{"title":"Supplemental Material for Inferences and Effect Sizes for Direct, Indirect, and Total Effects in Continuous-Time Mediation Models","authors":"","doi":"10.1037/met0000779.supp","DOIUrl":"https://doi.org/10.1037/met0000779.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"24 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254871","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}
{"title":"Supplemental Material for How to Synthesize Randomized Controlled Trial Data With Meta-Analytic Structural Equation Modeling: A Comparison of Various d-to-rpb Conversions","authors":"","doi":"10.1037/met0000790.supp","DOIUrl":"https://doi.org/10.1037/met0000790.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"255 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254883","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}
Tom Heyman,Ekaterina Pronizius,Savannah C Lewis,Oguz A Acar,Matúš Adamkovič,Ettore Ambrosini,Jan Antfolk,Krystian Barzykowski,Ernest Baskin,Carlota Batres,Leanne Boucher,Jordane Boudesseul,Eduard Brandstätter,W Matthew Collins,Dušica Filipović Ðurđević,Ciara Egan,Vanessa Era,Paulo Ferreira,Chiara Fini,Patricia Garrido-Vásquez,Hendrik Godbersen,Pablo Gomez,Aurelien Graton,Necdet Gurkan,Zhiran He,Dave C Johnson,Pavol Kačmár,Chris Koch,Marta Kowal,Tomas Kratochvil,Marco Marelli,Fernando Marmolejo-Ramos,Martín Martínez,Alan Mattiassi,Nicholas P Maxwell,Maria Montefinese,Coby Morvinski,Maital Neta,Yngwie A Nielsen,Sebastian Ocklenburg,Jaš Onič,Marietta Papadatou-Pastou,Adam J Parker,Mariola Paruzel-Czachura,Yuri G Pavlov,Manuel Perea,Gerit Pfuhl,Tanja C Roembke,Jan P Röer,Timo B Roettger,Susana Ruiz-Fernandez,Kathleen Schmidt,Cynthia S Q Siew,Christian K Tamnes,Jack E Taylor,Rémi Thériault,José L Ulloa,Miguel A Vadillo,Michael E W Varnum,Martin R Vasilev,Steven Verheyen,Giada Viviani,Sebastian Wallot,Yuki Yamada,Yueyuan Zheng,Erin M Buchanan
When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
在处理和分析经验数据时，研究人员经常面临可能显得武断的选择（例如，如何定义和处理异常值）。如果一个人选择专门关注一个特定的选项，并进行单一的分析，其结果可能是有限的效用。也就是说，对于结果的普遍性，人们仍然是不可知论者，因为合理的替代途径仍然没有被探索。多元宇宙分析通过探索与数据处理和/或模型构建相关的各种选择，并检查它们对研究结论的影响，为这个问题提供了解决方案。然而，尽管与典型的单路径方法相比，多元宇宙分析可以说不太容易受到偏差的影响，但仍然有可能选择性地添加或省略路径。为了解决这个问题，我们概述了一种新颖的、更有原则的方法，通过众包来进行多元宇宙分析。该方法将在一个循序渐进的教程中详细介绍，以促进其实现。我们还提供了一个针对跨多种语言语义启动项目的详细说明，从而展示了其可行性及其增加客观性和透明度的能力。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions: A tutorial.","authors":"Tom Heyman,Ekaterina Pronizius,Savannah C Lewis,Oguz A Acar,Matúš Adamkovič,Ettore Ambrosini,Jan Antfolk,Krystian Barzykowski,Ernest Baskin,Carlota Batres,Leanne Boucher,Jordane Boudesseul,Eduard Brandstätter,W Matthew Collins,Dušica Filipović Ðurđević,Ciara Egan,Vanessa Era,Paulo Ferreira,Chiara Fini,Patricia Garrido-Vásquez,Hendrik Godbersen,Pablo Gomez,Aurelien Graton,Necdet Gurkan,Zhiran He,Dave C Johnson,Pavol Kačmár,Chris Koch,Marta Kowal,Tomas Kratochvil,Marco Marelli,Fernando Marmolejo-Ramos,Martín Martínez,Alan Mattiassi,Nicholas P Maxwell,Maria Montefinese,Coby Morvinski,Maital Neta,Yngwie A Nielsen,Sebastian Ocklenburg,Jaš Onič,Marietta Papadatou-Pastou,Adam J Parker,Mariola Paruzel-Czachura,Yuri G Pavlov,Manuel Perea,Gerit Pfuhl,Tanja C Roembke,Jan P Röer,Timo B Roettger,Susana Ruiz-Fernandez,Kathleen Schmidt,Cynthia S Q Siew,Christian K Tamnes,Jack E Taylor,Rémi Thériault,José L Ulloa,Miguel A Vadillo,Michael E W Varnum,Martin R Vasilev,Steven Verheyen,Giada Viviani,Sebastian Wallot,Yuki Yamada,Yueyuan Zheng,Erin M Buchanan","doi":"10.1037/met0000770","DOIUrl":"https://doi.org/10.1037/met0000770","url":null,"abstract":"When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"1 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078142","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}
