Yingchi Guo,Jason D Rights
Yingchi Guo
In both single-level and multilevel regression analyses, researchers commonly report R-squared to quantify the proportion of outcome variance that is explained by a model or its component parts. It is well-established that the classic estim...
A causal framework for explaining effect heterogeneity in conceptual replications [0.03%]
解释概念重复实验中效应异质性的因果框架
Steffi Pohl,Marie-Ann Sengewald,Dennis Kondzic et al.
Steffi Pohl et al.
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...
Correction to "The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data" by Castro-Alvarez et al. (2025) [0.03%]
对卡斯特罗-阿尔韦茨等(2025)的《心理动力学的多种可靠性：估计强化纵向数据内部一致性的统计方法综述》一文的更正
Reports an error in "The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data" by Sebastian Castro-Alvarez, Laura F. Bringmann, J...
Published Erratum
Psychological methods. 2026 Apr;31(2):296. DOI:10.1037/met0000811 2026
Modeling psychological time series with multilevel hidden Markov models: A tutorial [0.03%]
多水平隐马尔可夫模型在心理时间序列建模中的应用教程
Emmeke Aarts,Jonas Haslbeck
Emmeke Aarts
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 a...
Assessing qualitative individual differences with Bayesian hierarchical latent-mixture models [0.03%]
基于Bayesian层次型潜类模型的定性个体差异评估方法研究
Martin Schnuerch,Jeffrey N Rouder
Martin Schnuerch
How do individuals vary in psychological experiments? Understanding how and why an effect differs across individuals provides a cornerstone for the development of precise psychological theories. Particularly important is the distinction bet...
Quantifying heteroscedasticity in linear models using quantile locally weighted scatterplot smoothing intervals [0.03%]
使用分位数局部加权平滑散点图间隔量化线性模型中的异方差性
Martina Sladekova,Andy P Field
Martina Sladekova
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 heterosce...
Minjeong Jeon,Michael Schweinberger
Minjeong Jeon
We introduce a novel approach to analyzing responses of individuals to items at two or more time points. Existing longitudinal item response models do not capture interactions among individuals and items that evolve over time. We construct ...
Attributing individual-level causal effects using experimental and observational data: A primer [0.03%]
基于实验和观察数据赋予个体因果效应的教程
Tim Kaiser,Stephen G West,Steffi Pohl
Tim Kaiser
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 o...
A largely univariate framework for understanding multivariate analysis of variance [0.03%]
一种理解多元方差分析的单变量框架
R Michael Furr
R Michael Furr
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 ...
Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology [0.03%]
超越炒作：评估心理学中机器学习模型预测性能的模拟研究
Kim-Laura Speck,Kristin Jankowsky,Florian Scharf et al.
Kim-Laura Speck et al.
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...