/met0000834","url":null,"abstract":"<p><p>Although previous research has described that intervention effects vary across replication studies, less effort has been devoted to identifying causes of this effect heterogeneity with regard to differences in study implementations. However, knowing in which way study characteristics (such as population, measurement instrument, setting, or treatment implementation) impact the study results may not only help to better infer the impact of research practices but also provide evidence for theory building. Causal effects can be easily identified if all study characteristics but the one under investigation are kept constant across two studies. This is, however, not always possible in practice and unintended differences between the studies to be compared may confound the relationship of the study characteristic of interest and the treatment effect. In this article, we present a statistical approach for identifying effects of study characteristics on study-specific treatment effects from randomized experiments in cases in which unintended differences in study implementation across studies cannot be prevented. We present formal definitions of the causal effects of interest, identification assumptions, and derive respective causal estimands. The assumptions can more likely be fulfilled in prospective replication studies or many-lab studies, where researchers have more control over design and measurement of covariates in both studies. We also provide ways to test the assumptions and illustrate consequences of not meeting the assumptions. The approach is illustrated using an empirical example on the imagined intergroup contact effect in social psychology. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1263-1279"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964534","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 tutorial on Bayesian multilevel latent time series models using stan with the mlts R package.","authors":"Kenneth Koslowski, Fabian Felix Münch, Tobias Koch, Jana Holtmann","doi":"10.1037/met0000866","DOIUrl":"10.1037/met0000866","url":null,"abstract":"<p><p>This tutorial introduces and illustrates the estimation of Bayesian multilevel latent time series models with the R package mlts. We provide a conceptual overview of dynamic structural equation modeling for intensive longitudinal data, highlighting how it combines time series analysis, multilevel modeling, and latent variable approaches. Step-by-step guidance is given on specifying, estimating, and interpreting two-level vector autoregressive models using mlts. We illustrate modeling extensions provided in mlts, including multiple-indicator measurement models, between-person covariates, latent interaction effects, the handling of censored variables, and multiple-group models. Practical considerations such as data preparation, model diagnostics, and handling of varying measurement intervals are discussed. Assessment of model fit is provided via posterior predictive checks. Using working examples with empirical data, we showcase how mlts enables flexible Bayesian dynamic structural equation modeling analyses while avoiding the complexities of custom Stan programming. This tutorial aims to make advanced dynamic modeling of intensive longitudinal data more accessible to applied researchers across the behavioral sciences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075853","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":"Model checking for vector autoregressive models.","authors":"Jonas Haslbeck, Joran Jongerling, Björn Siepe, Sacha Epskamp, Lourens Waldorp","doi":"10.1037/met0000855","DOIUrl":"10.1037/met0000855","url":null,"abstract":"<p><p>Time series data have become pervasive in psychological science and vector autoregressive (VAR) models are now widely used to study within-person dynamics. However, researchers rarely check systematically whether these models fit their data. This is problematic because model misfit can lead both to incorrect interpretations of model parameters and to missed structure in the data that would be theoretically interesting. This tutorial explains the theory behind model checking, discusses the most common types of VAR model misspecification in psychological time series, and introduces diagnostics for detecting them using plots and simulations. We provide code for extracting predictions and residuals from popular software packages and introduce the new R package VARcheck, which generates diagnostic plots with only a few lines of code. We apply these tools in a realistic setting by performing model checks for a multilevel VAR model estimated from emotion measurements from 179 people over 3 weeks. Finally, we discuss three ways in which model checking can advance psychological time series research: improving measurement, building better statistical models, and supporting theory development. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075814","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}