Psychological methods最新文献_第3页

nmax and the quest to restore caution, integrity, and practicality to the sample size planning process. Nmax以及对恢复样本量规划过程的谨慎性、完整性和实用性的追求。

IF 7 1区心理学

Psychological methods Pub Date : 2025-08-11 DOI: 10.1037/met0000776

Gregory R Hancock,Yi Feng

{"title":"nmax and the quest to restore caution, integrity, and practicality to the sample size planning process.","authors":"Gregory R Hancock,Yi Feng","doi":"10.1037/met0000776","DOIUrl":"https://doi.org/10.1037/met0000776","url":null,"abstract":"In a time when the alarms of research replicability are sounding louder than ever, mapping out studies with statistical and inferential integrity is of paramount importance. Indeed, funding agencies almost always require grant applicants to present compelling a priori power analyses to justify proposed sample sizes, as a critical part of the information considered collectively to ensure a sound investment. Unfortunately, even researchers' most sincere attempts at sample size planning are fraught with the fundamental challenge of setting numerical values not just for the focal parameters for which statistical tests are planned, but for each of the model's other, more peripheral or contextual parameters as well. As we plainly demonstrate, regarding the latter parameters, even in very simple models, any slight deviation in well-intentioned numerical guesses can undermine power for the assessment of the more focal parameters that are of key theoretical interest. Toward remedying this all-too-common but seemingly underestimated problem in power analysis, we adopt a hope-for-the-best-but-plan-for-the-worst mindset and present new methods that attempt to (a) restore appropriate conservatism and robustness, and in turn credibility, to the sample size planning process, and (b) greatly simplify that process. Derivations and suggestions for practice are presented using the framework of measured variable path analysis models as they subsume many of the types of models (e.g., multiple linear regression, analysis of variance) for which sample size planning is of interest. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"5 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144820002","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 Constructing a Binary Prediction Model With Incomplete Data: Variable Selection to Balance Fairness and Precision 构建不完全数据二元预测模型补充材料：平衡公平与精度的变量选择

IF 7 1区心理学

Psychological methods Pub Date : 2025-08-11 DOI: 10.1037/met0000786.supp

引用次数: 0

VISTA-SSM: Varying and irregular sampling time-series analysis via state-space models. VISTA-SSM：通过状态空间模型进行变采样和不规则采样时间序列分析。

IF 7.8 1区心理学

Psychological methods Pub Date : 2025-08-11 DOI: 10.1037/met0000785

Benjamin Brindle, Thomas Derrick Hull, Matteo Malgaroli, Nicolas Charon

{"title":"VISTA-SSM: Varying and irregular sampling time-series analysis via state-space models.","authors":"Benjamin Brindle, Thomas Derrick Hull, Matteo Malgaroli, Nicolas Charon","doi":"10.1037/met0000785","DOIUrl":"10.1037/met0000785","url":null,"abstract":"<p><p>We introduce varying and irregular sampling time-series analysis (VISTA), a clustering approach for multivariate and irregularly sampled time series based on a parametric state-space mixture model. VISTA is specifically designed for the unsupervised identification of groups in data sets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian state-space models (LGSSMs) to provide a flexible parametric framework for fitting a wide range of time series dynamics. The clustering approach itself is based on the assumption that the population can be represented as a mixture of a fixed number of LGSSMs. VISTA's model formulation allows for an explicit derivation of the log-likelihood function, from which we develop an expectation-maximization scheme for fitting model parameters to the observed data samples. Our algorithmic implementation is designed to handle populations of multivariate time series that can exhibit large changes in sampling rate as well as irregular sampling. We evaluate the versatility and accuracy of our approach on simulated and real-world data sets, including demographic trends, wearable sensor data, epidemiological time series, and ecological momentary assessments. Our results indicate that VISTA outperforms most comparable standard times series clustering methods. We provide an open-source implementation of VISTA in Python. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144822384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The practice of aggregating lower level predictors in clustered data: A reflection on reflective variables. 在聚类数据中聚合较低水平预测因子的实践：对反射变量的反思。

IF 7 1区心理学

Psychological methods Pub Date : 2025-08-07 DOI: 10.1037/met0000792

Timothy R. Konold, Elizabeth A. Sanders

引用次数: 0

Exploring how many categories are needed to model ordinal intensive longitudinal data as continuous with dynamic structural equation models. 探索用动态结构方程模型将有序密集纵向数据建模为连续数据需要多少类别。

IF 7 1区心理学

Psychological methods Pub Date : 2025-08-07 DOI: 10.1037/met0000784

Daniel McNeish, Andrea Savord

引用次数: 0

Supplemental Material for Integration of Latent Space and Confirmatory Factor Analysis to Explain Unexplained Person–Item Interactions 潜在空间与验证性因子分析整合解释无法解释的人-物交互作用补充材料