Psychological methods最新文献

Fitting two-level structural equation models to summary statistics: Leveling up meta-analytic structural equation modeling. 汇总统计的两级结构方程模型拟合：提升元分析结构方程模型的水平。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-31 DOI: 10.1037/met0000864

Suzanne Jak, Mike W-L Cheung

{"title":"Fitting two-level structural equation models to summary statistics: Leveling up meta-analytic structural equation modeling.","authors":"Suzanne Jak, Mike W-L Cheung","doi":"10.1037/met0000864","DOIUrl":"10.1037/met0000864","url":null,"abstract":"Standard two-level structural equation models (SEMs) require access to raw data. In this study, we propose a method for fitting two-level SEMs using meta-analytic structural equation modeling (MASEM) on summary statistics. Although our focus is on the meta-analytic case of individuals nested in studies, the approach could be applied to any two-level structure. An illustration using empirical data showed that fitting a two-level model on individual data versus fitting a two-level model using summary statistics produces essentially identical results. One advantage of the MASEM approach is that it is straightforward to model heterogeneity in the within-cluster covariances. Using simulated data, we demonstrated that the MASEM method performed well in such heterogeneous conditions. In contrast, two-level SEM analyses resulted in inflated Type I errors based on the test statistic and a significant underestimation of the parameters' standard errors. Through an empirical example, we show how two-level SEM through the MASEM method can be employed to evaluate measurement invariance across all studies and how the model can be expanded to incorporate study-level variables that explain some of the variation in parameters across studies. Implications and limitations of the proposed method are discussed, and directions for future research are provided. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148866704","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}

引用次数: 0

Within-person reliability of composite scores or single-item responses with missing intensive longitudinal data. 缺少密集纵向数据的综合得分或单项反应的人内可靠性。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-20 DOI: 10.1037/met0000865

Daniel McNeish

{"title":"Within-person reliability of composite scores or single-item responses with missing intensive longitudinal data.","authors":"Daniel McNeish","doi":"10.1037/met0000865","DOIUrl":"https://doi.org/10.1037/met0000865","url":null,"abstract":"Intensive longitudinal data (ILD) are commonly used to study within-person processes, and psychometric methods for computing within-person reliability with ILD have recently appeared in the methodological literature. The dense, frequent nature of data collection in ILD often leads to copious amounts of missing data-recent reviews find that 30%-40% missing data rates are typical. However, given the nascent state of psychometrics for ILD, the intersection of psychometrics and missing ILD has yet to be explored despite the pervasiveness of missing ILD in empirical studies. In addition, given that ILD are frequently used to study sensitive topics such as mental health and substance use, there is a heightened risk of missing not at random (MNAR) data. That is, the reason data are missing is associated with what the value would have been (e.g., depression responses are missing when a person is momentarily too depressed to respond). The goal of this article is to (a) clarify how missing values affect estimates of within-person reliability with ILD and (b) to extend one recently proposed method (the measurement error autoregressive model) with a Diggle-Kenward selection process to evaluate sensitivity to a potential MNAR mechanism. Simulations in the article find that the accuracy of within-person reliability estimates from standard approaches can deteriorate when missing data are present, but the proposed model can better recover population within-person reliability-even with a large amount of MNAR data-under the conditions and missingness mechanisms studied. Limitations and extensions to other methods for computing within-person reliability are also discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797423","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}

引用次数: 0

Columnwise neural imputation for incomplete ordinal psychometric data. 不完全有序心理测量数据的柱状神经归算。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-13 DOI: 10.1037/met0000867

Longfei Zhang, Minjeong Jeon, Ping Chen

引用次数: 0

Dynamic latent class structural equation modeling: A hands-on tutorial for modeling intensive longitudinal data. 动态潜在类结构方程建模：为密集纵向数据建模的动手教程。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-10 DOI: 10.1037/met0000849

Roberto Faleh, Sofia Morelli, Vivato Andriamiarana, Zachary J Roman, Christoph Flückiger, Holger Brandt

{"title":"Dynamic latent class structural equation modeling: A hands-on tutorial for modeling intensive longitudinal data.","authors":"Roberto Faleh, Sofia Morelli, Vivato Andriamiarana, Zachary J Roman, Christoph Flückiger, Holger Brandt","doi":"10.1037/met0000849","DOIUrl":"10.1037/met0000849","url":null,"abstract":"In this tutorial, we provide a hands-on guideline on how to implement complex dynamic latent class structural equation models (DLCSEMs) in the Bayesian software JAGS. We provide building blocks starting with simple confirmatory factor and time-series analysis and then extend these blocks to multilevel models and dynamic structural equation models. Subsequently, we introduce hidden Markov switching models and demonstrate their integration with dynamic structural equation models to yield DLCSEM. Leading through the tutorial is an example from clinical psychology using data on a generalized anxiety treatment that includes scales on anxiety symptoms and the Working Alliance Inventory, which measures the alliance between therapists and patients. Within each block, we provide an overview, specific hypotheses we want to test, the resulting model and its implementation, and an interpretation of the results. The aim of this tutorial is to provide a step-by-step guide for applied researchers that enables them to use this flexible DLCSEM framework for their own analyses. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707492","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}

引用次数: 0

Quantifying heteroscedasticity in linear models using quantile locally weighted scatterplot smoothing intervals. 用分位数局部加权散点图平滑区间量化线性模型中的异方差。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2026-05-07 DOI: 10.1037/met0000821

