Psychological methods最新文献

{"title":"Working with circular data: A tutorial for cognitive and behavioral research.","authors":"Stella Wernicke, Julie de Falco, Zahara Gironés, Chris Jungerius, Ivan Tomić, Adam Triabhall, Xiaolu Wang, Paul M Bays","doi":"10.1037/met0000861","DOIUrl":"https://doi.org/10.1037/met0000861","url":null,"abstract":"<p><p>Introductory statistics courses and conventional methods in behavioral and cognitive science are typically restricted to Euclidean space without explicitly stating this, leaving many researchers unaware of how to handle data with circular or periodic properties (e.g., time of day or orientation on a screen). This tutorial provides an accessible yet rigorous introduction, designed to bridge the gap between statistical theory and practical application. After discussing different ways of representing circular data mathematically and fundamental principles for working with them, we explain angular and vector representations and operations, including key considerations for implementing them in computer code. We then survey methods for visualizing circular data, including linear and polar plots and presentation of summary statistics. Next, we introduce circular measures of central tendency, dispersion, and shape: how they relate to their Euclidean counterparts and why they differ. Finally, we turn to more advanced topics of the kind needed to construct models of circular data. We introduce the most common circular distribution families, including circular analogs of the Gaussian distribution and their properties, as well as flexible distribution families that can describe skewed and heavy-tailed data. We survey common applications in the supplemental materials. The tutorial takes a method-oriented approach, not tied to specific software, but highlighting factors relevant to experimental design, data analysis, and statistical modeling. A consistent theme is the need to rigorously account for the periodicity of circular data. We also provide a consistent notation for circular statistics and parameters, addressing the lack of established conventions in the field. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220348","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":"Structural-before/after-measurement approaches for estimating treatment effects in latent outcomes with causal machine learning.","authors":"Amota Ataneka, Benjamin Kelcey, Jean Baptiste Habarurema","doi":"10.1037/met0000878","DOIUrl":"https://doi.org/10.1037/met0000878","url":null,"abstract":"<p><p>Prior research has developed a deep and diverse set of methods specifically designed to accommodate reflective latent factors (e.g., structural equation models). A critical limitation of conventional latent variable methods is that they typically operate under assumptions of linearity and almost always presume correct model specification (e.g., all nonlinearities and n-way interactions are known and included). Recent research in machine learning has relaxed such limitations by developing data-adaptive methods that identify treatment effects by empirically estimating the functional forms of relationships among covariates, treatment, and an outcome. A significant weakness of causal machine learning methods, however, is that they have been almost exclusively developed for observed outcome variables and have not considered reflective latent outcomes that are measured with error. In this study, we developed structural-after/before-measurement methods suitable for targeted machine learning with latent outcomes. The results suggested that the proposed estimators are nearly unbiased in finite samples even when the functional relationships are unknown and unspecified. We demonstrate the methods with an application to teachers' burnout study and compare their performance with conventional linear structural equation models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196264","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":"Regularized multigroup exploratory approximate factor analysis with orthogonal factors.","authors":"Katrijn Van Deun,Trà Lê,Jakub Malinowski,Floortje Mols,Dounya Schoormans","doi":"10.1037/met0000874","DOIUrl":"https://doi.org/10.1037/met0000874","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"409 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149093345","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\"Applying multivariate generalizability theory to psychological assessments\" by Vispoel et al. (2023).","authors":"","doi":"10.1037/met0000870","DOIUrl":"https://doi.org/10.1037/met0000870","url":null,"abstract":"<p><p>Reports an error in \"Applying multivariate generalizability theory to psychological assessments\" by Walter P. Vispoel, Hyeryung Lee, Hyeri Hong and Tingting Chen (<i>Psychological Methods</i>, Advanced Online Publication, Sep 07, 2023, np; see record 2024-05048-001). 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 terms involving true score variances and covariances. The corrected equation is noted in this Erratum. (The following abstract of the original article appeared in record 2024-05048-001.) 