When comparing relationships between latent variables across groups, measurement invariance (MI) needs to be established to ensure that the test results are valid and meaningful conclusions can be drawn. Common tests of MI are not ideal for investigating many groups and are of limited value during the development of measurement models. In addition, popular network-based alternatives to latent variable modeling lack established methods for MI testing. Therefore, we propose exploratory graph analysis trees (EGA trees) that apply the idea of model-based recursive partitioning to correlation matrices and combine it with EGA-which can be used instead of exploratory factor analysis. In a simulation study, we test the approach regarding its ability to detect configural or metric noninvariance in common factor models given numerous covariates and illustrate its usefulness in conditions with severe violations of configural invaraince based on the diverging number of factors. The results demonstrate that EGA trees can be a valuable tool for the exploration of MI when constructing scales and working on measurement models. We provide R functions within the R package EFAtree to easily implement EGA trees. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
在比较各组间潜在变量之间的关系时，需要建立测量不变性（MI），以确保检验结果有效，并能得出有意义的结论。MI的普通测试对于调查许多群体并不理想，并且在开发测量模型期间价值有限。此外，流行的基于网络的潜在变量建模替代方法缺乏MI测试的既定方法。因此，我们提出探索性图分析树（EGA树），将基于模型的递归划分思想应用于相关矩阵，并将其与EGA相结合，可以代替探索性因子分析。在模拟研究中，我们测试了该方法在给定大量协变量的公共因素模型中检测组态或度量非不变的能力，并说明了其在基于分散的因素数量严重违反组态不变性的条件下的有用性。结果表明，EGA树在构建尺度和处理测量模型时可以成为探索MI的一个有价值的工具。我们在R包EFAtree中提供R函数来轻松实现EGA树。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"Exploratory graph analysis trees-A network-based approach to investigate measurement invariance with numerous covariates.","authors":"David Goretzko,Philipp Sterner","doi":"10.1037/met0000796","DOIUrl":"https://doi.org/10.1037/met0000796","url":null,"abstract":"When comparing relationships between latent variables across groups, measurement invariance (MI) needs to be established to ensure that the test results are valid and meaningful conclusions can be drawn. Common tests of MI are not ideal for investigating many groups and are of limited value during the development of measurement models. In addition, popular network-based alternatives to latent variable modeling lack established methods for MI testing. Therefore, we propose exploratory graph analysis trees (EGA trees) that apply the idea of model-based recursive partitioning to correlation matrices and combine it with EGA-which can be used instead of exploratory factor analysis. In a simulation study, we test the approach regarding its ability to detect configural or metric noninvariance in common factor models given numerous covariates and illustrate its usefulness in conditions with severe violations of configural invaraince based on the diverging number of factors. The results demonstrate that EGA trees can be a valuable tool for the exploration of MI when constructing scales and working on measurement models. We provide R functions within the R package EFAtree to easily implement EGA trees. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"67 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058900","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}
Oscar L Olvera Astivia,Xijuan Zhang,Edward Kroc,Bruno D Zumbo
Moderated multiple regression is one of the most established, popular methods to model nonlinear associations in social sciences. A mostly unacknowledged fact is that a particular type of nonnormality can make the coefficient capturing this association nonzero. To further understand this connection, a theoretical investigation was conducted. A generalization of Isserlis' theorem from multivariate normal densities to all elliptical densities is presented. Through this generalization, it was found that the family of elliptical densities (which includes the multivariate normal) cannot generate a product-interaction term. Moreover, asymmetry in lower and/or higher dimensions can induce a product-interaction term. Special case studies are presented where the variables are unidimensional symmetric, but jointly nonsymmetric, resulting in a moderated multiple regression model. A call is made for researchers to think carefully and decide when they have a true interaction term, theorized a priori, and when nonnormality is mimicking an interaction effect. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
