Psychological methods最新文献

筛选
英文 中文
Improving inferential analyses predata and postdata. 改进推理分析的前数据和后数据。
IF 7 1区 心理学
Psychological methods Pub Date : 2024-09-09 DOI: 10.1037/met0000697
David Trafimow,Tingting Tong,Tonghui Wang,S T Boris Choy,Liqun Hu,Xiangfei Chen,Cong Wang,Ziyuan Wang
{"title":"Improving inferential analyses predata and postdata.","authors":"David Trafimow,Tingting Tong,Tonghui Wang,S T Boris Choy,Liqun Hu,Xiangfei Chen,Cong Wang,Ziyuan Wang","doi":"10.1037/met0000697","DOIUrl":"https://doi.org/10.1037/met0000697","url":null,"abstract":"The standard statistical procedure for researchers comprises a two-step process. Before data collection, researchers perform power analyses, and after data collection, they perform significance tests. Many have proffered arguments that significance tests are unsound, but that issue will not be rehashed here. It is sufficient that even for aficionados, there is the usual disclaimer that null hypothesis significance tests provide extremely limited information, thereby rendering them vulnerable to misuse. There is a much better postdata option that provides a higher grade of useful information. Based on work by Trafimow and his colleagues (for a review, see Trafimow, 2023a), it is possible to estimate probabilities of being better off or worse off, by varying degrees, depending on whether one gets the treatment or not. In turn, if the postdata goal switches from significance testing to a concern with probabilistic advantages or disadvantages, an implication is that the predata goal ought to switch accordingly. The a priori procedure, with its focus on parameter estimation, should replace conventional power analysis as a predata procedure. Therefore, the new two-step procedure should be the a priori procedure predata and estimations of probabilities of being better off, or worse off, to varying degrees, postdata. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"104 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165970","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
Consistency of Bayes factor estimates in Bayesian analysis of variance. 贝叶斯方差分析中贝叶斯因子估计值的一致性。
IF 7 1区 心理学
Psychological methods Pub Date : 2024-09-09 DOI: 10.1037/met0000703
Roland Pfister
{"title":"Consistency of Bayes factor estimates in Bayesian analysis of variance.","authors":"Roland Pfister","doi":"10.1037/met0000703","DOIUrl":"https://doi.org/10.1037/met0000703","url":null,"abstract":"Factorial designs lend themselves to a variety of analyses with Bayesian methodology. The de facto standard is Bayesian analysis of variance (ANOVA) with Monte Carlo integration. Alternative, and readily available methods, are Bayesian ANOVA with Laplace approximation as well as Bayesian t tests for individual effects. This simulation study compared the three approaches regarding ordinal and metric agreement of the resulting Bayes factors for a 2 × 2 mixed design. Simulation results indicate remarkable disagreement of the three methods in certain cases, particularly when effect sizes are small and studies include small sample sizes. Findings further replicate and extend previous observations of substantial variability of ANOVAs with Monte Carlo integration across different runs of one and the same analysis. These observations showcase important limitations of current implementations of Bayesian ANOVA. Researchers should be mindful of these limitations when interpreting corresponding analyses, ideally applying multiple approaches to establish converging results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"82 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165971","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
Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach. 在构造测量的潜在变化中建立构造随时间变化的模型:纵向调节因子分析方法。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-29 DOI: 10.1037/met0000685
Siyuan Marco Chen, Daniel J Bauer
{"title":"Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach.","authors":"Siyuan Marco Chen, Daniel J Bauer","doi":"10.1037/met0000685","DOIUrl":"https://doi.org/10.1037/met0000685","url":null,"abstract":"<p><p>In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding actual construct change with changes in measurement (i.e., differential item functioning [DIF]), threatening the validity of conclusions. An improved method that avoids such confounding is the second-order growth curve (SGC) model. It specifies a measurement model at each occasion of measurement that can be evaluated for invariance over time. The applicability of the SGC model is hindered by key limitations: (a) the SGC model treats time as continuous when modeling construct growth but as discrete when modeling measurement, reducing interpretability and parsimony; (b) the evaluation of DIF becomes increasingly error-prone given multiple timepoints and groups; (c) DIF associated with continuous covariates is difficult to incorporate. Drawing on moderated nonlinear factor analysis, we propose an alternative approach that provides a parsimonious framework for including many time points and DIF from different types of covariates. We implement this model through Bayesian estimation, allowing for incorporation of regularizing priors to facilitate efficient evaluation of DIF. We demonstrate a two-step workflow of measurement evaluation and growth modeling, with an empirical example examining changes in adolescent delinquency over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142111363","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