Martina Sladekova, Andy P Field

{"title":"Quantifying heteroscedasticity in linear models using quantile locally weighted scatterplot smoothing intervals.","authors":"Martina Sladekova, Andy P Field","doi":"10.1037/met0000821","DOIUrl":"10.1037/met0000821","url":null,"abstract":"Ordinary least squares (OLS) estimation, which is frequently applied in psychology, assumes constant variance of errors across predictor levels. This assumption is known as homoscedasticity, whereas its violation is referred to as heteroscedasticity. In categorical predictors, heteroscedasticity can be quantified by calculating the ratio of variances across groups. For continuous predictors, diagnostic residual plots are often used to assess whether the assumption had been met, but there is currently no measure that can quantify the amount of heteroscedasticity in an interpretable way. In this study, we have developed and evaluated a measure that constructs a quantile locally weighted scatterplot smoothing interval (QLI) around the residuals and estimates the linear, quadratic, cubic, and quartic change in the width of this interval as a function of the predictor or the fitted values. Furthermore, we evaluated simple linear models under different patterns of heteroscedasticity in a simulation to provide benchmark values of QLI estimates associated with inadequate control over false-positive results, loss of power, and loss of coverage probability of confidence intervals. The QLI method provided consistent estimates of trends for models with 60 or more cases, and this was true across variance patterns. We discuss QLI-generated estimates in relation to performance of OLS linear models. Finally, we present an example of how to apply the QLI method to quantify heteroscedasticity and how to interpret the estimates it provides, focusing on the implications for the OLS analysis. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"676-696"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842019","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}

引用次数: 0

The monotonic linear model: Testing for removable interactions. 单调线性模型：测试可去除的相互作用

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2024-02-29 DOI: 10.1037/met0000626

John C Dunn, Laura M Anderson

引用次数: 0

Measures of metacognitive efficiency across cognitive models of decision confidence. 跨决策信心认知模型的元认知效率测量。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2023-12-14 DOI: 10.1037/met0000634

Manuel Rausch, Sebastian Hellmann, Michael Zehetleitner

{"title":"Measures of metacognitive efficiency across cognitive models of decision confidence.","authors":"Manuel Rausch, Sebastian Hellmann, Michael Zehetleitner","doi":"10.1037/met0000634","DOIUrl":"10.1037/met0000634","url":null,"abstract":"Meta-d'/d' has become the quasi-gold standard to quantify metacognitive efficiency because meta-d'/d' was developed to control for discrimination performance, discrimination criteria, and confidence criteria even without the assumption of a specific generative model underlying confidence judgments. Using simulations, we demonstrate that meta-d'/d' is not free from assumptions about confidence models: Only when we simulated data using a generative model of confidence according to which the evidence underlying confidence judgments is sampled independently from the evidence utilized in the choice process from a truncated Gaussian distribution, meta-d'/d' was unaffected by discrimination performance, discrimination task criteria, and confidence criteria. According to five alternative generative models of confidence, there exist at least some combination of parameters where meta-d'/d' is affected by discrimination performance, discrimination criteria, and confidence criteria. A simulation using empirically fitted parameter sets showed that the magnitude of the correlation between meta-d'/d' and discrimination performance, discrimination task criteria, and confidence criteria depends heavily on the generative model and the specific parameter set and varies between negligibly small and very large. These simulations imply that a difference in meta-d'/d' between conditions does not necessarily reflect a difference in metacognitive efficiency but might as well be caused by a difference in discrimination performance, discrimination task criterion, or confidence criteria. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"607-626"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807090","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}

引用次数: 0

A time-varying interaction map approach for longitudinal assessments. 纵向评估的时变相互作用图方法。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2026-05-04 DOI: 10.1037/met0000825

Minjeong Jeon, Michael Schweinberger

引用次数: 0

Latent growth factors as predictors of distal outcomes. 作为远端结果预测因素的潜伏生长因子。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2024-06-03 DOI: 10.1037/met0000642

Ethan M McCormick, Patrick J Curran, Gregory R Hancock

{"title":"Latent growth factors as predictors of distal outcomes.","authors":"Ethan M McCormick, Patrick J Curran, Gregory R Hancock","doi":"10.1037/met0000642","DOIUrl":"10.1037/met0000642","url":null,"abstract":"A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"661-675"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200563","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}

引用次数: 0

Planning falsifiable confirmatory research. 规划可证伪的证实性研究。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2024-12-12 DOI: 10.1037/met0000639

James E Kennedy

{"title":"Planning falsifiable confirmatory research.","authors":"James E Kennedy","doi":"10.1037/met0000639","DOIUrl":"10.1037/met0000639","url":null,"abstract":"Falsifiable research is a basic goal of science and is needed for science to be self-correcting. However, the methods for conducting falsifiable research are not widely known among psychological researchers. Describing the effect sizes that can be confidently investigated in confirmatory research is as important as describing the subject population. Power curves or operating characteristics provide this information and are needed for both frequentist and Bayesian analyses. These evaluations of inferential error rates indicate the performance (validity and reliability) of the planned statistical analysis. For meaningful, falsifiable research, the study plan should specify a minimum effect size that is the goal of the study. If any tiny effect, no matter how small, is considered meaningful evidence, the research is not falsifiable and often has negligible predictive value. Power ≥ .95 for the minimum effect is optimal for confirmatory research and .90 is good. From a frequentist perspective, the statistical model for the alternative hypothesis in the power analysis can be used to obtain a p value that can reject the alternative hypothesis, analogous to rejecting the null hypothesis. However, confidence intervals generally provide more intuitive and more informative inferences than p values. The preregistration for falsifiable confirmatory research should include (a) criteria for evidence the alternative hypothesis is true, (b) criteria for evidence the alternative hypothesis is false, and (c) criteria for outcomes that will be inconclusive. Not all confirmatory studies are or need to be falsifiable. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"627-642"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819026","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}

引用次数: 0