适度多元回归是社会科学中最成熟、最流行的非线性关联建模方法之一。一个大多数未被承认的事实是，一种特殊类型的非正态性可以使捕获这种关联的系数非零。为了进一步了解这种联系，进行了理论研究。将Isserlis定理从多元正态密度推广到所有椭圆密度。通过这种推广，发现椭圆密度族（包括多元正态）不能产生积相互作用项。此外，低维和/或高维的不对称可以诱导产物相互作用项。提出了特殊的案例研究，其中变量是一维对称的，但联合不对称，导致一个有调节的多元回归模型。研究人员需要仔细思考，并决定什么时候他们有一个真正的相互作用条件，理论化的先验，什么时候非正常是模仿相互作用的效果。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"On the uncanny relationship between nonnormality and moderated multiple regression.","authors":"Oscar L Olvera Astivia,Xijuan Zhang,Edward Kroc,Bruno D Zumbo","doi":"10.1037/met0000797","DOIUrl":"https://doi.org/10.1037/met0000797","url":null,"abstract":"Moderated multiple regression is one of the most established, popular methods to model nonlinear associations in social sciences. A mostly unacknowledged fact is that a particular type of nonnormality can make the coefficient capturing this association nonzero. To further understand this connection, a theoretical investigation was conducted. A generalization of Isserlis' theorem from multivariate normal densities to all elliptical densities is presented. Through this generalization, it was found that the family of elliptical densities (which includes the multivariate normal) cannot generate a product-interaction term. Moreover, asymmetry in lower and/or higher dimensions can induce a product-interaction term. Special case studies are presented where the variables are unidimensional symmetric, but jointly nonsymmetric, resulting in a moderated multiple regression model. A call is made for researchers to think carefully and decide when they have a true interaction term, theorized a priori, and when nonnormality is mimicking an interaction effect. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"14 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018258","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}
Henrik R. Godmann, František Bartoš, Eric-Jan Wagenmakers
{"title":"Truncating the likelihood allows outlier exclusion without overestimating the evidence in the Bayes factor t test.","authors":"Henrik R. Godmann, František Bartoš, Eric-Jan Wagenmakers","doi":"10.1037/met0000782","DOIUrl":"https://doi.org/10.1037/met0000782","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"23 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910621","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}
{"title":"Coefficient of agreement between two raters corrected for category prevalence: Alternative to kappa.","authors":"Rashid Saif Almehrizi","doi":"10.1037/met0000732","DOIUrl":"https://doi.org/10.1037/met0000732","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"40 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899765","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}
The statistical and pragmatic tension between explanation and prediction is well recognized in psychology. Yarkoni and Westfall (2017) suggested focusing more on predictions, which will ultimately produce better calibrated interpretations. Variable selection methods, such as regularization, are strongly recommended because it will help construct interpretable models while optimizing prediction accuracy. However, when the data contain a nonignorable proportion of missingness, variable selection and model building via penalized regression methods are not straightforward. What further complicates the analysis protocol is when the model performance is evaluated on both prediction accuracy and fairness, the latter is of increasing attention when the predictive outcome has societal implications. This study explored two methods for variable selection with incomplete data: the bootstrap imputation-stability selection (BI-SS) method and the stacked elastic net (SENET) method. Both methods work with multiply imputed data sets but in different ways. BI-SS implements variable selection separately on each imputed bootstrap data set and aggregates the results via stability selection, while SENET stacks all imputed data sets and fits a single pooled model. We thoroughly evaluated their performance using a suite of metrics (including area under the curve, F1 score, and fairness criteria) via three increasingly complex simulation studies. Results reveal that while BI-SS and SENET methods perform almost equally well in settings with generalized linear models, only BI-SS fares well with nested data design because of high computation demand in fitting the regularized generalized linear mixed effects models. Finally, we demonstrated both methods with an example using rich electronic health data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