Factorization of person response profiles to identify summative profiles carrying central response patterns. 对人的反应特征进行因式分解,以确定包含中心反应模式的总结性特征。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-03-27 DOI: 10.1037/met0000568
Se-Kang Kim
{"title":"Factorization of person response profiles to identify summative profiles carrying central response patterns.","authors":"Se-Kang Kim","doi":"10.1037/met0000568","DOIUrl":"10.1037/met0000568","url":null,"abstract":"<p><p>A data matrix, where rows represent persons and columns represent measured subtests, can be viewed as a stack of person profiles, as rows are actually person profiles of observed responses on column subtests. Profile analysis seeks to identify a small number of latent profiles from a large number of person response profiles to identify central response patterns, which are useful for assessing the strengths and weaknesses of individuals across multiple dimensions in domains of interest. Moreover, the latent profiles are mathematically proven to be summative profiles that linearly combine all person response profiles. Since person response profiles are confounded with profile level and response pattern, the level effect must be controlled when they are factorized to identify a latent (or summative) profile that carries the response pattern effect. However, when the level effect is dominant but uncontrolled, only a summative profile carrying the level effect would be considered statistically meaningful according to a traditional metric (e.g., eigenvalue ≥ 1) or parallel analysis results. Nevertheless, the response pattern effect among individuals can provide assessment-relevant insights that are overlooked by conventional analysis; to achieve this, the level effect must be controlled. Consequently, the purpose of this study is to demonstrate how to correctly identify summative profiles containing central response patterns regardless of the centering techniques used on data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"723-730"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10016289","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 comprehensive model framework for between-individual differences in longitudinal data. 纵向数据个体间差异的综合模型框架。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-06-12 DOI: 10.1037/met0000585
Anja F Ernst, Casper J Albers, Marieke E Timmerman
{"title":"A comprehensive model framework for between-individual differences in longitudinal data.","authors":"Anja F Ernst, Casper J Albers, Marieke E Timmerman","doi":"10.1037/met0000585","DOIUrl":"10.1037/met0000585","url":null,"abstract":"<p><p>Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level, our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level, our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed. Recommendations for selecting and specifying longitudinal models are made for empirical researchers who aim to account for between-individual differences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"748-766"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9612872","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
Inclusion Bayes factors for mixed hierarchical diffusion decision models. 混合分层扩散决策模型的包容贝叶斯因子。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000582
Udo Boehm, Nathan J Evans, Quentin F Gronau, Dora Matzke, Eric-Jan Wagenmakers, Andrew J Heathcote
{"title":"Inclusion Bayes factors for mixed hierarchical diffusion decision models.","authors":"Udo Boehm, Nathan J Evans, Quentin F Gronau, Dora Matzke, Eric-Jan Wagenmakers, Andrew J Heathcote","doi":"10.1037/met0000582","DOIUrl":"10.1037/met0000582","url":null,"abstract":"<p><p>Cognitive models provide a substantively meaningful quantitative description of latent cognitive processes. The quantitative formulation of these models supports cumulative theory building and enables strong empirical tests. However, the nonlinearity of these models and pervasive correlations among model parameters pose special challenges when applying cognitive models to data. Firstly, estimating cognitive models typically requires large hierarchical data sets that need to be accommodated by an appropriate statistical structure within the model. Secondly, statistical inference needs to appropriately account for model uncertainty to avoid overconfidence and biased parameter estimates. In the present work, we show how these challenges can be addressed through a combination of Bayesian hierarchical modeling and Bayesian model averaging. To illustrate these techniques, we apply the popular diffusion decision model to data from a collaborative selective influence study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"625-655"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9796969","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