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":"31 5","pages":"iii"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220532","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 screen-time-based mixture model for identifying and monitoring careless and insufficient effort responding in ecological momentary assessment data.","authors":"Esther Ulitzsch, Steffen Nestler, Oliver Lüdtke, Gabriel Nagy","doi":"10.1037/met0000636","DOIUrl":"10.1037/met0000636","url":null,"abstract":"<p><p>Ecological momentary assessment (EMA) involves repeated real-time sampling of respondents' current behaviors and experiences. The intensive repeated assessment imposes an increased burden on respondents, rendering EMAs vulnerable to respondent noncompliance and/or careless and insufficient effort responding (C/IER). We developed a mixture modeling approach that equips researchers with a tool for (a) gauging the degree of C/IER contamination of their EMA data and (b) studying the trajectory of C/IER across the study. For separating attentive from C/IER behavior, the approach leverages collateral information from screen times, which are routinely recorded in electronically administered EMAs, and translates theoretical considerations on respondents' behavior into component models for attentive and careless screen times as well as for the functional form of C/IER trajectories. We show how a sensible choice of component models (a) allows disentangling short screen times due to C/IER from familiarity effects due to repeated exposure to the same measures, (b) aids in gaining a fine-grained understanding of C/IER trajectories by distinguishing within-day from between-day effects, and (c) allows investigating interindividual differences in attentiveness. The approach shows good parameter recovery when attentive and C/IER screen time distributions exhibit sufficient separation and yields valid conclusions even in scenarios of uncontaminated data. The approach is illustrated on EMA data from the German Socio-Economic Panel innovation sample. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"808-824"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139997241","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":"Beta-binomial meta-analysis of individual differences based on sample means and standard deviations: Studying reliability of sum scores of binary items.","authors":"Philipp Doebler, Susanne Frick, Anna Doebler","doi":"10.1037/met0000649","DOIUrl":"10.1037/met0000649","url":null,"abstract":"<p><p>Individual differences are studied with a multitude of test instruments. Meta-analysis of tests is useful to understand whether individual differences in certain populations can be detected with the help of a class of tests. A method for the quantitative meta-analytical evaluation of test instruments with dichotomous items is introduced. The method assumes beta-binomially distributed test scores, an assumption that has been demonstrated to be plausible in many settings. With this assumption, the method only requires sample means and standard deviations of sum scores (or equivalently means and standard deviations of percent-correct scores), in contrast to methods that use estimates of reliability for a similar purpose. Two parameters are estimated for each sample: mean difficulty and an overdispersion parameter which can be interpreted as the test's ability to detect individual differences. The proposed bivariate meta-analytical approach (random or fixed effects) pools the two parameters simultaneously and allows to perform meta-regression. The bivariate pooling yields a between-sample correlation of mean difficulty parameters and overdispersion parameters. As a side product, reliability estimates are obtained which can be employed to disattenuate correlation coefficients for insufficient reliability when no other estimates are available. A worked example illustrates the method and R code is provided. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"871-887"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140132459","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":"Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.","authors":"Qian Zhang, Xiao Liu, Zijun Ke","doi":"10.1037/met0000836","DOIUrl":"10.1037/met0000836","url":null,"abstract":"<p><p>In multilevel analysis, Level-1 dichotomous predictors (e.g., treatment vs. control, minority vs. majority; <i>D</i>) are commonly used to examine the existence of average within-cluster mean differences. To address this research question, we examine the best practices for coding <i>D</i>, considering within-cluster group compositions and between-cluster heterogeneity in them, as well as possible between-group heterogeneity in residual variances. We compare traditional dummy coding and unweighted effect coding with two proposed methods: overall and within-cluster weighted effect coding. Each is combined with cluster-mean centering to clearly define the within-cluster mean difference. Analytical results show that the intraclass correlation of <i>D</i> (ICC-D) substantially influences differences across coding methods regarding test results for the average within-cluster effect (group mean difference). When ICC-D is zero, all strategies yield identical test results (i.e., the significance tests based on the point estimates and standard error estimates) for the average within-cluster effect. However, as the ICC-D increases, test statistics (e.g., the t statistic) decrease for all coding strategies. An empirical study and simulations further confirm that ICC-D strongly influences results. Based on the simulations, we recommend dummy coding, unweighted effect coding, and overall weighted effect coding, which generally provide comparable or higher power than within-cluster weighted effect coding. The power contrast is most pronounced when both ICC-D and Level-1 residual variance heterogeneity increase, with the difference in power reaching up to 40%. Furthermore, cluster-mean centering not only disaggregates within- and between-cluster effects but also achieves generally reasonable Type I error rates even when homogeneity assumptions are not met. