Psychological methods最新文献

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 7.8 1区心理学

Psychological methods Pub 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":"https://doi.org/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":""},"PeriodicalIF":7.8,"publicationDate":"2026-05-07","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

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

IF 7.8 1区心理学

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

Minjeong Jeon, Michael Schweinberger

引用次数: 0

Attributing individual-level causal effects using experimental and observational data: A primer. 使用实验和观察数据归因个人水平的因果效应：引物。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-04-30 DOI: 10.1037/met0000808

Tim Kaiser, Stephen G West, Steffi Pohl

{"title":"Attributing individual-level causal effects using experimental and observational data: A primer.","authors":"Tim Kaiser, Stephen G West, Steffi Pohl","doi":"10.1037/met0000808","DOIUrl":"https://doi.org/10.1037/met0000808","url":null,"abstract":"Causal inference of the effect of a treatment on an outcome is usually done on the group or subgroup level. Although the typically reported average treatment effect may be positive, suggesting that the treatment is effective, at the level of individual participants, the treatment effect may be zero or even negative-the treatment may even harm some individuals. For making decisions on whether a specific person should take the treatment, information on the probability of benefiting or being harmed by the treatment for a single person is necessary. Estimating the probability of possible benefit or possible harm for a person involves counterfactual reasoning and thus strong assumptions about unobservable events. Precise statements about the causal effect of a treatment for an individual are only possible to a limited extent. This tutorial introduces the method of causal attribution to psychology that allows for estimating bounds in which the probability of benefit or harm lies. These bounds can be calculated using data at the group level, which can come from experimental or observational studies. The bounds can be narrowed by simultaneously using data from both randomized trials and observational studies and by using information from pretreatment covariates. R functions are provided for calculating these bounds from binary data and are illustrated with examples from basic laboratory research and clinical intervention research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820012","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 largely univariate framework for understanding multivariate analysis of variance. 一个很大程度上的单变量框架，用于理解方差的多变量分析。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-04-30 DOI: 10.1037/met0000819

R Michael Furr

{"title":"A largely univariate framework for understanding multivariate analysis of variance.","authors":"R Michael Furr","doi":"10.1037/met0000819","DOIUrl":"https://doi.org/10.1037/met0000819","url":null,"abstract":"Multivariate analysis of variance (MANOVA) has a long history of use in psychological science, is a staple of many advanced statistics textbooks and classes, and remains widely used in diverse areas of psychology. However, the way in which it is typically explained and taught relies on multivariate concepts, terms, and procedures that may be highly nonintuitive for many students, teachers, and applied researchers. This may limit many individuals' ability to learn, teach, interpret, use, and communicate MANOVA effectively and comfortably. Fortunately, MANOVA can be understood via a largely univariate perspective that is likely intuitive and accessible for many who are interested in MANOVA. Although other sources allude to this alternative perspective, those allusions are minimal and are not comprehensive, systematic, or illustrated in accessible ways. Moreover, no existing sources illustrate how, or even whether, this perspective applies to factorial MANOVA. The current tutorial explains and illustrates a largely univariate framework for understanding both one-way and factorial MANOVA. From this perspective, MANOVA begins with combinations of dependent variables, followed by a univariate analysis of variance (ANOVA) conducted on each combination, and by aggregation of effect sizes obtained from those ANOVAs to obtain multivariate effect sizes and multivariate inferential statistics. Thus, all key MANOVA results can be interpreted in relation to familiar results emerging from univariate ANOVA. This alternative perspective may help many students, teachers, and researchers gain a more intuitive understanding of how MANOVA works, what its results mean, and when/how to use it effectively. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147819937","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

Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology. 超越炒作：一项评估心理学中机器学习模型预测性能的模拟研究。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-04-30 DOI: 10.1037/met0000832

Kim-Laura Speck, Kristin Jankowsky, Florian Scharf, Ulrich Schroeders

{"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":"https://doi.org/10.1037/met0000832","url":null,"abstract":"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 (N = 1,000, perfectly reliable predictors, predominantly linear effects, and an exceptionally large effect size of R² = .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).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-04-30","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}

引用次数: 0

A novel multidimensional dynamic difficulty adjustment algorithm: Use case in a cognitive training video game. 一种新的多维动态难度调整算法：在认知训练类电子游戏中的应用。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-23 DOI: 10.1037/met0000805

Angela Pasqualotto,Marios Fanourakis,Zeno Menestrina,Friedhelm C Hummel,Elmars Rancans,Frank Padberg,Omer Bonne,Amit Lotan,Esther Bukowski,Lena Lipskaya-Velikovsky,Mor Nahum,Daphne Bavelier