Multivariate analysis of covariance for heterogeneous and incomplete data. 异质和不完整数据的多元协方差分析。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-02-16 DOI: 10.1037/met0000558
Guillermo Vallejo, María Paula Fernández, Pablo Esteban Livacic-Rojas
{"title":"Multivariate analysis of covariance for heterogeneous and incomplete data.","authors":"Guillermo Vallejo, María Paula Fernández, Pablo Esteban Livacic-Rojas","doi":"10.1037/met0000558","DOIUrl":"10.1037/met0000558","url":null,"abstract":"<p><p>This article discusses the robustness of the multivariate analysis of covariance (MANCOVA) test for an emergent variable system and proposes a modification of this test to obtain adequate information from heterogeneous normal observations. The proposed approach for testing potential effects in heterogeneous MANCOVA models can be adopted effectively, regardless of the degree of heterogeneity and sample size imbalance. As our method was not designed to handle missing values, we also show how to derive the formulas for pooling the results of multiple-imputation-based analyses into a single final estimate. Results of simulated studies and analysis of real-data show that the proposed combining rules provide adequate coverage and power. Based on the current evidence, the two solutions suggested could be effectively used by researchers for testing hypotheses, provided that the data conform to normality. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"731-747"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10787830","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 posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science. 干预科学多阶段优化战略中的后预期值决策方法。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-04-13 DOI: 10.1037/met0000569
Jillian C Strayhorn, Linda M Collins, David J Vanness
{"title":"A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science.","authors":"Jillian C Strayhorn, Linda M Collins, David J Vanness","doi":"10.1037/met0000569","DOIUrl":"10.1037/met0000569","url":null,"abstract":"<p><p>In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2<i><sup>k</sup></i> factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"656-678"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9367545","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
Bayesian regularization in multiple-indicators multiple-causes models. 多指标多原因模型中的贝叶斯正则化。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-08-01 Epub Date: 2023-07-27 DOI: 10.1037/met0000594
Lijin Zhang, Xinya Liang
{"title":"Bayesian regularization in multiple-indicators multiple-causes models.","authors":"Lijin Zhang, Xinya Liang","doi":"10.1037/met0000594","DOIUrl":"10.1037/met0000594","url":null,"abstract":"<p><p>Integrating regularization methods into structural equation modeling is gaining increasing popularity. The purpose of regularization is to improve variable selection, model estimation, and prediction accuracy. In this study, we aim to: (a) compare Bayesian regularization methods for exploring covariate effects in multiple-indicators multiple-causes models, (b) examine the sensitivity of results to hyperparameter settings of penalty priors, and (c) investigate prediction accuracy through cross-validation. The Bayesian regularization methods examined included: ridge, lasso, adaptive lasso, spike-and-slab prior (SSP) and its variants, and horseshoe and its variants. Sparse solutions were developed for the structural coefficient matrix that contained only a small portion of nonzero path coefficients characterizing the effects of selected covariates on the latent variable. Results from the simulation study showed that compared to diffuse priors, penalty priors were advantageous in handling small sample sizes and collinearity among covariates. Priors with only the global penalty (ridge and lasso) yielded higher model convergence rates and power, whereas priors with both the global and local penalties (horseshoe and SSP) provided more accurate parameter estimates for medium and large covariate effects. The horseshoe and SSP improved accuracy in predicting factor scores, while achieving more parsimonious models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"679-703"},"PeriodicalIF":7.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10241486","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 framework for studying environmental statistics in developmental science. 发展科学环境统计研究框架。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000651
Nicole Walasek, Ethan S Young, Willem E Frankenhuis
{"title":"A framework for studying environmental statistics in developmental science.","authors":"Nicole Walasek, Ethan S Young, Willem E Frankenhuis","doi":"10.1037/met0000651","DOIUrl":"https://doi.org/10.1037/met0000651","url":null,"abstract":"<p><p>Psychologists tend to rely on verbal descriptions of the environment over time, using terms like \"unpredictable,\" \"variable,\" and \"unstable.\" These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying \"unpredictability\" in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different \"unpredictability statistics\" across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634306","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信