Psychological methods最新文献_第4页

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

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

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

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

Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference. 估计曲线时变治疗效果：将结构嵌套均值模型的 g-估计与时变效应模型相结合，用于纵向因果推断。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-01 Epub Date: 2024-02-15 DOI: 10.1037/met0000637

Wen Wei Loh

{"title":"Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference.","authors":"Wen Wei Loh","doi":"10.1037/met0000637","DOIUrl":"10.1037/met0000637","url":null,"abstract":"Longitudinal designs can fortify causal inquiries of a focal predictor (i.e., treatment) on an outcome. But valid causal inferences are complicated by causal feedback between confounders and treatment over time. G-estimation of a structural nested mean model (SNMM) is designed to handle the complexities beset by measured time-varying or treatment-dependent confounding in longitudinal data. But valid inference requires correctly specifying the functional form of the SNMM, such as how the effects stay constant or change over time. In this article, we develop a g-estimation strategy for linear structural nested mean models whose causal parameters adopt the form of time-varying coefficient functions. These time-varying coefficient functions are smooth semiparametric functions of time that permit probing how the treatment effects may change curvilinearly. Further effect modification by time-invariant and time-varying covariates can be readily postulated in the SNMM to test fine-grained effect heterogeneity. We then describe a g-estimation strategy for estimating such an SNMM. We utilize the established time-varying effect model (TVEM) approach from the prevention and psychotherapy research literature for modeling flexible changes in covariate-outcome associations over time. Moreover, we exploit a known benefit of g-estimation over routine regression methods: its double robustness conferring protection against biases induced by certain forms of model misspecification. We encourage psychology researchers seeking correct causal conclusions from longitudinal data to use an SNMM with time-varying coefficient functions to assess curvilinear causal effects over time, and to use g-estimation with TVEM to resolve measured treatment-dependent confounding. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"643-660"},"PeriodicalIF":7.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139735974","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

Leveraging lavaan for iterated conditional expectations: A streamlined parametric g-formula. 利用lavaan迭代条件期望：流线型参数g公式。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-07-27 DOI: 10.1037/met0000844

Wen Wei Loh

{"title":"Leveraging lavaan for iterated conditional expectations: A streamlined parametric g-formula.","authors":"Wen Wei Loh","doi":"10.1037/met0000844","DOIUrl":"10.1037/met0000844","url":null,"abstract":"Longitudinal designs are widely used to assess causal effects that develop over time. But treatment-confounder causal feedback complicates adjusting for time-varying confounding. An established solution from the causal inference literature is parametric g-formula. In this tutorial, I introduce a streamlined variant: the iterated conditional expectation (ICE). Parametric g-formula using ICE requires only a sequence of regression models for the mean outcomes at consecutive time points. It is thus ideally suited for repeatedly measured outcomes, as frequently encountered in psychological research. Unlike conventional Monte Carlo parametric g-formula, ICE does not require modeling or simulating random draws from the joint distribution of time-varying confounders and outcomes. This makes ICE less susceptible to model misspecification biases and more versatile in handling continuous and noncontinuous (e.g., binary or categorical) time-varying confounders. A doubly robust version of ICE using correctly specified treatment models can further protect against biases from incorrectly specified outcome models. To make parametric g-formula with ICE accessible to a broad audience of applied researchers, I offer an implementation using lavaan, a popular, user-friendly, and free statistical modeling software in R. A running example of the causal effect of social exclusion on depression over time aids in perceiving the causal assumptions, interpreting the causal effect estimands, and following the estimation procedure. Finally, I illustrate how to apply ICE using data from a real-world study. Parametric g-formula using ICE is elegant, computationally efficient, and easy to apply in practice. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148593280","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

Adjustment set selection for estimating optimal treatment rules under confounding. 混杂条件下估计最优处理规则的调整集选择。

IF 7 1区心理学

Psychological methods Pub Date : 2026-07-23 DOI: 10.1037/met0000812

Nina Galanter,Susan M Shortreed,Erica E M Moodie

{"title":"Adjustment set selection for estimating optimal treatment rules under confounding.","authors":"Nina Galanter,Susan M Shortreed,Erica E M Moodie","doi":"10.1037/met0000812","DOIUrl":"https://doi.org/10.1037/met0000812","url":null,"abstract":"There is an increasing call for individualized treatment rules, which leverage individual patient characteristics to recommend treatments or interventions, tailoring recommendations based on their covariates. This is particularly of interest for the care of conditions such as depression, for which many treatment options are available with similar average effectiveness but with large heterogeneity in individual responses. In parallel, there has been a growing interest in machine learning methods for causal inference and variable selection. We compared several strategies for variable adjustment in a dynamic marginal structural modeling approach to estimating an optimal individualized treatment rule and investigated the performance of outcome adaptive lasso, group lasso and doubly robust estimation, double-index propensity score, the high-dimensional balancing propensity score, and the causal ball lasso as variable selection methods for the propensity score. Our results demonstrate that these all provided similar unbiased estimates. However, methods differed in their ability to exclude extraneous variables and in computational burden. We found statistical efficiency is gained when variable selection approaches were for the propensity score were used and by including variables in the outcome model. We applied all methods to determine the optimal treatment rule, treating with either selective serotonin reuptake inhibitors or serotonin and norepinephrine reuptake inhibitors, for unipolar depression in individuals aged 13 years and older. This analysis, which used electronic health records from 74,058 Kaiser Permanente Washington patients with a new antidepressant dispensing between 2008 and 2018, suggested tailoring treatment based on baseline symptom severity did not impact symptom severity 6 months later. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"13 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148565038","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