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1218-1244"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148252737","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 deep learning method for comparing Bayesian hierarchical models.","authors":"Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T Radev","doi":"10.1037/met0000645","DOIUrl":"10.1037/met0000645","url":null,"abstract":"<p><p>Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"917-945"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140857735","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":"Modeling psychological time series with multilevel hidden Markov models: A tutorial.","authors":"Emmeke Aarts, Jonas Haslbeck","doi":"10.1037/met0000830","DOIUrl":"10.1037/met0000830","url":null,"abstract":"<p><p>Time series (or intensive longitudinal) data are now widely used in psychological science. These data allow researchers to study the dynamics of human functioning at an unprecedented level of granularity. Capturing these dynamics requires appropriate statistical models. One powerful class of models for such data is the hidden Markov model (HMM), which captures processes that switch between a number of latent states over time, each associated with a different probability distribution. Unlike the currently predominant linear models, such as the vector autoregressive model, HMMs can capture more complex behaviors. They can characterize multiple equilibrium states (for instance, manic and depressed states as seen in bipolar disorder) and quantify how the system transitions between them. HMMs can also fit common empirical data patterns such as highly skewed or multimodal distributions. Despite their effectiveness in modeling within-person dynamics, HMMs are not widely used. We see three reasons: (1) limited familiarity among researchers, (2) only recent availability of software for multilevel HMMs, and (3) the relative complexity of estimating, analyzing, and reporting HMMs. Here, we address these issues by providing a gentle introduction to HMMs for researchers working with time series data in psychology, including a fully reproducible tutorial that walks the reader through each of the steps of analyzing psychological time series data using the R-package mHMMbayes. We hope our tutorial helps researchers working with time series data with adding (multilevel) HMMs into their methodological toolbox. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1091-1114"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869271","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 \"Worse than measurement error: Consequences of inappropriate latent variable measurement models\" by Rhemtulla et al. (2020).","authors":"","doi":"10.1037/met0000871","DOIUrl":"https://doi.org/10.1037/met0000871","url":null,"abstract":"<p><p>Reports an error in \"Worse than measurement error: Consequences of inappropriate latent variable measurement models\" by Mijke Rhemtulla, Riet van Bork and Denny Borsboom (<i>Psychological Methods</i>, 2020[Feb], Vol 25[1], 30-45, http://dx.doi.org/10.1037/met0000220; see record 2019-31247-001). In the article, an error in code produced incorrect results depicted in Figure 10, in the associated text, and in the parameter sweep results that were presented in the supplemental materials. The results shown in Figure 10 have been corrected in the online article. The code and supplemental materials have been updated (both the original and updated versions are available on the project OSF page located at https://osf.io/f8g4d). Corrections to the text are shown. The authors thank Leonhard Volz for identifying the error in the posted code. (The following abstract of the original article appeared in record 2019-31247-001.) Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data that are not generated from a common factor model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical reanalysis, we show that when a common factor model is misused, structural parameter estimates that indicate the relations among psychological constructs can be severely biased. Moreover, this bias can arise even when model fit is perfect. In some situations, composite models perform better than common factor models. These demonstrations point to a need for models to be justified on substantive, theoretical bases in addition to statistical ones. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"31 5","pages":"1286"},"PeriodicalIF":7.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220341","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}