Psychological methods最新文献_第7页

Supplemental Material for Utilizing Primary Study Quality in Meta-Analyses in Psychology: A Step-by-Step Tutorial 在心理学荟萃分析中利用初级研究质量的补充材料：一步一步的教程

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-02 DOI: 10.1037/met0000751.supp

引用次数: 0

Why the use of segmented regression analysis to explore change in relations between variables is problematic: A simulation study. 为什么使用分段回归分析来探索变量之间关系的变化是有问题的：模拟研究。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-03-27 DOI: 10.1037/met0000576

Moritz Breit, Julian Preuß, Vsevolod Scherrer, Franzis Preckel

引用次数: 0

A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models. 用于检验关于线性两水平模型的固定效应的零假设的默认贝叶斯因子。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-04-27 DOI: 10.1037/met0000573

Nikola Sekulovski, Herbert Hoijtink

{"title":"A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models.","authors":"Nikola Sekulovski, Herbert Hoijtink","doi":"10.1037/met0000573","DOIUrl":"10.1037/met0000573","url":null,"abstract":"Testing null hypotheses of the form \"β = 0,\" by the use of various Null Hypothesis Significance Tests (rendering a dichotomous reject/not reject decision), is considered standard practice when evaluating the individual parameters of statistical models. Bayes factors for testing these (and other) hypotheses allow users to quantify the evidence in the data that is in favor of a hypothesis. Unfortunately, when testing equality-contained hypotheses, the Bayes factors are sensitive to the specification of prior distributions, which may be hard to specify by applied researchers. The paper proposes a default Bayes factor with clear operating characteristics when used for testing whether the fixed parameters of linear two-level models are equal to zero. This is achieved by generalizing an already existing approach for linear regression. The generalization requires: (a) the sample size for which a new estimator for the effective sample size in two-level models containing random slopes is proposed; (b) the effect size for the fixed effects for which the so-called marginal R² for the fixed effects is used. Implementing the aforementioned requirements in a small simulation study shows that the Bayes factor yields clear operating characteristics regardless of the value for sample size and the estimation method. The paper gives practical examples and access to an easy-to-use wrapper function to calculate Bayes factors for hypotheses with respect to the fixed coefficients of linear two-level models by using the R package bain. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"579-598"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9356536","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

Simulation-based design optimization for statistical power: Utilizing machine learning. 基于仿真的统计功率设计优化：利用机器学习

IF 7.8 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-12-14 DOI: 10.1037/met0000611

Felix Zimmer, Rudolf Debelak

{"title":"Simulation-based design optimization for statistical power: Utilizing machine learning.","authors":"Felix Zimmer, Rudolf Debelak","doi":"10.1037/met0000611","DOIUrl":"10.1037/met0000611","url":null,"abstract":"The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, for example, in terms of monetary costs, are a relevant target for optimization. In this context, optimal design parameters can imply a desired level of power at minimum cost or maximum power at a cost threshold. We introduce a surrogate modeling framework based on machine learning predictions to solve these optimization tasks. In a simulation study, we demonstrate the efficiency for a wide range of hypothesis testing scenarios with single- and multidimensional design parameters, including t tests, analysis of variance, item response theory models, multilevel models, and multiple imputations. Our framework provides an algorithmic solution for optimizing study designs when no analytic power analysis is available, handling multiple design dimensions and cost considerations. Our implementation is publicly available in the R package mlpwr. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"513-536"},"PeriodicalIF":7.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807105","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 receiver operating characteristic area under the curve (or mean ridit) as an effect size. 曲线下的接收者工作特征面积（或平均波幅）作为效应大小。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-07-13 DOI: 10.1037/met0000601

Michael Smithson

{"title":"The receiver operating characteristic area under the curve (or mean ridit) as an effect size.","authors":"Michael Smithson","doi":"10.1037/met0000601","DOIUrl":"10.1037/met0000601","url":null,"abstract":"Several authors have recommended adopting the receiver operator characteristic (ROC) area under the curve (AUC) or mean ridit as an effect size, arguing that it measures an important and interpretable type of effect that conventional effect-size measures do not. It is base-rate insensitive, robust to outliers, and invariant under order-preserving transformations. However, applications have been limited to group comparisons, and usually just two groups, in line with the popular interpretation of the AUC as measuring the probability that a randomly chosen case from one group will score higher on the dependent variable than a randomly chosen case from another group. This tutorial article shows that the AUC can be used as an effect size for both categorical and continuous predictors in a wide variety of general linear models, whose dependent variables may be ordinal, interval, or ratio level. Thus, the AUC is a general effect-size measure. Demonstrations in this article include linear regression, ordinal logistic regression, gamma regression, and beta regression. The online supplemental materials to this tutorial provide a survey of currently available software resources in R for the AUC and ridits, along with the code and access to the data used in the examples. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"678-686"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9776732","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

Meta-analyzing the multiverse: A peek under the hood of selective reporting. 多元宇宙的元分析：选择性报道背后的窥视。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000559