{"title":"A novel multidimensional dynamic difficulty adjustment algorithm: Use case in a cognitive training video game.","authors":"Angela Pasqualotto,Marios Fanourakis,Zeno Menestrina,Friedhelm C Hummel,Elmars Rancans,Frank Padberg,Omer Bonne,Amit Lotan,Esther Bukowski,Lena Lipskaya-Velikovsky,Mor Nahum,Daphne Bavelier","doi":"10.1037/met0000805","DOIUrl":"https://doi.org/10.1037/met0000805","url":null,"abstract":"This study introduces a novel methodological framework for cognitive control training, embedded in a video game that incorporates action-based gameplay and a multidimensional dynamic difficulty adjustment (DDA) system. This system adapts to individual player performance in real time, ensuring a personalized and engaging experience. The game architecture is modular, including a configurable set of cognitive training modules that are tailored according to one's training goals. Transitioning between modules occurs through an action-based central hub following the literature on action video games and their positive impact on brain plasticity. Analysis of data from 34 players demonstrates how they progress through each module, with most players reaching their zone of proximal development after approximately 30-45 min of playing a module. Once players stabilize in their skill progression, the DDA system maintains variability in gameplay, a feature that has been suggested to promote the transfer of skills to novel situations. This analysis also highlights how our novel multidimensional DDA system accommodates to a wide range of skill levels, offering a seamless onboarding experience across a variety of players. Together, this novel architecture and DDA framework provide a new, rigorous methodological blueprint for the design of computerized cognitive training tools. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"143 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147733903","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

How to apply Bayesian stochastic search variable selection with multiply imputed data. 如何将贝叶斯随机搜索应用于多输入数据的变量选择。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-16 DOI: 10.1037/met0000837

Sierra A Bainter,Zhixin Mao,J Sunil Rao

{"title":"How to apply Bayesian stochastic search variable selection with multiply imputed data.","authors":"Sierra A Bainter,Zhixin Mao,J Sunil Rao","doi":"10.1037/met0000837","DOIUrl":"https://doi.org/10.1037/met0000837","url":null,"abstract":"Modern regularization and variable selection methods, such as least absolute shrinkage and selection operator (lasso) and Bayesian variable selection, are important tools for psychological researchers to reduce the risk of overfitting, improve prediction in future samples, and increase model interpretability. Although missing data are common in psychological data, it is not straightforward to combine principled methods for addressing missing data with these modern variable selection methods. This challenge is well illustrated in a recent article by Gunn et al. (2023) with a comparison of three approaches for combining lasso with multiple imputation to address missing data. Each of the surveyed approaches results in markedly different results in terms of predictors selected. Their findings underscore limitations of the lasso for the purpose of variable selection. In this article, we show how to implement a Bayesian variable selection method, stochastic search variable selection (SSVS), with multiply imputed data. SSVS is a principled and consistent method for variable selection, and we demonstrate advantages relative to lasso in an example data set and simulation study. It is straightforward to apply an ITS strategy for SSVS using existing software. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"19 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695540","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

Binomial effect size displays and gain-probability: Alternative ways to interpret hierarchical regression findings, with tutorial. 二项效应大小显示和增益概率：解释层次回归结果的替代方法，教程。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-16 DOI: 10.1037/met0000839

David Trafimow

引用次数: 0

A two-stage approach to account for measurement error when using empirical Bayes estimates of random slopes. 当使用随机斜率的经验贝叶斯估计时，考虑测量误差的两阶段方法。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-16 DOI: 10.1037/met0000838

Mark H C Lai,Siwei Liu

{"title":"A two-stage approach to account for measurement error when using empirical Bayes estimates of random slopes.","authors":"Mark H C Lai,Siwei Liu","doi":"10.1037/met0000838","DOIUrl":"https://doi.org/10.1037/met0000838","url":null,"abstract":"Psychological researchers have increasingly used individual- or cluster-specific slopes, also called random slopes in multilevel models, to operationalize dynamic constructs, such as individual growth, emotion inertia, and stress reactivity. However, the empirical Bayes estimates of random slopes, commonly used as predictors of cluster-level outcomes, are generally not reliable and could lead to biased results. Whereas multilevel structural equation modeling (MSEM) accounts for such measurement error, we propose a flexible two-stage approach that uses separate multilevel modeling and single-level structural equation modeling, which is computationally more efficient and gives similar estimates to MSEM. The proposed approach is demonstrated through an empirical example of stress reactivity. Results from three simulation studies show that the two-stage approach gives accurate estimates and inferences across a wide range of conditions, has substantially shorter computational time than MSEM for large cluster sizes, and has improved convergence when using penalized estimation in the first stage. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"194 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147695586","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

Supplemental Material for Extending Bias Adjustments for R-Squared to Multilevel Models 将R-Squared的偏置调整扩展到多水平模型的补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-13 DOI: 10.1037/met0000813.supp

引用次数: 0