Psychological methods最新文献_第6页

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

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

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":"In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2k 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).","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":"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).","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

Inference with cross-lagged effects-Problems in time. 交叉滞后效应推断--时间问题。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000665

Charles C Driver

{"title":"Inference with cross-lagged effects-Problems in time.","authors":"Charles C Driver","doi":"10.1037/met0000665","DOIUrl":"https://doi.org/10.1037/met0000665","url":null,"abstract":"The interpretation of cross-effects from vector autoregressive models to infer structure and causality among constructs is widespread and sometimes problematic. I describe problems in the interpretation of cross-effects when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps (as in e.g., structural equation modeling)-zeroes in a discrete-time temporal matrix do not necessarily correspond to zero effects in the underlying continuous processes, and vice versa. This has implications for the common case when the presence or absence of cross-effects is used for inference about underlying causal processes. I demonstrate these problems via simulation, and also show that when an underlying set of processes are continuous in time, even relatively few direct causal links can result in much denser temporal effect matrices in discrete-time. I demonstrate one solution to these issues, namely parameterizing the system as a stochastic differential equation and focusing inference on the continuous-time temporal effects. I follow this with some discussion of issues regarding the switch to continuous-time, specifically regularization, appropriate measurement time lag, and model order. An empirical example using intensive longitudinal data highlights some of the complexities of applying such approaches to real data, particularly with respect to model specification, examining misspecification, and parameter interpretation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","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":"141634308","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":"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).","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

Coefficients of determination measured on the same scale as the outcome: Alternatives to R² that use standard deviations instead of explained variance. 以与结果相同的尺度衡量的决定系数：R² 的替代品，使用标准差代替解释方差。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-07-18 DOI: 10.1037/met0000681

Mathias Berggren

{"title":"Coefficients of determination measured on the same scale as the outcome: Alternatives to R² that use standard deviations instead of explained variance.","authors":"Mathias Berggren","doi":"10.1037/met0000681","DOIUrl":"https://doi.org/10.1037/met0000681","url":null,"abstract":"The coefficient of determination, R², also called the explained variance, is often taken as a proportional measure of the relative determination of model on outcome. However, while R² has some attractive statistical properties, its reliance on squared variations (variances) may limit its use as an easily interpretable descriptive statistic of that determination. Here, the properties of this coefficient on the squared scale are discussed and generalized to three relative measures on the original scale. These generalizations can all be expressed as transformations of R², and alternatives can therefore also be calculated by plugging in related estimates, such as the adjusted R². The third coefficient, new for this article, and here termed the CoDSD (the coefficient of determination in terms of standard deviations), or Rπ (R-pi), equals R²/(R²+1-R²). It is argued that this coefficient most usefully captures the relative determination of the model. When the contribution of the error is c times that of the model, the CoDSD equals 1/(1 + c), while R² equals 1/(1 + c²). (PsycInfo Database Record (c) 2024 APA, all rights reserved).","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":"141634307","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

Using group level factor models to resolve high dimensionality in model-based sampling. 在基于模型的抽样中使用组级因子模型解决高维度问题。

IF 7.6 1区心理学

Psychological methods Pub Date : 2024-06-24 DOI: 10.1037/met0000618

Niek Stevenson, Reilly J Innes, Quentin F Gronau, Steven Miletić, Andrew Heathcote, Birte U Forstmann, Scott D Brown

{"title":"Using group level factor models to resolve high dimensionality in model-based sampling.","authors":"Niek Stevenson, Reilly J Innes, Quentin F Gronau, Steven Miletić, Andrew Heathcote, Birte U Forstmann, Scott D Brown","doi":"10.1037/met0000618","DOIUrl":"10.1037/met0000618","url":null,"abstract":"Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141446886","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 comparison of random forest-based missing imputation methods for covariates in propensity score analysis. 比较倾向评分分析中基于随机森林的协变量缺失估算方法。

IF 7 1区心理学

Psychological methods Pub Date : 2024-06-13 DOI: 10.1037/met0000676

Yongseok Lee, Walter L Leite

引用次数: 0

Correcting bias in the meta-analysis of correlations. 纠正相关性荟萃分析中的偏差。

IF 7 1区心理学

Psychological methods Pub Date : 2024-06-03 DOI: 10.1037/met0000662

T D Stanley, Hristos Doucouliagos, Maximilian Maier, František Bartoš

引用次数: 0