在心理学中，解释和预测之间的统计学和语用学张力是公认的。Yarkoni和Westfall（2017）建议更多地关注预测，这最终将产生更好的校准解释。变量选择方法，如正则化，是强烈推荐的，因为它将有助于构建可解释的模型，同时优化预测精度。然而，当数据包含不可忽略的缺失比例时，通过惩罚回归方法进行变量选择和模型构建并不简单。使分析方案进一步复杂化的是，当模型性能同时评估预测准确性和公平性时，后者在预测结果具有社会影响时越来越受到关注。本文探讨了两种不完全数据下的变量选择方法：自举法（BI-SS）和叠弹性网法（SENET）。这两种方法都适用于多输入数据集，但方式不同。BI-SS分别对每个输入的自举数据集进行变量选择，并通过稳定性选择汇总结果，而SENET将所有输入的数据集叠加并拟合单个池模型。我们通过三个日益复杂的模拟研究，使用一系列指标（包括曲线下面积、F1分数和公平性标准）彻底评估了他们的表现。结果表明，虽然BI-SS和SENET方法在广义线性模型设置中表现几乎相同，但只有BI-SS方法在嵌套数据设计中表现良好，因为在拟合正则化广义线性混合效应模型时需要大量的计算量。最后，我们通过一个使用丰富电子健康数据的示例演示了这两种方法。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"Constructing a binary prediction model with incomplete data: Variable selection to balance fairness and precision.","authors":"He Ren, Chun Wang, Gongjun Xu, David J Weiss","doi":"10.1037/met0000786","DOIUrl":"10.1037/met0000786","url":null,"abstract":"<p><p>The statistical and pragmatic tension between explanation and prediction is well recognized in psychology. Yarkoni and Westfall (2017) suggested focusing more on predictions, which will ultimately produce better calibrated interpretations. Variable selection methods, such as regularization, are strongly recommended because it will help construct interpretable models while optimizing prediction accuracy. However, when the data contain a nonignorable proportion of missingness, variable selection and model building via penalized regression methods are not straightforward. What further complicates the analysis protocol is when the model performance is evaluated on both prediction accuracy and fairness, the latter is of increasing attention when the predictive outcome has societal implications. This study explored two methods for variable selection with incomplete data: the bootstrap imputation-stability selection (BI-SS) method and the stacked elastic net (SENET) method. Both methods work with multiply imputed data sets but in different ways. BI-SS implements variable selection separately on each imputed bootstrap data set and aggregates the results via stability selection, while SENET stacks all imputed data sets and fits a single pooled model. We thoroughly evaluated their performance using a suite of metrics (including area under the curve, F1 score, and fairness criteria) via three increasingly complex simulation studies. Results reveal that while BI-SS and SENET methods perform almost equally well in settings with generalized linear models, only BI-SS fares well with nested data design because of high computation demand in fitting the regularized generalized linear mixed effects models. Finally, we demonstrated both methods with an example using rich electronic health data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856196","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}
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).
在一个研究可复制性的警报比以往任何时候都响亮的时代，绘制具有统计和推理完整性的研究是至关重要的。事实上，资助机构几乎总是要求赠款申请人提出令人信服的先验能力分析，以证明拟议的样本量是合理的，这是集体考虑的信息的关键部分，以确保合理的投资。不幸的是，即使是研究人员在样本量规划方面最真诚的尝试也充满了基本的挑战，即不仅要为计划进行统计测试的重点参数设置数值，还要为模型的其他更外围或背景参数设置数值。正如我们清楚地表明的那样，对于后一种参数，即使在非常简单的模型中，善意的数值猜测的任何轻微偏差都可能破坏对具有关键理论兴趣的更重要参数的评估能力。为了纠正这个在功率分析中太常见但似乎被低估的问题，我们采用了一种抱最好的希望但做最坏的计划的心态，并提出了新的方法，试图(a)恢复适当的保守性和稳健性，以及反过来的可信度，以样本量计划过程，并且(b)大大简化该过程。使用测量变量路径分析模型的框架提出了推导和实践建议，因为它们包含了许多类型的模型（例如，多元线性回归，方差分析），其中样本量规划是感兴趣的。（PsycInfo Database Record (c) 2025 APA，版权所有）。
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{"title":"Supplemental Material for Constructing a Binary Prediction Model With Incomplete Data: Variable Selection to Balance Fairness and Precision","authors":"","doi":"10.1037/met0000786.supp","DOIUrl":"https://doi.org/10.1037/met0000786.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"16 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899812","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}
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