Anton Olsson-Collentine, Robbie C M van Aert, Marjan Bakker, Jelte Wicherts

{"title":"Meta-analyzing the multiverse: A peek under the hood of selective reporting.","authors":"Anton Olsson-Collentine, Robbie C M van Aert, Marjan Bakker, Jelte Wicherts","doi":"10.1037/met0000559","DOIUrl":"10.1037/met0000559","url":null,"abstract":"Researcher degrees of freedom refer to arbitrary decisions in the execution and reporting of hypothesis-testing research that allow for many possible outcomes from a single study. Selective reporting of results (p-hacking) from this \"multiverse\" of outcomes can inflate effect size estimates and false positive rates. We studied the effects of researcher degrees of freedom and selective reporting using empirical data from extensive multistudy projects in psychology (Registered Replication Reports) featuring 211 samples and 14 dependent variables. We used a counterfactual design to examine what biases could have emerged if the studies (and ensuing meta-analyses) had not been preregistered and could have been subjected to selective reporting based on the significance of the outcomes in the primary studies. Our results show the substantial variability in effect sizes that researcher degrees of freedom can create in relatively standard psychological studies, and how selective reporting of outcomes can alter conclusions and introduce bias in meta-analysis. Despite the typically thousands of outcomes appearing in the multiverses of the 294 included studies, only in about 30% of studies did significant effect sizes in the hypothesized direction emerge. We also observed that the effect of a particular researcher degree of freedom was inconsistent across replication studies using the same protocol, meaning multiverse analyses often fail to replicate across samples. We recommend hypothesis-testing researchers to preregister their preferred analysis and openly report multiverse analysis. We propose a descriptive index (underlying multiverse variability) that quantifies the robustness of results across alternative ways to analyze the data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"441-461"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9498812","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 factored regression model for composite scores with item-level missing data. 具有项目级缺失数据的综合分数的因子回归模型。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-25 DOI: 10.1037/met0000584

Egamaria Alacam, Craig K Enders, Han Du, Brian T Keller

{"title":"A factored regression model for composite scores with item-level missing data.","authors":"Egamaria Alacam, Craig K Enders, Han Du, Brian T Keller","doi":"10.1037/met0000584","DOIUrl":"10.1037/met0000584","url":null,"abstract":"Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score applications. Many studies have investigated this issue, and the near-universal theme is that item-level missing data treatment is superior because it maximizes precision and power. However, item-level missing data handling can be challenging because missing data models become very complex and suffer from the same \"curse of dimensionality\" problem that plagues the estimation of psychometric models. A good deal of recent missing data literature has focused on advancing factored regression specifications that use a sequence of regression models to represent the multivariate distribution of a set of incomplete variables. The purpose of this paper is to describe and evaluate a factored specification for composite scores with incomplete item responses. We used a series of computer simulations to compare the proposed approach to gold standard multiple imputation and latent variable modeling approaches. Overall, the simulation results suggest that this new approach can be very effective, even under extreme conditions where the number of items is very large (or even exceeds) the sample size. A real data analysis illustrates the application of the method using software available on the internet. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"462-481"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9892840","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 true score imputation method to account for psychometric measurement error. 一种解释心理测量误差的真实分数计算方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-25 DOI: 10.1037/met0000578

Maxwell Mansolf

{"title":"A true score imputation method to account for psychometric measurement error.","authors":"Maxwell Mansolf","doi":"10.1037/met0000578","DOIUrl":"10.1037/met0000578","url":null,"abstract":"Scores on self-report questionnaires are often used in statistical models without accounting for measurement error, leading to bias in estimates related to those variables. While measurement error corrections exist, their broad application is limited by their simplicity (e.g., Spearman's correction for attenuation), which complicates their inclusion in specialized analyses, or complexity (e.g., latent variable modeling), which necessitates large sample sizes and can limit the analytic options available. To address these limitations, a flexible multiple imputation-based approach, called true score imputation, is described, which can accommodate a broad class of statistical models. By augmenting copies of the original dataset with sets of plausible true scores, the resulting set of datasets can be analyzed using widely available multiple imputation methodology, yielding point estimates and confidence intervals calculated with respect to the estimated true score. A simulation study demonstrates that the method yields a large reduction in bias compared to treating scores as measured without error, and a real-world data example is further used to illustrate the benefit of the method. An R package implements the proposed method via a custom imputation function for an existing, commonly used multiple imputation library (mice), allowing true score imputation to be used alongside multiple imputation for missing data, yielding a unified framework for accounting for both missing data and measurement error. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"636-659"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9707324","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

Evaluating avoidable heterogeneity in exploratory factor analysis results. 评估探索性因素分析结果中可避免的异质性。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000589

Patrick D Manapat, Samantha F Anderson, Michael C Edwards

引用次数: 0

A Bayesian classifier for fractal characterization of short behavioral series. 短行为序列分形特征的贝叶斯分类器。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-01 DOI: 10.1037/met0000562

Alessandro Solfo, Cees van Leeuwen

{"title":"A Bayesian classifier for fractal characterization of short behavioral series.","authors":"Alessandro Solfo, Cees van Leeuwen","doi":"10.1037/met0000562","DOIUrl":"10.1037/met0000562","url":null,"abstract":"Serial tasks in behavioral research often lead to correlated responses, invalidating the application of generalized linear models and leaving the analysis of serial correlations as the only viable option. We present a Bayesian analysis method suitable for classifying even relatively short behavioral series according to their correlation structure. Our classifier consists of three phases. Phase 1 distinguishes between mono- and possible multifractal series by modeling the distribution of the increments of the series. To the series labeled as monofractal in Phase 1, classification proceeds in Phase 2 with a Bayesian version of the evenly spaced averaged detrended fluctuation analysis (Bayesian esaDFA). Finally, Phase 3 refines the estimates from the Bayesian esaDFA. We tested our classifier with very short series (viz., 256 points), both simulated and empirical ones. For the simulated series, our classifier revealed to be maximally efficient in distinguishing between mono- and multifractality and highly efficient in assigning the monofractal class. For the empirical series, our classifier identified monofractal classes specific to experimental designs, tasks, and conditions. Monofractal classes are particularly relevant for skilled, repetitive behavior. Short behavioral series are crucial for avoiding potential confounders such as mind wandering or fatigue. Our classifier thus contributes to broadening the scope of time series analysis for behavioral series and to understanding the impact of fundamental behavioral constructs (e.g., learning, coordination, and attention) on serial performance. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"537-578"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9374013","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